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The analysis of the masticatory cycle plays a fundamental role in studying the functions of the stomatognathic system and evaluating temporomandibular dysfunctions (TMD). The primary objective of this study is to investigate the complex interplay between mandibular kinematics and surface electromyography (sEMG) activity during the masticatory cycle using advanced 4D dentistry technology in 22 healthy subjects (without TMD). By employing electromyography, it becomes feasible to capture the electrical activity of the masticatory muscles throughout the chewing process. The BTS TMJOINT (© 2023 BTS Bioengineering, Garbagnate Milanese, MI, Italy) electromyograph was utilized in this study. Mandibular tracking, on the other hand, allows for recording the movements of the mandible during chewing and condylar slopes. This latest technology (ModJaw®, Tech in motion™, Villeurbanne, France) utilizes motion sensors placed on the jaw to accurately track three-dimensional movements, including jaw opening, closing, and lateral movements. Nowadays, in clinical gnathology, it is common practice to examine masticatory function by analyzing mandibular kinematics and muscle contraction as distinct entities. Similarly, the results obtained from these analyses are typically assessed independently. The investigation of a correlation between electromyography data and mandibular kinematics during the masticatory cycle could provide several advantages for clinicians in diagnosis and lead to a combined analysis of muscle activities and intraarticular dynamics. In conclusion, it can be inferred from the results obtained in the present study that the chewing cycle with a greater vertical movement results in increased masseter muscular activity, and condylar slopes are positively correlated to an increase in temporalis muscle activation. This comprehensive approach can provide valuable insights into the relationship between muscle activity and mandibular movement, enabling clinicians to gain a deeper understanding of the functional dynamics of the stomatognathic system.

To provide consistent air purification benefits from urban forests, it is crucial to identify common characteristics that allow for similar experimental setups. This study aimed to analyze PM10 concentrations in urban forests near pollution sources and understand their mitigation effects. Data from the Asian Initiative for Clean Air Networks, Korea, were used, focusing on three urban forests adjacent to road and industrial pollution sources in Korea, with PM10 concentrations collected during 2021. Considering high PM10 concentrations during winter and spring, these seasons were divided into two sub-periods, resulting in six seasonal periods for analysis. To address the right-skewed PM10 distribution and reduce outlier influence, the Kruskal–Wallis test was used. The results showed that “good” PM10 levels were lowest in early spring, increasing to a peak in summer before declining. High PM10 events were concentrated in spring, early spring, and early winter. The Kruskal–Wallis test indicated lower median PM10 concentrations in urban forests compared to pollution sources in the latter half of the year, while no significant median differences were found in the first half. Distribution visualizations further confirmed that even during high PM10 periods, all urban forests showed lower PM10 values compared to pollution sources. In conclusion, PM10 concentrations in urban forests were consistently lower than in pollution sources across all seasons, demonstrating their effectiveness in air purification at both road and industrial pollution sources. Future research should consider additional variables, such as PM2.5, to further explore differences between pollution sources.

Background: Peroxisome-proliferator-activated receptors (PPARs) constitute nuclear transcription factors controlling gene expression associated with cell growth and proliferation, diverse proteins, lipids, and glucose metabolism, being related to several other pathophysiological states such as metabolic disorders, atherogenesis, carcinogenesis, etc. The present survey aims to analyze the natural compounds that can act as agonists for the PPAR-α, PPAR-β/δ, and PPAR-γ system targeting, highlighting how the amazing biochemical diversity of natural compounds can yield new insights into this “hotspot” of the scientific field. Methods: A narrative review was performed by searching the recent international literature for the last two decades in the most authoritative scientific databases, like PubMed, Scopus, Web of Science, and Embase, using appropriate keywords. Results: Several natural compounds and/or their synthetic derivatives can act as ligands of PPARs, stimulating their transcriptional activity and enabling their use as preventive and/or therapeutic agents for several disease states, such as inflammation, oxidative stress, metabolic disturbances, atherogenesis, and carcinogenesis. Although synthetic compounds are increasingly used as drugs to manage health problems, serious side effects have been observed, while their natural analogues exhibit only few minor side effects. Conclusions: Further clinical studies on natural compounds such as ligands of PPARs and the evaluation of the related molecular mechanisms are needed to implement an effective strategy concerning the pharmaco-technology, food chemistry, and nutrition to introduce them as part of clinical and dietary practice.

The National Basketball Association (NBA) is the major professional basketball league in the United States. Home-court advantage (HA) and home win percentage (HW) have been identified as decisive factors in determining the final outcome of a game. This study analyzes a total of 24 regular seasons, with two main objectives: (i) comparing HA and HW within the two conferences and (ii) examining the influence of team ability on HA and HW. HA (%) was calculated as (total home wins/total wins) × 100, and HW (%) as (total home wins/total home games) × 100. Teams were categorized into four different groups based on their season win percentage using cluster analyses. Regarding the first aim, we used an Independent Samples T-test and Mann–Whitney to assess differences between conferences. Subsequently, we evaluated the second objective using one-way ANOVA or Kruskal–Wallis. The results indicated no significant differences in HA within conferences. However, Western teams tended to have a higher HW than Eastern teams. The analyses revealed significantly higher HA in contender teams compared to the rest of the team abilities (p < 0.001). High–medium-ability teams also showed a significantly higher HA (p < 0.001) compared to low-ability teams. However, non-significant differences were found between the remaining groups for HA. Regarding HW, we observed significantly higher values in the contender teams compared to the teams with other ability levels (p < 0.001). Additionally, the high–medium-ability teams also exhibited significantly higher HW values (p < 0.001) when compared to the medium–low- and low-ability teams. Furthermore, the medium–low-ability teams showed significantly higher HW values (p < 0.001) when compared to the low-ability teams. Those findings suggest that team ability influences HA and HW in the NBA, where contender teams have significantly higher HW and HA values compared to the teams with other team ability levels. Understanding these dynamics provides valuable insights for team strategies, particularly during playoff stages where home-court advantage can be critical for success.

The modeling and analysis of epidemic processes in networks have attracted much attention over the past few decades. A major underlying assumption is that the recovery process and infection process are homogeneous, allowing the associated theoretical studies to be conducted in a convenient manner. However, the recovery and infection processes usually exhibit heterogeneous rates in the real world, which makes it difficult to characterize the general relations between the dynamics and the underlying network structure. In this work, we focus on the susceptible–infected–susceptible (SIS) epidemic process with heterogeneous recovery rates in a finite-size complete graph. Specifically, we study the metastable-state statistical properties of SIS epidemic dynamics with two different nodal recovery rates in complete graphs. We propose approximate solutions to the metastable-state expectation and the variance in the number of infected nodes within the framework of the mean-field approximation method. We also derive several upper and lower bounds of the steady-state probability that a node is in the infected state. We verify the proposed approximate solutions of the mean and variance via simulations. This work provides insights into the fluctuations in the statistical properties of epidemic processes with complex dynamical behaviors in networks.

This study investigated Portulaca oleracea (purslane) as a potential antioxidant supplement in cooked sausages, focusing on its effects on lipid oxidation, fatty acid composition, and antioxidant activity. The fatty acid profile of the sausages enriched with 1.2% purslane powder revealed a 1.3-fold increase in alpha-linolenic acid (ALA), an essential omega-3 fatty acid. Improved oxidative stability during refrigerated storage was observed, with peroxide values of 10.9 meq/kg in the sausages with purslane by day 10 compared with 12.5 meq/kg in the control sausages. The thiobarbituric acid (TBA) values, reflecting lipid peroxidation, were also significantly lower in the sausages with purslane. The antioxidant capacity of the sausages containing purslane was significantly enhanced, demonstrating a ferric-reducing antioxidant power (FRAP) of 13.5 mg GAE/g, whereas the control sausages showed undetectable FRAP levels. Additionally, the DPPH radical-scavenging activity in the sausages with purslane was 21.70% compared with 13.73% in the control. These findings suggest that purslane improves the nutritional profile of meat products by increasing beneficial fatty acids while providing substantial protection against oxidative spoilage. Purslane offers a promising natural alternative to synthetic antioxidants, enhancing the shelf life and quality of processed meats.

This paper reports the results of a systematic investigation into the magnetic properties of Bi1−yCayFe1−xMnxO3 (0.1 ≤ y ≤ 0.175, 0.35 ≤ x ≤ 0.45) solid solutions. The substitution of Bi3+ with Ca2+ and the concurrent introduction of Mn3+/Mn4+ ions result in the stabilization of various structural phases, with each exhibiting distinct magnetic characteristics. The investigation indicates that the samples containing the polar rhombohedral phase display metamagnetic transitions at low temperatures, characterized by pronounced jumps in magnetization. Single-phase samples with a nonpolar orthorhombic structure exhibit weak ferromagnetic behavior without metamagnetic features. The observed metamagnetic behavior, accompanied by anomalies in temperature-dependent magnetization and significant remnant magnetization at low temperatures, particularly in samples near the polar-anti(non)polar phase boundary, highlighted the presence of both antiferromagnetic and glassy magnetic components.

Kiwifruit, as a climacteric fruit, undergoes rapid ripening and senescence after harvest, making it highly susceptible to softening, rotting, and spoilage. Therefore, monitoring the key quality parameters of kiwifruit, particularly the accurate detection of soluble solids content (SSC), is considered crucial. The performance of two spectral acquisition methods—diffuse reflectance and diffuse transmission—in detecting SSC in kiwifruit was compared. Various preprocessing methods and feature wavelength selection techniques were employed, and regression models were constructed using partial least squares (PLS) analysis. The stability and accuracy of the models were validated through an independent validation set. The results indicated that the spectral data acquired by the diffuse reflectance method, preprocessed using Savitzky–Golay smoothing and combined with competitive adaptive reweighted sampling (CARS), yielded a coefficient of determination (R2) of 0.98 for the prediction set, with a root-mean-square error (RMSE) of 0.66. In contrast, the spectral data obtained by the diffuse transmission method, preprocessed using multiplicative scatter correction and combined with CARS, achieved an R2 of 0.95 and an RMSE of 0.93 for the prediction set. This study demonstrated that both methods were effective for detecting SSC in kiwifruit, with the diffuse reflectance method showing the greater advantage.

Saudi Arabia faces significant challenges in managing the rising energy consumption in buildings driven largely by its hot climatic conditions. As a result, retrofitting building facades to enhance energy efficiency has become a critical strategy. This study assesses the effectiveness of various façade retrofit strategies in reducing cooling electricity consumption using a real-time case study in Dhahran, Saudi Arabia. The strategies explored include external wall upgrades, window replacements, and installation of shading devices. Each strategy was evaluated individually, considering the reduction in heat gains, cooling load, and payback period as key performance indicators. To further maximize energy efficiency, these strategies were also analyzed in combination using the genetic algorithm optimization method, yielding 224 possible facade configurations. The optimal solution included the use of an External Thermal Insulation Composite System (ETCIS) in walls, louvers in windows, and low-emissivity coating with Argon gas-filled glazing, achieving a cooling energy reduction of approximately 16% and a payback period of 14.8 years. This study provides several recommendations for improving the efficiency of retrofitting building façades in hot climatic conditions.

Disease is one of the primary threats to maize growth. Currently, maize disease detection is mainly conducted in laboratories, making it difficult to promptly respond to diseases. To enable detection in the field, a lightweight model is required. Therefore, this paper proposes a lightweight model, YOLOv8-GO, optimized from the YOLOv8 (You Only Look Once version 8) model. The Global Attention Mechanism was introduced before the SPPF (Spatial Pyramid Pooling Fast) layer to enhance the model’s feature extraction capabilities without significantly increasing computational complexity. Additionally, Omni-dimensional Dynamic Convolution was employed to optimize the model’s basic convolutional structure, bottleneck structure, and C2f (Faster Implementation of CSP (Cross Stage Partial) Bottleneck with two convolutions) module, improving feature fusion quality and reducing computational complexity. Compared to the base model, YOLOv8-GO achieved improvements across all metrics, with mAP@50 increasing to 88.4%, a 2% gain. The computational complexity was 9.1 GFLOPs, and the model could run up to 275.1 FPS. YOLOv8-GO maintains a lightweight design while accurately detecting maize disease targets, making it suitable for application in resource-constrained environments.

The efficiency of photopolymerisation significantly impacts achieving a high degree of conversion and, consequently, determines the success and strength of resin-based composite (RBC) restorations. The study aimed to measure the light irradiance of selected LED curing lamps, taking into account various exposure modes and the increased distance of the light source from the radiometer surface. The study material consisted of 21 LED polymerisation lamps of a single type (Woodpecker Medical Instrument Co., Guilin, China) with three exposure modes: standard, soft start, and pulse. During the measurement, the distance was increased from 0 mm to 8 mm, every 2 mm. Light irradiance measurements were made with a Bluephase Meter II photometer (Ivoclar Vivadent, Opfikon, Switzerland). Increasing the distance affected the soft mode the most, causing a significant drop in light irradiance on the photometer. Standard mode coped best with distance. Even at a distance of 0 mm, the soft start mode does not reach the power of the standard and pulse modes. The standard mode seems to be the most clinically effective, especially if it is planned to polymerise a material in a deep cavity. The soft start mode, as the least resistant to increasing distance, is recommended for use in front teeth or the cervical area.

The main aim of this narrative review is to assess the existing body of scientific literature on heart rate variability (HRV) in relation to basketball, focusing on its use as a measure of internal load and vagal nerve responses. Monitoring HRV offers insights into the autonomic function and training-induced adaptations of basketball players. Various HRV measurement protocols, ranging from short-term to longer durations, can be conducted in different positions and conditions, such as rest, training, and sleep, to determine this key metric. Consistency and individualization in measurement protocols, responding to the athlete’s specific characteristics, is crucial for reliable HRV data and their interpretation. Studies on HRV in basketball have explored psychological adaptation, training effects, individual differences, recovery, and sleep quality. Biofeedback techniques show positive effects on HRV and anxiety reduction, potentially enhancing performance and stress management. The scientific literature on HRV in basketball could benefit from studies involving longer monitoring periods to identify significant trends and results related to training and recovery. Longitudinal HRV monitoring in teams with intense travel schedules could reveal the impact on athletes of all levels and ages, and, in this regard, individualized interpretation, considering the subjective recovery and fitness levels of athletes, is recommended to optimize training programs and performance. HRV provides insights into training and competitive loads, aiding in determining exercise intensities and training status. Additionally, HRV is linked to recovery and sleep quality, offering valuable information for optimizing player performance and well-being. Overall, HRV is a reliable tool for adjusting training programs to meet the specific needs of basketball players.

In this paper, a contrastive learning approach for morphological disambiguation (MD) using large language models (LLMs) is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct analysis and contextual embeddings while maintaining a margin between correct and incorrect embeddings. One of the aims of the paper is to analyze the effects of fine-tuning an LLM on MD in morphologically complex languages (MCLs) with special reference to low-resource languages such as Kazakh, as well as Turkish. Another goal of the paper is to consider various distance measures for this contrastive loss function, aiming to achieve better results when performing disambiguation by computing the distance between the context and the analysis embeddings. The existing approaches for morphological disambiguation, such as HMM-based and feature-engineering approaches, have limitations in modeling long-term dependencies and in the case of large, sparse tagsets. These challenges are mitigated in the proposed approach by leveraging LLMs, thus achieving better accuracy in handling the cases of ambiguity and OOV tokens without the need to rely on other features. Experiments were conducted on three datasets for two MCLs, Kazakh and Turkish—the former is a typical low-resource language. The results revealed that the proposed approach with contrastive loss improves MD performance when integrated with knowledge from large language models.

Traditional numerical analyses often overlook the potential impact of adjacent building basements on ground surface deformation. This study investigated the influence of neighboring structures on diaphragm walls and ground surface deformation during deep excavation for building foundations using PLAXIS 3D finite element software. This study simulated the top–down construction method with plate elements for diaphragm walls retaining H-shaped steel for support and pre-stressed anchors. The adjacent structures were modeled using plate elements. Numerical analysis results were compared with field observations for model validation. The results show that the lateral displacement of the retaining wall varies with the depth of neighboring basements. At 0.5 times the excavation depth, displacement was significant, and it stabilized at 1.0 times the depth. When the distance between adjacent buildings and the retaining wall was about twice the excavation depth, the deformation curve converged, indicating negligible influence beyond this distance. Ground surface settlement increased as the neighboring basement depth reached half the excavation depth, and stabilized at 1.6 times the depth. A closer proximity resulted in greater ground surface settlement. These findings offer practical references for deep excavation design and assessment, aiding engineers in ensuring project stability and safety.

In the field of aeronautics, aircraft, as a critical aviation tool, exert a decisive influence on the structural integrity and safety of the entire system. Accurate prediction of the stress field distribution and variations within the aircraft structure is of great importance to ensuring its safety performance. To facilitate such predictions, a rapid assessment method for stress fields based on a multilayer perceptron (MLP) neural network is proposed. Compared to the traditional machine learning algorithm, the random forest algorithm, MLP demonstrates superior accuracy and computational efficiency in stress field prediction, particularly exhibiting enhanced adaptability when handling high-dimensional input data. This method is applied to predict stresses in the wing rib structure. By performing finite element meshing on the wing ribs, the angle of attack, inflow velocity, and node coordinates are utilized as input tensors for the model, enabling it to learn the stress distribution in the wing ribs. Additionally, a peak stress prediction model is separately established for regions experiencing peak stresses. The results indicate that the MAPE of the stress field prediction model is within 5%, with a coefficient of determination R2 exceeding 0.994. For the peak stress model, the MAPE is within 2%, with an R2 exceeding 0.995. This method offers faster computation and greater flexibility, presenting a novel approach for structural strength assessment.

As a critical component of the transportation system, the safety of bridges is directly related to public safety and the smooth flow of traffic. This study addresses the aforementioned issues by focusing on the identification of bridge structure deterioration and the updating of finite element models, proposing a systematic research framework. First, this study presents a preprocessing method for bridge point cloud data and determines the parameter ranges for key algorithms through parameter tuning. Subsequently, based on the massive point cloud data, this research explores and optimizes the methods for identifying bridge cracks and spatial deformations, significantly enhancing the accuracy and efficiency of identification. On this basis, the particle swarm optimization algorithm is employed to optimize the key parameters in crack detection, ensuring the reliability and precision of the algorithm. Additionally, the study summarizes the methods for detecting bridge structural deformations based on point cloud data and establishes a framework for updating the bridge model. Finally, by integrating the results of bridge crack and deformation detection and combining Bayesian model correction and adaptive nested sampling methods, this research sets up the process for updating finite element model parameters and applies it to the analysis of actual bridge point cloud data.

A finite element model, which includes reservoir rock, cement stone, casing, and perforation channels, was developed. The purpose of the study is to create a geomechanical model of the zone around the well, which includes support elements and perforation channels. This model will help predict changes in the productivity coefficient of a terrigenous reservoir and determine the most efficient mode of operation of a producing well. In order to exclude the stress concentration within the casing–cement stone and cement stone–rock, the numerical model applies contact elements. As a result, structural elements slip, while the stresses are redistributed accurately. The numerical simulation of a stress state in the near-well zone was carried out by using the developed model with differential pressure drawdown on the terrigenous reservoir, one of the oil fields in the Perm region. It is shown that the safety factor of the casing reaches roughly 3–4 units. The only exceptions are the upper and lower parts of the perforations, where this parameter is close to one unit. The safety factor of cement stone accounts for 2–3 units. However, parts with its lowest value (1.35) are also concentrated near the perforation channels. In order to analyze the change in permeability, the dependence of the safety factor on effective stresses was taken into account. Therefore, it was found that, in the upper and lower parts of perforations, the stresses decreased, while permeability rose by up to 20% of the initial value. An increase in differential pressure drawdown, on the contrary, can lead to a permeability reduction of 25%, especially in the lateral parts of the perforations. Areas of rock destruction under tensile and compressive forces were identified by using the Mohr–Coulomb criterion. It is estimated that with an increase in pressure drawdown, the areas of rock destruction under tensile force disappear, while the areas of rock destruction under compression increase. After further analysis, it was found that, with the maximum pressure drawdown of 12 MPa, the well productivity index can decrease by 15% due to the reservoir rock compaction.

The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches.

This study investigates the potential of Plasma-Activated Water (PAW) technology for the production of nitrate fertilizer, focusing on the dielectric properties of plasma-treated water, such as the dielectric constant and dielectric loss factor. The research aims to elucidate the impact of plasma treatment and varying water flow rates on these properties. An experimental approach was employed wherein ion-free water was subjected to plasma treatment at different flow rates (0.5, 1.0, and 2.0 L/min) and durations (1, 2, and 3 h). The results reveal a marked enhancement in the dielectric properties of the water following plasma treatment, with the most significant improvements observed at a flow rate of 0.5 L per minute and a treatment duration of 3 h, and dielectric efficiencies of 97.82%, 97.21%, and 96.61% achieved at flow rates of 0.5, 1.0, and 2.0 L/min, respectively. These findings demonstrate that PAW technology enhances the efficiency of nitrate fertilizer production by optimizing energy storage and reducing energy losses. The study underscores the potential of PAW as a sustainable, environmentally benign alternative to conventional chemical fertilizers, contributing to more efficient and sustainable agricultural practices.

Based on the principles of reinjection converter technology and submodule operation, a novel topology for a reinjection multilevel voltage source converter (RMVSC) is proposed. In this topology, submodules are dynamically connected in series as the reinjection circuit. This new topology retains all the functions of the RMVSC while offering flexible control of the reinjection circuit and ease of modularization, making it highly suitable for high-voltage, high-power applications. A circulating drive pulse sequence was developed for the 7-level serial submodule RMVSC, ensuring dynamic stability of the submodule voltage and maintaining low harmonic distortion when interfaced with the grid. Building on this, a double-loop control strategy—comprising an outer power loop and an inner current loop—was proposed for the static synchronous compensator (STATCOM) of RMVSC. A simulation model was constructed in PSCAD/EMTDC, and the simulation results confirm the excellent performance of the proposed topology and the effectiveness of both the modulation strategy and the double-loop control strategy. The RMVSC-STATCOM demonstrates continuous and bidirectional reactive power regulation, with high control accuracy and superior compensation current quality.

Crops such as tree nuts, corn and peanut are highly susceptible to infestation by the aspergilli Aspergillus flavus or Aspergillus parasiticus and subsequent aflatoxin (AF) contamination, a serious threat to public food safety. Conventional control of the aspergilli has been through the application of fungicides; however, certain fungicides at sub-optimal doses have been correlated with increased production of mycotoxins including AF. Natural products (NP) have been a potential source of antifungal agents. In this study, we performed risk assessment testing, for which thirteen NP/derivatives (generally recognized as safe) were examined at sub-inhibitory concentrations to determine the enhancement of AF production in aspergilli. We found that benzaldehyde derivatives or thymol (THY) enhanced AF production in aspergilli, while 4-isopropyl-3-methylphenol (4I3M), a synthetic analog of the NP THY and carvacrol, or salicylaldehyde (SLD) exerted a potent antifungal or mycotoxin-inhibitory effect. In seed testing (corn, pistachio kernels), SLD effectively prevented fungal growth as a fumigant, while 4I3M completely inhibited AF production at ≥1.0 mM. Therefore, we concluded that NP/derivatives that do not have any significant environmental impact can be a potent source of antifungal or anti-mycotoxigenic agents, either in their nascent form or as leads for more effective derivatives; however, NP should be applied at optimum concentrations to prevent the abnormal enhancement of mycotoxin production by fungi.

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using previous methods. To address the challenges of color painting segmentation, an optimized strategy based on U-Net is proposed in this paper. The residual blocks of a residual network (ResNet) are added to the original U-Net architecture, and a UR-Net is constructed for the semantic segmentation of color paintings. The following steps are taken. First, datasets of color paintings are obtained as training and test samples and are labeled with the two following pixel colors: earth red and ultramarine blue. Second, residual blocks are improved and added to fit the U-Net architecture. Then, a UR-Net is constructed and trained using the samples to obtain the semantic segmentation model. Finally, the effectiveness of the trained UR-Net model for segmenting the test samples is evaluated, and it is compared with the K-means clustering algorithm, ResNet, and U-Net. Data from several studies suggest that the segmentation accuracy of the UR-Net model is higher than that of other methods for the color segmentation of painted images, and the IoUs of the segmented earth red and ultramarine blue pixels are 0.9346 and 0.9259, respectively, achieving the desired results. The proposed UR-Net model provides theoretical and methodological support for further in-depth research on color recognition and segmentation of cultural color paintings.

The fungi and bacteria on the human scalp play important roles in both health and disease. Scalp biofilms have pathogenic effects on cutaneous tissues, such as seborrheic dermatitis. However, investigations into scalp biofilms and their physiological effects on scalp skin are limited. In this study, we suggest an evaluation method through which the scalp is stained a reddish color using erythrosine to visualize scalp biofilms, which strongly depends on the presence of bacteria and fungi. We found that the physiological properties of the scalp significantly differed between high and low levels of stained red areas (sRAs) on the scalp. The sRA levels showed a strong positive correlation with IL-8 levels and sebum production. It is worth noting that the production of sebum has a dominant effect on the scalp microbiome via the growth of microbes, leading to the formation of a biofilm, as evidenced by changes in the sRA levels. Furthermore, the sRA levels could be reduced through the use of antimicrobial agents, such as climbazole and hexamidine diisethionate (HD), as well as the manipulation of the physical properties of the scrubs used in scalp care products. These scalp care products could potentially disrupt the formation and accumulation of a biofilm on the scalp, providing strong evidence for the importance of considering the scalp microbiome and its interactions with sebum in the development of biofilms. Consequently, we suggested that the administration of anti-microbial agents, such as climbazole and HD, could be an effective strategy to alleviate biofilm accumulation for the maintenance of scalp health.

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

Currently, modern hydrogen technologies, due to their low or zero emissions, constitute one of the key elements of energy transformation and sustainable development. The growing interest in hydrogen is driven by the European climate policy aimed at limiting the use of fossil fuels for energy purposes. Although not all opinions regarding the technical and economic potential of hydrogen energy are positive, many prepared forecasts and analyses show its prospective importance in several areas of the economy. The aim of this article is to provide a comprehensive review of modern materials, current hydrogen technologies and strategies, and show the opportunities, problems, and challenges Poland faces in the context of necessary energy transformation. The work describes the latest trends in the production, transportation, storage, and use of hydrogen. The environmental, social, and economic aspects of the use of green hydrogen were discussed in addition to the challenges and expectations for the future in the field of hydrogen technologies. The main goals of the development of the hydrogen economy in Poland and the directions of actions necessary to achieve them were also presented. It was found that the existence of the EU CO2 emissions allowance trading system has a significant impact on the costs of hydrogen production. Furthermore, the production of green hydrogen will become economically justified as the costs of energy obtained from renewable sources decrease and the costs of electrolysers decline. However, the realisation of this vision depends on the progress of scientific research and technical innovations that will reduce the costs of hydrogen production. Government support mechanisms for the development of hydrogen infrastructure and technologies will also be of key importance.

Noise reduction is a key process in digital image detection technology for concrete cracks and bug-holes. In this study, the threshold range of the flood-filling noise reduction algorithm was investigated experimentally. Surface cracks and bug-holes in RC bridge concrete were detected using mobile terminal images based on the flood-filling noise reduction algorithm. The results showed that the error range was within 10% when threshold range Θ was confined in [60, 80] as the crack width was from 0.1 mm to 2 mm. It is suitable that the threshold range Θ was selected as 70 while the measured crack width range was 0.2 mm to 2 mm. However, by reducing the values of the threshold range Θ to 50, the miscalculation was obviously eliminated. The influences of reducing values of the threshold range on bug-holes of the equivalent diameter and area were not significant. It is suitable that the threshold range Θ was elected on 50 to detect bug-holes in the concrete surface. The threshold range can be selected as a suitable value for the detection of cracks and bug-holes in order to reduce noise.

There is an increasing need for virtual reality (VR) health applications. In the field of pain, VR has been used mainly as a distraction, with minimal use of VR to answer basic clinical questions. Pain is multifactorial and inherently threatening. Our lab recently designed two VR clinical environments with varying threat values; the present study sought to validate these environments. Subjects were randomly allocated into either the threatening or non-threatening VR consultation room and both subjective (threat questionnaire) and physiological (salivary cortisol) measurements were taken. As hypothesised, subjects in the threat condition recorded a higher threat score (p < 0.001; effect size = 0.76). There was a cortisol change across time in the threat condition (χ2(2) = 13.83, p < 0.001), but there were unexpected decreases at both 20 (p = 0.001) and 26 min (p = 0.03) following VR. While the physiological findings need further clarification, this study provides some validation of the threat value of our VR clinical tools. As such, these VR environments can potentially be used in pain experiments to help better our understanding of basic pain mechanisms. It is only with such understanding that we might offer new avenues for pain management.

The preservation of the alveolar ridge has gained increasing importance for various types of rehabilitation, including dental implant placement. Consequently, researchers have explored different bone grafts, such as mineralized dentin matrix grafts. However, a comprehensive review of the efficacy of autogenous mineralized dentin (AMD) for alveolar ridge preservation remains lacking. In this review, we evaluated the efficacy of AMD as a method for alveolar ridge preservation in cases of delayed implant placement. A comprehensive search through PubMed, Google Scholar, Cochrane Library, and B-on repositories was conducted without time constraints up to July 2024 to identify peer-reviewed human studies. These studies assessed the percentage of newly formed bone and residual graft following bone regeneration with AMD grafts after tooth extraction, specifically in the context of delayed implant placement. Our analysis included four selected studies involving 55 patients and 67 sockets. The findings suggest that AMD grafts resulted in an average (and 95% confidence interval) of 43.8% [36.6%, 50.8%] newly formed bone, and delayed implant placement was a feasible surgical option for all patients. Although the available literature is scarce, AMD grafting has yielded promising outcomes as a method for bone reconstruction. Nevertheless, additional randomized controlled trials with larger sample sizes and longer follow-ups are required to substantiate these findings.

In the realm of autonomous driving, practical driving scenarios are fraught with numerous complexities, including inclement weather conditions, nighttime blurriness, and ambient light sources that significantly hinder drivers’ ability to discern road indicators. Furthermore, the dynamic nature of road indicators, which are constantly evolving, poses additional challenges for computer vision-based detection systems. To address these issues, this paper introduces a road indicator light detection model, leveraging the advanced capabilities of YOLOv8. We have ingeniously integrated the robust backbone of YOLOv8 with four distinct attention mechanism modules—Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), Shuffle Attention (SA), and Global Attention Mechanism (GAM)—to significantly enhance the model performance in capturing nuanced features of road indicators and boosting the accuracy of detecting minute objects. Additionally, we employ the Asymptotic Feature Pyramid Network (AFPN) strategy, which optimizes the fusion of features across different scales, ensuring not only an enhanced performance but also maintaining real-time capability. These innovative attention modules empower the model by recalibrating the significance of both channel and spatial dimensions within the feature maps, enabling it to hone in on the most pertinent object characteristics. To tackle the challenges posed by samples rich in small, occluded, background-similar objects, and those that are inherently difficult to recognize, we have incorporated the Focaler-IOU loss function. This loss function deftly reduces the contribution of easily detectable samples to the overall loss, thereby intensifying the focus on challenging samples. This strategic balancing of hard-to-detect versus easy-to-detect samples effectively elevates the model’s detection performance. Experimental evaluations conducted on both a public traffic signal dataset and a proprietary headlight dataset have yielded impressive results, with both mAP50 and mAP50:95 metrics experiencing significant improvements exceeding two percentage points. Notably, the enhancements observed in the headlight dataset are particularly profound, signifying a significant step forward toward realizing safer and more reliable assisted driving technologies.

The purpose of this study was to fully explore the mechanical properties of five different doses of an Argon–Oxygen Decarburization slag mixture in an unconfined compressive strength test. The peak stress, elastic modulus, and stress–strain curve of the mixture were studied for 90 days. Based on the experimental data and according to the theory of damage mechanics, the concept of damage threshold (t) was introduced to construct a damage constitutive model. Referring to the damage threshold of concrete, that of the mixture was determined to be 0.7 times higher than the peak strain, and the correlation coefficient between the established model and the test curve was above 0.85. These results indicate that the addition of AOD slag and fly ash can cause hydration reactions, increase the quantity of hydration products, and enhance the peak stress and elastic modulus of the mixture. The maximum increases were 94.9% and 43.1%, respectively. Parameters a and b reflect the peak stress and brittleness of the mixture, respectively. The incorporation of Argon–Oxygen Decarburization slag can make the mixture less brittle and improve its properties. The incorporation of Argon–Oxygen Decarburization slag can protect the mixture from damage. The maximum decrease is 40.2%.

The downtown area of the city of Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. The present research proposes a mobile augmented reality (MAR) application aimed at visualising the effect of possible scenarios of flooding in an area of higher risk in the city. A realistic 3D model of the city was created, using data extracted with BLosm and processed through Blender, followed by its integration into Unity with Vuforia for AR visualisation. The methodology encompasses the extraction and simplification of 3D models, mapping real-world coordinates in Unity, analysing several datasets, obtaining a model through regression and implementing a workflow to manage interactions between various Unity objects. The MAR application enables users to visualise potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. The system’s efficacy was evaluated by simulating various use-case scenarios, demonstrating the application’s capability to provide real-time, interactive flood risk assessments. The results underline the potential of integrating AR and machine learning for enhancing urban flood management and prevention.

Mattress comfort, often associated with firmness, is a complex construct influenced by factors such as material composition, construction, and personal preference. In this short communication paper, we indirectly investigated the effects of long-term mattress use on its hardness and sleep quality by observing the changes in the mattress. A mechanical durability test was performed on two structurally different mattress samples (with polyurethane core and pocket spring core) using a modified method based on the EN 1957 standard, aiming to understand the long-term effects of mattress characteristics on sleep quality. Preliminary results confirm that the mattress samples can maintain firmness and support during long-term use. The polyurethane foam mattress experienced initial compression but quickly stabilized, while the pocket spring mattress showed slight softening, maintaining overall firmness. For the polyurethane mattress, after the initial drop, the hardness value stabilized, varying between 7.53 and 9.03 N/mm, and at the end of the test, it stopped at 8.60 N/mm. The firmness rating stabilized at 4.3, showing minimal fluctuation between 4.0 and 4.6 throughout the process, while the total height loss was 3.79 mm. The hardness value of pocket spring mattresses generally decreased with increasing test cycles (it started at 5.86 N/mm and ended at 5.21 N/mm). The firmness remained relatively stable, varying between 7.3 and 7.1, and the total height loss was only 2.86 mm. The findings suggest that the firmness of a mattress can be changed with its use, highlighting the need for further research on a larger number of samples in the direction of the long-term implications of these changes on sleep comfort.

The vortex tube, also known as the Ranque–Hilsch vortex tube, is a mechanical device that separates compressed gas into hot and cold streams. It offers a reliable and cost-effective solution to a wide range of cooling applications, as it operates without moving parts, electricity, or refrigerants. Research on vortex tubes has primarily focused on understanding the mechanisms of energy separation and optimizing cooling performance by altering geometric operational parameters. In this study, a Computation Fluid Dynamics (CFD) analysis was conducted to enhance the prediction of energy separation performance and improve the overall energy efficiency of the vortex tube. First, the geometry of the experimental device was modeled to closely match its actual shape, unlike the simplified geometries commonly used in previous CFD studies. Simulations were then carried out with variation in grid systems and turbulence models, and the results demonstrated improved agreement with experimental data compared to those reported in previous studies. Finally, simulations with a modified shape of the inlet nozzle shape were performed, revealing that the energy separation effect of the vortex tube could be enhanced by approximately 15% with an increased inlet expansion ratio (ϵ) while maintaining a constant nozzle length.

In Bandpass Sampling Fourier Transform Spectroscopy, a comprehensive method for evaluating signal-to-noise ratio (SNR) has not yet been established. This paper employs an energy conservation approach to analyze the relationship of SNR between interferogram and spectra in Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy (BPS-FTS). It systematically presents models for the average SNR of the system interferogram and the average SNR of reconstructed spectra under different parameters. These models are compared with SNR models in traditional Fourier transform spectroscopy (FTS) and are mutually validated through theoretical modeling and simulation analysis. It provides a theoretical basis and simulation verification for the design and implementation of the system.

The aim of this study was to evaluate the content of selected minerals and total phenolic and flavonoid content of wheat bread and bread enriched with varying amounts of carrot powder (CP) and pumpkin powder (PP). In addition, the coverage of daily requirements of selected minerals was evaluated after the consumption of 100 g of each type of bread. The research included seven types of bread: wheat bread (WB) and bread enriched with 10%, 20%, and 30% CP and PP. The vegetable powders were obtained by freeze-drying. The concentrations of minerals—sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn)—were determined by atomic absorption spectroscopy (AAS) method. Coverage of daily requirements after consumption of a serving of bread was determined based on Recommended Daily Allowance (RDA) and Adequate Intake (AI) ratios for minerals, according to the nutritional standards for the Polish population. The addition of 10% PP led to a greater increase in Na, K, Mg, and Cu in the bread compared to CP, while for Ca, Fe, Zn, and Mn a greater increase was observed with the addition of 10% CP than PP. Among macronutrients, the greatest changes in content were recorded for Ca—the addition of 10 and 20% CP and PP resulted in increases of 66, 113, 51 and 59%. Among micronutrients, the addition of CP and PP to wheat bread caused the largest changes in Cu (46–150% increase) and Mn (25–99% increase) content. Additionally, there was a tenfold increase in total phenolic content (TPC) when 30% CP was added to the bread. Consuming 100 g of bread with CP and PP provided the greatest coverage of K (41–60%), Cu (8–17.5%), and Mn (6–17%). These findings suggest that fortification of bread with vegetable powders has the potential to be a useful method of enriching the mineral composition and increasing the proportion of selected minerals in the human diet.

Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change.

Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.

In recent years, additive manufacturing has been widely used in industrial, medical, and educational fields. Material extrusion is used in most industries to increase development efficiency and reduce costs. This study used the material extrusion to discuss the print quality of additive manufacturing and optimized the processing parameters based on material properties. Based on the literature, this study summarized the fishbone diagram influencing printing quality. The layer height, nozzle temperature, printing speed, infill pattern, and filling spacing were selected as the control factors of the Taguchi method. An orthogonal array L16 was used for parameter design. The optimal parameters were analyzed using the variance and the response surface method. The results of the study are as follows.

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition.

Deep learning is revolutionizing various scientific fields, with medical applications at the forefront. One key focus is automating image segmentation, a process crucial in many clinical services. However, medical images are often ambiguous and challenging even for experts. To address this, reliable models need to quantify their uncertainty, allowing physicians to understand the model’s confidence in its segmentation. This paper explores how the performance and uncertainty of a model are influenced by the number of annotations per input sample. We examine the effects of both single and multiple manual annotations on various deep learning architectures. To tackle this question, we employ three widely recognized deep learning architectures and evaluate them across four publicly available datasets. Furthermore, we explore the effects of dropout rates on Monte Carlo models by examining uncertainty models with dropout rates of 20%, 40%, 60%, and 80%. Subsequently, we evaluate the models using various measurement metrics. The findings reveal that the influence of multiple annotations varies significantly depending on the datasets. Additionally, we observe that the dropout rate has minimal or no impact on the model’s performance unless there is a substantial loss of training data, primarily evident in the 80% dropout rate scenario.

The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (p>0.05). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.

As Deep Neural Networks (DNNs) continue to increase in complexity, the computational demands of their training have become a significant bottleneck. Low-precision training has emerged as a crucial strategy, wherein full-precision values are quantized to lower precisions, reducing computational overhead while aiming to maintain model accuracy. While prior research has primarily focused on minimizing quantization noise and optimizing performance for specific models and tasks, a comprehensive understanding of the general principles governing low-precision computations across diverse DNN architectures has been lacking. In this paper, we address this gap by systematically analyzing the factors that influence low-precision matrix computations, which are fundamental to DNN training. We investigate three critical factors—accumulation in matrix calculations, the frequency of element usage, and the depth of matrices within the model—and their impact on low-precision training. Through controlled experiments on standard models, as well as customized experiments designed to isolate individual factors, we derive several key insights: layers with higher accumulation and matrices with lower usage frequencies demonstrate greater tolerance to low-precision noise, without significantly compromising the stability of model training. Additionally, while the depth of matrices influences the stability of matrix operations to some extent, it does not have a noticeable effect on the overall training outcomes. Our findings contribute to the development of generalizable principles for low-precision training, offering a systematic framework applicable across various DNN architectures. We provide empirical evidence supporting the strategic allocation of training bit-widths based on the analyzed factors, thereby enhancing the efficiency and effectiveness of DNN training in resource-constrained environments.

The staggered grid finite difference method has emerged as one of the most commonly used approaches in finite difference methodologies due to its high computational accuracy and stability. Inevitably, discretizing over time and space domains in finite difference methods leads to numerical artifacts. This paper introduces a novel approach that combines the widely used Taylor series expansion with the least squares method to effectively suppress numerical dispersion. We have derived the coefficients for the staggered grid finite difference method by integrating Taylor series expansions with the least squares method. To validate the effectiveness of our approach, we conducted analyses on accuracy, dispersion, and stability, alongside simple and complex numerical examples. The results indicate that our method not only inherits the capabilities of the original Taylor series and least squares methods in suppressing numerical dispersion across small and medium wavenumber ranges but also surpasses the original methods. Moreover, it demonstrates robust dispersion suppression capabilities at high wavenumber ranges.

With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users’ intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts.

In view of the fact that the technology of polarization detection performs better at identifying targets through clouds and fog, the recognition ability of the space target detection system under haze conditions will be improved by applying the technology. However, due to the low ambient brightness and limited target radiation information during space target detection, the polarization information of space target is seriously lost, and the advantages of polarization detection technology in identifying targets through clouds and fog cannot be effectively exerted under the condition of haze detection. In order to solve the above problem, a dehazing enhancement strategy specifically applied to polarization images of space targets is proposed. Firstly, a hybrid multi-channel interpolation method based on regional correlation analysis is proposed to improve the calculation accuracy of polarization information during preprocessing. Secondly, an image processing method based on full polarization information inversion is proposed to obtain the degree of polarization of the image after inversion and the intensity of the image after dehazing. Finally, the image fusion method based on discrete cosine transform is used to obtain the dehazing polarization fusion enhancement image. The effectiveness of the proposed image processing strategy is verified by carrying out simulated and real space target detection experiments. Compared with other methods, by using the proposed image processing strategy, the quality of the polarization images of space targets obtained under the haze condition is significantly improved. Our research results have important practical implications for promoting the wide application of polarization detection technology in the field of space target detection.

This study experimentally investigated the effects of selected factors on the bending moment capacity (BMC) of case furniture joints. The main aim was to explore mixed applications of wood-based materials and fasteners in manufacturing case furniture to reduce material costs. The study examined the effects of the face member material—particle board (PB), plywood (PL), and block board (BB)—edge member material (PB, PL, and BB), and joint shape (T-shape and L-shape) on BMC. Additionally, the study evaluated the effects of joint type (two eccentrics (TE), two dowels (TD), and one eccentric and one dowel (ED)), and material type (PB, PL, and BB) on BMC for L-shaped joints. The results showed that joint shape and face member material significantly affected the BMC of case furniture joint. The BMCs of T-shaped joints were significantly greater than those of L-shaped joints, regardless of the material of the face and edge members, except when the face member was made of PL. For L-shaped joints with PL face members, the BMCs were significantly higher compared to others. Joints constructed with TE exhibited significantly higher BMC compared to ED and TD for the same material type. For PB, TE joints exhibited an increase of approximately 3.0 Nm and 2.0 Nm compared to TD and ED, respectively. For PL, TE showed an increase of 9.1 Nm and 4.1 Nm compared to ED and TD, respectively. For BB, the increases were 7.0 Nm and 6.6 Nm compared to ED and TD. The BMC of joints made with PL and constructed with TE and ED was significantly greater than those of BB, followed by PB. However, for joints assembled with TD, there was no significant difference among the three materials. The ratios of BMC for joints constructed with ED compared to the half-sum of TE and TD were 0.73, 1.04, and 0.79 for PB, PL, and BB, respectively. These results suggest that the face member material predominantly influences the BMC of case furniture joints, indicating the potential to reduce costs by combining different materials and joint types.

This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.

A unique triboelectric nanogenerator (TENG) using carbon nanotube (CNT) composite papers is proposed in this work. CNT composite papers can be fabricated easily using a method based on the production of Japanese washi paper. To obtain and evaluate the proposed TENGs, several types of CNT composite papers and aluminum plates were prepared. As the CNT composite paper contained many paper fibers, it was expected to be negatively charged, and the aluminum plate was expected to be positively charged across the triboelectric series. Through various experiments, it was confirmed that CNT composite papers could be used as TENGs. In addition, it was found that when a CNT composite paper was used, it contributed to triboelectricity generation, and the contained CNTs efficiently transferred the generated charge to the electrodes. Furthermore, output voltages approaching a maximum RMS value of 300 mV could be obtained. The results of this study will aid in the practical application of paper-based TENGs in the near future.