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High-speed rubber cup polishing can exacerbate tooth surface damage, especially when preexisting conditions such as early caries or cracks exist. This study aimed to quantify the extent of damage to sound teeth based on rotating rubber cup speed and assess the damage in relation to the tooth surface condition. Using a rubber cup, 36 sound teeth were polished at 100, 3000, and 10,000 rpm, and 24 teeth with early carious lesions and 24 cracked teeth were polished at 3000 and 10,000 rpm. Polishing was performed using a rubber cup and prophylaxis paste, applying an on–off method (3.0 N force for 3 s). Damage depth was quantified using a surface profilometer and examined using scanning electron microscopy. Polishing at 10,000 rpm caused significantly more damage to sound teeth than polishing at lower speeds (depth increase: 71.45 ± 15.12 µm at 100 rpm; 61.91 ± 17.82 µm at 3000 rpm; p < 0.001). Teeth with early carious lesions or cracks demonstrated more damage after polishing than sound teeth (p < 0.05). Therefore, the rotational speed of the rubber cup has a critical impact on the extent of enamel damage. Higher speeds can increase the damage depth in both sound and damaged tooth surfaces.

This study introduces an innovative two-range, 12-stage Marx pulse generator employing thyristor switches designed specifically for the electroporation of biological cells. The generator consists of two module capacitors of different capacitances (1 μF and 0.25 μF), which enable the generation of electrical pulses with different durations and amplitudes of up to 25 kV. Safety aspects, including overcurrent and overvoltage protection mechanisms, are implemented in both the software and the hardware. In the experimental section, the tests of the Marx generator with resistive load are described in detail, and the results for the voltage fluctuations, pulse duration, and output characteristics of the generator are presented. The advantages of the design, including the high output voltage, the wide range of repetition rates, and the flexibility of the pulse parameters, are emphasized. Additionally, the research showcases the utilization of the devised generator for industrial purposes. Hence, an investigation into the efficiency of protein extraction from microalgae (Chlorella vulgaris) and the impacts of pulsed electric fields (PEFs) on the structural characteristics of casein micelles (CSMs) was chosen as an illustrative example. The obtained results provide valuable insights into the application of PEF in food processing and biotechnology and underline the potential of the developed generator for sustainable and environmentally friendly practices.

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Knee injuries represent a significant health concern for young male football players in Saudi Arabia. Despite global research on football-related injuries, there is a distinct lack of studies focusing on this demographic in the Saudi context. This research aims to fill this gap, offering insights into injury prevalence and risk factors, thereby contributing to athlete well-being and informing tailored interventions. This study aimed to investigate the prevalence of knee injuries among youth male football players in Saudi Arabia, with a focus on injury patterns, risk factors, and associated factors. A cross-sectional study was conducted, involving 104 male football players who represent five Saudi clubs and are aged 18.82 ± 0.68 years. Injury data, including severity, timing, and mechanisms, were collected. Logistic regression analyses were performed to assess the impact of various factors on the likelihood of knee injuries. The study revealed that 37.5% of participants reported prior knee injuries, predominantly muscle injuries (61.5%) occurring during training. Ligamentous injuries, particularly anterior cruciate ligament injuries, were also notable (25.6%). Logistic regression analyses indicated that factors such as age, weight, height, body mass index, playing position, duration of playing football, and playing surface significantly influenced the odds of sustaining a knee injury. This study provides insights into the prevalence and patterns of knee injuries among youth male football players in the central region of Saudi Arabia. Muscle injuries are common. Factors such as age, weight, and playing position contribute to the risk of knee injuries. The findings underscore the need for targeted injury prevention strategies and player education programs.

This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused by worms. A blue stain is a defect that is difficult to detect based on the color of the wood, because it can be easily confused with wood defects or dirt that do not impair its strength properties. In particular, the issues of detecting blue stain in wood, the use of artificial neural networks, and improving the operation of the system in production conditions are discussed in this article. While training the network, 400 boards, 4 m long, and their cross-sections of 100 × 25 [mm] were used and photographed using special scanners with laser illuminators from four sides. The test stages were carried out during an 8-hour workday at a sawmill (8224 m of material was scanned) on material with an average of 10% blue stain (every 10th board has more than 30% of its length stained blue). The final learning error was assessed based on defective boards detected by humans after the automatic selection stage. The system error for 5387 boards, 550 m long, which had blue staining that was not detected by the scanner (clean) was 0.4% (25 pieces from 5387), and 0.1 % in the case of 3412 boards, 610 mm long, on which there were no blue stains, but were wrongly classified (blue stain). For 6491 finger-joint boards (180–400 mm), 48 pieces were classified as class 1 (clean), but had a blue stain (48/6491 = 0.7%), and 28 pieces did not have a blue stain, but were classified as class 2 (28/3561 = 0.7%).

The pneumatic centralized fertilizer discharge system is an important part of pneumatic fertilization machinery and mainly includes a fertilizer discharge device, an air–fertilizer mixing device and a pneumatic distribution device. In this paper, the mechanical structure, key parameters and research methods of pneumatic centralized discharge devices, air–fertilizer mixing devices and pneumatic distribution devices at home and abroad are briefly analyzed. The advantages and disadvantages of these existing devices are summarized, the existing problems are discussed and improvement methods are put forward. In this paper, the structural adaptability, uniformity and stability of the fertilizer discharge of different types of fertilizer discharge devices, such as external groove wheel types, spiral types and centrifugal types, are comprehensively analyzed. The working principle of air–fertilizer mixing devices using the Venturi effect to achieve the uniform mixing of fertilizer and airflow is expounded. The effects of air–fertilizer mixing devices with different structural forms and parameters on mixing performance and the motion characteristics of air–solid two-phase flows are analyzed. The influence of the internal structure, key parameters and distribution mode of pneumatic distribution devices on the uniformity and accuracy of fertilizer distribution are analyzed. This paper focuses on how to improve the uniformity, stability and consistency of discharge across rows provided by the pneumatic centralized fertilizer discharge system. The research status and progress made regarding the core components of the pneumatic centralized fertilizer discharge system at home and abroad are summarized. Based on different research results, the key factors and methods for improving the uniformity of fertilizer discharge are discussed.

Weight loss solutions are on the rise, and there is growing interest in natural alternatives to the pharmaceuticals in the market, which are not devoid of relevant side effects. To this end, the objective of the current study was to assess the effects of a botanical-based blend comprised of lemon verbena and hibiscus extracts on weight management, body fat, appetite, lipid, and glucose profiles in an overweight population for 90 days. The volunteers presented significant weight loss in absence of diet (approximately 3 kg, −3.4% total body weight), which coincided with decreased body fat (−1.7%, equivalent to an almost 6% decrease compared to initial value). Body fat was assessed by anthropometry, electrical bioimpedance and DEXA scans. Cholesterol levels were also significantly decreased (approximately 7% decrease). Satiety assessment revealed that the product contributed to increased feeling of fullness and satisfaction after a meal compared to the placebo group, which could contribute to the weight loss observed. Blood glucose and blood pressure levels remained unchanged, which was expected as the volunteers were normoglycemic and their blood pressure was in the healthy range. In conclusion, daily supplementation of a botanical-based blend contributes to weight loss in overweight individuals in absence of diet, while not affecting other parameters that are in the healthy range. This product could be a candidate alternative or add-on to other weight loss products as part of a healthy lifestyle.

This study layed the groundwork for the creation of haploid and triploid celestial goldfish, presenting essential data derived from preliminary investigations. The research delved into three main areas: the short-term preservation of celestial goldfish gametes, the interaction between oocytes and foreign sperm, and the effects of temperature on fertilized eggs concerning hatching rates and late-stage ocular development. Initially, the study explored the optimal semen dilution ratio for celestial goldfish under microscopic examination. And the hybridization of the largemouth bass and celestial goldfish was investigated: largemouth bass sperm was crossbred with celestial goldfish eggs, and it was found that their sperm could not stimulate the development of celestial eye eggs. At last, celestial goldfish fertilized eggs were stimulated at 4 °C and −20 °C, respectively, to observe their impact on the hatching rate and later celestial eye rate. The results revealed no significant differences in hatching rate and celestial eye rate between the cold stimulus groups and the control group, but numerically, the 4 °C cold stimulation reduced the celestial eye rate of celestial goldfish fertilized eggs. The research provided fundamental data for artificial breeding and hybridization experiments in celestial goldfish.

Structures must provide strength, stability, and stiffness to buildings and at the same time be efficient. This study addressed the effect of design elements and parameters on the strength of bolted lateral connections in steel beams under bending using the component-based finite element method. The variables evaluated were plate thickness, horizontal and vertical spacing between bolts, and geometric arrangement of bolts. Finite element software was used to evaluate the stress state of the junction plate, its plastic deformation, and bolt shear. A sensitivity analysis was performed to determine which bolt arrangements result in safer and more efficient designs using the same components. Stress distribution within the junction plate and plastic deformation values were used to evaluate the structural performance of the joints according to EuroCode 3. The results showed that placing bolts near the edge of a plate affected the bolts’ utilization, especially with thinner plates. Additionally, introducing an offset between central and outer bolt rows is not recommended as it worsened the stress distribution and the structural performance.

Silicate and borate bioactive glasses have been reported to create alkaline conditions by rapidly releasing ions when reacting in aqueous solution. At certain levels, this alkaline solution can negatively affect cell viability. Adding phosphate ions to the glass composition can control the degradation rate of bioactive glasses and create a neutral pH environment. This study evaluated a series of borophosphate bioactive glasses (BPBGs) with nominal molar compositions 16Na2O-24CaO-xB2O3-(60-x)P2O5, where x = 0, 40, or 60. The phosphate (X0) glass (PBG) produced an acidic solution when dissolved in water; the borate (X60) glass (BBG) produced an alkaline solution, and the BPBG glass produced a pH-neutral solution. These three glasses were evaluated using adipose stem cells (ASCs), a cell population known for their therapeutic abilities. The effects of each glass on the pH of cell culture, ions released during degradation, and on ASC functions, including viability, migration, angiogenic ability, differentiation, and protein secretions, were evaluated. The X40 BPBG created a physiologically neutral pH in cell culture media after 24 h. The X0 phosphate glass promoted ASC migration, while the highly alkaline X60 borate increased the angiogenic ability of ASCs. These results indicate that BPBG can be used safely in cell culture studies and customized for specific biomedical applications.

This paper presents an ultra-wideband, low insertion loss, and high accuracy 6-bit digital step attenuator (DSA). To improve the accuracy of amplitude and phase shift of the attenuator, two innovative compensation structures are proposed in this paper: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) intended for large attenuation bits. Additionally, we propose insertion loss reduction techniques (ILRTs) to reduce insertion loss. The fabricated 6-bit DSA core area is only 0.51 mm2, and it exhibits an attenuation range of 31.5 dB in 0.5 dB steps. Measurements reveal that the root-mean-square (RMS) attenuation and phase errors for the 64 attenuation states are within 0.18 dB and 7°, respectively. The insertion loss is better than 2.54 dB; the return loss is better than −17 dB; and the input 1 dB compression point (IP1 dB) is 29 dBm at IF 12 GHz. To the best of our knowledge, this chip presents the highest attenuation accuracy, the lowest insertion loss, the best IP1dB, and a good matching performance in the range of 2–22 GHz using the 0.25 μm GaAs p-HEMT process.

Leakage in underground structures, especially tunnels, may cause seepage erosion in the surrounding soil, which in turn leads to ground subsidence, posing a great threat to urban safety. The current literature mainly focuses on seepage erosion in the sand but lacks a systematic study on the development process of seepage erosion induced by tunnel leakage in different strata. To investigate the different seepage erosion modes induced by tunnel leakage in different stratum types, a series of reduced-scale model tests were carried out. A coupled fluid–solid numerical model was further established to analyze the fine-scale characteristics of different seepage erosion modes. The results show that (1) the soil seepage erosion modes can be divided into three categories: no soil cave, unstable soil cave, and stable soil cave; (2) the adopted coupled fluid–solid numerical model based on DEM, which takes into account the degradation of clay during seepage erosion, can effectively simulate the erosion process of soil with different seepage erosion modes; (3) the phenomena of the three erosion modes are different in the process of erosion development; and (4) the micro-mechanisms of the three seepage erosion modes are different, which are manifested in the erosion range, soil arching effect, and displacement.

The stability of roadway sidewalls is crucial to ensuring people’s safety and production efficiency in coal mining. This paper investigated the deformation and failure of deep roadway sidewalls, particularly the effects of height-width ratios and lateral pressure coefficients. Our research results indicate that brittle failure occurred in the diabase sidewall rock of the Datai coal mine, and a V-shaped pit was formed as a result of shear damage caused by high stress. When the height-width ratio of a roadway increases from 0.25 to 2.00, the tensile and shear plastic failure area of the sidewall increases, and vertical stress is transferred to a deep part of the roadway sidewall. There are two stress concentration zones and two stress peak points in the sidewall of a roadway. When the lateral pressure coefficient increases from 0.10 to 1.00, the tensile plastic zone of rock mass in the sidewall first decreases and gradually reaches stability. On the other hand, the shear failure area increases and then decreases. Similarly, the sidewall horizontal displacement decreases and then increases. Additionally, the vertical stress concentration position is located near the roadway sidewall.

Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods.

Increased performance with reduced overall cost, and precise design and optimization of launch systems are critical to affordability. In this respect, the use of hybrid motors has increased to ease handling based on motor selection. In the current study, the launch vehicle’s performance is enhanced by incorporating a hybrid rocket motor into the last stage and optimized using particle swarm optimization to develop a six-degrees-of-freedom tool. This modification aims to increase payload placement flexibility, facilitate handling, and reduce costs. Thanks to the interactive subsystems within this research, this innovative study more comprehensively considers the launch vehicle trajectory design problem, allowing the simultaneous consideration of the effect of launch vehicle geometry along with other parameters in the system. In this context, the proposed method is applied to the Minotaur-I launch vehicle, and contributions of the detailed design and optimization are presented. Optimization results show that the percentage differences between these models for the original vehicle were observed to be 11.55% in velocity and 8.02% in altitude. However, there were differences of 10.06% and 48.8%, 15.8% and 23.2%, and 19.5% and 78.9% in altitudes and velocities when the center of gravity and moment of inertia changes were neglected, and constant aerodynamic coefficients were assumed, respectively. In all these cases, it was observed that the flight path angle was not close to zero. Moreover, the same mission was achieved by the launch vehicle with the optimized hybrid last stage and the propulsion performance was increased by about 7.64% based on the specific impulse and total impulse-over-weight ratio.

A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as inputs to enhance the edge contour information of YOLOv7 for detecting small targets; then, the COT self-attention mechanism is added to reconstruct the SPPCSPC module to improve YOLOv7’s ability to capture and fuse small target features in complex backgrounds; next, the normalized Wasserstein distance (NWD) is used to replace the traditional loss function based on intersection over union (IoU) metric, reducing the boundary offset between the prior box and the closest real target box in the prediction process of the object detection model and reducing the sensitivity of the YOLOv7 network to small object position deviations; finally, by constructing a shortwave infrared polarization imaging system to obtain polarization images of photovoltaic cells and detect small targets with scratch defects in photovoltaic cells, the applicability and effectiveness of the proposed method are verified. The results show that the proposed method has good recognition ability for small target defects in photovoltaic cells. By applying the constructed dataset, the detection accuracy reaches 98.08%, the recall rate reaches 95.06% and the mAP reaches 98.83%.

Background: One of the rarest complications of fixed orthodontic retention is inadvertent tooth movement of the teeth bonded to the retainer. A 25-year-old patient presented at the orthodontist as she was preoccupied about the position of the lower teeth. The aim of this case report was to present a follow-up of anterior teeth alignment after fixed retainer removal and to analyze the structure of the removed fixed orthodontic retainer in a patient suffering from a twist effect. Materials and Methods: The retainer that caused inadvertent movement has been removed, and subsequent teeth displacement was assessed with scan superimposition. The retainer structure and diameter were analyzed with a laser confocal microscope. Results: The superimposition showed significant improvements in the position of the teeth. The sole removal of the activated retainer resulted in a partial self-correction of the twist effect. Under microscopic observation, changes in the dimensions of the wire were found but were too small to cause significant changes in tooth position. Conclusions: A fixed retainer should always be removed when a twist effect is diagnosed. The wire sections covered with composite are less likely to untwist. In the presented case, the changes in the dimensions of the round retainer wire could not have led to changes in the teeth’s positions.

A devastating earthquake took place on 8 April 1893, close to the town of Svilajnac, central Serbia. Over the past decade, significant historical data on the effects of this earthquake has been collected from a variety of sources, including books, scientific publications, reports, newspapers, and coeval chronicles. Additionally, this earthquake was recorded 750 km from the epicenter at the seismological station Rocca di Papa in Rome, Italy. Based on critical review and analysis of the historical data, we demonstrate that the epicentral area of this earthquake was 531 km2, and the macroseismic effects were recorded at epicentral distances up to 600 km towards the west (Vienna, Austria) towards the north, up to 500 km (Košice–Michalovce, Slovakia), towards the east up to 460 km (Brašov–Borsec, Romania); and towards the south up to about 300 km (Radoviš, North Macedonia). Finally, we show that the key parameters of the 1893 Svilajnac earthquake are as follows: (1) epicentral intensity, I0 = IX EMS-98, (2) the estimations of the moment magnitude and focal depth based on the observed intensities, MW = 6.8 and h = 13 km, respectively, and (3) the epicenter coordinates, 44.160° N and 21.354° E.

The analysis of odds ratio curves is a valuable tool in understanding the relationship between continuous predictors and binary outcomes. Traditional parametric regression approaches often assume specific functional forms, limiting their flexibility and applicability to complex data. To address this limitation and introduce more flexibility, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based odds ratio (OR) curves, taking a specific covariate value as reference. In this paper, we introduce an R package, flexOR, which provides a comprehensive framework for pointwise nonparametric estimation of odds ratio curves for continuous predictors. The package can be used to estimate odds ratio curves without imposing rigid assumptions about their underlying functional form while considering a reference value for the continuous covariate. The package offers various options for automatically choosing the degrees of freedom in multivariable models. It also includes visualization functions to aid in the interpretation and presentation of the estimated odds ratio curves. flexOR offers a user-friendly interface, making it accessible to researchers and practitioners without extensive statistical backgrounds.

Composite foundations have been widely used and promoted in practical engineering applications. However, research on the joint-bearing mechanism of piles and soil within composite foundations is still not comprehensive enough. This paper proposes a method for calculating the additional internal forces of piles and soil within composite foundations. Based on a three-dimensional finite element analysis, this study investigates the variation patterns of the stress, displacement, and additional internal forces of piles and soil in the depth direction under the action of upper loads when using friction piles and end-bearing piles. This research aims to reveal the bearing performance of piles and soil. The results showed that, under the same conditions and due to the presence of end-bearing effects, the internal forces experienced by the entire pile body of the end-bearing piles were more uniform, exhibiting significant advantages in resisting deformation and being able to withstand larger loads. Additionally, the diffusion mechanism of the vertical forces, stresses, and displacements of piles and soil is discussed. Due to the negative frictional resistance of soil and the influence of pile end-bearing effects, the distribution of internal forces and the displacements of piles and soil exhibited different characteristics. This study provides a scientific reference for the theoretical analysis and design of composite foundations.

This study aims to compare the effectiveness of ultrasonically and sonically activated irrigation in terms of extrusion risk, root canal debridement, and biofilm removal, considering distinct apical preparation sizes, through an ex vivo study in human teeth. Instrumented teeth, to an apical size of 35/.06 or 50/.06, were assigned to three different irrigation procedures: ultrasonically activated irrigation, sonically activated irrigation, and conventional manual irrigation. Apical extrusion risk was evaluated by quantifying irrigant and debris extrusion (n = 10/group). Debris evaluation and smear layer removal from the root canal wall were conducted by scanning electron microscopy (SEM) (n = 5/group), and the elimination of a mature biofilm of Enterococcus faecalis was assessed through resazurin assay and SEM (n = 10/group). For statistical analyses, Student’s paired t-test and the ANOVA with post-hoc Tukey were used. Activated irrigations exhibited a higher risk of extrusion for the larger apical size, while the risk for manual irrigation remained independent of the apical size. Substantially fewer residual debris and smear layers were observed after the activation of the irrigant, and there was a notable enhancement in biofilm elimination compared to manual irrigation (p < 0.05). Notably, the effectiveness of both activated irrigations was more pronounced in root canals prepared to a size 50/.06, with ultrasonic activation showing enhanced improvements. The findings of this study underscore the substantial impact of both ultrasonically and sonically activated irrigation on the effectiveness of root canal disinfection and debridement. This impact is especially prominent with larger apical size, albeit accompanied by an increased risk of extrusion.

In recent years, along with the remarkable development of women’s soccer, significant attention has been given to the study of asymmetry in lower limbs. However, there is uncertainty about whether and to what extent jumping asymmetries affect the performance of female soccer players. The aims of this study were to examine (a) possible asymmetries in jumping ability and (b) the correlations between asymmetries and performance of female soccer players in 10 m and 30 m speed tests, as well as in change-of-direction speed tests. The study involved 12 adolescent (age: 15.8 ± 0.8 years, body mass: 59.4 ± 7 kg and height: 160.5 ± 5.1 cm) and 10 adult female soccer players (age: 22.3 ± 4.4 years, body mass: 62.2 ± 7.5 kg and height: 165.7 ± 6.1 cm). Pearson’s correlations showed no statistically significant relationships between vertical and horizontal asymmetries and time in 10 m, 30 m and 505 change-of-direction speed tests for adolescent players. In adult players, a significantly high correlation was found between asymmetries in single-leg hop tests (for distance) and time in 505 change-of-direction speed tests (r = 0.68, p < 0.05). Adult players showed higher asymmetry values in vertical and horizontal jump tests, but these asymmetries were not significant (p > 0.05). Practitioners are recommended to implement strength and power training programs, as well as injury prevention protocols, aiming to reduce asymmetries, in order to minimize the risk of injuries, and potentially improve performance of female soccer players in certain fitness tests.

Alzheimer’s disease (AD), a slowly progressive neurodegenerative disorder, is the main cause of dementia worldwide. However, currently, the approved drugs to combat AD are effective only in treating its symptoms. In fact, an efficacious treatment for this complex and multifactorial disorder remains to be discovered, demanding the urgent development of new therapeutic approaches for the disease, such as the use of bioactive secondary metabolites (SMs) from natural sources. Sessile organisms, like plants, are unable to escape from adverse environmental conditions and must therefore create their own defense. Their main defense strategy is chemical defense that includes the production of an enormously diverse array of bioactive SMs, such as terpenes and their derivatives. This largest and most diverse group of plant SMs also provide the treatment of several diseases due to their broad-spectrum bioactivities, for example, anticancer, antioxidant, and anti-inflammatory properties. Thus, the evaluation of the neuroprotective potential of terpenes is imperative. It is known that the major AD clinical indications (CIs) are extracellular senile plaques of amyloid-β (Aβ) protein, intracellular hyperphosphorylated tau (τ) neurofibrillary tangles (NFTs), uncommon neuroinflammatory response, oxidative stress, and synaptic and neuronal dysfunction. Therefore, terpenes that may decrease these CIs might be used for AD treatment. Surely, terpenes targeting more than one AD pathogenic mechanism, multi-target drug ligands (MTDLs), have the potential to become a leading AD treatment. Thus, this review analyzes, for each CI, the scaffolds of the selected terpenes leading to the highest activity.

In the face of the challenges of limited urban space and the continuous increase of vehicles, stereo garages have been widely used as a solution in cities. In order to improve the automation and intelligence level of the stereo garage, this paper applies the digital twin technology to the lift-and-transverse stereo garage. A five-dimensional model of a digital twin has been developed based on an actual stereo garage. Combined with S7-PLCSIM Advanced, Botu TIA Portal, and NX MCD to build a virtual simulation platform, realizing the virtual simulation design and debugging of the digital twin-based stereo garage. This approach allows to test and optimize operational processes without relying on physical equipment, reducing labor and field debugging costs, shortening deployment cycles, and significantly reducing development costs. In addition, MCD allows real-time monitoring and control of security risks and failures detected. Finally, the feasibility of the virtual simulation and debugging scheme based on digital twin is verified by comparing the operation data of the virtual model and the actual stereo garage, which provides new ideas for the intelligent development of stereo garage but also can be used as an important reference for the development of equipment in other areas.

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

Soft rocks are prone to softening upon contact with water, and their rapid deterioration in mechanical properties is a significant cause of instability and failure soft rock masses. Besides, the macroscopic mechanical response of rocks is closely related to the mineral composition and microstructure. The purpose of this research is to consider the heterogeneity factors and softening effects, and systematically investigate the influence of confining pressure and softening time on the damage and failure characteristics of soft rocks. The Voronoi polygons generated using a built-in Voronoi diagram algorithm and contact elements (the substances with cementing capacity) of UDEC discrete element method are employed to represent the clastic grains and interfacial cemented bonding (ICB) structures in soft rock. Based on the Voronoi probabilistic method, the grain-based discrete element model (GB-DEM) considering the softening effect is established by introducing a meso-scale softening damage factor, along with a detailed calibration method for meso-scale parameters. The damage parameters such as the crack initiation threshold, the crack damage threshold, the damage degree, and the tensile and shear crack ratio are then analyzed. The study results indicate that the simulated strengths of the heterogeneous models under different water immersion time are in good agreement with the experimental results. The thresholds for crack initiation and damage, the proportions of tensile and shear cracks, and the degree of damage are positively correlated with the confining pressure. The attenuation patterns of the crack initiation threshold and damage threshold in the heterogeneous models with water immersion time are highly consistent with the meso-scale softening damage factor. The damage parameters show a trend of increasing first and then decreasing with the extension of water immersion time. The cement–cement contact elements are the main locations for crack initiation and propagation. The research outcomes have significant theoretical and practical implications for understanding and predicting the mechanical behavior of soft rocks under a water–rock interaction.

Cone-beam computed tomography (CBCT) remains the diagnostic modality of choice. The involvement of the cortical bone and adjacent teeth can be easily established via CBCT. Magnetic resonance can be helpful in the estimation of any other soft-tissue tumour spread within this anatomical area. The soft, hard-tissue, or mixed aetiology of tumours requires a differential diagnosis and accurate evaluation. If such pathologies arise, an adequate biopsy or incisional biopsy is essential to evaluate the type of tumour histopathologically. The occurrence of some neural tumours in the oral cavity is rare. Schwannomas (SCs), like some neuromas and other types of neural tumours, are rare and atypical. During clinical examination, a smooth, sponge-like, elastic mass could indicate other small salivary gland tumours rather than an oral neural tumour. Such pathologies of neural origins are quite rare and are uncommon findings in the oral cavity; therefore, their appearance may be conflated with other more typical benign or malignant tumours in the oral cavity. Establishing the status of bone via CBCT, the tooth involvement and the composition of the cortical bone may be helpful for establishing the best treatment of choice. The presented case report describes a rare schwannoma localised at the mandibular retromolar trigone.

The disruption of the blood–brain barrier (BBB) in Alzheimer’s Disease (AD) is largely influenced by amyloid beta (Aβ). In this study, we developed a high-throughput microfluidic BBB model devoid of a physical membrane, featuring endothelial cells interacting with an extracellular matrix (ECM). This paper focuses on the impact of varying concentrations of Aβ1–42 oligomers on BBB dysfunction by treating them in the luminal. Our findings reveal a pronounced accumulation of Aβ1–42 oligomers at the BBB, resulting in the disruption of tight junctions and subsequent leakage evidenced by a barrier integrity assay. Additionally, cytotoxicity assessments indicate a concentration-dependent increase in cell death in response to Aβ1–42 oligomers (LC50 ~ 1 µM). This study underscores the utility of our membrane-free vascular chip in elucidating the dysfunction induced by Aβ with respect to the BBB.

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

The cosmetics industry is one of the fastest growing markets in terms of searching for new ingredients. Recently, there has been a growing interest in products made during fermentation, which are being introduced into cosmetics with increasing frequency, creating a market that emphasizes the positive image of healthy, environmentally friendly components with a positive effect on skin. Scientists mainly focus on examining biological activity as well as the impact on changes in the production of bioactive ingredients in various plant species undergoing fermentation. The studies show that bioferments have scientifically proven anti-aging and anti-inflammatory effects, among other skin benefits. Due to the increasing emphasis on environmental protection, ecofriendly compounds are being sought. This group includes surfactants, which are also obtained by fermentation. Plant-based and microbial biosurfactants, due to their multifunctional properties, such as detergency, emulsifying, foaming, moisturizing, and antibacterial activity, can replace chemical surfactants in many skincare formulations. This review focuses especially on elucidating the importance of the bioferments and biosurfactants and their potential in the cosmetic industry.

Traditional federated learning relies heavily on mature datasets, which typically consist of large volumes of uniformly distributed data. While acquiring extensive datasets is relatively straightforward in academic research, it becomes prohibitively expensive in practical applications, especially in emerging or specialized medical fields characterized by data scarcity. This poses a significant challenge. To address this issue, our study introduces a federated learning model that integrates few-shot learning techniques and is complemented by personalized knowledge distillation to further enhance the model’s classification accuracy. This innovative approach significantly reduces the dependence on large-scale datasets, enabling efficient model training under limited data conditions. Our experimental evaluations conducted on small-scale datasets, including Omniglot, FC100, and mini-ImageNet, indicate that our model surpasses existing state-of-the-art federated learning models in terms of accuracy, achieving a substantial improvement. Specifically, on the FC100 dataset, the classification accuracy of the conventional federated learning algorithm FedAvg was merely 19.6%, whereas the method proposed in this study achieved a classification accuracy of 41%, representing an improvement of more than double. This advancement not only highlights our model’s superiority in alleviating the challenges of limited data availability, but also expands the applicability of federated learning to a broader range of applications.

The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.

Traditional model tests for soil and rock materials face challenges in observing the slurry diffusion within the soil mass, hindering the understanding of the relationship between grouting-induced ground deformation and grout diffusion. This study conducts grouting diffusion model tests using a self-developed experimental setup on both ordinary and transparent sand. We investigate cement slurry diffusion patterns, distribution characteristics, and temporal variations in ground uplift displacement during the grouting process. By leveraging a visualization grouting model and non-intrusive displacement measurements, we directly observe and verify the changes in cement slurry diffusion and ground displacement in transparent sand. The results indicate the following: during non-steady grouting in sand, slurry diffusion progresses from low-pressure infiltration to medium-pressure compaction, culminating in high-pressure fracturing; ground uplift displacement curves exhibit a consistent “step-like” increase with grouting time, featuring accelerated growth after each step; and visualization tests reveal a strong correlation between grouting pressure, slurry diffusion, and corresponding uplift displacement. Distinct features in the grouting pressure plot align with the acceleration phases of the displacement; at a water–cement ratio (w/c) of 0.8, the stratum’s vertical deformation shows a symmetric “higher in the middle, lower on the sides” distribution. As the burial depth decreases, the stratum’s uplift displacement tends to flatten horizontally, especially at w/c = 0.8 and 1.2.

Numerous studies have shown that a traditional model persists in the teaching of history, in which students are not allowed to think for themselves and are assigned a passive role based on the mere memorisation of information. This reality is in opposition to the technological and technical boom taking place in the current educational context and to the enhancement of innovative strategies and methodologies that mark the role that students must occupy as protagonists of their own learning. This paper aims to compare the perceptions and historical knowledge of 93 baccalaureate students (16–18 years of age) following the implementation of an intervention programme based on active learning situations mediated by augmented reality and historical thinking skills. A quasi-experimental quantitative design with a non-equivalent control group was employed to meet these objectives. The results showed higher scores in the perception and knowledge of students in the experimental group compared with those in the control group. This line of work should be continued in the future with new studies to corroborate these findings, prioritising pedagogical models based on student activity and protagonism via the use of technology and critical thinking.

The cyclist’s posture is typically characterized by a trunk flexion position to reach the handlebar of the bike. The pelvis serves as the base of the spine, and its tilt has been associated with the degree of extensibility of the hamstring, particularly in flexion postures of the trunk. The aim of this study was to determine whether, in professional cyclists, the degree of hamstring extensibility influences the pelvic tilt maintained while seated on the bicycle with support from the three handlebar grips of the road bike, as well as in other positions of the bicycle. To evaluate pelvic tilt, all participants were measured using the Spinal Mouse system. The results revealed statistically significant differences in pelvic tilt among the six positions assessed (p ≤ 0.05). Furthermore, the degree of hamstring extensibility of the hamstrings presented a strong and positive correlation with pelvic tilt in standing posture (r = 0.82), Sit-and-Reach (r = 0.76), and Toe-Touch (r = 0.88). However, the degree of hamstring extensibility showed no significant correlations with pelvic tilt in any posture maintained on the bicycle.

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

The purpose of this study was to compare the change of Body Composition (BC) (height, weight, body fat percentage and muscle mass) as a function of the trimester and category in a sample of young soccer players. Data collection was performed in five consecutive seasons (2016–2021). The sample consisted of 741 young male football players of different categories (Under 14 year old (U14), U15, U16, U17 and U18) belonging to a high-performance football academy. Considering the trimestral change of all the raw anthropometrics variables a set of new variables called the trimestral change in percentage (TC) of each raw variable was computed. Two-way repeated measures ANOVA (including the raw anthropometric variables as dependent and trimester and the age-category as independent) revealed differences for the anthropometric variables (p value < 0.001 in all cases), concluding that the effect of trimester reaches conventional levels of statistical significance. The trimester by age in contrast was significant (p < 0.05) in all raw variables except for the height. Considering the TC variables, the variable height-TC showed an increase (p value < 0.05) while the variable muscle mass-TC was near the significative value (p = 0.09). In this case the interaction trimester by age category was not significative (p > 0.05 in all cases). It seems that height suffers more changes in the first trimester but the weight, body fat percentage and muscle mass changes more in the second and third trimester. It is important to modulate the training load according to the trimester-specific response, although these improvements may vary according to factors such as genetics, diet, sleep and the specific training.

Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2N equations to solve for 2N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N+1, thereby corroborating the theoretical analysis.

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

It is probable that the term marine traffic engineering (MTE) was first used by Toyoda and Fuji [...]

The geological conditions evaluation of coalbed methane (CBM) is of great significance to CBM exploration and development. The CBM resources in the Southern Sichuan Coalfield (SSC) of China are very abundant; however, the CBM investigation works in this area are only just beginning, and the basic geological research of CBM is seriously inadequate, restricting CBM exploration and development. Therefore, in this study, a representative CBM block (Dacun) in the SSC was selected, and the CBM geological conditions were evaluated based on field injection/fall-off well testing, gas content and composition measurements, and a series of laboratory experiments. The results show that the CH4 concentrations of coal seams in the Dacun Block, overall, take on an increasing trend as the depth increases, and the CH4 weathering zone depth is 310 m. Due to the coupled control of temperature and formation pressure, the gas content shows a “increase→decrease” trend as the depth increases, and the critical depth is around 700 m. The CBM is enriched in the hinge zone of the Dacun syncline. The moisture content shows a negative correlation with CBM gas content. The porosities of coal seams vary from 4.20% to 5.41% and increase with the Ro,max. The permeabilities of coal seams show a strong heterogeneity with values ranging from 0.001mD to 2.85 mD and present a decreasing trend with the increase in depth. Moreover, a negative relationship exists between coal permeability and minimum horizontal stress magnitude. The reservoir pressure coefficients are between 0.51 and 1.26 and show a fluctuation change trend (increase→decrease→increase) as the depth increases, reflecting that three sets of independent superposed gas-bearing systems possibly exist vertically in the Longtan Formation of the study area. The Langmuir volumes (VL) of coals range from 22.67 to 36.84 m3/t, indicating the coals have strong adsorptivity. The VL presents a parabolic change of first increasing and then decreasing with the increase in depth, and the turning depth is around 700 m, consistent with the critical depth of gas content. The gas saturations of coal seams are, overall, low, with values varying from 29.10% to 116.48% (avg. 68.45%). Both gas content and reservoir pressure show a positive correlation with gas saturation. The CBM development in the Dacun Block needs a large depressurization of reservoir pressure due to the low ratio (avg. 0.37) of critical desorption pressure to reservoir pressure.

Stance information has a significant influence on market strategy, government policy, and public opinion. Users differ not only in their polarity but also in the degree to which they take a stand. The traditional classification of stances is quite simple and cannot fully depict the diversity of stances. At the same time, traditional approaches ignore user sentiment features when expressing their stances. As a result, this paper develops a multi-stance detection model by fusing sentiment features. First, a five-category stance indicator system is built based on the LDA model, then sentiment features are extracted from the reviews using the sentiment lexicon, and finally, stance detection is implemented using a hybrid neural network model. The experiment shows that the proposed method can classify stances into five categories and perform stance detection more accurately.

Pure Bi7Fe3Ti3O21 ceramic material and gadolinium ion (Gd3+)-doped ones were prepared by solid-state reaction method using simple oxides. The findings of the XRD measurements confirmed the initial author’s assumption that the dopant ions substituted in perovskite blocks influenced the dimensions of the unit cell parameters. All obtained materials are single-phase and show an orthorhombic structure with the Fm2m space group. Microstructure studies show that the admixture gadolinium doping changes the microstructure of the base material, changing grain shapes from plate-like to rounded. The temperature dependences of the electric permittivity have shown the existence of a maximum, the temperature location of which depends on both the frequency and the concentration of Gd3+ ions. The highest values of electric permittivity were characteristic of the material with an admixture of Gd3+ ions in the amount of x = 0.6 (f = 1 kHz), and the lowest values were for material with x = 0.2 (f = 1 kHz). Studies of the magnetoelectric effect have shown that the strongest coupling between magnetic and electrical properties was demonstrated by a material doped with Gd3+ ions in the amount of x = 0.2, for which the magnetoelectric coupling coefficient is equal to α = 12.58·10−9 s/m.

Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

Numerous studies derived from medicinal herbs have been conducted to explore bioactive compounds as potential alternatives to synthetic drugs, aiming to mitigate harmful side effects and alleviate economic burdens. In this study, we assessed the safety and potential biological activities of extracts from Fomes fomentarius L. (FFL). The FFL extracts were obtained through various ethanol concentrations, as follows: 0%, 30%, 50%, 70%, and 100%, respectively. All extracts did not induce mutagenicity even up to 5 mg/plate concentration. In the assessment of antioxidant activity, only the hot water extract exhibited weaker antioxidant activity than the other ethanol extracts. Notably, all extracts exhibited significant antimutagenetic effects only with a metabolically active enzyme system (S9 mix). The condition of 70% ethanol extract displayed the most robust antimutagenic activity; thus, the extract was sequentially fractionated with solvents of varying polarities to isolate inhibitory components. After the fractionization, the diethyl ether and butanol fractions effectively suppressed the growth of mutated colonies, suggesting that those such as essential oils, vitamins, alkaloids, and flavonoids can be considered major active compounds. Overall, our study demonstrated that FFL extracts induce potent antioxidant and antimutagenic effects. Further investigations are warranted to verify specific active compounds which induce an antimutagenic effect. Our findings provide valuable insights into FFL as a promising source for potential functional food development.

In this work, using a Richards-Wolf formalism, we derive explicit analytical relationships to describe vectors of the major and minor axes of polarization ellipses centered in the focal plane when focusing a cylindrical vector beam of integer order n. In these beams, the major axis of a polarization ellipse is found to lie in the focal plane, with the minor axis being perpendicular to the focal plane. This means that the polarization ellipse is perpendicular to the focal plane, with its polarization vector rotating either clockwise or anticlockwise and forming “photonic wheels”. Considering that the wave vector is also perpendicular to the focal plane, we conclude that the polarization ellipse and the wave vector are in the same plane, so that at some point these can coincide, which is uncharacteristic of transverse electromagnetic oscillations. In a cylindrical vector beam, the spin angular momentum vector lies in the focal plane, so when making a circle centered on the optical axis, at some sections, the handedness of the spin vector and circular motion are the same, being opposite elsewhere. This effect may be called an azimuthal transverse spin Hall effect, unlike the familiar longitudinal spin Hall effect found at the sharp focus. The longitudinal spin Hall effect occurs when opposite-sign longitudinal projections of the spin angular momentum vector are spatially separated in the focal plane. In this work, we show that for the latter, there are always an even number of spatially separated regions and that, when making an axis-centered circle, the major-axis vector of polarization ellipse forms a two-sided twisted surface with an even number of twists.

Preparing submicron and nanoscale phosphors with good optical properties for practical applications is a challenging task for current inorganic long afterglow luminescent materials. This study utilized commercialized SrAl2O4:Eu2+, Dy3+ phosphors (SAOED) as raw materials and employed solvents with lower polarity or non-polar solvents for dynamic solvothermal treatment. The commercialized phosphor’s overall average particle size was reduced from 42.3 μm to 23.6 μm while maintaining the fluorescence intensity at 91.39% of the original sample. Additionally, the study demonstrated the applicability of the dynamic solvothermal method to most other commercialized inorganic phosphors. The experiment produced a high-brightness nano-sized phosphor with a yield of 5.64%. The average diameter of the phosphor was 85 nm, with an average thickness of 16 nm. The quantum efficiency of the phosphor was 74.46% of the original sample. The fingerprint imaging results suggest that the nano-sized phosphors have potential for practical applications.

Air-to-air heat pumps are used in today’s ventilation systems increasingly often as they provide heating and cooling for buildings. The energy transformation modes of these units are subject to constant change due to the varying outdoor air state, including temperature and humidity. When choosing how to operate and control energy transformers, it is important to be able to adapt effectively to the changing outside air conditions. Nowadays, modern commercial heat pumps offer two levels of control flexibility: a compressor with a variable speed and an electronic expansion valve. This combination of control elements has boosted the seasonal energy efficiency of heat pumps. For a long time, cycle control possibilities have been dominated by electronic controls. The authors of this paper aim to present an additional element to the traditional heat pump controls, which provides a third level of control over the cycle. To achieve the objective, experimental investigations of a heat pump integrated into a ventilation unit have been carried out under real-life conditions. The experiments involved varying the operating modes of the unit by adjusting the compressor speed, the position of the expansion valve, and the volume of the system loop. The study examined the performance characteristics of the heat pump and found that the performance of a variable-volume heat pump is comparable to that of a conventionally operated typical constant-volume heat pump system. In addition, the study found that by adding a third level of volume control to the active heating circuit, in combination with conventional controls, the heat pump’s heat output range could be extended by 69.62%. The study determined the variation of the heat pump cycle in the p-h diagram with the variation of the loop volume. The benefits and drawbacks of a heat pump with a variable-volume loop are discussed in this study.

Provisional fixed partial dentures represent a critical phase in dental treatment, necessitating heightened mechanical durability, particularly in comprehensive and extended treatment plans. Strengthening these structures with various reinforcing materials offers a method to enhance their resilience. Utilizing a three-point testing methodology on standardized trial specimens allows for a comparative assessment of various materials and reinforcement techniques for pre-prosthetic applications. This study aims to validate and assess the significance of integrating different reinforcing materials into standardized test bodies. The study focuses on test specimens comprising three types of unreinforced laboratory and clinical polymers for provisional constructions (n = 6)—heat-cured PMMA (Superpont C+B, Spofa Dental, Czech Republic), CAD-CAM prefabricated PMMA (DD temp MED, Dental Direkt, Germany), CAD-CAM printing resin (Temporary CB Resin, FormLabs, USA), self-polymerizing PEMA (DENTALON plus, Kulzer, Germany), light-polymerizing composite (Revotek LC, GC, Japan), and dual-polymerizing composite (TempSpan, Pentron, USA). Additionally, laboratory polymers are evaluated in groups with five types of reinforcing filaments (n = 15)—Glass Fiber (Fiber Splint One-Layer, Polydentia, Switzerland), Polyethylene thread (Ribbond Regular 4.0 mm, Ribbond Inc., USA), triple-stranded chrome-cobalt wire for splinting 015″ (Leone S.p.a., Italy), Aesthetic ligature wire 012″ (Leone S.p.a., Italy), and Glass Fiber coated with light-cured composite 8.5 × 0.2 mm (Interlig, Angelus, Brazil). Analysis of the data using Generalized Linear Models (GLMs) reveals that the experimental bodies, produced via the subtractive digital method using PMMA (DD temp MED, Dental Direkt GmbH, Germany) as the polymer and glass filaments as the reinforcement, exhibit superior mechanical properties, particularly when pre-wetted with Interlig liquid composite (Angelus, Brazil).

Crowd anomaly detection is crucial in enhancing surveillance and crowd management. This paper proposes an efficient approach that combines spatial and temporal visual descriptors, sparse feature tracking, and neural networks for efficient crowd anomaly detection. The proposed approach utilises diverse local feature extraction methods, including SIFT, FAST, and AKAZE, with a sparse feature tracking technique to ensure accurate and consistent tracking. Delaunay triangulation is employed to represent the spatial distribution of features in an efficient way. Visual descriptors are categorised into individual behaviour descriptors and interactive descriptors to capture the temporal and spatial characteristics of crowd dynamics and behaviour, respectively. Neural networks are then utilised to classify these descriptors and pinpoint anomalies, making use of their strong learning capabilities. A significant component of our study is the assessment of how dimensionality reduction methods, particularly autoencoders and PCA, affect the feature set’s performance. This assessment aims to balance computational efficiency and detection accuracy. Tests conducted on benchmark crowd datasets highlight the effectiveness of our method in identifying anomalies. Our approach offers a nuanced understanding of crowd movement and patterns by emphasising both individual and collective characteristics. The visual and local descriptors facilitate high-level analysis by closely relating to semantic information and crowd behaviour. The analysis observed shows that this approach offers an efficient framework for crowd anomaly detection, contributing to improved crowd management and public safety. The proposed model achieves accuracy of 99.5 %, 96.1%, 99.0% and 88.5% in the UMN scenes 1, 2, and 3 and violence in crowds datasets, respectively.