Appl. Sci., Volume 14, Issue 9 (May-1 2024) – 416 articles

This paper introduces a novel filter algorithm termed as an MKMC-CSVSF which combined square-root cubature Kalman (SR-CKF) and smooth variable structure filtering (SVSF) under multi-kernel maximum correntropy criterion (MKMC) for accurately estimating the state of the fully submerged hydrofoil craft (FSHC) under the influence of uncertainties and multivariate heavy-tailed non-Gaussian noises. By leveraging the precision of the SR-CKF and the robustness of the SVSF against system uncertainties, the MKMC-CSVSF integrates these two methods by introducing a time-varying smooth boundary layer along with multiple fading factors. Furthermore, the MKMC is introduced for the adjustment of kernel bandwidths across different channels to align with the specific noise characteristics of each channel. A fuzzy rule is devised to identify the appropriate kernel bandwidths to ensure filter accuracy without undue complexity. The precision and robustness of state estimation in the face of heavy-tailed non-Gaussian noises are improved by modifying the SR-CKF and the SVSF using a fixed-point approach based on the MKMC. The experimental results validate the efficacy of this algorithm. Full article

Convolutional neural networks (CNNs) have achieved promising results in many tasks, and evaluating the model’s generalization ability based on the trained model and training data is paramount for practical applications. Although many measures for evaluating the generalization of CNN models have been proposed, the existing works are limited to small-scale or simplified model sets, which would result in poor accuracy and applicability of the derived methods. This study addresses these limitations by leveraging ResNet models as a case study to evaluate the model’s generalization ability. We utilized Intersection over Union (IoU) as a method to quantify the ratio of task-relevant features to assess model generalization. Class activation maps (CAMs) were used as a representation of the distribution of features learned by the model. To systematically investigate the generalization ability, we constructed a diverse model set based on the ResNet architecture. A total of 2000 CNN models were trained on the ImageNet subset by systematically changing commonly used hyperparameters. The results of our experiments revealed a strong correlation between the IoU-based evaluation method and the model’s generalization performance (Pearson correlation coefficient more than 0.8). We also performed extensive experiments to demonstrate the feasibility and robustness of the evaluation methods. Full article

Antimicrobial susceptibility tests, achieved through the use of antibiotic-impregnated disks in a suitable laboratory environment, are conducted to determine which antibiotics are effective against the bacteria present in the body of an infected patient. The Kirby–Bauer method, a type of disk diffusion antimicrobial susceptibility test, is currently widely applied in microbiology laboratories due to its proven effectiveness. In our study, we developed an algorithm that utilizes image processing techniques to detect the inhibition zones of bacteria. A certain color depth acts as the threshold for the inhibition zone, with its radius determined according to the size of the reference object. This approach facilitates the measurement of inhibition zones and employs machine learning and deep learning to categorize antibiograms, followed by determination of whether a bacterium on the disk is sensitive or resistant to the antibiotics applied. The focus of this research is creating an automated interpretation system for antimicrobial susceptibility testing using the disk diffusion technique, thus simplifying the measurement and interpretation of inhibition zone sizes. Full article

This paper investigates the effect of spatial variability of soil elastic modulus on the longitudinal responses of the existing shield tunnel to the new tunnel undercrossing using a random two-stage analysis method (RTSAM). The Timoshenko–Winkler-based deterministic method considering longitudinal variation in the subgrade reaction coefficient and the random field of the soil elastic modulus discretized by the Karhunen–Loeve expansion method are combined to establish the RTSAM. Then, the proposed RTSAM is applied to carry out a random analysis based on an actual engineering case. Results show that the increases in the scale of fluctuation and the coefficient of variation of the soil elastic modulus lead to higher variabilities of tunnel responses. A decreasing pillar depth and mean value of the soil elastic modulus and an increasing skew angle strengthen the effect of the spatial variability of the soil elastic modulus on tunnel responses. The variabilities of tunnel responses under the random field of the soil elastic modulus are overestimated by the Euler–Bernoulli beam model. The results of this study provide references for the uncertainty analysis of the new tunneling-induced responses of the existing tunnel under the random field of soil properties. Full article

Front loaders used in agriculture are characterized by a compact structure, which limits the scope of their application. The loading possibilities are expanded by designing front loaders equipped with telescopic arms. This design increases the loader’s working area, making it easier to load trucks. It is necessary to work on the arm extension drive and perform strength analyses on the new structures. This article presents a FEM numerical analysis of the structure of an extending front loader and an assessment of the state of stress and the value of displacements under the influence of load. This study discusses the advantages and disadvantages of front loaders compared to telehandlers and the legal requirements and standards for the design of front loaders in Europe. This work presents the concept of loader arm movement and assesses the effectiveness of using hydraulic motors coupled with a screw gear. The obtained results prove that the newly designed extending front loader system is safe and stable. Full article

Jumping robots possess the capability to surmount formidable obstacles and are well-suited for navigating through complex terrain environments. However, most of the existing jumping robots face challenges in achieving stable jumping and they also have low energy utilization efficiency, which limits their practical applications. In this work, a two-module jumping robot based on tensegrity structure is put forward. Firstly, the structural design and jumping mechanism of the robot are elaborated in the article. Then, dynamic models, including the two modules’ simultaneous jumping and step-up jumping process of the robot, are established utilizing the Lagrange dynamic modeling method. On this basis, the effects of parameters, including the stiffness of elastic cables and the initial tilt angle of the robot, on the jumping performance of the robot can be obtained. Finally, simulations are carried out and a prototype is developed to verify the rationality of the tensegrity-based jumping robot proposed in this work. The experiment results show that our jumping robot can achieve a stable jumping process and the step-up jumping of each module of the prototype can have higher energy efficiency than that of simultaneous jumping of each module, which enables the robot a better jumping performance. This research serves as a valuable reference for the design and analysis of jumping robots. Full article

Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size—320; batch size—32; epoch number—300; layers freeze option—10; data augmentation—on; learning rate—0.001; momentum—0.95; and weight decay—0.0007. These results may be useful for various tasks in which small and similar objects are analysed. Full article

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields. Full article

After the experience of the COVID-19 pandemic, it has become evident that efficient and secure interoperability of medical information is crucial for effective diagnoses and medical treatments. However, a significant challenge arises concerning the heterogeneity of the systems storing patient information in medical centers or hospitals. Memory management becomes a pivotal element for the effective operation of the proposed API, as it must seamlessly execute across various devices, ranging from healthcare units, such as mobile phones, to servers in cloud computing. This proposal addresses these issues through techniques designed to enhance the performance of the software architecture in creating a medical interoperability API. This API has the capacity to be cloned and distributed to facilitate the exchange of data related to a patient’s medical history. To tackle heterogeneity, efficient memory management was implemented by utilizing an object-oriented approach and leveraging design patterns like abstract factory and wrapper. Regarding the evaluation of the proposal, this study showed an estimated performance of 94.5 percent, which was indirectly demonstrated through the assessment of operation sequences. This result suggests a satisfactory level based on complexity and coupling. Full article

Single-walled carbon nanotubes (SWCNTs) loaded with magnetic iron oxide nanoparticles were prepared by the arc discharge method and air heat treatment. The nanocomposite was characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, vibrating sample magnetometry, etc. The results showed that the heat-treated nanocomposites (SWCNTs/Fe_xO_y) had iron oxide phases and superparamagnetic properties with a saturation magnetization of 33.32 emu/g. Compared with the non-heat-treated materials, SWCNTs/Fe_xO_y had a larger specific surface area and pore volume. Using SWCNTs/Fe_xO_y to remove the organic contaminant (bisphenol A, BPA), it was found that under the conditions of pH = 3 and adsorbent dosage of 0.2 g/L, the maximum adsorption capacity of the composite was 117 mg/g, and the adsorption could reach more than 90% in only 5 min when the BPA content was below 0.05 mmol/L. The fitting results of the Langmuir and D-R models are more consistent with the experimental data, indicating a relatively uniform distribution of the adsorption sites and that the adsorption process is more consistent with physical adsorption. The kinetic calculations showed that the SWCNTs/Fe_xO_y exhibits chemical effects on both the surface and the gap, and the adsorption process is controlled by the π-π bonds and the hydrophobicity of the SWCNTs/Fe_xO_y. Full article

In this study, we report on the isolation, purification, and anti-inflammatory evaluation of compounds from the plant species Ageratina pichinchensis. Using open-column chromatography, 11 known compounds were purified, which chemical structures were elucidated by nuclear magnetic resonance techniques (1D and 2D). All compounds were evaluated in an in vitro model of RAW 264.7 mouse macrophage cells, measuring the nitric oxide inhibition to determine the anti-inflammatory effect. The compound betuletol 3-O-β-glucoside (11) inhibited nitric oxide with a half-maximal inhibitory concentration (IC₅₀) of 75.08 ± 3.07% at 75 µM; additionally, it inhibited the secretion of interleukin 6 (IL-6) and activation of the nuclear factor (NF-kβ). These results suggest that the anti-inflammatory effect attributed to A. pichinchensis species is promoted by compound 11, which could be considered a potential anti-inflammatory agent by suppressing the expression of NF-kβ target genes, such as those involved in the proinflammatory pathway and inducible nitric oxide synthase (iNOS). Full article

Context modeling has always been the groundwork for the dialogue response generation task, yet it presents challenges due to the loose context relations among open-domain dialogue sentences. Introducing simulated dialogue futures has been proposed as a solution to mitigate the problem of low history–response relevance. However, these approaches simply assume that the history and future of a dialogue have the same effect on response generation. In reality, the coherence between dialogue sentences varies, and thus, history and the future are not uniformly helpful in response prediction. Consequently, determining and leveraging the relevance between history–response and response–future to aid in response prediction emerges as a pivotal concern. This paper addresses this concern by initially establishing three context relations of response and its context (history and future), reflecting the relevance between the response and preceding and following sentences. Subsequently, we annotate response contextual relation labels on a large-scale dataset, DailyDialog (DD). Leveraging these relation labels, we propose a response generation model that adaptively integrates contributions from preceding and succeeding sentences guided by explicit relation labels. This approach mitigates the impact in cases of lower relevance and amplifies contributions in cases of higher relevance, thus improving the capability of context modeling. Experimental results on public dataset DD demonstrate that our response generation model significantly enhances coherence by 3.02% in long sequences (4-gram) and augments bi-gram diversity by 17.67%, surpassing the performance of previous models. Full article

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources. Full article

An arbitrary Lagrangian-Eulerian lattice Boltzmann flux solver (ALE-LBFS) coupled with the mode superposition method is proposed in this work and applied to study two- and three-dimensional flutter phenomenon on dynamic unstructured meshes. The ALE-LBFS is applied to predict the flow field by using the vertex-centered finite volume method with an implicit dual time-stepping method. The convective fluxes are evaluated by using lattice Boltzmann solutions of the non-free D1Q4 lattice model and the viscous fluxes are obtained directly. Additional fluxes due to mesh motion are calculated directly by using local conservative variables and mesh velocity. The mode superposition method is used to solve for the dynamic response of solid structures. The exchange of aerodynamic forces and structural motions is achieved through interpolation with the radial basis function. The flow solver and the structural solver are tightly coupled so that the restriction on the physical time step can be removed. In addition, geometric conservation law (GCL) is also applied to guarantee conservation laws. The proposed method is tested through a series of simulations about moving boundaries and fluid–structure interaction problems in 2D and 3D. The present results show good consistency against the experiments and numerical simulations obtained from the literature. It is also shown that the proposed method not only can effectively predict the flutter boundaries in both 2D and 3D cases but can also accurately capture the transonic dip phenomenon. The tight coupling of the ALE-LBFS and the mode superposition method presents an effective and powerful tool for flutter prediction and can be applied to many essential aeronautical problems. Full article

Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This process typically involves using load-strain stiffness matrices, derived from ground calibration tests, to map measured flight parameters to loads at critical locations. Presently, deep learning neural network methods are rapidly developing, offering new perspectives for this task. This paper explores the potential of deep learning models in predicting flight parameter loads and strains, integrating the methods of flight parameter preprocessing techniques, flight maneuver recognition (FMR), virtual ground calibration tests for wings, dimensionality reduction of flight data through Autoencoder (AE) network models, and the application of Long Short-Term Memory (LSTM) network models to predict strains. These efforts contribute to the prediction of strains in critical areas based on flight parameters, thereby enabling real-time assessment of aircraft damage. Full article

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 R_o,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 (V_L) of coals range from 22.67 to 36.84 m³/t, indicating the coals have strong adsorptivity. The V_L 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. Full article

This paper evaluates the performance of the Rubin reduction methods, enhanced with static modal derivatives, for vibration analysis of geometrically nonlinear structures with friction contact. Static modal derivatives are computed numerically based on Rubin reduction, which includes free interface normal modes and residual flexibility attachment modes, by introducing a finite displacement around these modes. Then, the most relevant static modal derivatives are selected using an improved strategy that incorporates weighting factors derived from both a nonlinear static analysis and a geometrically linear transient run. This enhanced Rubin method is also compared with the previously used enhanced Craig–Bampton method, which is based on fixed normal modes, constraint modes, and their static derivatives. The effectiveness of these methods is demonstrated through vibration analysis of a geometrically nonlinear beam in different contact configurations. Both methods proved their robustness, achieving accurate results with a relatively small number of modes in the reduced space, thus ensuring low online computation times. Furthermore, the analyses show the significant impact of using a geometrically nonlinear model on the accurate prediction of a contact state. Full article

Oil spill cleanup in water remains a critical challenge due to the harmful secondary pollution from conventional methods such as burning or chemical degradation. Herein, we present a facile method to fabricate a superhydrophobic and superoleophilic polyurethane (PU) foil for efficient and environmentally friendly oil/water separation. More specifically, micropillar arrays were embedded onto the foil surface through a nanoimprinting process. Microporous structures were generated at the foil cross-section by a supercritical carbon dioxide (CO₂) saturation method. The dimensions of pillar and pore structures were optimized with the aim of boosting selective wetting (i.e., water repellency and oil attraction) properties. As a result, the developed PU foil shows an oil absorption efficiency nearly 4 times higher than a pristine reference. Moreover, the structured PU foil stably retains the absorbed oil for over a week, demonstrating an absorption capacity of nearly 400%, which is also much superior than the unstructured sample. Our concept of combining both topographical micropillars and cross-sectional micropores onto PU foil provides a novel approach for achieving efficient and environmental friendly oil/water separation. Full article

This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner’s dilemma, covert channels, qualitative metrics, and existing steganography techniques in computer communications. An architecture was developed to assess the effectiveness of this solution, and experiments were conducted, with their results presented. This contribution leverages established steganographic principles and seamlessly integrates with widely adopted IPsec protocols, offering a solution to improve covert communication within computer networks. Full article

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. Full article

Eight balls were manufactured with a 3D printer to resemble various types of 32-panel soccer balls. One ball was completely smooth, whereas the other seven possessed various dimple patterns on their surface panels. Seam width and seam depth were also varied. Wind-tunnel experiments were performed to extract aerodynamic coefficients, and also to determine the critical Reynolds number for each manufactured ball. A new surface roughness parameter is introduced, and a fitting formula is presented, which allows for the prediction of the critical Reynolds number if the new parameter is known. Full article

This paper presents the reconstruction framework of the Roman mosaic with the central scene from the abduction of Europa. The mosaic depicting Europa, discovered in Ptuj (Slovenia) and dated from the second half of the third to the beginning of the fourth century AD, once decorated the representative room of a Roman villa. The experimental section addresses the materials and methods used in the 2D reconstruction of the mosaic, including the creation of line drawings of the mosaic based on the preserved part of the mosaic, photogrammetric acquisition, and the creation and processing of 1:1 raster reconstructions of the entire mosaic. This is followed by color management and interpretation approaches which allow the mosaic elements to be implemented in a 3D animation. The presented approaches could be implemented in the reconstruction process of other mosaics and archaeological objects with adaptations to the specifics of related objects. Full article

This study aimed to investigate the role of the thymus in influencing long-term outcomes of COVID-19 by comparing the thymic appearance in patients with and without COVID-19 pulmonary sequelae at chest computed tomography (CT). A total of 102 adult patients previously hospitalized for COVID-19 underwent a follow-up chest CT three months after discharge. Pulmonary sequelae and thymic appearance were independently assessed by two experienced radiologists. The thymus was detectable in 55/102 patients (54%), with only 7/55 (13%) having any kind of pulmonary sequelae, compared to 33 out of 47 (70%, p < 0.001) in patients without thymic visibility, as confirmed in age-stratified analysis and at logistic regression analysis, where thymic involution had a 9.3 odds ratio (95% CI 3.0–28.2, p < 0.001) for the development of pulmonary sequelae. These results support the hypothesis that thymic reactivation plays a protective role against adverse long-term outcomes of COVID-19. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Consumers are becoming increasingly concerned about synthetic preservatives like nitrites in meat, prompting the meat industry to explore alternatives in order to lower nitrite levels. This study investigated the effects of incorporating hemp flour on the chemical and shelf-life characteristics of minced meat products with reduced nitrite content. Three types of products were prepared: HF0 (control) (0% hemp flour, 30 mg/kg NaNO₂), HF4 (4% hemp flour, 15 mg/kg NaNO₂), and HF6 (6% hemp flour, 15 mg/kg NaNO₂). Analyses were conducted on proximate composition, fatty acid composition, antioxidant properties, lipid oxidation, colour, texture, and sensory characteristics. The addition of hemp flour at 6% reduced moisture content and influenced ash and sodium chloride levels in minced meat products. Despite the favorable fatty acid profile of hemp flour, its inclusion did not significantly alter the composition of the products. However, it did lead to significantly lower levels of lipid oxidation and modified the antioxidant capacity. Colour attributes were affected, with a higher hemp flour content resulting in colour deterioration. Cooking loss increased with a higher hemp flour content, and the minced meat products were significantly harder. Visual and olfactory sensory evaluation indicated that there were no significant differences in most traits, suggesting consumer acceptance of hemp-flour-enriched minced meat products. Overall, this study highlights the potential of hemp as a functional ingredient in minced meat products, also exhibiting the ability to reduce lipid oxidation. Full article

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. Full article

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). Full article