Visible light positioning (VLP) technology is a classic application of visible light communication (VLC), which inherits the advantages of VLC and applies it to the field of positioning. LED (light-emitting diode) is a type of light source. Because of its high brightness, aesthetically pleasing characteristics, and ease of installation, it is used in a variety of indoor lighting applications. However, most of the current VLP technology is still in the laboratory simulation stage and cannot be used in industry or life on a large scale due to various reasons, such as accuracy and cost. Because of the large size of LED flat panel lamps, there are almost no VLP applications with LED flat panel lamps as the emitting light source. Therefore, this paper proposes a VLP technology combining LED flat panel light and a barcode, with a single flat panel light at the transmitting end and a smartphone with a camera at the receiving end, to achieve fuzzy positioning. The paper further uses the angle sensor to assist in designing the “pseudo-two-light positioning” algorithm and selects 16 test points for experiments, and the average positioning error can reach a minimum of 6.5023 cm, achieving centimeter-level positioning accuracy requirements. Full article

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on the chemical composition data of the glass, and developed a case-specific clustering algorithm (K-Means++) to further categorize the glass cultural relics. K-Means++ was developed to reduce the sensitivity of a traditional K-Means clustering algorithm, by choosing the next clustering center with probability inversely proportional to the distance from the current clustering center. Then we verified the validity of the six subcategories we defined by inertia and silhouette score and evaluated the sensitivity of the clustering algorithm. We obtained a robustness ratio that maintained over 0.9 in the random noise test and a silhouette score of 0.525 in the clustering, which illustrated significant divergence among different clusters and showed the result is reasonable. With our proposed algorithm and classification result, a more comprehensive understanding of glass relics can be gained. Full article

The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic. Many studies deploy machine learning and deep learning algorithms on QUIC traffic classification. However, standalone machine learning models are subject to overfitting and poor predictability in complex network traffic environments. Deep learning on the other hand requires a huge dataset and intensive parameter fine-tuning. On the contrary, ensemble techniques provide reliability, better prediction, and robustness of the trained model, thereby reducing the chance of overfitting. In this paper, we approach the QUIC network traffic classification problem by utilizing five different ensemble machine learning techniques, namely: Random Forest, Extra Trees, Gradient Boosting Tree, Extreme Gradient Boosting Tree, and Light Gradient Boosting Model. We used the publicly available dataset with five different services such as Google Drive, YouTube, Google Docs, Google Search, and Google Music. The models were trained using a different number of features on different scenarios and evaluated using several performance metrics. The results show that Extreme Gradient Boosting Tree and Light Gradient Boosting Model outperform the other models and achieve one of the highest results among the state-of-the-art models found in the literature with a simpler model and features. Full article

A museum is the destination of the crystallization of history and culture, and a museum is the center for the development of heritage culture. The display and exhibition of historical relics can promote the harmonious development of society. However, there are many defects in traditional museums. The collection in the museum is scattered. The exhibition needs a specific time and place to open, and the exhibition mode is boring and lacks entertainment, which seriously limits the museum’s dissemination of history and culture. In order to improve the public’s number of visits, museums need to keep pace with the times. Using augmented reality technology can digitize the information in the museum and integrate it with the user’s real environment, which solves the limitation of time and space for most users to visit the museum, and greatly increases the entertainment of users to visit the museum. In addition, the application of blockchain technology in digital museums can effectively ensure the security of museum cultural relics data and quickly restore the lost cultural relics information. The digital museum can help tourists realize cross-region viewing. Through multimedia software and hardware, it can greatly stimulate tourists’ interest and attract tourists of different ages to visit. This article uses digital technology to present physical museums in a digital form on the network, but it is difficult for digital museums to conduct targeted analysis on tourists of different ages. This article will analyze the attraction ability of digital museums to tourists of different ages, thereby attracting tourists of different ages to visit. This paper compared traditional museums with digital museums based on augmented reality technology and blockchain technology. The experimental results showed that from the perspective of young people, the average interactivity of traditional museums and digital museums was 47.20% and 78.20%, respectively; from the perspective of the elderly, the average interactivity of traditional museums and digital museums was 59.04% and 70.36%, respectively. Therefore, the digital museum built by using augmented reality technology and blockchain technology can effectively improve the interaction with users. The digital museum can improve the interaction with tourists of different ages, attract a large number of tourists for virtual visits, and improve the cultural transmission and economic development of the digital museum. Full article

Outreach projects are often used to a limited extent for dissemination purposes and rarely have a significant impact on the student’s teaching and technical skills. The RadioLab project requires a proactive interaction between researchers and students by experimental activities for measuring environmental radioactivity, in particular radon gas. Buildings considered to be of radiological interest, such as schools, have been selected to carry out radon gas activity concentration measurements using solid-state nuclear track passive detectors LR-115. The results of annual measurements, made over 6 years and involving a total of 952 rooms, distributed in 67 schools throughout the Campania region, were collected. These data, deemed scientifically reliable (i) can be overlapped over geological characterization data enhancing the relationship between lithology and radon, (ii) confirmed data from the radon potential map of the Campania region about the distribution of indoor radon, and finally (iii) contributed to the collection of radon indoor data of the Campania region. The results obtained highlighted the need and effectiveness of increasing the network of schools involved in the outreach activity and in the implementation of experimental activities with applicative effects in the scientific and research sectors. Full article

The in situ stress distribution is one of the driving factors for the design and construction of underground engineering. Numerical analysis methods based on artificial neural networks are the most common and effective methods for in situ stress inversion. However, conventional algorithms often have some drawbacks, such as slow convergence, overfitting, and the local minimum problem, which will directly affect the inversion results. An intelligent inverse method optimizing the back-propagation (BP) neural network with the particle swarm optimization algorithm (PSO) is applied to the back analysis of in situ stress. The PSO algorithm is used to optimize the initial parameters of the BP neural network, improving the stability and accuracy of the inversion results. The numerical simulation is utilized to calculate the stress field and generate training samples. In the application of the Shuangjiangkou Hydropower Station underground powerhouse, the average relative error decreases by about 3.45% by using the proposed method compared with the BP method. Subsequently, the in situ stress distribution shows the significant tectonic movement of the surrounding rock, with the first principal stress value of 20 to 26 MPa. The fault and the lamprophyre significantly influence the in situ stress, with 15–30% localized stress reduction in the rock mass within 10 m. The research results demonstrate the reliability and improvement of the proposed method and provide a reference for similar underground engineering. Full article

Drone brood is a little-known bee product, often treated as beekeeping waste or natural varroosis bait. Obtaining drone brood from beehives does not weaken the bee family, which is why this product is used as natural medicine in Eastern European countries. The main objective of this work was to design an innovative dietary supplement containing freeze-dried drone brood (DB) enriched with calcium ions (3:1). As the calcium component, inorganic calcium carbonate (CC) and ground chicken eggshells (ES) were used. Bioaccessibility of hormones, selected nutrients (proteins and amino acids), non-nutritive polyphenols from pure drone brood (DB), and designed supplements (DB + CC, DB + ES) were analyzed using an in vitro gastrointestinal system. It was shown that drone brood components are better bioaccessible from the DB + ES compared to DB + CC and DB capsules. An increase was achieved by up to 93.33%, 21.29%, 105.14%, and 52.34% for testosterone, estradiol, calcium, and polyphenols, respectively. Drone brood proteins were completely digested to free amino acids which was confirmed by SDS-PAGE electrophoresis and high-performance thin layer chromatography (HPTLC). Due to the demonstrated synergistic action of drone brood and the calcium of eggshells, the newly proposed two-ingredient supplement seems to be an efficient treatment to equalize hormonal and calcium deficiency in osteoporosis; however, its application requires further studies. Full article

Water can deteriorate the compositional properties of rock through softening and dissolution. The water content rate of rock has a certain effect and can cause changes in rock properties caused by the water action. In this research, to study the effects of the water content rate on the strength and brittle plasticity of limestone, uniaxial compression tests with different water content rate states were conducted, and the form of limestone damage under different water content rate conditions was analyzed. The effects of the different water content rates on the modulus of elasticity, uniaxial compressive strength, brittleness index B value, and brittleness correction index BIM value (BIM: the ratio of dissipated strain energy to releasable elastic strain energy at the peak point of the specimen) of limestone were investigated. It was found that as the rate of water content in the limestone increased from 0% to 0.27%, the penetration shear surface on the limestone’s damaged surface decreased. The modulus of elasticity decreased from 8.85 to 6.76 GPa, the uniaxial compressive strength decreased from 74.11 to 57.60 MPa, the brittleness index B value decreased from 1.17 to 1.04, and the brittleness correction index BIM value increased from 0.09 to 0.26. As the rate of water content on the limestone increased, the rock’s modulus of elasticity and uniaxial compressive strength decreased. Additionally, the rock’s brittleness decreased, and the percentage of plastic deformation in the total deformation increased. Full article

In various engineering fields, the fusion of infrared and visible images has important applications. However, in the current process of fusing infrared and visible images, there are problems with unclear texture details in the fused images and unbalanced displays of infrared targets and texture details, resulting in information loss. In this article, we propose an improved generative adversarial network (GAN) fusion model for fusing infrared and visible images. In the generator and discriminator network structure, we introduce densely connected blocks to connect the features between layers, improve network efficiency, enhance the network’s ability to extract source image information, and construct a content loss function using four losses, including an infrared gradient, visible intensity, infrared intensity, and a visible gradient, to maintain a balance between infrared radiation information and visible texture details, enabling the fused image to achieve ideal results. The effectiveness of the fusion method is demonstrated through ablation experiments on the TNO dataset, and compared with four traditional fusion methods and three deep learning fusion methods. The experimental results show that our method achieves five out of ten optimal evaluation indicators, with a significant improvement compared to other methods. Full article

The objective was to evaluate the accuracy of ClinCheck^® reliability in the sagittal, vertical, transverse, and arch length dimensions before and after Invisalign^® (Align Technology, Santa Clara, CA, USA) treatment. This retrospective study was conducted on 206 patients who underwent dual-arch clear aligner therapy exclusively with Invisalign^®. Digital models were obtained from iTero^® scanners from three different private practices where the treatment plans were performed and executed by multiple orthodontists with varying degrees of experience. The ClinCheck^® models of the initial, achieved, and predicted outcome were obtained from Align Technology^® and the values were compared using Pearson correlation (p < 0.05) to determine if predicted values were correlated with achieved values. ANOVA was used to compare the different centers. The highest reliabilities were associated with interincisal angle (96.23%), upper intercanine width (97.97%), lower intercanine width (97.67%), upper intermolar width (97.58%), and lower intermolar width (97.72%) (p < 0.001). The lowest reliabilities were associated with arch length parameters in which the upper was 38.79% and the lower at 30.02% (p = 0.03, p < 0.001). However, there were no statistically significant differences between the centers in terms of the accuracy of treatment provided. The mean predicted accuracy of Invisalign^® was 76.85% overall; however, Invisalign^® providers may need to exaggerate the digital tooth movements to achieve the desired outcome. Full article

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction. Full article

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients. Full article

Lemon essential oil (LEO) is associated with a multitude of health benefits due to its anticancer, antioxidant, antiviral, anti-inflammatory and bactericidal properties. Its drawback is that it is very sensitive to oxidation by heat. For this reason, researchers are increasingly investigating the use of LEO in nanoemulsions. In this work, we used laser diffraction, rheology and multiple light scattering techniques to study the effects of different HLB numbers (indicating different mixtures of Tween 80 and Span 20) on the physical stability of nanoemulsions formulated with LEO. We found that different HLB numbers induced different destabilization mechanisms in these emulsions. An HLB number lower than 12 resulted in an Ostwald ripening effect; an HLB number higher than 12 resulted in coalescence. In addition, all the developed nanoemulsions exhibited Newtonian behavior, which could favor the mechanism of creaming. All emulsions exhibited not only a growth in droplet size, but also a creaming with aging time. These findings highlight the importance of selecting the right surfactant to stabilize nanoemulsions, with potential applications in the food industry. Full article

Titanium is one of the most widely used metals in implantology owing to its reduced modulus, improved corrosion resistance and good biocompatibility. In spite of its excellent biocompatibility, it does not exhibit inherent antibacterial and antioxidant activity. Tannic acid is a naturally occurring polyphenol compound which exhibits excellent antibacterial, antioxidant and antimutagenic activity. The development of tannic acid-based coatings on the titanium surface holds great potential to reduce the risks associated with implant applications, thereby increasing the longevity of implants. In the present study, tannic acid was deposited on the titanium surface and the surface displayed a slightly improved hydrophilic character with an increase in surface energy. The release kinetics of tannic acid from titanium surface was analyzed and it showed an initial burst effect followed by a gradual decrease over time. Hemolysis tests revealed the erythrocyte compatibility of the developed surfaces. The improved hydrophilicity observed the release kinetics of tannic acid and reduced hemolysis rates revealed the potential of this facile technique for implant surface engineering applications. Full article

Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households’ responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes (“evacuation reluctant” and “evacuation keen”) by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. “Evacuation reluctant” individuals with a longer housing tenure are more likely to follow phased evacuation orders. “Evacuation keen” individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees’ compliance behavior to phased evacuation orders. Full article