Appl. Sci., Volume 13, Issue 15 (August-1 2023) – 484 articles

In this paper, the results obtained from a series of parametric analyses, where the influence that geometric and mechanical parameters have in the structural response of existing vernacular cob walls within an Irish context, are presented. A design of experiments using central composite designs was implemented along with analysis of variance following two computational approaches, namely, the finite element method and kinematic limit analysis. As results, a series of response surfaces and parametric equations with which it is possible to compute safety factors and collapse multipliers (within the range of values studied) are provided. Based on the results obtained, it could be concluded that traditional cob walls in Ireland are very robust. Relatively high acceleration values, unlikely to happen in a low seismic hazard region such as Ireland, would be needed to start the collapse mechanisms studied or cause yielding in typical vernacular cob walls. Furthermore, the equations generated with the refined regression models can be used by practitioners as a first approach to estimate the safety levels of existing cob buildings with similar characteristics. Full article

With the gradual increase in the mining depth of coal resources, the destruction of the rock structure of the inter-layered rock of the near coal seam under the influence of mining has led to the frequent occurrence of water-inrush disasters in mines, which seriously affects the safety of mine production and the safety of the people in the underground. Therefore, it is important to study the mechanism of the water inrush of the rock between the coal seams under the influence of mining to control the occurrence of water inrush disasters and protect the loss of groundwater resources. This paper takes the Hanjiawan coal mine with typical stratigraphic characteristics as the background for research and studies the structural characteristics of interlayer rock breakage and the solid–liquid coupling inrush water disaster mechanism during the mining of 2⁻² and 3⁻¹ coals. The study shows that according to the damage degree and destruction depth of the inter-layered rock caused by the mining of the upper and lower coal seams, combined with the slip line theory and the “three bands” collapse theory, the inter-layered rock is classified into a completely fractured inter-layer, a fractured–broken stacked inter-layer, and a fractured–broken–fractured combined inter-layered rock using $L\le h_m+H_{k2}{}^{\prime }$, $L>h_m+H_{k2}{}^{\prime }$, and $L\ge h_m+H_{li2}{}^{\prime }$ as the discriminating criteria. Combined with the structural classification of inter-layer rock and the discriminating criteria, we used similar simulation experiments and on-site research to analyze the evolution law and distribution characteristics of four types of inter-layer rock water-inrush fractures in different mines and put forward the classification of inter-layer rock water-inrush channels based on the width, length, and penetration of the fractures. Based on the characteristics of the water-inrush channel of inter-layer rock, we constructed the network-boundary inrush water calculation model of inter-layered rock and network-attach-boundary inrush water calculation model, solved the water movement of the water-inrush channel in the model by transforming the flat flow state, fracture to flow state, and pore-fracture flow state, and finally revealed the mechanism of the disaster by which water-inrush of inter-layer rocked was induced. Finally, we revealed its mechanism of inducing the inter-layer rock inrush water disaster. Our research enriches the theory and research ideas of the water-inrush disaster, provides theoretical support and a basis for the control of water-inrush disasters in similar conditions, and ensures the safe production of mines. Full article

The recent seismic activity in Croatia has inflicted significant damage upon numerous buildings, with masonry structures being particularly affected. Consequently, experimental investigations and structural condition assessments’ have garnered increased attention, as they have become integral to the renovation process for such buildings. Additionally, assessing the structural condition prior to seismic events is vital for determining the extent to which earthquakes impact the stiffness of systems, such as masonry structures. This paper presents the results of experimental investigations and numerical analysis conducted on a damaged high school building in Sisak, Croatia. The experimental investigation involved shear testing, flat jack analysis, and operational modal analysis. Utilizing the available drawings and mechanical properties determined experimentally, an initial numerical model was developed. Subsequently, through the iterative process of finite element model updating, the initial numerical model was refined based on the structural dynamic properties. The updated numerical model was then employed to assess the structural condition prior to the earthquake event. This study contributes to the field by providing insights into the post-earthquake estimation of dynamic properties in intact masonry buildings, utilizing a comprehensive approach that combines experimental investigations and finite element model updating. By quantifying the changes in dynamic parameters, such as natural frequencies and mode shapes, the study provides valuable insights into the response characteristics of damaged masonry building. The observed differences in natural frequencies between the damaged and undamaged states are as follows: 9% for the first mode shape, 6% for the second mode shape, and 2% for the third mode shape. Full article

A rocket’s turbulent jet radiates intense acoustic waves, which are an acoustic load for structural components like payload, launch structure, and rocket avionics, and impact communities near the launch site. Therefore, a careful characterization of the acoustic field produced by a rocket engine can provide crucial information during the design phase. In particular, this deals with improving the understanding of the acoustics of low-thrust hybrid rocket engines. Since an accurate jet noise detection around the entire launch site is time-consuming and extremely cost-prohibitive, a fast and reliable predictive tool is invaluable. For this purpose, a semi-empirical model was employed, using the exhaust plume property and geometric characteristics of the nozzle as input. Experimental data collected during a firing test campaign, conducted in the framework of HYPROB-NEW project by the Italian Aerospace Research Center, were decisive to discuss the validity of the model also for low-thrust hybrid propulsion and support the goodness of the noise curves and metrics estimated for nearby regions and provide considerations about the implications of engine geometric characteristics on noise emissions. Full article

Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visual robots based on the 3D point cloud. The current solution is converting the 3D point clouds to 2D images, and then processing these with a convolutional neural network (CNN) classification. Although the popular scan-context descriptor obtained from the 3D data can retain parts of the 3D point cloud characteristics, its accuracy is slightly low. This is because the scan-context image under the adjacent label inclines to be confusing. This study reclassifies the image according to the CNN global features through image feature extraction. In addition, the dictionary-based coding is leveraged to construct the retrieval dataset. The experiment was conducted on the North-Campus-Long-Term (NCLT) dataset under four-seasons conditions. The results show that the proposed method is superior compared to the other methods without real-time Global Positioning System (GPS) information. Full article

Guangzhou and Shenzhen are two core cities in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It is increasingly important to regulate water quality in urban development. The Forel–Ule Index (FUI) can be obtained by optical data and is an important indicator. Therefore, we used Sentinel-2 to calculate the FUI of 41 lakes and reservoirs in Guangzhou and Shenzhen from January to December in 2016–2021, and analyzed their spatio-temporal variations, including spatial distributions, seasonal variations, and inter-annual variations. We also performed a correlation analysis of driving factors. In Guangzhou, the FUI was low in the north and west, and high in the south and east. In Shenzhen, the FUI was high in the west and low in the east. Moreover, 68% of the lakes and reservoirs in Guangzhou exhibited seasonal variations, with a low FUI in summer and autumn, and high levels in spring and winter. Shenzhen had the lowest FUI in autumn. Furthermore, 36% of the lakes and reservoirs in Guangzhou exhibited increasing inter-annual variations, whereas Shenzhen exhibited stable and decreasing inter-annual variations. Among the 41 lakes and reservoirs analyzed herein, the FUI of 10 water areas were positively correlated with precipitation, while the FUI of 31 water areas were negatively correlated with precipitation. Increased precipitation leads to an increase in external pollutants and sediment, as well as the resuspension of substances in the water, resulting in more turbid water. Therefore, an increase in precipitation is positively correlated with the FUI, whereas a decrease in precipitation is negatively correlated with the FUI. These findings can be used to design suitable management policies to maintain and control the local water quality. Full article

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight $\omega$, self-learning factor $c_1$, and social factor $c_2$; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R² was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries. Full article

To prevent pedicle screw implant failure, a diagnostic technique that allows surgeons to evaluate implant stability easily, quickly, and quantitatively in clinical orthopedic situations is required. This study aimed to predict the insertion torque equivalent to laboratory-level evaluation accuracy. This serves as an index of the implant stability of pedicle screws placed in cadaveric bone, which relies on laser resonance frequency analyses (L-RFA) when irradiating with two types of lasers. The machine learning analysis was optimized using a dataset with artificial bone as teaching data. In this analysis, many explanatory variables extracted from the laser-induced vibration spectra obtained during an analysis/RFA evaluation were predicted by selecting important variables using the least absolute shrinkage and selection operator and performing a non-linear approximation using support vector regression. It was found that combining both artificial and cadaveric bone data with the bone densities as teaching data dramatically improved the determination coefficient from R² = −0.144 to R² = 0.858 as the prediction accuracy and reduced the influence of differences between artificial and cadaveric bones. This technology will contribute to the development of preventive diagnostic technologies that can be used during surgery, which is necessary in order to further advance treatment technologies. Full article

Managing sediment transport in streams is crucial to the surface water resource development strategy and has several implications for flood risk and water management, hydropower use, and balancing river morphology. This paper summarises the movement and behaviour of suspended sediment within the Slovenian portion of the River Drava, covering a span of thirteen years from 2005 to 2018. An analysis of relevant data collected during this period is also presented. Suspended-sediment dynamics strongly depend on flow velocity, seasonal variations in sediment sources, and human interventions in the riverbed. The transportation of material in the River Drava results in the accumulation of sediments in reservoirs and riverbeds, consequently impeding the natural hydrological cycle by reducing the outflow into aquifers. The 2018 high-water event is analysed in terms of the dependence of concentration of suspended sediments on discharge, where counterclockwise hysteresis was observed, providing an essential clue to the origin of sediment. Sediments from the River Drava in Slovenia are managed with some conventional processes and are mainly deposited or reintegrated into rivers and aquatic ecosystems. Some additional sediment management strategies with long-term solutions for efficient and comprehensive water management, hydropower, and ecological problems are proposed. Full article

Falls in older adults are leading causes of fatal and non-fatal injuries, negatively impacting quality of life among those in this demographic. Most elderly falls occur due to unrecoverable limb collapse during balance control in the single-limb support (SLS) phase. To understand why older adults are more susceptible to falls than younger adults, we investigated age-related differences in lower limb kinematics, kinetics, and muscle synergy patterns during SLS, as well as their relationship to postural control strategies. Thirteen older and thirteen younger healthy adults were compared during the SLS phase of balance recovery following an unexpected surface drop perturbation. Compared to younger adults, older adults demonstrated (1) greater trunk flexion, (2) increased hip extension torque and reduced hip abduction torque of the perturbed leg, and (3) higher postural sway. Trunk flexion was correlated with a delayed latency to the start of lateral-to-medial displacement of center of mass from the perturbation onset. The group-specific muscle synergy revealed that older adults exhibited prominent activation of the hip extensors, while younger adults showed prominent activation of the hip abductors. These findings provide insights into targeted balance rehabilitation and indicate ways to improve postural stability and reduce falls in older adults. Full article

Background: Alveolar socket preservation is a topic of serious interest, and researchers have investigated this problem quite extensively. In terms of aesthetics, it is very important to avoid bone resorption if the clinician decides to insert the implant immediately after the extraction. Recently, a new approach utilizing a barrier external to the socket has been developed, which has advanced the evolution of this technique. Immediate implants have also created some difficulty when re-evaluated in long-term follow-up, especially when an aesthetic result is part of the goal of the procedure. Methods: The modified periosteal inhibition (MPI) technique, which has shown interesting outcomes, is evaluated in this paper on a large group of patients. In this case series, among 14 patients, 11 received immediate implants using the MPI technique and immediate provisionalization, and 3 received immediate implants using the MPI technique and customized healing abutment. All patients showed ridge preservation to different degrees, ranging from 0.02 to 1.17 mm, with an average gain of 0.51 mm. Results: all of the 14 patients maintained the original ridge shape, and 1 showed an increase in bucco-lingual size. Conclusions: This case series confirms the promising information reported in earlier studies on this technique. Larger samples will be necessary to confirm the predictability of this new approach. Full article

The cavitation phenomenon and shaft deformation have a significant impact on the tribological performance of the journal bearing. A mixed lubrication model is developed that takes into account surface roughness and asperity contact, as well as the effects of cavitation and deflection. The fluid–solid coupling effect in bearing deformation, asperity contact, and film pressure are investigated. The effect of boundary conditions on the lubrication regimes is discussed. The results of simulations with and without cavitation are compared under steady-state conditions. The results show that when cavitation is considered by the mixed lubrication model under a given load, the eccentricity is reduced, and the maximum oil film pressure is also reduced. The speed range of the bearing simulated with the mixed lubrication model increases after considering deflection deformation. The mixed lubrication model proposed in this paper is able to provide accurate results of pressure distribution and coefficient of friction and can be applied in the design and analysis of journal bearings. Full article

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider. Full article

Globally, gastrointestinal (GI) tract diseases are on the rise. If left untreated, people may die from these diseases. Early discovery and categorization of these diseases can reduce the severity of the disease and save lives. Automated procedures are necessary, since manual detection and categorization are laborious, time-consuming, and prone to mistakes. In this work, we present an automated system for the localization and classification of GI diseases from endoscopic images with the help of an encoder–decoder-based model, XceptionNet, and explainable artificial intelligence (AI). Data augmentation is performed at the preprocessing stage, followed by segmentation using an encoder–decoder-based model. Later, contours are drawn around the diseased area based on segmented regions. Finally, classification is performed on segmented images by well-known classifiers, and results are generated for various train-to-test ratios for performance analysis. For segmentation, the proposed model achieved 82.08% dice, 90.30% mIOU, 94.35% precision, and 85.97% recall rate. The best performing classifier achieved 98.32% accuracy, 96.13% recall, and 99.68% precision using the softmax classifier. Comparison with the state-of-the-art techniques shows that the proposed model performed well on all the reported performance metrics. We explain this improvement in performance by utilizing heat maps with and without the proposed technique. Full article

Diamond wire sawing is one of the most widely used methods of cutting Al₂O₃ ceramic because it has good machining accuracy and causes less surface damage. However, its material removal rate (MRR) needs to be improved with the increasing demand for Al₂O₃ ceramic parts. In this paper, spark discharges are generated around the diamond wire based on the electrochemical discharge machining (ECDM) process. An oil film-assisted ECDM process is applied to solve the difficulty of generating spark discharges when the thickness of the workpiece exceeds 5.0 mm due to the difficulty of forming a hydrogen gas film. Experimental results show that the combination of oil film-assisted ECDM and diamond wire sawing improved the MRR of Al₂O₃ ceramic. Oil film-assisted ECDM may improve the surface quality of machined parts and reduce the wear on diamond wire. Therefore, this research focuses on the surface quality of Al₂O₃ ceramic and tool wear in diamond wire sawing combined with oil film-assisted ECDM. Surface roughness and topography, recast layer, and elements of the machined surface are analyzed. The tool wear is studied using SEM images of diamond wire. The results provide a valuable basis for application of diamond wire sawing combined with oil film-assisted ECDM. Full article

In the research of Facial Expression Recognition (FER), the inter-class of facial expression data is not evenly distributed, the features extracted by networks are insufficient, and the FER accuracy and speed are relatively low for practical applications. Therefore, a lightweight and efficient method based on class-rebalancing fusion cumulative learning for FER is proposed in our research. A dual-branch network (Regular feature learning and Rebalancing-Cumulative learning Network, RLR-CNet) is proposed, where the RLR-CNet uses the improvement in the lightweight ShuffleNet with two branches (feature learning and class-rebalancing) based on cumulative learning, which improves the efficiency of our model recognition. Then, to enhance the generalizability of our model and pursue better recognition efficiency in real scenes, a random masking method is improved to process datasets. Finally, in order to extract local detailed features and further improve FER efficiency, a shuffle attention module (SA) is embedded in the model. The results demonstrate that the recognition accuracy of our RLR-CNet is 71.14%, 98.04%, and 87.93% on FER2013, CK+, and RAF-DB, respectively. Compared with other FER methods, our method has great recognition accuracy, and the number of parameters is only 1.02 MB, which is 17.74% lower than that in the original ShuffleNet. Full article

The rail inductance gradient is an important parameter of the electromagnetic launcher. The calculation of its value is important for the design of the launcher structure and for predicting the motion behavior of the armature. The current research on the inductance gradient analysis method of the electromagnetic rail launcher mostly does not take into account the effects of launcher size and current diffusion. This method cannot describe its dynamic characteristics, and it results in a large error compared with the actual launch. Therefore, the paper first establishes an electromagnetic rail launcher armature motion model to obtain the rail velocity skin depth under a U-shaped armature. Second, an analytical method for calculating the inductance gradient based on the dynamic skin depth of the rail is obtained, which takes into account the launcher size and velocity skin effect. Finally, the experimental results verify the correctness and accuracy of the method to achieve an accurate prediction of armature speed. Full article

This research introduces a mobile application specifically designed to enhance tourist safety in warm and humid destinations. The proposed solution integrates advanced functionalities, including a comprehensive warning system, health recommendations, and a life rescue system. The study showcases the exceptional effectiveness of the implemented system, consistently providing tourists with precise and timely weather and safety information. Notably, the system achieves an impressive average accuracy rate of 100%, coupled with an astonishingly rapid response time of just 0.001 s. Furthermore, the research explores the correlation between the System Usability Scale (SUS) score and tourist engagement and loyalty. The findings reveal a positive relationship between the SUS score and the level of tourist engagement and loyalty. The proposed mobile solution holds significant potential for enhancing the safety and comfort of tourists in hot and humid climates, thereby making a noteworthy contribution to the advancement of the tourism business in smart cities. Full article

In the prediction and modeling analysis of wear degree in the field of industrial parts processing, there are problems such as poor prediction ability for long sequence data and low sensitivity of output feedback to changes in input signals. In this paper, a combined prediction model is proposed that integrates dual attention mechanisms and self-regressive correction. Firstly, pre-processing is performed on the collected wear data to eliminate noise and aberrant mutation data. Then, the feature attention mechanism is introduced to analyze the input data sequence, and the weights of each feature under the temporal condition are set based on the contribution of the prediction results, thereby obtaining the LSTM hidden state at the current time. Subsequently, the temporal attention mechanism is introduced to perform a weighted calculation of the hidden state information, analyze the correlation of long-term sequential wear data, and decode and output the analysis results. Finally, the ARIMA model is used to perform linear correction on the predicted results to improve the accuracy of wear degree prediction. The proposed model is compared and analyzed with the models that are highly related in recent research on real-world wear degree datasets. The experimental results show that the improved model has a better ability to improve the corresponding problems and has a significant increase in prediction accuracy. Full article

The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties’ estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics library, which plays a significant role in dataset input for network training, validation, and testing to improve elastic properties in this field. The deep learning network architecture comprising UNET and RESNET-18 with weak supervision networks has proven to be useful to enhance computational work efficiency and prediction accuracy while handling the non-linearity of the data and the non-uniqueness of the solutions. We successfully validated the proposed method on actual field data from a clastic fluvial-dominated field in the Malay basin. Upon comparative analysis with the conventional method, both inversion results are comparable and capable of identifying the reservoir occurrence and distribution. The conventional method exposed the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir. Meanwhile, the proposed method showed more stable, clearer definition and fewer noise inversion results but with a faster turn-around time and a more efficient workflow. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing the proposed method for elastic properties prediction compared to the conventional. The result implies that the proposed method can provide a good elastic properties prediction framework while addressing data limitations and sparsity issues in typical deep learning-based inversions. Full article

Water pollution is a significant environmental challenge with implications for both the natural world and human well-being. To better understand and manage the complex interactions within water pollution systems, such as waste dumping in the sea, system dynamics modeling has emerged as a valuable tool. This simulation-based approach employs feedback loops and cause-and-effect relationships to capture the dynamic behavior of such systems over time. By simulating various waste disposal scenarios and assessing their impacts on the environment and human health, system dynamics modeling aids policymakers and waste managers in devising effective strategies for the sustainable management of dumping sites into the sea. In this manuscript, we present a system dynamics approach to model water pollution control. Our study entails the development of a conceptual model that encompasses pollution sources, pollutant transport and fate, and their effects on water quality and human health. By calibrating and validating the model using data from a case study in Charleston Harbor, South Carolina, United States, we ensure its accuracy and reliability. The results highlight the model’s versatility in simulating different pollution control scenarios, particularly those involving dredging discharge and powerhouse effluent. Through these simulations, we gain valuable insights into the potential impacts of various pollution control measures on water pollution dynamics. Our research underscores the significance of system dynamics modeling in comprehending intricate water pollution systems, including those associated with waste dumping in the sea. By identifying effective strategies for water pollution control, this approach offers invaluable support in safeguarding marine ecosystems and human communities. In conclusion, system dynamics modeling proves to be a powerful tool for sustainable water pollution management. This research demonstrates its utility in analyzing dumping sites in the sea and provides essential findings to inform effective pollution control strategies. Emphasizing the broader context of water pollution, this study contributes to advancing knowledge and fostering sustainable practices to protect our precious water resources. Full article

Proper operation of the grid-tie transformerless converters under unbalanced and distorted conditions entails a precise detection of the frequency and fundamental component of the grid voltage. One of the main problems that could arise during the estimation of grid parameters is the existence of a DC offset generated from measurement and A/D conversion. This undesirable induced DC offset could appear as a part of the reference sinusoidal current of grid-tie converters. Although literature has proposed the use of an extended complex Kalman filter (ECKF) for the estimation of positive and negative sequence voltage components as a promising competitor to phase locked loops, mitigating the effect of possible DC offsets when a Kalman filter is employed remains scarce. This paper proposes a new extended complex Kalman filter to improve the filter stability for estimating the frequency and the fundamental positive and negative symmetrical components of the grid voltages, where DC offset, scaling error, and noise can successfully be rejected. The theoretical findings are experimentally validated. Full article

Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models. Full article

In order to increase industrial production quality and efficiency, it is essential to understand how the aeration and no-aeration condition affects liquid and solid material mixing in the stirred tank. Due to complicated shear flows, the related mass-transfer mechanism confronts numerous difficulties. This paper put forward an improved computational fluid dynamics and discrete element method (CFD–DEM) modeling approach to explore the effect mechanism of aeration conditions on liquid–solid material mixing. Firstly, a mass-transfer dynamic model is set up with a volume of fluid and piecewise linear interface construction (VOF–PLIC) coupling strategy to explore flow modes and vorticity evolution trends under aeration control. Then, a self-developed interphase coupling interface is utilized to modify the coupling force and porosity of the porous media model in the DEM module, and random dispersion properties of the particle phase under non-aeration and aeration are obtained. Results show that the aeration and flow-blocking components transform fluid tangential speeds into axial and radial speeds, which can improve the material mixing quality and efficiency. The mixed flow field can reach a greater turbulent process under the impeller rotation, making the particles have an intensive disorder and complex flow patterns. The enhanced motion efficiency of the vortex clusters encourages their nesting courses and improves cross-scale mixed transport. It can serve as some reference for the three-phase flow mixing mechanism, vorticity distribution law, and particle motion solution and has a general significance for battery homogeneous mixing, biopharmaceutical processes, and chemical process extraction. Full article

This scientific article investigates the mechanical properties of a novel three-dimensional-printing resin specifically designed for orthodontic aligners and compares it to other commonly used resins. The resin was made by blending two commercially available and certified resins to produce aligners with improved mechanical properties compared to each separate resin. The study examined the effect of the addition of NextDent Ortho Rigid resin on the mechanical properties of the specimens, more specifically analyzing the relationship between the amount of the added rigid resin and the mechanical properties of the specimens. The mechanical properties of the specimens and the nickel-titanium wires were analyzed using a three-point bending test setup. The results showed no statistically significant differences within the different groups of specimens. The study provides valuable insights into the potential of the resin to meet the mechanical demands of orthodontic treatments. It will contribute to the advancement of personalized orthodontic care through three-dimensional-printing technology. Blending commercially available and certified resins enables orthodontic practitioners to select the most suitable resin for each case, providing better outcomes for patients and increasing the efficiency of the treatment process. Further research and evaluation would be required to determine the suitability of the blended resin for orthodontic treatments. Full article

Donkey milk represents an efficient substitute for human milk in infants’ diets being unlikely to cause allergic reactions. In this study, different donkey milks were collected at two lactation times (T₀ and T₁), subjected to freezing–thawing and freeze-drying, and analyzed by Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) and ATR-FT-IR. The data collected on freeze–thaw (FT-) and reconstituted (R-)milks were investigated by ANOVA–Simultaneous Component Analysis (ASCA) and Principal Component Analysis (PCA). The following concentrations (µg/mL) for FT and R-milks, respectively, at T₀, were found: Ca: 712 ± 71, 600 ± 72; Fe: 0.7 ± 0.3, 0.1 ± 0.1; K: 595 ± 49, 551 ± 59; Mg: 75 ± 5, 67 ± 4; Na: 117 ± 16, 114 ± 16; P: 403 ± 30, 404 ± 38; Zn: 1.6 ± 0.2, 1.6 ± 0.3. At T₁, the concentrations (µg/mL for FT and R-milks, respectively) were: Ca: 692 ± 60, 583 ± 43; Fe: 0.13 ± 0.02, 0.13 ± 0.03; K: 641 ± 71, 574 ± 61; Mg: 72 ± 4, 63 ± 1; Na: 116 ± 9, 109 ± 8; P: 412 ± 30, 405 ± 24; Zn: 1.6 ± 0.3, 1.6 ± 0.3. ASCA demonstrated the treatment has a substantial effect, and PCA revealed that the largest quantities of metals, specifically Fe, Mg, and Ca for T₀ and K, P, and Na for T₁, are present in the FT-milk samples. The IR spectra of FT- and R-milks revealed no macroscopic changes among them or between lactation periods, indicating this technique may not suitably capture variability in lactation or conservation processes in donkey milk. Despite the relatively small sample size, this study offers insight on the mineral composition changes in donkey milk and emphasizes the significance of milk preprocessing and the lactation period on it. Full article

Over the course of the last twenty years, building information modeling (BIM) has emerged as a firmly established construction methodology integrating fundamental principles. The implementation of BIM methodologies possesses the capability to augment the attainment of quality, cost, and schedule objectives in construction endeavors. Notwithstanding the widespread adoption of BIM in the construction sector, the execution of BIM-related tasks frequently suffers from the absence of established methodologies. The objective of this study was to create a BIM application model through an examination of the correlation between BIM integration and the achievement of overall project success (OPS) in construction endeavors. In order to develop the BIM application model, feedback was solicited from a cohort of fourteen industry experts who assessed a range of BIM activities in light of prior research. The data that were gathered underwent exploratory factor analysis (EFA) in order to authenticate the results acquired from the expert interviews. Furthermore, construction professionals participated in structured surveys in order to evaluate the importance of said BIM practices. This study utilized partial least squares–structural equation modeling (PLS-SEM) to ascertain and authenticate the underlying framework and correlations between BIM implementation and OPS. The findings indicate a moderate correlation between the implementation of BIM and the success of a project wherein BIM is responsible for approximately 52% of the project’s overall success. To optimize project outcomes, it is recommended that construction companies prioritize the implementation of BIM practices. This study highlights the correlation between the utilization of BIM and favorable project results, emphasizing the necessity for the construction sector to adopt BIM as a revolutionary instrument to attain enhanced project achievements. Full article

The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources. Full article

When performing cooperative search operations underwater, multi-autonomous underwater vehicles formations may encounter array-type obstacles such as gullies and bumps. To safely traverse the obstacle domain, this paper balances convergence time, transformation distance and sensor network power consumption, and proposes a Formation Comprehensive Cost (FCC) model to achieve collision avoidance of the formations. The FCC model is used instead of the fitness function of the genetic algorithm to solve the assignment of capture positions and the improved neural self-organizing map (INSOM) algorithm is proposed to achieve efficient path-planning during the capture process. The simulation experiments in 3D space verify that the proposed scheme can improve the efficiency of robot deployment while ensuring safety. Full article