The classic Slack-Based Measure (SBM) model has been posited to be a favorable non-parametric tool to cope with undesirable output. Nevertheless, this model has two significant drawbacks that should be addressed in practice. Thus, this paper aims to revise the classic SBM model to estimate container terminals’ environmental efficiency with undesirable output. The originality of this article includes: (1) introducing the energy consumption method to calculate the quantity of CO₂ emitted by container terminal operators (CTOs), (2) adopting cluster analysis to identify homogeneous CTOs acting as Decision-Making Units (DMUs), and (3) introducing the modified SBM model to measure and analyze environmental efficiency for CTOs. Based on this research, the efficiency of the analyzed terminals and the management of the local port sector are improved. Full article

To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M₁ and M₂) and a battery supplier is studied. Firstly, three cooperation strategy models were constructed for the battery supplier and the EV manufacturers, namely: Strategy N (neither the battery supplier nor the two manufacturers cooperate with each other); Strategy I (M₁ cooperates with the battery supplier); and Strategy II (M₂ cooperates with the battery supplier). Then, the Stackelberg solution method was used to obtain the optimal equilibrium decisions under the three strategic models. Finally, the effect of the preference coefficient of consumers for leasing EVs per unit on the optimal equilibrium decision was analyzed. We found that: (1) The wholesale price of batteries provided by the battery supplier to M₁ is always greater than to M₂. (2) Strategies I and II prompt M₁ and M₂ to reduce the unit and fixed rental prices of EVs to some extent, while intensifying the competition between the two manufacturers in terms of EV lease prices. (3) When the consumer preference coefficient (θ) for leasing EVs per unit provided by manufacturer M₁ is relatively small, the cooperation alliance S₂ and the supply chain achieve the maximum profit under Strategy II; however, while θ is large, M₁, cooperative alliance S₁, and the entire supply chain could benefit the most under Strategy I. Full article

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article

In the Pistoia Nursery-Ornamental Rural District (Italy), a leader in Europe in ornamental nurseries covering over 5200 hectares with over 2500 different species of plant, plant-parasitic nematodes represent a serious concern. The potential efficacy of a pot cultivation system using commercial substrates to control plant-parasitic nematodes was assessed. On two different plant species, two different pot cultivation managements, potted plants, and potted plants previously cultivated in natural soil were compared to plants only cultivated in natural soil. The entire soil nematode structure with and without plants was evaluated. The relationship between soil properties and soil nematode community was investigated. All the studied substrates were free from plant-parasitic nematodes. Regarding free-living nematodes, Peat–Pumice showed nematode assemblage established by colonizer and extreme colonizer bacterial feeders, whereas Peat–Perlite included both bacterial and fungal feeders, and, finally, coconut fiber also included omnivores and predators. In farming, the substrates rich in organic matter such as coconut fiber could still play an important role in suppressing plant-parasitic nematodes because of the abundance of free-living nematodes. In fact, they are of crucial importance in both the mineralization of organic matter and the antagonistic control of plant-parasitic nematodes. Potting systems equally reduce virus-vector nematodes and improve the prey/predator ratio favoring natural control. Full article

In this paper, we prove the existence of at least two weak solutions to a class of singular two-phase problems with variable exponents involving a $\psi$-Hilfer fractional operator and Dirichlet-type boundary conditions when the term source is dependent on one parameter. Here, we use the fiber method and the Nehari manifold to prove our results. Full article

Composite materials, valued for their adaptability, face challenges associated with degradation over time. Characterising their durability through traditional experimental methods has shown limitations, highlighting the need for accelerated testing and computational modelling to reduce time and costs. This study presents an overview of the current landscape and future prospects of multi-scale modelling for predicting the long-term durability of composite materials under different environmental conditions. These models offer detailed insights into complex degradation phenomena, including hydrolytic, thermo-oxidative, and mechano-chemical processes. Recent research trends indicate a focus on hygromechanical models across various materials, with future directions aiming to explore less-studied environmental factors, integrate multiple stressors, investigate emerging materials, and advance computational techniques for improved predictive capabilities. The importance of the synergistic relationship between experimental testing and modelling is emphasised as essential for a comprehensive understanding of composite material behaviour in diverse environments. Ultimately, multi-scale modelling is seen as a vital contributor to accurate predictions of environmental effects on composite materials, offering valuable insights for sustainable development across industries. Full article

Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators. Full article

Sustainable development has been a global concern worldwide for the last decades now, but only recently have the challenges faced by small towns, especially in regions experiencing population contraction been addressed. (1) Background: This article delves into the case of Romania, a country in Eastern Europe that has witnessed significant demographic, social and economic changes in recent decades. Population contraction in small towns can significantly impact their future development. (2) Methods: The research was conducted in three stages: first, we selected relevant demographic, economic, financial and social indices (16 in total), then we analysed their changes over time, and forecast their values based on statistical data to assess economic development sustainability for 215 small towns with less than 20,000 inhabitants. (3) Results: Following the aggregation of the quantitative indicators and the demographic changes, we identified four categories of small towns. (4) Conclusions: the study underlines the importance of adopting proper policies targeting small towns in Romania to ensure their long-term viability by implementing targeted policies and strategies such as incentives for local businesses, improving educational and healthcare facilities, and promoting entrepreneurship. The ultimate goal is to mitigate the adverse effects of population contraction and pave the way for more sustainable and resilient communities. Full article

Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change’s effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities. Full article

High diffraction signal-to-background ratios (SBRs), the ratio of diffraction peak integrated intensity over its background intensity, are desirable for a neutron diffractometer to acquire good statistics for diffraction pattern measurements and subsequent data analysis. For a given detector, while the diffraction peak signals primarily depend on the characteristics of the neutron beam and sample coherent scattering, the background largely originates from the sample incoherent scattering and the scattering from the instrument space. In this work, we investigated the effect of collimation on neutron diffraction SBRs of Si powder measurements using one high-angle area detector bank coupled with six different collimation configurations in a large and complex instrument space at the engineering materials diffractometer VULCAN, SNS, ORNL. The results revealed that the diffraction SBRs can be significantly improved by a proper coarse collimator that leaves no gap between the detector and the collimator, and the improvement of SBRs by a fine radial collimator was remarkable with a proper coarse collimator in place but not distinguishable without one. It was also found that the diffraction SBRs were not effectively improved by adding the neutron-absorbing element boron to the fine radial collimator body, which indicates that either the absorption of secondary scattered neutrons by the added boron is insignificant or the collimator base material (resin and ABS) alone attenuates background scattering sufficiently. These findings could serve as a useful reference for diffractometer developers and/or operators to optimize their collimation to achieve higher diffraction SBRs. Full article

Mycelium-based composites (MBCs) are biomaterials with scientifically proven potential to improve sustainability in construction. Although mycelium-based products are not entirely new, their use in engineering presents challenges due to the inherent properties of this fungal material. This study investigated professional architects’ and interior designers’ perceptions of MBCs, focusing on familiarity, aesthetic appeal, and willingness to use. The first phase of the survey explored respondents’ views on material-related ecological design principles. In the second phase, respondents evaluated ten small architectural objects crafted from MBCs, focusing on form, detail, and visual appeal. The last phase of the survey measured their interest in using mycelium in their design work. The results revealed that MBCs were relatively unknown among the surveyed professionals; only every second respondent knew this material. Despite this, 90% found MBCs visually appealing after seeing the examples. Interestingly, the natural, unprocessed appearance of the material was assessed as less aesthetically pleasing, with thermal treatment improving its perceived value. Architects were more receptive to using MBCs in their professional projects for customers than for personal use. This observation points to a ‘double standard’: professional architects are more open to using MBCs in projects not intended for their own use. Full article

To accurately identify the key features influencing the fisheries distribution of Pacific yellowfin tuna, this study analyzed data from 43 longline fishing vessels operated from 2008 to 2019. These vessels operated in the Pacific Ocean region (0° to 30° S; 110° E to 170° W), with a specific focus on 25 features of yellowfin tuna derived from marine environment data. For this purpose, this study opted for the Lasso regression analysis method to select features to predict Pacific yellowfin tuna fishing grounds, exploring the relationship between the catch per unit effort (CPUE) of yellowfin tuna and multiple features. This study reveals that latitude and water temperature at various depths, particularly the sea surface temperature of the preceding and subsequent months and the temperature at depths between 300 and 450 m, are the most significant features influencing CPUE. Additionally, chlorophyll concentration and large-scale climate indices (ONI and NPGIO) also have a notable impact on the distribution of CPUE for yellowfin tuna. Lasso regression effectively identifies features that are significantly correlated with the CPUE of yellowfin tuna, thereby demonstrating superior fit and predictive accuracy in comparison with other models. It provides a suitable methodological approach for selecting fishing ground features of yellowfin tuna in the Pacific Ocean. Full article

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data. Full article

This study provides a method for the rapid, non-destructive testing of the internal quality of Korla fragrant pears. The dielectric constant (ε′) and dielectric loss factor (ε″) of pear samples were tested at 100 frequency points (range = 0.1–26.5 GHz) using a vector network analyzer and coaxial probe. The variations in the dielectric parameters of fragrant pears were analyzed. The linear relationships between the dielectric parameters and internal quality were explored. Internal quality prediction models for Korla fragrant pears were built using partial least squares regression (PLSR), support vector regression (SVR) and particle swarm optimization–least squares support vector regression (PSO-LSSVR). The optimal model was then determined. There was a weak correlation between the dielectric parameters and soluble solid content (SSC) under a single frequency. The model based on PLSR and using ε′ as a variable predicted hardness the best, while the model based on PLSR using ε″ as a variable predicted SSC the best. Its R and MSE values were 0.77 and 0.073 in hardness prediction, respectively, and 0.91 and 0.087 in SSC prediction. This study provides a new method for the non-destructive online testing of the internal quality of Korla fragrant pears. Full article

Leucine-rich repeat extensin (LRX) is involved in the regulation of crucial cellular processes, such as cell wall growth and development, as well as signaling. However, the presence of the LRX gene family in Brassica rapa (B. rapa) has not been previously reported. This study identified 17 BrLRXs within the Brassica rapa genome by bioinformatic analysis, and these genes were distributed on seven chromosomes. Phylogenetic and covariance analyses indicate that BrLRXs can be categorized into two distinct branches: the trophic branch and the reproductive branch, with a close relationship observed between BrLRXs and AtLRXs. According to cis-acting element analysis, this gene family is rich in hormone-responsive and stress-responsive elements such as drought-inducibility, abscisic acid, methyl jasmonate, and gibberellic acid responsive elements, suggesting a potential role in abiotic stress response. Transcriptomic, proteomic, and RT-qPCR analyses demonstrated significant up-regulation of BrLRX2 and BrLRX6 under salt stress, while BrLRX3, BrLRX6, and BrLRX8 were significantly down-regulated under osmotic stress. Our analysis of the protein tertiary structure predicts a strong association between LRX proteins and RALF. Protein–protein interaction prediction revealed that LRX interacts with the RALF protein and the receptor FER, which have been previously reported to jointly regulate plant stress responses. We propose that BrLRX6 and BrLRX8 are implicated in osmotic stress, while BrLRX2 and BrLRX6 are involved in the modulation of salt stress. Full article

Tomatoes are globally renowned for their nutritional value and culinary versatility. However, environmental stresses, particularly salinity, present significant challenges to tomato production, impacting both yield and fruit quality. In light of these challenges, this study investigates the effect of salinity on yield and fruit quality of a local cultivar tomato named ‘Pisanello’ in a closed soilless rockwool cultivation system. Total yield, fruit size, and number were investigated in both control (10 mM of NaCl) and salinity-treated plants (salinity 1 (S1)~30 mM of NaCl and salinity 2 (S2)~60 mM of NaCl), alongside various physicochemical parameters in fully ripened tomato fruits. The results indicated a decrease in crop production with rising sodium chloride concentration in the nutrient solution (25% and 41% for S1 and S2 treatment, respectively). Conversely, salinity-treated fruits exhibited an increase in total phenolic content of +21.9% in S1 and +36.7% in S2 and in antioxidant capacity (+33.5% and +34.7%, for the S1 and S2 treatments, respectively). Salinity treatments registered in general higher quality parameters such as titratable acidity (+8.9 for S1 and +16.5% for S2), total soluble solids (+18.5% for S1 and +43.0% for S2) and fruit firmness (+30.7% for S1 and +60.3% for S2) in comparison with control tomato fruits. Sensory profile analysis further validated the preference for fresh consumption of tomato fruits grown with saline water. These findings suggests that salinity stress can enhance the nutritional quality and taste of the Pisanello tomato. Further investigation could explore the optimal NaCl concentration to balance tomato production and nutritional quality. Full article

The effect of a transverse magnetic field on two-phase stratified flow in horizontal and inclined channels is studied. The lower heavier phase is assumed to be an electrical conductor (e.g., liquid metal), while the upper lighter phase is fully dielectric (e.g., gas). The flow is defined by prescribed flow rates in each phase, so the unknown frictional pressure gradient and location of the interface separating the phases (holdup) are found as part of the whole solution. It is shown that the solution of such a two-phase Hartmann flow is determined by four dimensionless parameters: the phases’ viscosity and flow-rate ratios, the inclination parameter, and the Hartmann number. The changes in velocity profiles, holdups, and pressure gradients with variations in the magnetic field and the phases’ flow-rate ratio are reported. The potential lubrication effect of the gas layer and pumping power reduction are found to be limited to low magnetic field strength. The effect of the magnetic field strength on the possibility of obtaining countercurrent flow and multiple flow states in concurrent upward and downward flows, and the associated flow characteristics, such as velocity profiles, back-flow phenomena, and pressure gradient, are explored. It is shown that increasing the magnetic field strength reduces the flow-rate range for which multiple solutions are obtained in concurrent flows and the flow-rate range where countercurrent flow is feasible. Full article

Arrhythmogenic right ventricular cardiomyopathy (ARVC) can lead to sudden cardiac death and life-threatening heart failure. Due to its high fatality rate and limited therapies, the pathogenesis and diagnosis biomarker of ARVC needs to be explored urgently. This study aimed to explore the lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network in ARVC. The mRNA and lncRNA expression datasets obtained from the Gene Expression Omnibus (GEO) database were used to analyze differentially expressed mRNA (DEM) and lncRNA (DElnc) between ARVC and non-failing controls. Differentially expressed miRNAs (DEmiRs) were obtained from the previous profiling work. Using starBase to predict targets of DEmiRs and intersecting with DEM and DElnc, a ceRNA network of lncRNA-miRNA-mRNA was constructed. The DEM and DElnc were validated by real-time quantitative PCR in human heart tissue. Protein–protein interaction network and weighted gene co-expression network analyses were used to identify hub genes. A logistic regression model for ARVC diagnostic prediction was established with the hub genes and their ceRNA pairs in the network. A total of 448 DEMs (282 upregulated and 166 downregulated) were identified, mainly enriched in extracellular matrix and fibrosis-related GO terms and KEGG pathways, such as extracellular matrix organization and collagen fibril organization. Four mRNAs and two lncRNAs, including COL1A1, COL5A1, FBN1, BGN, XIST, and LINC00173 identified through the ceRNA network, were validated by real-time quantitative PCR in human heart tissue and used to construct a logistic regression model. Good ARVC diagnostic prediction performance for the model was shown in both the training set and the validation set. The potential lncRNA-miRNA-mRNA regulatory network and logistic regression model established in our study may provide promising diagnostic methods for ARVC. Full article

The internal mechanisms of dental air turbine handpieces (DATHs) have become increasingly intricate over time. To enhance the operational reliability of dental procedures and guarantee patient safety, this study formulated temporal convolution network (TCN) prediction models with the functions of causality in time sequence, transmitting memory, learning, storing, and fast convergence for monitoring the health and diagnosing the rotor and collet failure of DATHs. A handpiece mimicking a dentist’s hand load of 100 g was employed to repeatedly mill a glass porcelain block back and forth for cutting. An accelerometer was employed to capture vibration signals during free-running of unrestrained operation of the handpiece, aiming to discern the characteristic features of these vibrations. These data were then utilized to create a diagnostic health classification (DHC) for further developing a TCN, a 1D convolutional neural network (CNN), and long short-term memory (LSTM) prediction models. The three frameworks were used and compared for machine learning to establish DHC prediction models for the DATH. The experimental results indicate that, in terms of DHC predicted for the experimental dataset, the square categorical cross-entropy loss function error of the TCN framework was generally lower than that of the 1D CNN, which did not have a memory framework or the drawback of the vanishing gradient problem. In addition, the TCN framework outperformed the LSTM model, which required a longer history to provide sufficient diagnostic ability. Still, high accuracies were achieved both in the direction of feed-drive milling and in the gravity of the handpiece through vibration signals. In general, the failure classification prediction model could accurately predict the health and failure mode of the dental handpiece before the use of the DATH when an embedded sensor was available. Therefore, this model could prove to be a beneficial tool for predicting the deterioration patterns of real dental handpieces in their remaining useful life. Full article

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3−4−1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process. Full article

Obesity is a global health problem and is increasing in prevalence in most countries. Although obesity affects all age groups, children are the most vulnerable sector. Functional foods are novel formulated foods containing substances (i.e., nutrients, phytochemicals, probiotics, etc.) that have potential health-enhancing or disease-preventing value. The research objective was to study the possible beneficial effects of providing a functional food made with amaranth flour, chia seed, and curcumin extract on the metabolism and behavior of a rat model of childhood obesity. Male Wistar rat pups from two litters of different sizes, a normal litter (NL) (10 pups) and a small litter (SL) (4 pups), were used. After weaning, the rats were fed a hypercaloric diet (HD) or an HD supplemented with the functional food mixture. Body weight and energy intake were measured for seven weeks, and locomotor activity, learning, and memory tests were also performed. At the end of the experiment, glucose and lipid metabolism parameters were determined. The results showed that in this model of obesity produced by early overfeeding and the consumption of a hypercaloric diet, anxiety-like behaviors and metabolic alterations occurred in the rat offspring; however, the provision of the functional food failed to reduce or prevent these alterations, and an exacerbation was even observed in some metabolic indicators. Interestingly, in the NL rats, the provision of the functional food produced some of the expected improvements in health, such as significant decreases in body weight gain and liver cholesterol and non-significant decreases in adipose tissue and leptin and insulin serum levels. Full article

To address the effect of a ship’s rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed without internal members added (R-FBWIM). The transient motion, as well as the behavioral characteristics of the bubbles within the R-FBLIM, was analyzed; the variation patterns of the number of bubbles, as well as the equivalent diameter of the bubbles, were compared for different apparent gas velocities, oscillation periods, and amplitudes; and the mechanism of the action of the longitudinal internal members was investigated. The results show that the structural design of the longitudinal internal members can effectively improve the gas–solid fluidization quality of the rolling fluidized bed. The horizontal support plate and the cap hole structure can effectively break the air bubbles, the cap hole structure promotes the radial mixing of the gas–solid fluid, and the internal and outer rings of the curved surface plate roll in rows, which inhibit the aggregation behavior of the gas–solid fluid to the two sides of the oscillating planes, respectively, by cooperating with the cap hole structure. Compared with R-FBWIM, the gas–solid phase within R-FBLIM is more spatially distributed, with the number of bubbles increasing by about 2–4 times and the mean diameter decreasing by about 50–60%. The number of bubbles increases with the gas velocity but decreases with the rolling amplitude; the mean diameter decreases with the gas velocity but responds less to the rolling amplitude change. Full article