Applied Sciences

Bone tissue engineering (BTE) is an interdisciplinary discipline that focuses on bone structure–function relationships for improving the replacement and/or regeneration of bone tissues. Thereby, the architecture and load-bearing capacity of embedded scaffolds play an important role in the generation of artificial tissues. The aim of this study was to develop a parametric numerical model and the accompanying fabrication of polycaprolactone (PCL) scaffolds for BTE applications. Therefore, we manufactured layered PCL-based constructs using three-dimensional (3D) printing. The material properties of PCL and constructs were determined by mechanical testing, and numerical models based on Beam188 Timoshenko elements were developed in the software environment ANSYS. PCL constructs were coated with collagen and seeded with osteoblasts, mesenchymal stem cells (MSCs), MLO-Y4 and MG63 cell types. We demonstrated the successful production of PCL constructs with 3D interconnected pores suitable for BTE applications. Furthermore, we provided for the first time geometrical parametric numerical models that determined the mechanical behavior of layered PCL scaffolds consisting of interconnected compartments for strains up to 3%. The parametric structures of the model allowed us to flexibly study new geometries in silico, which demonstrated its role as an important tool for supporting the fabrication of customized PCL constructs in planning and performing suitable mechanical characterizations for BTE applications. Full article

In this study, experiments and numeric studies were conducted to analyze the causes of abnormal behavior in temperature, heat flux, and combustion efficiency that occurred before a compartment fire was converted to the under-ventilated condition. The fire experiments were performed using a 1/4 reduced-scale ISO 9705 compartment and propane fuel. Considering that the occurrence and termination of abnormal behavior are related to ventilation conditions, vertical openings of various shapes designed to have the identical ventilation factor ($0.52\mathrm{A}\sqrt{\mathrm{h}}$) were used. As a result, an abnormal phenomenon was observed in which the measured heat release rate did not increase despite the increase in the fuel flow rate under the over-ventilation condition in all opening shapes. Due to this phenomenon, the combustion efficiency was reduced to 70% regardless of the opening shape. In order to understand these abnormal behaviors and to analyze the causes of their occurrence, the temperature and chemical species concentrations of the opening discharge flow were measured. The results indicated that the abnormal behavior of thermal physical quantities, HRR, and combustion efficiency occurred because fuel that was not burned inside the compartment did not reach the lower combustion limit even outside the compartment and was discharged. Full article

This paper considers the positivity-preserving model reduction for discrete-time positive systems. Given a stable high-order positive system, we aim to find a reduced-order model such that the approximation error is minimized within a prescribed $H_{\infty }$ performance and positivity is preserved. Regarding the bounded real lemma, the sufficient and necessary condition for the existence of a reduced-order model is established in terms of bilinear matrix inequality and convex semi-definite constraint, which ensures that the reduced-order system is positive and the resulted error system is stable and has an $H_{\infty }$ performance level. Based on the inner-approximation strategy, we approximate the bilinear constraints with convex ones, under which an iterative procedure is provided to calculate the desired reduced-order model. Finally, an example is provided to demonstrate the effectiveness and potential benefits of the presented results. Full article

Photodynamic therapy (PDT) in small animals’ oncology has been under research focus, pointing to new treatment possibilities. Moreover, several animal studies constitute experimental human disease models due to the similarity of tumor biology between animals and man. PDT uses photosensitizing compounds without toxicity per se. When subjected to a specific wavelength, the photosensitizers are activated, triggering the production of reactive oxygen species (ROS) that lead to cell death. Additionally, antiangiogenic effects and immune stimulation may also be elicited. PDT is minimally invasive, non-toxic, and does not induce carcinogenic or mutagenic side effects. Thus, it is safe for non-neoplastic tissues compared with other neoplasms treatment modalities. This review describes the applications of PDT in the cancer treatment of small animals, particularly dogs and cats, focusing on the respective photosensitizers and treatment protocols used in trials in this therapeutic modality. Full article

Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants. Full article

With the development of urbanization, the transportation network of underground tunnels has been gradually formed and improved. It is a complicated issue for engineering construction when two municipal road tunnels intersect at one point. Based on a construction site of the crossing point of the Huayuan Road Tunnel and Luzhou Road Tunnel in Hefei City, China, the finite element analysis method is used to calculate and analyze the deformation characteristics of the crossing point of the tunnels during the asymmetric construction of connecting parts. The deformation behaviors of the crossing point of tunnels subjected to symmetrical construction are also studied for comparison. Results show that the deformations of the supporting pile and tunnel frame structures increase rapidly when they are subjected to asymmetric construction, while the lateral movement of the supporting pile and the deformation of the tunnel structure can be greatly limited when the symmetrical construction method is adopted. Some suggestions for engineering construction are put forward to ensure the safety of the structure, such as multi-stage construction and temporary supporting measure. Full article

The results of a theoretical and experimental study of the processes of ignition and combustion of a single composite liquid fuel (CLF) droplet based on wet coal processing waste and combustible municipal solid waste under radiant and convective heating are presented. Based on the results of a detailed analysis of video recordings and previously obtained experimental data from the ignition and combustion of a single CLF droplet, a mathematical model was developed. The advantage of the developed mathematical model lies in the specification of sequential physical and chemical processes of the high-temperature decomposition of fuel in a high-temperature gaseous medium. A numerical simulation of combustion characteristics was carried out in the Ansys Fluent commercial software for five different CLF compositions. The ignition-delay times were established for fuel droplets that were in a preheated motionless air environment, a temperature in the range of 723–1273 K, and an air flow heated to 723–973 K moving at a velocity of 3 m/s. Using the asymptotic procedure, satisfactory analytical solutions are obtained for the multistage nonlinear problem of ignition and combustion of a single CLF droplet. The possibility for the practical application of the developed program in Ansys Fluent in predicting the characteristics of the ignition processes of CLF droplets is substantiated. Full article

In the simulation of compressible turbulent flows via a high-order flux reconstruction framework, the artificial viscosity model plays an important role to ensure robustness in the strongly compressible region. However, the impact of the artificial viscosity model in under-resolved regions on dissipation features or resolving ability remains unclear. In this work, the performance of a dilation-based (DB) artificial viscosity model to simulate under-resolved turbulent flows in a high-order flux reconstruction (FR) framework is investigated. Comparison is conducted with results via several typical explicit subgrid scale (SGS) models as well as implicit large eddy simulation (iLES) and their impact on important diagnostic quantities including turbulent kinetic energy, total dissipation rate of kinetic energy, and energy spectra are discussed. The dissipation rate of kinetic energy is decomposed into several components including those resulting from explicit SGS models or Laplacian artificial viscosity model; thus, an explicit evaluation of the dissipation rate led by those modeling terms is presented. The test cases consist of the Taylor-Green vortex (TGV) problem at $Re=1600$, the freely decaying homogeneous isotropic turbulence (HIT) at $M{a}_{t0}=0.5$ (the initial turbulent Mach number ), the compressible TGV at Mach number 1.25 and the compressible channel flow at $R{e}_b=$ 15,334 (the bulk Reynolds number based on bulk density, bulk velocity and half-height of the channel), Mach number 1.5. The first two cases show that the DB model behaves similarly to the SGS models in terms of dissipation and has the potential to improve the insufficient dissipation of iLES with the fourth-order-accurate FR method. The last two cases further demonstrate the ability of the DB method on compresssible under-resolved turbulence and/or wall-bounded turbulence. The results of this work suggest the general suitability of the DB model to simulate under-resolved compressible turbulence in the high order flux reconstruction framework and also suggest some future work on controlling the potential excessive dissipation caused by the dilation term. Full article

The main barrier to the wide use of composite liquid fuels in the energy sector is the significant sedimentation of solid particles during fuel storage and transportation. As a result, the composition of fuel slurries changes quite fast and considerably when yet another portion of fuel is pumped from a storage tank. Stabilizing additives are one of the possible solutions to this problem. The technology of primary and secondary slurry fuel atomization is generally considered promising for obtaining a spray of small fragments (droplets and particles). This way, droplets of liquid components and solid particles can be produced with a size of less than 10 μm. A fuel aerosol with particles and droplets this small burns out rapidly. The most effective secondary droplet atomization technology is based on their microexplosive breakup in combustion chambers by superheating the water in the fuel to exceed its nucleation (boiling) point. As part of this research, we studied the impact of the main stabilizing additives to slurry fuels on droplet breakup behavior: heating time until breakup, breakup delay and duration, and the number, size, and velocities of secondary fragments. Soy lecithin and sodium lignosulfonate were used as stabilizers. The main components of the fuel slurries were water, rapeseed oil, diesel fuel, coal processing waste (filter cake), coking bituminous coal, soy lecithin, and sodium lignosulfonate. Droplets were heated at an ambient gas temperature ranging from 450 to 1050 K until the breakup conditions were achieved. Mathematical expressions were obtained for the relationship between input parameters and the key characteristics of the process. Principal differences and overall patterns of droplet breakup were established for slurries with and without stabilizing additives. Full article

In previous studies, the meshing force of a gear system is usually treated as being uniformly distributed for the convenience of analysis. In practical applications, however, it is nonuniformly distributed along the line of action due to machining errors, assembly errors, misalignment errors, etc. When a nonuniformly distributed meshing force is coupled with the shaft deformation, dynamic center distance, and time-varying meshing stiffness, the transmission performance of the gear system will be seriously degraded. Therefore, a nonuniformly distributed meshing force cannot be ignored when considering the gear systems used in complicated working conditions. In this study, the gear’s nonuniformly distributed meshing force is analyzed. Then, an 18 degrees-of-freedom bending-torsion-swing-coupled dynamic model of a pair of involute spur gears is put forward. Through this model, the coupling relationship between the nonuniformly distributed meshing force, shaft bending deformation, and dynamic center distance is accurately described. The influence of meshing frequency, stiffness excitation, damping, and error excitation on the nonlinear dynamic characteristics of the gear system was researched through bifurcation diagrams, phase diagrams, Poincaré maps, and time-domain diagrams. Various complicated nonlinear dynamic behaviors, such as quasiperiodic motion, bifurcation, chaotic motion, and chaotic banding, are revealed. Reasonable parameter ranges that guarantee the gear system is in a stable motion were extracted. By evading complicated nonlinear dynamic behavior, the transmission performance of a gear system was improved. This research will contribute to reducing the vibration and noise of gear systems. Full article

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA). Full article

Purpose: In recent years, a method for improving gummy smiles in adults using an orthodontic anchor screw has been reported, but there is yet to be a treatment for the gummy smiles of those in the growth period. Methods: We improved the gummy smiles of three class II patients with vertical excessive growth of the upper jaw, during their growth period, using a simple bite jumping appliance (SBJA) and a high pull J-hook headgear. Results: It was found that SBJA promoted mandibular growth and the high-pull J-hook headgear inhibited the vertical growth of the maxillary anterior teeth. Conclusion: It is suggested that a combined use of high-pull J-hook headgear and SBJA is an effective way of improving gummy smiles in class II patients with vertical excessive growth of the upper jaw during their growth period. Full article

Urban planning has a crucial role in helping cities meet the United Nations’ Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community’s urban region based on their accessibility performance. Full article

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R²) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R² and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R² values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient. Full article

Considering the time dimension in big data analytics allows for a more complete insight into the analyzed phenomena and thus for gaining a competitive advantage on the market. The entrepreneurs also reported the need for temporal big data analytics, when interviewed by the author. Hence, the main goal of this article is to create a conceptual framework for applying temporal big data analytics (TBDA) in businesses. It is determined that a temporal framework is required. Existing big data implementation frameworks are discussed. The requirements for the successful implementation of temporal big data analytics are shown. Finally, the conceptual framework for organizational adoption of temporal big data analytics is offered and verified. The most important findings of this study are: proving that effective implementation of big data analytics in companies requires open consideration of time; demonstrating the usefulness of the leagile approach in the implementation of TBDA in companies; proposing a comprehensive conceptual framework for TBDA implementation; indicating possible success measures of the TBDA implementation in the company. The study has been conducted according to the Design Science Research in Information Systems (DSRIS) methodology. IT, business leaders, and policymakers can use the findings of this article to plan and develop temporal big data analytics in their enterprises. The report provides useful information on how to implement temporal big data in companies. Full article

Bubbly liquids are widely present in the natural environment and industrial fields, such as seawater near the ocean bottom, the multiphase flow in petroleum reservoirs, and the blood with bubbles resulting in decompression sickness. Therefore, accurate measurement of the gas content is of great significance for hydroacoustic physics, oil and gas resources exploration, and disease prevention and diagnosis. Trace bubbles in liquids can lead to considerable changes in the acoustic properties of gas–liquid two-phase media. Acoustic measurements can therefore be applied for trace bubble detection. This study derived the reflection coefficient of acoustic waves propagating in a sandwich layering model with liquid, bubbly liquid, and liquid. The influences of gas contents on the reflection coefficient at the layer interface were analyzed based on theoretical calculations. It was revealed that the magnitude of the reflection coefficient and the frequency interval between its valleys have a quantitative correlation with the gas contents. Thus, a novel means to detect the contents of trace bubbles was proposed by evaluating the reflection coefficients. The reflection features of a thin layer with bubbly liquid were then studied through experiments. It was validated by acoustical measurements and theories that the reflection coefficient is considerably sensitive to the change of gas contents as long as the gas content is tiny. With the increasing gas content, the maximum value of the reflection coefficient increases; meanwhile, the frequency intervals between the valleys become smaller. However, when the gas content is extensive enough, e.g., greater than 1%, the effect of the change of gas content on the reflection coefficient becomes inapparent. In that case, it is not easy to measure the gas content by the acoustic reflection signals with satisfying precision. This proposed method has potential applications for the detection of trace gas bubble content in several scenarios, e.g., decompression illness prevention and diagnosis. Full article

Port fuel injection is an important technical route in methanol engines. To obtain a theoretical basis for injector arrangement and injection strategy development in methanol engines, an optimal experimental platform based on diffuse back-illumination and the refractive index matching method (RIM) was designed and built in this study. The experiments on the behavior of low-pressure methanol spray-wall impingement and wall film were carried out and the influence of the three boundary conditions of spray distance (D_imp), wall temperature (T_wall), and injection pressure (P_inj) were analyzed comprehensively. Results showed that with the increase of D_imp, the overall shape of spray before impinging the wall changed from conical to cylindrical. The impinging spray height H_i and impinging spray width W_i increased with the decrease of D_imp and the increase of P_inj. Adhesive fuel film mass M_f increased with the increase of D_imp due to the decrease of kinetic energy during wall impact. In addition, the increase of the wall temperature T_wall reduced M_f due to evaporation, but when T_wall reached 423 K, M_f rebounded due to the Leidenfrost effect. The results of this study are helpful to improve the accuracy of the numerical methanol engine model. Full article

Road safety is a major global concern, as millions of lives are lost every year because of road accidents. Towards an effort to increase road safety, several Internet-of-Vehicle systems have been developed over the last years in order to better monitor vehicle and driver behavior and issue warnings that effectively prevent life-threatening accidents. These systems face a number of challenges including connectivity issues and high installation and/or maintenance costs. The current work introduces the ODOS2020 system, an integrated Internet-of-Vehicles system aiming to increase road safety. The system comprises several On-the-Road Units for vehicle-related data collection from affordable, energy-efficient magnetometers and calculation of critical parameters, such as each passing vehicle’s speed and direction. A Road-Side Unit accumulates data from the On-the-Road Units, sends data to a cloud infrastructure for further analysis and sends dedicated warnings to the drivers based on their road behavior and/or specific traffic conditions via a dedicated Human–Machine Interface. The overall system architecture and the key features of its modules are being presented, as well as the evaluation results of specially designed tests performed in an actual motorway under real use case scenarios. The evaluation results showed both a very good technical performance of the system and a high level of user acceptance. This in turn means that the system can be employed for effective traffic control and road accident avoidance via monitoring of critical vehicle parameters and early warning of the drivers based on their and other drivers’ behavior, road conditions and real-time, unpredictable events. Full article

One of the strategies to detect the precursors of an eruption is to define the background dynamical state of a volcano for a prompt recognition of deviations from the basic condition. Mt. Vesuvius (Italy), currently in a quiescent state, is one of the most monitored volcanoes in the world, inciting multidisciplinary advanced studies. Hence an understanding of the links among the different monitored parameters is mandatory. In recent decades the joint analyses of ground tilt and seismicity have added to the understanding of the volcano’s activity. In this paper, we outline the first steps towards a comprehension of the link between Mt. Vesuvius earthquakes and co-seismic ground tilt, after excluding the contribution of other external forces acting on the ground, such as tides, landslides or exceptional meteorological phenomena. We used the seismicity with a duration magnitude ≥ 2.0 recorded at Mt. Vesuvius in the period 2018–2020 to estimate the source parameters and to calculate the associated static displacement. Then, we compared the ground inclination retrieved from the estimated seismic deformation with the long-term ground motion trend measured by tiltmeters. We found that in most cases the two vectors have a comparable size and direction. Full article