Applied Sciences

The structural characteristics of polymer track-etched membranes (TM) were obtained by atomic force microscopy (AFM) for a set of samples (polypropylene, polycarbonate, polyethylene terephthalate, with average pore diameters ~183, 375, and 1430 nm, respectively). The analysis of AFM experimental data was performed by using a specially developed technique for computer analysis of AFM images. The method allows one to obtain such parameters of TM as distribution of pore diameters, distribution of the minimum distances between the nearest pores, pore surface density, as well as to identify defective pores. Spatial inhomogeneities in the distribution of pore parameters were revealed. No anisotropy (some specific selected direction) was found in the surface distribution of the pores in the samples under study. Full article

The combustion process in diesel engines is controlled by the injection rate shape. The stricter emission regulations requiring simultaneous reduction of nitrogen oxides and particulate matter imposes intense research and development activity for achieving clean and robust combustion. This work describes the experimental investigation made for calibration of an engine model and the numerical investigation performed to assess the influences of different injection rate shapes on performances of a diesel engine fuelled with diesel and rapeseed biodiesel B20. The engine model was developed with the AVL-BOOST code using the AVL-MCC combustion mode. The model was calibrated for the reference Top-Hat injection rate shape using experimental data registered for maximum brake torque and maximum brake power speed conditions. Other injection rate shapes such as triangular, trapezoidal, and boot having the same area, start, and duration of injection were investigated in terms of combustion characteristics, performance, and pollutant emissions. The link existing between the injection characteristics and the NO_x and Soot emissions highlights that, for the optimal rate of injection shape, a simultaneous reduction of NO_x and Soot by 11%, respectively 4% for maximum brake torque and by 22%, respectively 7% for maximum brake power, can be obtained using biodiesel B20. Full article

The introduction of any new disruptive technology in a traditionally well-established industry, such as the road construction industry, is usually associated with considerable resistance. This is especially relevant when the new technology is based on the use of granular materials traditionally considered to be of an unacceptable quality in combination with relatively new concepts such as New-age (Nano) Modified Emulsions (NME). In such cases, the fact that the material design methods are based on fundamental scientific principles and have been proven in both laboratories and through Accelerated Pavement Testing (APT) may be of little influence. However, the general acceptance of new disruptive technologies, e.g., telecommunications and Information Technologies (IT), have been based on the considerable advantages it presented. The same principles are applicable to the general acceptance and use of the NME stabilisation/enhancement of materials in the road construction industry. This article is aimed at the practical cost-effective demonstration of the general application of the use of nanos-silane-modified emulsions in the construction of the highest order roads, i.e., inter-city multi-lane highways, lower-order roads (including Low-Volume Roads (LVR)), and even local accesses to farms and in villages/townships. The implementation of NME technologies is directly associated with ease of use, time, and cost savings, and with the addressing and reduction of risks applicable to the use thereof. Full article

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment. Full article

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open-source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc. Full article

Uniaxial fatigue testing of micro-mechanical metallic specimens can provide valuable insight into damage formation. Magnetic and piezomagnetic testing are commonly used for qualitative characterization of damage in ferromagnetic specimens. Sensitive and accurate measurements with magnetic sensors is a key part of such a characterization. This work presents an experimental setup to induce structural defects in a micro-mechanical fatigue test. Simultaneously, the resulting piezomagnetic signals are measured during the complete lifetime of the tested specimen. The key component is a highly sensitive optically pumped magnetometer (OPM) used to measure the piezomagnetic hysteresis of a small specimen whose structural defects can be analyzed on a small scale by other metallographic characterization methods as well. This setup aims to quantify the magnetic signatures of damage during the fatigue process, which could enable non-destructive mechanical testing of materials. This paper reports the initial results obtained from this novel micro-magneto-mechanical test setup for a ferritic steel specimen. Full article

Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. Since manual segmentation requires considerable time and effort from experts, research to automatically perform segmentation is in progress. Recent studies using deep learning have improved performance but need many labeled data. Although there are public datasets for research, manual labeling is required in an area where labeling is not performed to train a model. We propose a deep-learning-based tool that can easily create training data to alleviate this inconvenience. The proposed tool receives a CT image and the pixels of organs the user wants to segment as inputs and extract the features of the CT image using a deep learning network. Then, pixels that have similar features are classified to the identical organ. The advantage of the proposed tool is that it can be trained with a small number of labeled data. After training with 25 labeled CT images, our tool shows competitive results when it is compared to the state-of-the-art segmentation algorithms, such as UNet and DeepNetV3. Full article

This study aimed to utilize enzymatic treatment and pressurized hot water extraction (PHWE) to recover soluble food-grade protein and collagen peptides from mechanically deboned chicken meat (MDCM), a side-stream from the meat industry. Food-grade enzyme blends Ermitase 1 and Ermitase 2 were used to fractionate the mechanically deboned meat into fat, soluble protein, and solids. Response surface methodology was utilized to optimize treatments to maximize the protein yield. At the optimum conditions (hydrolysis time 240 min, E:S 0.27%, and a hydromodule 1 L/kg), the enzymatic treatment produced high protein yield, approximately 90%. The protein hydrolysates showed a good solubility index, but weak gelling properties. The PHWE of the bones resulted in a high nitrogen yield, approximately 87%, at the optimum conditions of 190 °C and 83 min. Peptides in the bone extract were in the range of 0.5–13.7 kDa. Overall, our study highlights the importance of response surface methodology to optimize parameters for mechanically deboned chicken meat enzymatic and PHWE treatments to achieve high yields of protein for food applications and low-molecular-weight collagen peptides for cosmetic applications. The crucial role of protein and peptide prices was observed in preliminary profitability analysis. Full article

The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures. Full article

The plate embedded with acoustic black hole (ABH) indentations is potential for structural vibration and noise control. This work focuses on the mid- and low-frequency performance of plates embedded with the array of ABH for energy focalization and vibration and noise suppression. Plates embedded with two-dimensional ABHs are modelled with detailed Finite Element models, and the power flow method is introduced to analyze the energy propagation characteristics arising from the ABH effect. Then, the distribution of average vibration power density along the ABH radius is studied. Next, the energy dissipation effects of the plate model embedded with the ABH array with two types of damping layers are investigated. Finally, the sound pressure levels of the ABH structure are calculated and discussed. This work is helpful to understand the characteristics of plates embedded with the ABH array in reducing vibration and noise radiation. Results show that the ABH array can realize more than 100 times energy focalization effect at some frequencies, which indicates a potential in vibration and noise control when coupled with damping materials. Full article

The objective was to compare pain relief in alveolar osteitis achieved by the application of platelet-rich fibrin (PRF) and aspirin cones, and to assess the influence of both treatments on bacterial concentrations in post-extraction wounds. A split-mouth, single-center, non-randomized controlled trial was conducted in 30 patients. Pain relief was assessed using the visual analog scale at three time points (before, 24 h after, and 48 h after intervention). Bacterial concentrations were evaluated from cultures of swab samples collected at the three time points. A Wilcoxon matched-pairs signed-rank test was performed to detect differences in distributions of scalar variables between treatment with PRF and aspirin cones. Pain relief at 24 h and 48 h after treatment was significantly higher (p = 0.003) with PRF application than with aspirin cone placement. Additionally, PRF application caused a significantly higher reduction in aerobic bacteria concentration, an average increase, instead of a decrease, in lactic acid bacilli concentration in the first 24 h, and a significantly greater increase in streptococci concentration at 48 h follow-up. PRF provides better pain relief than aspirin cones in alveolar osteitis. Bacterial concentrations in the extraction wounds are consistent with the pain relief achieved after PRF or aspirin application. Full article

Coherent terahertz control of magnetization dynamics is an area of current interest due to its great potential for the realization of magnetization control on ultrafast timescales in commercial devices. Here we report on an experiment realized at the THz beamline of the free electron laser FLASH at DESY which offers a tunable terahertz radiation source and spontaneously synchronized free-electron laser X-ray pulses to resonantly probe the magnetization state of a ferromagnetic film. In this proof-of-principle experiment, we have excited a thin Permalloy film at different THz wavelengths and recorded the induced magnetization dynamics with photons resonantly tuned to the Ni $M_{2,3}$ absorption edge. For THz pump pulses including higher orders of the undulator source we observed demagnetization dynamics, which precise shape depended on the employed fundamental wavelength of the undulator source. Analyzing the shape in detail, we can reconstruct the temporal profile of the electric field of the THz pump pulse. This offers a new method for the realization of an in-situ terahertz beamline diagnostic which will help researchers to adjust the pulse characteristics as needed, for example, for future studies of THz induced coherent control of magnetization dynamics. Full article

The existence of local soft interlayer can significantly amplify or attenuate the ground motion and thus might influence the lagged spatial coherency between spatially varying earthquake ground motions. A target site with a local soft interlayer was assumed first, and then two numerical examples were set. In example 1, linear soil behavior was considered and a large amount of quasi-stationary spatially varying earthquake ground motions were generated by combining the one-dimensional wave propagation theory and the classical spectral representation method. The influence regularity of varying shear wave velocity, buried depth, and thickness of the soft interlayer on the characteristics of lagged spatial coherency was investigated. In example 2, non-linear soil behavior was taken into account and fully non-stationary spatially varying earthquake ground motions were thus generated by using time-varying transfer function and spectral representation method. An overall evaluation was carried out to shed light on the differences of characteristics of spatial coherency between non-linear soil and linear soil cases. It showed that: (i) As the shear wave velocity of interlayer declines and as the buried depth and thickness increase, remarkable reduction of spatial coherency showed up; (ii) the reduction of lagged spatial coherency caused by varying buried depth may be more inclined to concentrate in the lower frequency range; (iii) the non-linear soil behavior can cause greater further reduction of lagged spatial coherency in comparison with linear soil behavior, especially in the higher frequency range; (iv) the troughs of lagged spatial coherency curve tend to be located in the variation range of vibration frequency of time-varying spectral ratio. Full article

Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other. Social networks have become so indispensable in our everyday life that we cannot imagine what daily life would be like without social networks. Through social networks, we can access friends’ opinions and behaviors easily and are influenced by them in turn. Thus, an effective algorithm to find the top-K influential nodes (the problem of influence maximization) in the social network is critical for various downstream tasks such as viral marketing, anticipating natural hazards, reducing gang violence, public opinion supervision, etc. Solving the problem of influence maximization in real-world propagation scenarios often involves estimating influence strength (influence probability between two nodes), which cannot be observed directly. To estimate influence strength, conventional approaches propose various humanly devised rules to extract features of user interactions, the effectiveness of which heavily depends on domain expert knowledge. Besides, they are often applicable for special scenarios or specific diffusion models. Consequently, they are difficult to generalize into different scenarios and diffusion models. Inspired by the powerful ability of neural networks in the field of representation learning, we designed a hierarchical generative embedding model (HGE) to map nodes into latent space automatically. Then, with the learned latent representation of each node, we proposed a HGE-GA algorithm to predict influence strength and compute the top-K influential nodes. Extensive experiments on real-world attributed networks demonstrate the outstanding superiority of the proposed HGE model and HGE-GA algorithm compared with the state-of-the-art methods, verifying the effectiveness of the proposed model and algorithm. Full article

Fluid film bearings lubricated with supercritical carbon dioxide (sCO₂) eliminate the infrastructural requirement for oil lubricant supply and sealing in turbomachinery for sCO₂ power systems. However, sCO_2′s thermohydrodynamic properties, which depend on pressure and temperature, pose a challenge, particularly with computational model development for such bearings. This study develops a computational model for analyzing sCO₂-lubricated tilting pad journal bearings (TPJBs) with external pressurization. Treating sCO₂ as a real gas, the Reynolds equation for compressible turbulent flows solves the pressure distribution using the finite element method, and the Newton−Raphson method determines the static equilibrium position by simultaneously calculating forces, moments, flow rates of externally pressurized sCO₂, and pressure drop due to flow inertia. The finite difference method solves the energy equation for temperature distribution. The density and viscosity of sCO₂ are converged using the successive substitution method. The obtained predictions agree with the previous and authors’ computational fluid dynamics predictions, thus validating the developed model. Hybrid lubrication increases the minimum film thickness and stiffness up to 80% and 65%, respectively, and decreases the eccentricity ratio by up to 65% compared to those of pure hydrodynamic TPJB, indicating significant improvement in the load capacity. The bearing performance is further improved with increasing sCO₂ supply pressure. Full article

Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module. Full article

The French tokamak WEST supports the ITER design and operation. IRFM is designing a new Thomson scattering diagnostic to measure plasma density and temperature profiles. The diagnostic system consists of an endoscope inside a vacuum vessel, composed of actively cooled optical components. In order to validate and guarantee the diagnostic performances during normal operations, mechanical, thermal, hydraulic and vibratory behavior must be checked. Moreover, perpendicular displacement of the optical surface shall not be higher than 40 µm. Since this diagnostic operates in near infrared light, the temperature of all components must stay lower than 200 °C as not to bias the measurements. The differences in water temperature and pressure between the inlet and outlet of the diagnostic must be lower than 50 °C and 5.6 bar, respectively. The natural frequencies of the structure must be higher than 20 Hz and far enough from the frequency of external components. In this study, the worst radiative plasma scenario was chosen. The results of this study validate the accuracy of the measurements. Before manufacturing, electromagnetic disruption events must also be considered. Full article

Due to its importance in prolonging the lifetime of battery-restricted wireless sensor networks, network longevity has garnered considerable research attention, with the rechargeable wireless sensor network emerging as a viable solution. In this research, the novel methodology of a trust-based mechanism for enhanced security integrated with an energy utility and re-usability model is proposed with software-defined networking (SDN) to maximize energy utilization. We proposed a novel framework with SDN for the service station in a wireless sensor network (WSN). The results showed that the life capacity of the network increases to a maximum of 290% when compared with no charging, with the charge increasing by 30% intervals. We also present how the network survives through this choice of sink. As there is variation in the network size while it increases, the proposed approach with the static method works well until the network size reaches 200. Furthermore, the proposed approach also uses the heuristic method to achieve the best performance. Full article

Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models. Full article