Applied Sciences

Zou, Xuan;Xiong, Yibo;Wang, Leiyuan;Zhou, You;Wang, Wanpeng;Zhong, Fangping

2024 Applied Sciences

doi: 10.3390/app14166852

To accurately characterize the damage of rock-like materials under simultaneous or alternating tensile and compressive loading, a dynamic damage constitutive model for rock-like materials based on elastic tensile strain is developed by integrating the classical compressive plastic damage model and the tensile elastic damage model. The model is based on the Holmquist–Johnson–Cook (HJC) and Kuszmaul (KUS) models, categorizing the element stress state into tensile and compressive states through positive and negative elastic volumetric strain. It utilizes elastic tensile strain to enhance the calculation method for tensile cracks, determining the tensile strength of the principal direction based on the contribution rate of tensile principal stress for uniaxial/multiaxial loading. Additionally, it establishes a maximum elastic tensile strain rate function to rectify the model’s effect on the tensile strain rate. Through the LS-DYNA subroutine development, the model proficiently delineates the distribution of ring-shaped cracks on the frontal side and strip-shaped cracks on the rear side of the reinforced concrete slab subjected to impact loading. Numerical simulations demonstrate that the model provides more accurate damage prediction results for stress conditions involving simultaneous or alternating compression and tension, offering valuable insights for damage analysis in engineering blasting or impact penetration.

Khabiyev, Alibek;Dilibal, Savas;Mussulmanbekova, Assel;Kanapiya, Magzhan;Kerimkulov, Daniyar

2024 Applied Sciences

doi: 10.3390/app14166853

In this study, an AM-based continuous processing reactor system was designed, manufactured, and assembled on a laboratory scale for the generation of pharmaceutical substances with an improved process control. The developed AM-based (additively manufactured) continuous pharmaceutical reactor system for the synthesis of metronidazole derivatives aimed to optimize both the physical and the chemical processes with time savings. Using AM, we were able to build reactor subcomponents with complex designs and precise dimensions, which facilitated the precise control of the reaction parameters and reduced the amount of chemicals required compared to macroscale reactors. The assembly of the whole reactor system consisted of main reactor bodies, mixers, valves, heat exchangers, electrical motors, and a microcontroller system. The assembled reactor system revealed a continuous flow of reagents and ensured uniform mixing and reaction conditions, thereby increasing the process efficiency and product quality. Five metronidazole derivatives were synthesized via two continuous processes, involving metronidazole reduction and its subsequent reactions with terephthalic aldehyde and anthracen-9(10H)-one to form Schiff bases. The optimal conditions were determined as follows: compound A (72% yield, 120 min, 55 °C), compounds B and C (63% and 68% yield, respectively, 8 h, 65 °C), and compounds D and E (74% and 85% yield, respectively, 8 h, 45 °C).

Maitin, Ana M.;Nogales, Alberto;Delgado-Martos, Emilio;Intra Sidola, Giovanni;Pesqueira-Calvo, Carlos;Furnieles, Gabriel;García-Tejedor, Álvaro J.

2024 Applied Sciences

doi: 10.3390/app14166854

Computer vision has advanced much in recent years. Several tasks, such as image recognition, classification, or image restoration, are regularly solved with applications using artificial intelligence techniques. Image restoration comprises different use cases such as style transferring, improvement of quality resolution, or completing missing parts. The latter is also known as image inpainting, virtual image inpainting in this case, which consists of reconstructing missing regions or elements. This paper explores how to evaluate the performance of a deep learning method to do virtual image inpainting to reconstruct missing architectonical elements in images of ruined Greek temples to measure the performance of different activation functions. Unlike a previous study related to this work, a direct reconstruction process without segmented images was used. Then, two evaluation methods are presented: the objective one (mathematical metrics) and an expert (visual perception) evaluation to measure the performance of the different approaches. Results conclude that ReLU outperforms other activation functions, while Mish and Leaky ReLU perform poorly, and Swish’s professional evaluations highlight a gap between mathematical metrics and human visual perception.

Li, Ruiyao;Wang, Xucheng;Zhang, Yuantao

2024 Applied Sciences

doi: 10.3390/app14166855

In recent years, the in situ resource utilization of CO2 in the Martian atmosphere by low-temperature plasma technology has garnered significant attention. However, numerical simulation is extremely time-consuming for modeling the complex CO2 plasma, involving tens of species and hundreds of reactions, especially under Martian pressure. In this study, a deep neural network (DNN) with multiple hidden layers is introduced to investigate the CO2 conversion in radio-frequency (RF) discharges at a given power density under Martian pressure in almost real time. After training on the dataset obtained from the fluid model or experimental measurements, the DNN shows the ability to accurately and efficiently predict the various discharge characteristics and plasma chemistry of RF CO2 discharge even in seconds. Compared with conventional fluid models, the computational efficiency of the DNN is improved by nearly 106 times; thus, a real-time calculation of RF CO2 discharge can almost be achieved. The DNN can provide an enormous amount of data to enhance the simulation results due to the very high computational efficiency. The numerical data also suggest that the CO2 conversion increases with driving frequency at a fixed power density. This study shows the ability of the DNN-based approach to investigate CO2 conversion in RF discharges for various applications, providing a promising tool for the modeling of complex non-thermal plasmas.

Ciavarella, Domenico;Montaruli, Graziano;Giuliani, Lidia;Bisceglia, Maria;Laurenziello, Michele;Fanelli, Carlotta;Lorusso, Mauro;Esposito, Rosa;Tepedino, Michele

2024 Applied Sciences

doi: 10.3390/app14166856

Nail biting (NB) is a repetitive and uncontrolled parafunctional activity that can affect oral health by altering tooth shape and intraoral position. Nowadays, there is not enough scientific evidence about the impact of NB on teeth; therefore, this study aimed to evaluate the effects of NB on the length, width and inclination of upper central incisors. This retrospective study involved 76 patients, 40 males and 36 females, with a mean age of 10.6 ± 0.3 years. Digital scans of the maxillary arch of each patient were recorded. Next, the length, width and inclination of upper central incisors used and not used for NB were measured. Finally, data were analysed with a paired t-test. Statistical analysis showed statistically significant differences in the length and inclination of upper central incisors used for NB compared with those of upper central incisors not used for NB, while the width did not show a significant difference. There were relevant changes in upper central incisors subjected to NB, demonstrating that NB impairs the shape, morphology and inclination of teeth. Therefore, because of the potentially negative consequences of NB, it is recommended that NB not be underestimated.

Radpour, Roxanne;Kleynhans, Tania;Facini, Michelle;Pozzi, Federica;Westerby, Matthew;Delaney, John K.

2024 Applied Sciences

doi: 10.3390/app14166857

Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts.

Zhang, Xia;Zhang, Mao;Wei, Gang;Wang, Jia

2024 Applied Sciences

doi: 10.3390/app14166858

Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis.

Jung, Jinwoo;Lee, Kihak;Kim, Bonghwan

2024 Applied Sciences

doi: 10.3390/app14166859

In this study, we thoroughly analyzed the linearity and repeatability of force-sensing resistor (FSR) sensors through static load tests to ensure their reliability. The novelty of this research lies in its comprehensive evaluation and direct comparison of two widely used FSR sensors, i.e., Flexiforce A201-1 and Interlink FSR-402, under various loading conditions by employing a robust calibration methodology. This study provides detailed insights into the sensors’ performances, offering practical calibration equations that enhance measurement precision and reliability, which have not been extensively documented in previous studies. Our results demonstrate that the linearity of thin film FSR sensors is highly accurate, closely resembling a straight line. We employed M1 Class weights, applying loads ranging from 20 g to 300 g. The resistance of the FSR sensors, which varies with the applied load, was measured using a voltage divider circuit and an analog-to-digital converter of a microcontroller. MATLAB was used to calculate the average output voltage for each applied load and fixed resistance. Additionally, we examined the relationships among load, FSR sensor resistance, and conductivity. Our research indicates that with precise calibration, thin film FSR sensors can be highly reliable for force measurement applications.

Gaweł, Eliza;Soto-Rey, Javier;Zwierzchowska, Anna;Perez-Tejero, Javier

2024 Applied Sciences

doi: 10.3390/app14166860

The aim of this systematic review was twofold: to identify the main trends and issues that are being addressed by researchers in the context of physical fitness and sports performance in deaf and hard-of-hearing (D/HH) athletes and to indicate the needs and future directions that should be implemented in the training process of athletes with hearing impairments. The methodology of this systematic review was planned according to PRISMA guidelines. A search of electronic databases (PubMed, EBSCO, Scopus) was conducted to identify all studies on physical fitness, sports performance and participation, and D/HH athletes from 2003 to 2024. In total, 87 full-text articles were assessed to determine eligibility, while 34 studies met the inclusion criteria and were subjected to detailed analysis and assessment of their methodological quality. The presented systematic review indicates evidence that D/HH athletes are characterized by a similar or higher level in selected motor abilities compared to hearing athletes. Moreover, it seems that hearing impairment is not a barrier in the development of an athlete’s physical fitness, including aerobic capacity, muscular strength and power or speed of reaction. Furthermore, inclusion in sports participation and specific tools (i.e., communication aids) appear to be crucial factors for performance enhancement.

Jiang, Dazuo;Zou, Huanqing;Guo, Yike;Tian, Fuqiang;Liu, Hongqi;Yin, Yufeng

2024 Applied Sciences

doi: 10.3390/app14166861

When the electric locomotive pantograph is dropping, the interruption of pantograph catenary contact causes electromagnetic oscillation and arcing. The frequent arc burning that occurs due to charge accumulation results in the amplitude of overvoltage increasing gradually, posing a threat to locomotive high-voltage equipment. However, the physical mechanisms and characteristics of overvoltage are still unclear. This paper proposes a simulation model of operating overvoltage due to a dropping pantograph based on the pantograph–catenary arc and variable capacitance. Distributed RLC electromagnetic oscillation is considered, which allows the real-time calculation of arc resistance and capacitance. Under the same working conditions, the error between the simulation and test results is less than 4.0%, which proves the credibility of the model. The variation law of overvoltage under different dropping speeds or catenary phases was investigated, which shows the max amplitude is 298.20 kV and steepness is 2096.80 kV/μs at 0.30 m/s speed. The waveform shows the characteristics of high amplitude and high steepness, similar to very fast transient overvoltage (VFTO). There is a sinusoidal relationship between the catenary phase and overvoltage amplitude. The closer the catenary phase to 90°, the higher the overvoltage amplitude. The research has important guiding significance for the overvoltage formation mechanism of a traction power supply system and the insulation coordination design of high-voltage equipment.