Applied Sciences

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EISSN : 20763417
Current Publisher: MDPI (10.3390)
Total articles ≅ 11,248
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Ignatius Nurprasetio, Bentang Budiman, Ahmad Afwan, Putri Halimah, Sarah Utami, Muhammad Aziz
Published: 18 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041366

Abstract:This work aims to investigate piezoresistive behavior in plain-woven carbon fiber reinforced polymer (CFRP). Measurement method for electric resistant alteration in the woven CFRP under tensile loading by using a Wheatstone bridge circuit is introduced. Reversibility of the resistant alteration is also investigated whereas the gauge factor of the woven CFRP is evaluated. The result shows that the positive piezoresistive properties of the woven CFRP can be observed by the Wheatstone bridge circuit. The specific resistances of 43.8 μΩm and 10.1 μΩm are obtained for wrap and thickness directions, respectively. Reversibility with a hysteresis of the woven CFRP can also be confirmed with the gauge factor of 22.9 at loading conditions and 17.7 at unloading conditions. Positive piezoresistive behavior which has been revealed in this work can be utilized for structural health monitoring technology development.
Guan-Chen Liu, Li Xu, Jie Li, Qiang Sun, Zong-Qiang Liu, Hai-Wen Chen
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041349

Abstract:Under the erosion of seawater–ice two-phase flow, seawater in pipelines of polar ships can cause the pipeline failures that threaten the safety of navigations. The discrete phase model (DPM) and erosion wear model (EWM) were established by using the computational fluid dynamics (CFD) method for numerical analysis of the 90° elbow with relatively severe erosion. This paper explores the erosion effect of pipelines under different conditions and puts forward optimal measures for pipeline protection. Compared with the existing multiphase flow research, the novelty of this study is that vibration conditions are considered and parameters such as two-phase flow velocity, ice packing factor (IPF), ice particle diameter and ice particle rotation characteristics are combined with vibration conditions. Combined with the comprehensive analysis of erosion effects of static pipelines, a general law of seawater pipeline wear under vibration is obtained. The results show that pipeline wear under vibration is more serious than under static conditions. Under static conditions, the wear of the same section in the pipeline increases with the increases of two-phase flow velocity and IPF. However, under vibration conditions, when the velocity is less than 3 m/s, the wear of the pipeline has no significant change, while when the velocity is over 3 m/s, the wear rate increases significantly. The particle diameter has little effect on the wear of static pipes, but under the vibration condition, the pipe wear rate decreases with the increase of particle diameter, and it starts to stabilize when the diameter exceeds 0.3 mm. If the rotation characteristics of ice particles are taken into account, the wear rate along the pipeline is significantly higher than that without particle rotation.
Jamel Riahi, Silvano Vergura, Dhafer Mezghani, Abdelkader Mami
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041350

Abstract:An agricultural greenhouse is a complex and Multi-Input Multi-Output MIMO system in which the internal parameters create a favorable microclimate for agricultural production. Temperature and internal humidity are two parameters that have a major impact on greenhouse yield. The objective of this study was to propose a simulated dynamic model in a MATLAB/Simulink environment for experimental validation. Moreover, a fuzzy controller was designed to manage a greenhouse indoor climate by means of an asynchronous motor for ventilation, heating, humidification, etc. An intelligent system to control these actuators for an optimal inside climate was implemented in the model. The dynamic model was validated by comparing the simulation results to experimental measurements. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate. Finally, a photovoltaic generator was modeled, with the aim of reducing the costs of agricultural production. It feeds the asynchronous motor with a vector control optimized by fuzzy logic that drives a variable speed fan.
Tamás Orosz, David Pánek, Pavel Karban
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041361

Abstract:Since large power transformers are custom-made, and their design process is a labor-intensive task, their design process is split into different parts. In tendering, the price calculation is based on the preliminary design of the transformer. Due to the complexity of this task, it belongs to the most general branch of discrete, non-linear mathematical optimization problems. Most of the published algorithms are using a copper filling factor based winding model to calculate the main dimensions of the transformer during this first, preliminary design step. Therefore, these cost optimization methods are not considering the detailed winding layout and the conductor dimensions. However, the knowledge of the exact conductor dimensions is essential to calculate the thermal behaviour of the windings and make a more accurate stray loss calculation. The paper presents a novel, evolutionary algorithm-based transformer optimization method which can determine the optimal conductor shape for the windings during this examined preliminary design stage. The accuracy of the presented FEM method was tested on an existing transformer design. Then the results of the proposed optimization method have been compared with a validated transformer design optimization algorithm.
Jinjing Shi, Shuhui Chen, Jiali Liu, Fangfang Li, Yanyan Feng, Ronghua Shi
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041353

Abstract:A novel encryption algorithm called the chained phase-controlled operation (CPCO) is presented in this paper, inspired by CNOT operation, which indicates a stronger correlation among message states and each message state depending on not only its corresponding key but also other message states and their associated keys. Thus, it can prevent forgery effectively. According to the encryption algorithm CPCO and the classical dual signature protocols, a quantum dual signature scheme based on coherent states is proposed in this paper. It involves three participants, the customer Alice, the merchant Bob and the bank Trent. Alice expects to send her order message and payment message to Bob and Trent, respectively. It is required that the two messages must be linked to guarantee the payment is paid for the corresponding order. Thus, Alice can generate a quantum dual signature to achieve the goal. In detail, Alice firstly signs her two messages with the shared secret key. Then She connects the two signatures into a quantum dual signature. Finally, Bob and Trent severally verify the signatures of the order message and the payment message. Security analysis shows that our scheme can ensure its security against forgery, repudiation and denial. In addition, simulation experiments based on the Strawberry Fields platform are performed to valid the feasibility of CPCO. Experimental results demonstrate that CPCO is viable and the expected coherent states can be acquired with high fidelity, which indicates that the encryption algorithm of the scheme can be implemented on quantum devices effectively.
Jungsoo Cho, Kyoungchul Kong
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041365

Abstract:Legged robots suffer from the impact due to the consistent collisions with the ground. At the moment of collision, the sudden impact force not only causes the legs to lose contact off the ground, but can also reduce controllability and durability. This phenomenon becomes worse for the robots in running. In order to mitigate such an impact effectively, this study focuses on the mechanical structure of the legs, unlike the previous studies, which focused on the component level. The mechanical structures include actuator configuration, segment ratio, total length, and flexion direction. Contact inertia (CI), closely related to the impact, is derived and utilized to analyze the mechanical structure in terms of impact mitigation. A series of impact experiments with a fabricated leg verify that the mechanical structure affects mitigating the impact.
Mario Acevedo, María Orvañanos-Guerrero, Ramiro Velázquez, Vigen Arakelian
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041351

Abstract:The problem of shaking force balancing of robotic manipulators, which allows the elimination or substantial reduction of the variable force transmitted to the fixed frame, has been traditionally solved by optimal mass redistribution of the moving links. The resulting configurations have been achieved by adding counterweights, by adding auxiliary structures or, by modifying the form of the links from the early design phase. This leads to an increase in the mass of the elements of the mechanism, which in turn leads to an increment of the torque transmitted to the base (the shaking moment) and of the driving torque. Thus, a balancing method that avoids the increment in mass is very desirable. In this article, the reduction of the shaking force of robotic manipulators is proposed by the optimal trajectory planning of the common center of mass of the system, which is carried out by “bang-bang” profile. This allows a considerable reduction in shaking forces without requiring counterweights, additional structures, or changes in form. The method, already presented in the literature, is resumed in this case using a direct and easy to automate modeling technique based on fully Cartesian coordinates. This permits to express the common center of mass, the shaking force, and the shaking moment of the manipulator as simple analytic expressions. The suggested modeling procedure and balancing technique are illustrated through the balancing of the 3RRR planar parallel manipulator (PPM). Results from computer simulations are reported.
Guoxiang Zhang, QiQi Fu, Zetian Fu, Xinxing Li, Maja Matetić, Marija Brkić Bakaric, Tomislav Jemrić
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041348

Abstract:Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.
Wen-Chien Ting, Horng-Rong Chang, Chi-Chang Chang, Chi-Jie Lu
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041355

Abstract:: Colorectal cancer is ranked third and fourth in terms of mortality and cancer incidence in the world. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five machine learning classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. A total of 4287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.
Krzysztof Skowron, Karolina Jadwiga Skowron, Justyna Bauza-Kaszewska, Ewa Wałecka-Zacharska, Joanna Kwiecińska-Piróg, Katarzyna Grudlewska-Buda, Natalia Wiktorczyk, Eugenia Gospodarek-Komkowska
Published: 17 February 2020
by MDPI
Applied Sciences, Volume 10; doi:10.3390/app10041364

Abstract:The decontamination of food contact surfaces is a major problem for the food industry. The radiant catalytic ionization (RCI) method, based on the ionization process, may be an alternative for conventional decontamination procedures. The advantage of this technique is the possibility of its application to household refrigerating appliances and industrial cold rooms. This study aimed to assess the effect of RCI on the reduction of Campylobacter jejuni, Listeria monocytogenes, and Salmonella Enteritidis from the biofilms formed on a glass surface under refrigeration conditions. Bacterial biofilms were exposed to RCI for 24 h and after 12 (variant I) and 72 h (variant II) of the glass surface contamination. In the last variant (III), the contaminated meat was placed on the glass surface in the refrigerator and subjected to RCI treatment for 72 h. The significantly highest values of absolute reduction efficiency coefficient E were found for the bacterial attachment stage of biofilm formation (variant I). The research proves the efficiency of the RCI method in the reduction of bacteria number from a glass surface.