Selecting lubricating oil for two-stroke gasoline engines: a multi-criteria decision-making approach

The two-stroke engine boasts advantages in terms of simpler manufacturing and a smaller size when compared to the four-stroke engine. Vehicles powered by two-stroke engines can thus effortlessly overcome road obstacles compared to their four-stroke counterparts. However, the use of a two-stroke engine results in higher carbon monoxide and hydrocarbon emissions than that of a four-stroke engine. This discrepancy places greater demands on the selection of lubricating oil for two-stroke engines compared to four-stroke engines. In market, there exists a multitude of lubricating oil options tailored for two-stroke engines, each characterized by varying technical parameters. These disparities are expressed through factors such as density, viscosity index, viscosity, and combustion temperature, among others. Consequently, the task of choosing the optimal lubricant becomes a complex endeavor for consumers. In this study, an examination of lubricant selection is presented using a Multi-Criteria Decision-Making (MCDM) approach. The MCDM method employed in this article is the Combined Compromise Solution (COCOSO) method. The selection of the best lubricant is based on an evaluation of four distinct types. Each type of oil is described by four key parameters (criteria): density, viscosity index, viscosity at 100 °C, and viscosity at 40 °C. The weights for these four criteria are determined through three different methods, including the Entropy method, Criteria Importance Through Intercriteria Correlation (CRITIC) method, and Standard Deviation (SD) method. Thus, the ranking of lubricants is conducted three times, corresponding to these three weighting methods. The results indicate that the best oil choice remains consistent regardless of the weighting method applie

Comparison of two methods in multi-criteria decision-making: application in transmission rod material selection

Transmission rod is an indispensable part in diesel and gasoline engines. Its job is to convert rotation into translational motion or vice versa. The transmission rod material selection plays a very important role, affecting its working function and durability. This study was conducted to compare two Multi Criteria Decision Making (MCDM) methods in transmission rod material selection. They are PIV (Proximity Indexed Value) method, and FUCA (Faire Un Choi Adéquat) method. Seven types of steel commonly used in transmission rods were reviewed for ranking, inclusive of: 20 steel, 40 steel, 45 steel, 18Cr2Ni4WA steel, 30 CrMoA steel, 45Mn2 steel and 40CrNi steel. Nine parameters were used as criteria to evaluate each steel including minimum yield strength, ultimate tensile strength, minimum elongation ratio, contraction ratio, modulus of elasticity, mean coefficient of thermal expansion, thermal conductivity, specific thermal capacity, and density. The weights of the criteria were calculated using three methods inclusive of MEAN weight method, Entropy weight method and MEREC weight method (Method based on the Removal Effects of Criteria). Each MCDM method was combined with the three weight methods mentioned above to rank the alternatives. The obtained results show that when using both PIV and FUCA methods to rank the alternatives, the best and worst alternatives are found regardless of the weight of the criteria. The best alternative determined using the PIV method is also the best alternative determined using the FUCA method. It means that the two PIV and FUCA methods have been shown to be equally effective. Among the seven transmission rod materials reviewed, 20 steel was identified as the best, and 40CrNi steel was identified as the wors

An Efficient Spectral Leakage Filtering for IEEE 802.11af in TV White Space

Orthogonal frequency division multiplexing (OFDM) has been widely adopted for modern wireless standards and become a key enabling technology for cognitive radios. However, one of its main drawbacks is significant spectral leakage due to the accumulation of multiple sinc-shaped subcarriers. In this paper, we present a novel pulse shaping scheme for efficient spectral leakage suppression in OFDM based physical layer of IEEE 802.11af standard. With conventional pulse shaping filters such as a raised-cosine filter, vestigial symmetry can be used to reduce spectral leakage very effectively. However, these pulse shaping filters require long guard interval, i.e., cyclic prefix in an OFDM system, to avoid inter-symbol interference (ISI), resulting in a loss of spectral efficiency. The proposed pulse shaping method based on asymmetric pulse shaping achieves better spectral leakage suppression and decreases ISI caused by filtering as compared to conventional pulse shaping filters

Modeling of milling forces in facing process of aluminum alloy AL7075 using the square inserts

Cutting forces play very important in designing the tool machine, cutting tool, and in optimization of machining processes. Modeling and prediction of cutting forces by theoretical methods are quite difficult, so, this study was focused on modeling the cutting force in face milling process using combination of theoretical and experimental methods. This study was performed to model the milling forces (MFs) and determine the milling force coefficients (MFCs) in the face milling process of aluminum alloy Al7075 using square inserts. From theoretical and experimental methods, the relationship of average milling forces (AMFs) and feed per flute (ft) were determined as the linear regression. Using experimental data, the linear regressions of AMFs and feed per flute were determined with high values of determination coefficients (larger than 95 %). MFCs were determined including shear and edge MFCs (tangential shear MFC (Ktc) of 538.127 N/mm2, radial shear MFC (Krc) of 185.967 N/mm2, axial shear MFC (Kac) of -691.297 N/mm2, tangential edge MFC (Kte) of 11.253 N/mm, radial edge MFC (Kre) of 6.991 N/mm, and axial edge MFC (Kae) of –32.971 N/mm. The MF models were successfully verified by comparing the measured and predicted MFs in face milling process of Al7075. The tendency and shape of predicted MFs were quite close to the measured ones. The differences between the predicted and the measured MFs can be due to the several reasons such as the influence of vibrations, the influence of cutting heat, etc., and these are also the limitations of this study. The modeling and prediction methods of this study can be used to model and predict the cutting forces in face milling of other milling types and other pairs of cutting tool and workpiece material as wel

Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study. Our method achieved a remarkable accuracy of 0.9999986\% on the test suite, surpassing the performance of the previous method. From this result, we use 3D printing technology to illustrate the shape of the wound filling. The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design. By automating facial wound segmentation and improving the accuracy of wound-filling extraction, our approach can assist in carefully assessing and optimizing interventions, leading to enhanced patient outcomes. Additionally, it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants. Our source code is available at \url{https://github.com/SIMOGroup/WoundFilling3D}

Static and Dynamic Analysis of Piezoelectric Laminated Composite Beams and Plates

In this chapter, the mechanical behavior analysis of piezoelectric laminated composite beams and plates is influenced subjected to static, dynamic, and aerodynamic loads. Algorithm for dynamic, stability problem analysis and vibration control of laminated composite beams and plates with piezoelectric layers is presented. In addition, numerical calculations, considering the effect of factors on static, dynamic, and stability response of piezoelectric laminated composite beams and plates are also clearly presented. The content of this chapter can equip readers with the knowledge used to calculate the static, dynamic, and vibration control of composite beams, panels made of piezoelectric layers applied in the field different techniques

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three method

Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures

In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative method for regenerating flawed facial structures, yielding 3D printable outputs that effectively support physicians in their patient treatment process. Our results highlight the model's capacity for concealing scars and achieving comprehensive facial reconstructions without discernible scarring. By capitalizing on pre-trained models and necessitating only a few hours of supplementary training, our methodology adeptly devises an optimal model for reconstructing damaged and imperfect facial features. Harnessing contemporary 3D printing technology, we institute a standardized protocol for fabricating realistic, camouflaging mask models for patients in a laboratory environment

Risk Management at Military Commercial Joint Stock Bank in Vietnam

This research is conducted for examining the framework for risk management in the Basel II accord, the Basel II risk management model at the Military Commercial Joint Stock Bank. Data were collected from annual reports for the period from 2015 to 2017 of the Military Commercial Joint Stock Bank. The results show that the implementation of risk management under Basel II at Military Bank still faces many difficulties in the pressure of capital increase, database system, human resource quality, and cost of implementation. The study suggest some solutions for Military Bank to implement successfully Basel II, emphasizing the role of human resource quality, modernizing the data system and the specific mechanism for raising capital. The results of this research is a reference for Vietnamese commercial banks in identifying, controlling and responding various risks in banking activities in the context of Vietnam. Keywords: Basel II, Risk management, Military Bank DOI: 10.7176/RJFA/10-12-06 Publication date:June 30th 201