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Electro-chemical machining (ECM) can safely be categorized as being a unique unconventional machining technique, in which the role of thermal defects are not involved. The NiTiCo shape memory alloy has high yield strength coupled with good resistance to aggressive aqueous environments, which makes it a potential candidate for use in several bio-medical applications. This research concentrates on electro-chemical machining of a NiTiCo alloy. Taguchi's Design of Experiment (DOE) containing three-parameters and three-factors was used to conduct the experimental study. The influence of concentration of the sodium chloride electrolyte, voltage, and frequency on output characteristics, such as (i) material removal rate (MRR), (ii) surface roughness (SR), and (iii) over-cut (OC), was analyzed. Experimental outputs revealed the voltage (45.29%) to be the most influential among the three controlling parameter. Grey relational analysis (GRA) was utilized to discover the ideal parametric relation and optimize machining characteristics. [ABSTRACT FROM AUTHOR]

Electrical parameters are vital for electrical discharge machining (EDM) process performance. The study utilized the combination of Taguchi and grey relational analysis (GRA) to optimize sinking EDM. Optimization of peak current, pulse duration, duty ratio and electrode lifting time affecting material removal rate (MRR), relative electrode wear rate (REWR) and surface roughness (SR) in sinking EDM. Analysis of variance (ANOVA) shows that peak current and pulse duration significantly affect MRR and REWR, respectively. Peak current and pulse duration significantly affect SR. The optimized parameters were subjected to EDM experiments and the results are 8.54% increase in MRR, 11.11% decrease in REWR and 9.42% decrease in SR. It was found that at a peak current of 1A, the discharge pit not only depends on peak current and pulse duration but also on pulse off time. Compared to conventional optimization methods, this study analyzed the microstructure and recast layer of the workpieces. [ABSTRACT FROM AUTHOR]

Knock combustion has become the main barrier in the development of hydrogen internal combustion engine (ICE). This study investigated the factors (engine speed, spark timing, injection timing, and injection pressure) influencing knock combustion based on the data of a bench test and a 1D simulation model on GT-Power. And the Taguchi-Grey method was applied to rank the four factors. Results showed that knock combustion occurs under lower relative air-fuel equivalence ratio, the percentage of the knock cycle in 100 continuous cycles decreased from 45.8% to 0 when λ raised from 2.47 to 3.37. The advancement of injection timing and the decreasing of injection pressure, along with higher engine speed, facilitate the occurrence of knock. However, the knock tendency increases first and then declines with the advancement of spark timing. Among all the factors considered, engine speed has the greatest influence on knock combustion, whose contribution rate is 50.64%. • Injection timing and pressure, spark timing, and engine speed can affect knock. Engine speed has the largest impact on knock. • The advancement of spark timing makes the knock tendency increase first and then decline. • Engine speed has the largest contribution on knock combustion through the Taguchi-Grey method. [ABSTRACT FROM AUTHOR]

We attempted to optimize the process-based parameters during friction stir welding of AZ80A - AZ31B Mg alloys with the objective of enhancing the mechanical properties of the fabricated joints. A response surface method based grey relational analysis was employed using three factors and three distinctive levels. A central composite design based multi-objective numerical model using the technique of grey relational analysis was formulated for optimizing the tool dependent parameters, namely tool's rotational speed, its speed of traverse and geometry of the pin. Grey relational grade was determined for all the responses: tensile strength and elongation percentage. Analysis of variance was employed for attaining grey relational grade to determine the most influential parameter of the FSW process. It was observed that the geometry of the tool pin had a greater impact in ascertaining the quality of the fabricated Mg alloy joints, and the tool possessing tapered cylindrical pin geometry exhibited larger values of grey relational grade. Optimized process parameter settings based on the attained GRG values were recorded to be 1100 rpm rotational speed, speed of traverse of 1.5 mm/sec and a tool with taper cylindrical pin geometry. The anticipated values were validated through confirmation investigational runs performed during employment of optimized parameter combinations, which exhibited a perfect agreement with the investigational run values and the confirmatory joint exhibited a tensile strength of 260.42 MPa and elongation percentage of 6.53. [ABSTRACT FROM AUTHOR]

The literature on exceptionalism is preoccupied with its distinctive national sources and resultantly differing styles. Exceptionalism has thus become almost synonymous with culture and identity, rather than international relations (IRs). The paper instead argues that exceptionalism reveals a relational identity that both informs and is informed by a multilateral relation prior to the emergence of exceptionalism. It also argues that all relational systems seek expansion and coexistence. Based upon a comparative study of Confucian and liberal multilateral relationalities, a similar cycle of engagement, conversion, disengagement, and learning is applicable to both. Two exceptionalisms in multilateral relationality differ, however. American exceptionalism embraces a transcendent identity with which to contrast with the rest, who share the same identity and are expected to follow the same rules. Chinese exceptionalism contrarily envisions a superior, benevolent identity to ensure harmony and peace among all, who share no collective identity. The paper traces how Confucianism diverges from liberalism with regard to what accounts for multilateralism—inclusiveness versus rule-based governance, and benevolent exceptions versus universal rights—and the resulting orientations during encounters with strangers. The last section before the conclusion corresponds to the growing attention in IRs theorising to the factor of emotion. Such rational–emotional connectivity—between exceptionalism and emotion—can further attest to the promise of the relational agenda in explaining pluriversal IRs. [ABSTRACT FROM AUTHOR]

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as CL and CD. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment. [ABSTRACT FROM AUTHOR]

To improve the forming quality of agricultural tractor engine hood and effectively solve the problems of wrinkling and cracking during the drawing forming process, DYNAFORM 5.9 finite element software was used for numerical simulation of the part. Based on a comprehensive scoring method of orthogonal experiments and grey relational analysis, the critical process parameters affecting the "maximum thickening rate" and "maximum thinning rate" were determined. Latin hypercube sampling was used to randomly sample the key process parameters, and the sampled samples served as the data basis for the optimization stage of the neural network genetic algorithm function; an optimization model of the neural network genetic algorithm function was established, with the key process parameters, and the "maximum thickening rate" and "maximum thinning rate" as inputs and outputs respectively, to construct a nonlinear mapping relationship and optimize the process parameters. The study showed that the blank holder force and die clearance had the greatest impact on the "maximum thickening rate" and "maximum thinning rate", identifying them as key process parameters; the optimal process parameter combination was the blank holder force of 462.71 kN, the die clearance of 1.09 mm, and corresponding "maximum thickening rate" and "maximum thinning rate" of 3.97% and 25.96%, respectively. Overall, this research provides a practical optimization strategy to solve the quality issues of the engine hood, offering a theoretical basis for its actual production and processing with significant practical application value. [ABSTRACT FROM AUTHOR]

The present research examines the impact of Si3N4–BN hybrid reinforcement on the machinability of a magnesium hybrid composite in wire electrical discharge machine (WEDM). The composite is fabricated through inert gas assisted stir casting route with silicon nitride and boron nitride as reinforcement with varying weight percentages of 0%, 5%, and 10%. Subsequently, the fabricated composites were machined through WEDM according to Taguchi 27 orthogonal array with varying pulse on time (PON), pulse off time (POFF), wire feed rate (WFR), and wire tension (WT). The machinability of the composite was evaluated by measuring Surface Roughness (SR), Kerf Width (KW), and Cutting Velocity (CV) during WEDM. Results reveals that the % of Si3N4 has greater influence over kerf width and cutting velocity whereas BN % has higher influence over surface roughness. The optimization of process parameters using the Taguchi method resulted in different combinations of parameters for each output response. Therefore, Grey Relational Analysis was applied to determine the common optimal process parameters for all three considered output responses. The identified input parameters that yielded the higher CV, minimal SR and KW were as follows: 0% Si3N4 and BN, 6 µs PON, 14 µs POFF, 6 m/min WFR, and 10 g WT. Artificial Neural Network model has been developed to predict the output response CV, SR and KW. The 6–8–3 network model predicts the output responses with better accuracy with overall R2 value of 99.3%. [ABSTRACT FROM AUTHOR]

In this investigation, the factors were oil type ( O t ), powder type ( T p ), and powder concentration ( C p ) level, and the machining parameters were the voltage (V), capacitance (pf), pulse-on time ( T on ), and pulse-off time ( T off ). The output parameters were the material removal rate (MRR), and tool wear rate (TWR). Three types of oils and powders, along with their respective particle concentrations, were selected for this experiment. Neem, Karanja, and sesame oils were mixed with aluminum oxide (Al2O3), chromium (Cr), and nickel (Ni) powder at 4, 6, and 8 gm/l, respectively, using an overhead stirrer to investigate the conditions under which the green oil powder mixed mixture yielded a higher MRR and lower TWR during the micro-EDM process of titanium alloy (Ti-6Al-4V) using a 0.8 mm tungsten carbide (WC) tool with fixed machining parameters (240 V, 100 pf, 20 μ s T on , and 5 μ s T off ). All the powders used in this experiment had a 32-micron (450 mesh) size. The Taguchi L 2 7 orthogonal array and grey relational analysis methods were used to design and optimize the input process parameters for both responses. SEM, optical microscopic analysis, and EDS analysis were performed to examine the surface morphology, characteristics of the WC tool, and elemental composition of both the tool and the workpiece material. The optimum conditions for Karanja oil with a Cr powder mixture concentration of 4 g/l resulted in higher MRR and lower TWR. However, machining with neem and sesame oils resulted in higher TWR and lower MRR. [ABSTRACT FROM AUTHOR]

The turbidite channel of South China Sea has been highly concerned. Influenced by the complex fault and the rapid phase change of lithofacies, predicting the channel through conventional seismic attributes is not accurate enough. In response to this disadvantage, this study used a method combining grey relational analysis (GRA) and support vector machine (SVM) and established a set of prediction technical procedures suitable for reservoirs with complex geological conditions. In the case study of the Huangliu Formation in Qiongdongnan Basin, South China Sea, this study first dimensionalized the conventional seismic attributes of Gas Layer Group I and then used the GRA method to obtain the main relational factors. A higher relational degree indicates a higher probability of responding to the attributes of the turbidite channel. This study then accumulated the optimized attributes with the highest relational factors to obtain a first-order accumulated sequence, which was used as the input training sample of the SVM model, thus successfully constructing the SVM turbidite channel model. Drilling results prove that the GRA-SVM method has a high drilling coincidence rate. Utilizing the core and logging data and taking full use of the advantages of seismic inversion in predicting the sand boundary of water channels, this study divides the sedimentary microfacies of the Huangliu Formation in the Lingshui 17-2 Gas Field. This comprehensive study has shown that the GRA-SVM method has high accuracy for predicting turbidite channels and can be used as a superior turbidite channel prediction method under complex geological conditions. [ABSTRACT FROM AUTHOR]

Determining the optimal intermix of various input parameters for an end milling process is an important task to explore its fullest performance along with the achievement of the desired responses. Several multi-criteria decision-making techniques, like technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis and multiobjective optimisation on the basis of ratio analysis (MOORA), have been applied for solving parametric optimisation problems of milling processes while assigning equal importance to the considered responses. In this paper, the optimal combinations of cutting speed, feed rate and depth of cut of an end milling process during the machining of Al 1070 alloy are determined using the weighted sum model (WSM), weighted product model (WPM), weighted aggregated sum product assessment (WASPAS), MOORA, TOPSIS, evaluation based on distance from average solution (EDAS), additive ratio assessment (ARAS) and complex proportional assessment (COPRAS) methods with the allocation of unequal weights to the responses determined by standard deviation and entropy techniques. The derived solutions are also compared when the same importance is assigned to the responses. From the results, a parametric combination of 255 rpm cutting speed, 82 mm/min feed rate and 0.75 depth of cut is identified by WSM, WPM, WASPAS, COPRAS, TOPSIS and ARAS methods to achieve the optimal values of material removal rate (MRR) and average surface roughness (Ra) as 595.23 mm3/min and 13.79 µm, respectively. It is also noticed that both feed rate and depth of cut significantly influence MRR and Ra during the machining of Al 1070 alloy. [ABSTRACT FROM AUTHOR]

To tackle the challenges of environmentally friendly dielectric fluid, the current research focuses on the diverse biodegradable oils that are utilised as dielectric fluids in electro-discharge machining (EDM) processes. When it comes to machining extremely hard materials, EDM is a cutting-edge, non-traditional technology. As part of this research, Ti-6Al-4 V material has been machined in two phases: the first phase, using conventional EDM oil, and the second phase, utilising four distinct bio-dielectric fluids. Comparisons have been made between EDM oil and other bio-dielectric fluids in terms of machining performance measures such as material removal rates (MRR), tool wear rate (TWR), and surface roughness (SR). The primary goal of this research is to identify an appropriate bio-dielectric fluid that will be environmentally acceptable, cost-effective, and provide better machining performance when compared to other dielectric fluids on the market. When the MRR, TWR, and SR are taken into account, mixed vegetable oil is found to be the best bio-dielectric fluid, and in cases where only the MRR is considered, sunflower oil is the best dielectric. It was discovered that the surface obtained with sunflower oil is the best in terms of crack size and number of cracks when compared to other dielectric fluids. [ABSTRACT FROM AUTHOR]

This paper attempts to optimise the single screw extrusion (SSE) process parameters for extruding Acrylonitrile-butadiene-styrene (ABS) and Polycarbonate (PC) blend filament, having improved tensile strength and melt flow rate. The melt flow and mechanical properties of ABS-PC blend filament are highly reliant on the SSE process parameters. Thus, optimising the process parameters to fabricate the blend filaments with high performance has become essential. Grey relational analysis has been integrated with the Taguchi method to optimise the SSE process parameters by evaluating the grey relational grades in terms of the performance index. Experimentation has been carried out according to Taguchi's L9 orthogonal array (OA), which has been designed considering three SSE process parameters, i.e. screw rotation, blend composition and chamber temperature. The co-continuous structure and compatibility between the phases of the blend obtained at ABS:PC 50:50 by wt%. Micrographs and thermographs support the results. The tensile strength of the 3D printed ABS:PC 50:50 by wt.% composite specimen has improved by 38.02% as compared to virgin ABS. [ABSTRACT FROM AUTHOR]

Slicing of material using a thin wire reduces the loss of material and provides better cutting quality compared to any other machining techniques. Moving wire electrochemical spark machining (WECSM) is a hybrid, non-conventional wire slicing process, specially for non-conducting materials such as ceramics, composites and glasses. In this research article, alumina epoxy nanocomposite (AENC) has been taken as a workpiece. AENC is a polymer-based nanocomposite with a property of mixed nature such as ceramics and polymer. Due to its high electrical insulation performance, AENC is in demand in various applications. However, its mixed properties limit the machining capabilities for various applications. In the present work, modelling of the moving WECSM process has been performed using response surface methodology, and GRA with entropy method was applied for finding the optimum set of parameters. The results show that the optimal machining performances for material removal rate, surface roughness and kerf taper were obtained at 55 V applied voltage, 2.5 m/min wire velocity, 200 g/l electrolyte concentration and 200 µs pulse-on time. [ABSTRACT FROM AUTHOR]

The conventional machining of the widely used nickel-based superalloy Inconel 625 is challenging since it exhibits high strength at elevated temperatures. Cryogenic machining is an effective cooling technique to extract the heat developed during machining and enhance the machining performance of superalloys by eliminating the high-temperature related machining obstacles. The maximum machining performance of cryogenic machining can be achieved by optimising the input parameters considered for the study. This work attempts to improve the cryogenic machining performance of Inconel 625 using grey relational analysis. Multiple responses that are having the most significance on the performance of the machining such as main cutting force, tool wear and surface roughness were considered to optimise the process. The experiments were designed on the basis of the orthogonal array. Taguchi's L9 design with three levels of cutting speed, feed and depth of cut was considered for conducting the experiments. A cutting speed of 60 m/min, feed of 0.05 mm/rev and depth of cut of 0.2 mm were obtained as the optimal parameters. The analysis of modes of wear and morphology of chips was also conducted using SEM imaging to understand the wear mechanisms and machining performance at the optimal level under cryogenic cooling. [ABSTRACT FROM AUTHOR]

BACKGROUND: Optimizing biochar application is vital for enhancing crop production and ensuring sustainable agricultural production. A 3‐year field experiment was established to explore the effects of varying the biochar application rate (BAR) on crop growth, quality, productivity and yields. BAR was set at 0, 10, 50 and 100 t ha−1 in 2018; 0, 10, 25, 50 and 100 t ha−1 in 2019; and 0, 10, 25 and 30 t ha−1 in 2020. Crop quality and growth status and production were evaluated using the dynamic technique for order preference by similarity to ideal solution with the entropy weighted method (DTOPSIS‐EW), principal component analysis (PCA), membership function analysis (MFA), gray relation analysis (GRA) and the fuzzy Borda combination evaluation method. RESULTS: Low‐dose BAR (≤ 25 t ha−1 for cotton; ≤ 50 t ha−1 for sugar beet) effectively increased biomass, plant height, leaf area index (LAI), water and fertility (N, P and K) productivities, and yield. Biochar application increased the salt absorption and sugar content in sugar beet, with the most notable increases being 116.45% and 20.35%, respectively. Conversely, BAR had no significant effect on cotton fiber quality. The GRA method was the most appropriate for assessing crop growth and quality. The most indicative parameters for reflecting cotton and sugarbeet growth and quality status were biomass and LAI. The 10 t ha−1 BAR consistently produced the highest scores and was the most economically viable option, as evaluated by DTOPSIS‐EW. CONCLUSION: The optimal biochar application strategy for improving cotton and sugar beet cultivation in Xinjiang, China, is 10 t ha−1 biochar applied continuously. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Background: Paeoniae Radix Rubra (PRR) has been usedwidely to promote blood circulation and eliminate blood stasis in China clinical practice owing to its extensive pharmacological effects. However, the "quality markers" (Q-markers) of the antioxidant effects remains unknown. Object: To explore the Q-markers of antioxidant activity based on multiple strategies, which would provide reference for the quality evaluation of PRR based on specific pharmacodynamic-oriented. Methods: Firstly, the "fingerprint" profiles of 15 batches of PRR were acquired and identified by ultrahigh performance liquid chromatography-quadrupole time-of- flight tandem mass spectrometry (UHPLC-Q-TOF MS/MS) and the common peaks extracted. Meanwhile, the MTT assay was used to evaluate the effect of 15 batches of PRR on H2O2-induced oxidative stress in HT-22 cells. The antioxidant activity of PRR was investigated simultaneously by superoxide dismutase (SOD), glutathione (GSH), and malondialdehyde (MDA) commercial kits. The relationship between common peaks and antioxidant indexes were constructed by grey relational analysis (GRA) and partial least squares- discriminant analysis (PLS-DA) for the identification of preselected Q-markers. Secondly, experimental verification was conducted to investigate the protective effect of the preliminary components on HT-22 cells undergoing oxidative stress. Finally, for the further validation of effectiveness of antioxidant Q-markers, network pharmacology was applied to explore potential targets, and the molecular docking technology was used to value the binding ability of the potential active components of PRR to the antioxidant targets. Results: Thirty-seven common peaks from 15 batches of PRR were identified qualitatively by UHPLC-Q-TOF MS/MS. The MTT assay showed that PRR could reduce the oxidative damage induced by H2O2 upon HT-22 cells according to the index of MDA, SOD and GSH. Eight potential antioxidant components were screened by spectrum-effect correlation analysis: paeoniflorin, galloylpaeoniflorin, albiflorin, 1,2,3,4,6-o-pentagalloylglucose, benzoylpaeoniflorin, pinocembrin, oleanic acid, and isorhamnetin-3-o-nehesperidine. Each of these preliminary components showed significant protections on cellular oxidative stress (P < 0.05). Interleukin- 6 (IL-6), protein kinase B (AKT1), and tumor necrosis factor (TNF) were predicted to be the major potential targets of PRR, and the good binding ability were presented between the potential active components of PRR and each target as a whole. Conclusion: Eight components were identified as the antioxidant Q-markers of PRR based on an integrated multimodal strategy. [ABSTRACT FROM AUTHOR]

In order to study the relationship between China's safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China's safety production, and the country's three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research. [ABSTRACT FROM AUTHOR]

Background: Understanding the potential risk factors of heart diseases is key to effectively managing cardiac diseases. The present study quantifies the association of identified risk factors. In addition, the study has compared the association of mortality with hypertension, uncontrolled diabetes, and uncontrolled hyperlipidemia using Grey Relational Approach (GRA) for stroke, lung diseases, smoking, stress, obesity, and lack of exercise. Method: Data on risk factors of heart failure were collected from the Global Burden of Disease (GBD) study (2001–2017). From the GBD database, variables have selected the top leading risk factors responsible for mortality from cardiac diseases. Data on risk factors was analyzed using the GRA procedure (utilizing Grey [8.0] software). In the GRA method, the correlation was categorized into three components: GRA – Deng (assesses the effect of one variable specified by data on the other variables), GRA- absolute (assesses the association between variables measured), and GRA-SS (assessed the overall association between the variables measured). Stroke, lung diseases, smoking, stress, obesity, and lack of exercise were taken as dependent variables and their impact was assessed. Hypertension (high grade) uncontrolled diabetes, and uncontrolled hyperlipidemia were considered as independent variables. The relationship between dependent and independent variables was assessed. Results: Overall correlational analysis showed that type 2 diabetes (T2DM) is the risk factor that has a strong relationship with causing heart failure and thereby increases morbidity and mortality among Chinese patients. After T2DM, the second highest risk factor associated was severe dyslipidemia which is responsible for causing heart failure. High-grade hypertension is one-third most common risk factor in causing heart failure. GRA – Deng analysis showed that T2DM is the top risk factor associated with heart failure, followed by high-grade hypertension and severe dyslipidemia (uncontrolled). GRA-absolute analysis showed that severe dyslipidemia is the top risk factor associated with heart failure, followed by high-grade hypertension and T2DM (uncontrolled). GRA-SS analysis showed that high-grade hypertension is the top risk factor associated with heart failure, followed by severe dyslipidemia and T2DM (uncontrolled). Conclusions: The study reported that T2DM, severe dyslipidemia, and high-grade hypertension as strongly correlated with the development of heart failure after considering other several key risk factors (stroke, lung diseases, smoking, stress, obesity, and lack of exercise). Level of evidence: IV. Technical efficacy: Stage 5. [ABSTRACT FROM AUTHOR]

Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River. [ABSTRACT FROM AUTHOR]

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction. [ABSTRACT FROM AUTHOR]

This study investigates the effects of laser power, scan speed, and hatch distance on the features of aluminium specimens produced using direct metal laser sintering (DMLS). By systematically varying these parameters, we identified optimal combinations for producing high-quality metal components. Our findings were validated through reproducible printing processes. Analysis of variance (ANOVA) and grey relational analysis (GRA) were employed to optimize the production parameters further. We found a significant trade-off between laser power, tensile strength, and fatigue resistance, with laser power having the most substantial impact on mechanical properties, microstructure, and surface roughness. Statistical analysis confirmed that higher laser power improves mechanical characteristics but may increase surface roughness. These insights are crucial for enhancing the efficiency and quality of DMLS-produced metal components. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of vegetation and its influencing factors is crucial for understanding the relationship between vegetation and climate change, which helps guide the management of regional ecosystems effectively. Utilizing the Fractional Vegetation Cover (FVC) data and various meteorological elements from 1982 to 2021, this research employed methodologies, such as grey relational analysis, path analysis, and the time-lag effect, to examine the impact of climate change on FVC fluctuations. It introduced a comprehensive qualitative and quantitative analysis of the multi-factor climate–vegetation relationship, enhancing the understanding of the interaction between the climate and vegetation growth. The findings indicate that 77.41% of the wetland vegetation cover in the Yellow River Basin (YRB) has significantly decreased. Precipitation and evapotranspiration emerged as the primary factors affecting FVC, with soil moisture and temperature having a lesser impact. Given the crucial influence of climate factors' time lag on vegetation dynamics, especially the notable cumulative lag effects observed in arid regions, such as precipitation accumulating over approximately 1.963 months (on average) and evapotranspiration lagging by about 1.727 months (on average), this study offers valuable theoretical insights on vegetation restoration efforts amidst the challenges posed by climate change. [ABSTRACT FROM AUTHOR]

The rapid development of new energy vehicles demands significant improvements in connector structures and performance standards. Wire harness connectors, crucial for linking various electrical components, face challenges due to their small size and thin-walled structure, which can lead to dimensional shrinkage and warping during injection molding. To address these issues, this study optimizes the injection molding process by fine-tuning parameters such as melt temperature, mold temperature, injection time, holding pressure/time, and cooling time. By integrating the Taguchi method with grey relational analysis, the study enhances the molding process for thin-walled composite connectors. This combined approach provides a comprehensive framework for optimizing multiple quality objectives and improving the overall performance of injection-molded composite components. [ABSTRACT FROM AUTHOR]

In the pursuit of improved productivity and efficiency, the search for alternative fuels that reduce dependence on fossil fuels is of utmost importance. This research study aims to investigate the influence of process parameters, namely blend percentage, fuel injection pressure, exhaust gas recirculation (EGR) rate, and engine load, on various engine performance parameters such as torque, brake thermal efficiency (BTE), brake mean effective pressure (BMEP), hydrocarbon emissions (HC), and mechanical efficiency. The experiments are conducted following the Taguchi orthogonal array (L25) design of experiments. Additionally, grey relational analysis, a multi-objective optimization technique, is applied to identify the optimal levels of the process variables that optimize all the response parameters. The optimal values obtained through grey relational analysis are determined as 0% blend, 600 bar fuel injection pressure, 12% EGR rate, and 12 kg engine load. Moreover, the Genetic Algorithm-based Multi-Objective Genetic Algorithm (MOGA) is employed for multi-objective optimization of the engine input variables, yielding optimal levels of 19.45% blend, 594.35 bar fuel injection pressure, 14.12% EGR rate, and 12 kg engine load. Furthermore, multivariable regression models are developed to predict the response variables within the experimental domain. These models are validated through a confirmation test. The findings of this study provide insights into the optimization of process variables for enhanced engine performance, with the developed models serving as valuable tools for future predictions and optimization. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period. [ABSTRACT FROM AUTHOR]

One kind of light crude oil and one kind of heavy crude oil were selected respectively, blended in different ratios, and asphalt was prepared by atmospheric and vacuum distillation process. Research on the impact of crude oil blending ratio on the physical properties and rheological properties of asphalt, Fourier spectroscopic tests were performed and functional group indices of crude oil and asphalt were calculated, the study of crude oil refining asphalt in the process of the change of the functional group index, based on the gray relational analysis method to establish the correlation between the functional group index of asphalt and its road performance. The results show that, with the incorporation of light crude oil, the penetration and ductility of asphalt tend to increase, and the softening point, rotational viscosity, complex modulus and rutting factor tend to decrease; the relative content of aliphatic groups during the refining process decreases, and the relative content of =C--H on the benzene ring and sulfoxide group increases; the content of aromatic groups mainly determines the penetration and softening point, and aliphatic groups have a greater impact on the ductility, and =C--H on the benzene ring is related to the rotational viscosity, complex modulus and rutting factor. [ABSTRACT FROM AUTHOR]

The present work deals with the influence of the graphite and tin particles in the wear behavior of A356 alloy-based composites. The A356 alloy consists of 92% aluminum and 7% silicon. The A356/Gr/Sn composites were fabricated with the proportion of 10 wt.% Gr and varying wt.% of Sn (0, 5, and 10 wt.%) particles through the bottom pouring stir casting route. The inclusion of Sn with Gr in A356 alloy matrix took significant improvement in the wear characteristics of the composites. Process parameters such as wt.% of tin, normal load, and sliding speed have been considered for performing the wear test. The Box–Behnken design in response surface methodology was chosen for conducting the wear study because of its ability to efficiently explore the response surface with fewer experiments, ensuring statistical rigor in optimizing multiple variables within a balanced experimental domain. Grey relational analysis (GRA) has been used to optimize the wear study parameters. Grey-fuzzy reasoning grade analysis was used to check the uncertainty in the GRA results. The study used main effect plots to analyze the GFRG values for the various input variables. An artificial neural network model was created to create the relationship model between the input conditions and output responses. Scanning electron microscope micrographs were used to test the worn samples' surfaces. [ABSTRACT FROM AUTHOR]

In the early stages of residential project investment, accurately estimating the engineering costs of residential projects is crucial for cost control and management of the project. However, the current cost estimation of residential engineering in China is primarily carried out by cost personnel based on their own experience. This process is time-consuming and labour-intensive, and it involves subjective judgement, which can lead to significant estimation errors and fail to meet the rapidly developing market demands. Data collection for residential construction projects is challenging, with small sample sizes, numerous attributes, and complexity. This paper adopts a hybrid method combining a grey relational analysis, Lasso regression, and Backpropagation Neural Network (GAR-LASSO-BPNN). This method has significant advantages in handling high-dimensional small samples and multiple correlated variables. The grey relational analysis (GRA) is used to quantitatively identify cost-driving factors, and 14 highly correlated factors are selected as input variables. Then, regularization through Lasso regression (LASSO) is used to filter the final input variables, which are subsequently input into the Backpropagation Neural Network (BPNN) to establish the relationship between the unit cost of residential projects and 12 input variables. Compared to using LASSO and BPNN methods individually, the GAR-LASSO-BPNN hybrid prediction method performs better in terms of error evaluation metrics. The research findings can provide quantitative decision support for cost estimators in the early estimation stages of residential project investment decision-making. [ABSTRACT FROM AUTHOR]

The current study explores the precision enhancement of AISI420 Martensitic Stainless Steel (MSS) using sinking-electrical discharge machining (S-EDM) with Copper electrodes, which is a unique combination of materials and machining process and conducts a comprehensive multivariate analysis to investigate the correlation between machine control variables (MCV) and measured machining performance (MMP) in the context of AISI420 Martensitic Stainless Steel and Sinking-Electrical Discharge Machining. The analysis of variance (ANOVA) establishes the hierarchy of machine control variables influence: Pulse current (B) > Gap voltage (A) > Pulse on Time (C). Remarkably, Pulse current (B) emerges as the paramount parameter, thus constituting a cornerstone of this study's findings. This research article utilizes the RSM–GRA–PCA methodology, which combines response surface methodology (RSM), grey relational analysis (GRA), and principal component analysis (PCA) to optimize the machining process. Using traditional RSM–GRA technique and RSM–GRA–PCA methodology, the experimental Grey Relational Grade (GRGexperiment) are achieved 0.8048 and 0.9817, respectively. The validation test has been performed to confirm the fittest method positions. The percentage significance of significant factor is also improved from 64.63 to 79.71% and error is reduced from 5.22 to 1.68% using RSM–GRA–PCA methodology with improved GRG of 0.068. This integrated approach improves the grey relational grade (GRG) and reduces errors, leading to more accurate and efficient machining. [ABSTRACT FROM AUTHOR]

Drilling precise micro-holes in glass material has brought a new challenge, primarily due to its application in microfluidic devices. It is required to reduce machining time and simultaneously achieve repeatability of the process. The machined hole should have minimum overcut to get desired hole diameter. Maximum hole circularity and minimum heat-affected zone are the essential hole characteristics to achieve controlled machining. The multi-criteria decision-making (MCDM) method is quite effective in selection of best possible combination of outputs from several alternative solutions. The experimental data from the previously published paper is used in the current study. As input parameters, the experiment data include voltage, tool feed rate, and machining time. The radial overcut (ROC), circularity of the machined hole and heat-affected zone (HAZ) were calculated as output responses. The experiments were conducted using copper and nickel-coated copper tools in previous study and response data were used in current work. The present study uses the methods which include hybrid grey relational analysis (GRA), technique for order performance by similarity to ideal solution (TOPSIS), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods for the selection of the best combination of process parameters in drilling to obtain the optimal geometric characteristic of a hole, i.e. optimal values of ROC, circularity and HAZ, in glass using electrochemical discharge machining (ECDM). In this study, weight calculation for MCDM was proposed using entropy and analytic hierarchy process (AHP) methods and combined to get final weights using the fuzzy logic tool, which determines the importance of AHP and entropy weights using the expert opinion. The final weights were used to calculate the ranks from each MCDM method and determine optimal process parameter selection in ECDM hole drilling with copper and nickel-coated copper tools. The fuzzy logic method determined that the weight contribution from AHP is 0.586 in the present study. Genetic algorithm-based multi-objective optimization was conducted, and a non-dominated Pareto front was generated. The Spearman correlation method was used to determine the relationship between different MCDM methods. The current study will be helpful in selecting the best combination of process parameters in drilling holes during ECDM machining using MCDM methods. [ABSTRACT FROM AUTHOR]

• The indicators were collected based on three levels of situation awareness (SA). • The drivers' SA assessment system was built using grey correlation analysis. • The constructed backpropagation neural network model can effectively predict SA. Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training's mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Refrigeration is the property of Journal of Refrigeration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aims: This study evaluated millet germplasms in Liaoning Province to support the collection, preservation and innovation of millet germplasm resources. Methods: The study was conducted from 2018 to 2020, involved the selection of 105 millet germplasm resources from the Germplasm Bank of the Liaoning Academy of Agricultural Sciences (LAAS), the observation and recording of 31 traits, and the application of multivariate analysis methods to assess phenotypic diversity. Results: From the diversity analysis and correlation analysis, it was found that the tested traits had abundant diversity and complex correlations among them. Principal component analysis (PCA) comprehensively analyzed all quantitative traits and extracted seven principal components. Grey relational analysis (GRA) highlighted the varied contributions of different traits to yield. Through systematic cluster analysis (SCA), the resources were categorized into six groups at Euclidean distance of 17.09. K-mean cluster analysis determined the distribution interval and central value of each trait, then identified resources with desirable traits. Conclusion: The results revealed resources that possess characteristics such as upthrow seedling leaves, more tillers and branches, larger and well-formed ears, and lodging resistance prefer to higher grain yield. It was also discovered that the subear internode length (SIL) could be an indicator for maturity selection. Four specific resources, namely, Dungu No. 1, Xiao-li-xiang, Basen Shengu, and Yuhuanggu No. 1, were identified for further breeding and practical applications. [ABSTRACT FROM AUTHOR]

Glass fiber-reinforced polymer (GFRP) composites are extensively used in many sectors because of their high strength, low weight, and resistance to corrosion properties. Drilling operations are needed to make holes for the joining of the components. Various defects were reported during the drilling, and the authors in this study tried to provide an approach to minimize the drilling defects. Selection of the optimum level of the input parameters can reduce defects generated during drilling. This study used grey relational analysis to optimize input parameters during glass fiber-reinforced composite drilling. Three tools (T1, T2, T3) with point angles (118°, 135°, 140°), feed rate in mm/min (10, 20, 30), and spindle speed in rpm (1000, 2000, 3000) were taken as input parameters. Taguchi L27 orthogonal arrays were selected for the three factors at three levels. Machining force (Fm), surface roughness (Ra), peel-up delamination factor (DF1), push-out delamination factor (DF2), hole size error, and circularity error were taken as output responses. Drilling GFRP composite by considering these input parameters combined with all these output responses using grey relational analysis was not explored before. The effect of input parameters on individual output response was analyzed using Taguchi and analysis of variance. Feed rate influenced machining force the most (65.34%), tool geometry influenced surface roughness the most (38.32%), and spindle speed influenced the push-out delamination factor the most (18.11%). SEM analysis was done to explain the aftereffects of drilling. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A timely and systematic assessment can provide valuable insights for decision‐making aimed at reducing vulnerability and enhancing adaptive capacity. This study assesses the vulnerability and adaptation of China's provinces to climate change by employing a combination of the criteria importance intercriteria correlation method and Grey relational analysis. We establish a comprehensive three‐level index system for evaluation, allowing us to prioritize various factors. Our findings reveal significant disparities in the levels of adaptation and vulnerability to climate change across various regions, Generally, regions with well‐developed economies exhibit greater resilience to the impacts of climate change. the ecological system emerges as a key determinant influencing provincial vulnerability to climate change. Moreover, our study underscores the pivotal role of energy system transition in facilitating adaptation to climate change. The findings suggest policy recommendations in areas of ecosystem protection, energy intensity, roadmap, and funding allocation. [ABSTRACT FROM AUTHOR]

The tourism country's competitiveness is important especially when countries strive for bigger market shares as European countries are. The COVID-19 pandemic has had devastating effects on tourism and the entire tourism industry must be rethinking and reshaping given some opportunities and challenges to the entrepreneurs, local communities, local administrations, governments of competitiveness destinations. In this paper we measured if the Google and Apple mobility indices cand predict (or not) the movement of TTCI during pandemic compared with pre-pandemic TTCI ranks by using grey system theory and statistical methods. The aim of the paper is to validate the Grey Relational Analysis (GRA) as identifier of the good predictor (statistically significant for p-value< 0.05) for European tourism competitiveness (measured by TTCI) in particular conditions, especially before and during COVID-19 pandemic time by take into consideration the Google Mobility and Apple Mobility data and their relationship with overnight stays for 11 European countries. To validate the GRA as method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during COVID-19 pandemic time) a GLM – General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons was applied. Our results validated the powerful of GRA for tourism competitiveness, statistically significant according to GLM with interaction effects, and emphasis (especially for entrepreneurs) that the overnight stays will give the right rank on top of tourism competitiveness, prior to other ITC support as Google and Apple Mobility indices proved by reflection of overnights on TTCI 2021 – during pandemic. [ABSTRACT FROM AUTHOR]

Disc cutter wear has emerged as a critical concern impacting the efficiency and cost budgets of TBMs (tunnel boring machines). Through statistical analysis of field data on cutter wear in a TBM tunnel, this study explores the wear rules of different types of disc cutters in granite. Grey sensitivity analysis is employed to investigate the sensitivity between the cutter ring wear rate of face cutters and two types of cutter wear influence parameters. Subsequently, reasonable parameters are selected to develop a new cutter life prediction model for face cutters. The results show that, with increases in the installation radius, the accumulated wear extent shows a linearly increasing trend for both the center and the face cutter, while it first increases and then decreases for gauge cutters, and the accumulated replacement number shows a linear growth trend for face cutters. The accumulated wear extent of the average single cutter position of gauge cutters is about 3 times that of face cutters and 7 times that of center cutters; the number of replaced cutter rings of the average single cutter position for gauge cutters is about 3–4 times that for center cutters and face cutters; and the average utilization rate of gauge cutters is the highest (80.97%). The cutter ring wear rate of face cutters is the most sensitive to three intact rock parameters (uniaxial compressive strength (UCS), Cerchar abrasion index (CAI), and equivalent quartz content (EQC)) and two TBM tunnelling parameters (cutterhead thrust (F) and cutterhead rotational speed (RPM)). Finally, a new cutter life prediction model (R2 = 0.964) for face cutters is developed based on F, UCS, and RPM. The research results can provide a certain theoretical basis for cutter wear and cutter life prediction for the face cutters of TBM projects in similar geological conditions and TBM specifications. [ABSTRACT FROM AUTHOR]

Accurate prediction of estuarine salinity can effectively mitigate the adverse effects of saltwater intrusion and help ensure the safety of water resources in estuarine regions. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity and obtained considerable achievements. Due to the nonlinear and nonstationary features of estuarine salinity sequences, this paper proposed a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters for the LSTM model were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). The proposed ISSA-LSTM model was applied to predict salinity at the Cangqian and Qibao stations in the Qiantang Estuary of China, based on measured data from 2011–2012. The model performance is evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The results show that compared to other models including Back Propagation neural network (BP), Gate Recurrent Unit (GRU), and LSTM model, the new model has smaller errors and higher prediction accuracy, with NSE improved by 8–32% and other metrics (MAP, MAPE, RMSE) improved by 15–67%. Meanwhile, compared with LSTM optimized with the original SSA (SSA-LSTM), MAE, MAPE, and RMSE values of the new model decreased by 13–16%, 15–16%, and 11–13%, and NSE value increased by 5–6%, indicating that the ISSA has a better hyperparameter optimization ability than the original SSA. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity. [ABSTRACT FROM AUTHOR]

As new advanced vehicles, the safety and stability of electromagnetic suspension maglev trains have always been a subject of concern. This study introduces the improved R index and τ-distance index into the performance evaluation of the suspension control system, respectively assessing the stability of the suspension gap and the smoothness of train operation, combining them with grey relational analysis to achieve data-driven comprehensive evaluation. Furthermore, feasibility tests on the Fenghuang Maglev Express validate the effectiveness and superiority of the comprehensive evaluation method based on measured data. Experimental results demonstrate that the data-driven comprehensive evaluation method, through designing specialized evaluation metrics and increasing assessment dimensions, effectively evaluates the performance of the suspension system control loop. Compared to a traditional error integral comprehensive performance index, it offers greater comprehensiveness and accuracy, along with real-time state-monitoring capabilities. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology, specifically Digital Light Processing (DLP), offers a unique approach to fabricating intricate objects without the need for traditional molds or machining. The technique described entails the incremental construction of layers through the curing process polymer, facilitated by a projection light source. Digital Light Processing 3D printing is particularly suited for creating complex structures with small cross-sections, requiring high surface polish and durability. In this study, DLP-printed test coupons were produced using an Ultraviolet-sensitive resin, and the investigation focused on examining the impact of surface quality and toughness, adhering to guidelines outlined by the ASTM specifications. To optimize the DLP printing process, Taguchi and Grey relational optimization techniques were employed to find the best values for multiple factors. The parameters considered in this study were speed, layer thickness, orientation, and pattern type. By employing these techniques, the optimal parameters were identified as follows: The printing process is conducted at a speed of 3 mm per second, with each layer being 0.05 mm thick and the orientation of the printed object is set at 90 degrees, and the pattern employed is of the square type. Experimental results revealed that utilizing these parameters yielded a surface roughness of 2.58 µm and an impact strength of 9.1J. The results showed that the Grey relational grade improved from 0.6244 to 0.7974, with a deviation of 0.173 and 27.71%, indicating a significant enhancement in the optimization process. Additionally, contour plots depicting the relationships between speed and orientation, speed and layer thickness, and orientation and layer thickness were generated for surface roughness, impact strength, and the Grey relational grade. This research offers valuable insights into optimizing DLP 3D printing processes to achieve enhanced surface quality, toughness, and material efficiency while reducing costs and production time. [ABSTRACT FROM AUTHOR]

Intelligent electronic devices (IEDs) are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions. In the context of the heightened security challenges within smart grids, IEDs pose significant risks due to inherent hardware and software vulnerabilities, as well as the openness and vulnerability of communication protocols. Smart grid security, distinct from traditional internet security, mainly relies on monitoring network security events at the platform layer, lacking an effective assessment mechanism for IEDs. Hence, we incorporate considerations for both cyber-attacks and physical faults, presenting security assessment indicators and methods specifically tailored for IEDs. Initially, we outline the security monitoring technology for IEDs, considering the necessary data sources for their security assessment. Subsequently, we classify IEDs and establish a comprehensive security monitoring index system, incorporating factors such as running states, network traffic, and abnormal behaviors. This index system contains 18 indicators in 3 categories. Additionally, we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid, combining grey relational analysis (GRA), analytic hierarchy process (AHP), and entropy weight method (EWM). According to the proposed assessment method, the security risk level of IEDs can be graded into 6 levels, namely 0, 1, 2, 3, 4, and 5. The higher the level, the greater the security risk. Finally, we assess and simulate 15 scenarios in 3 categories, which are based on monitoring indicators and real-world situations encountered by IEDs. The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation. Thus, the reasonableness and effectiveness of the proposed index system and assessment method are validated. Graphic Abstract [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research. [ABSTRACT FROM AUTHOR]