Analysis of Factors Influencing Hospitalization Expenses of Patients with Gastric Cancer in Shanghai, 2014-2021: Based on Grey Relational Analysis and Structural Equation Modeling.

OBJECTIVES: This study analyzed the basic condition and the influencing factors of hospitalization costs of patients with gastric cancer in Shanghai from 2014 to 2021, so as to provide a scientific reference for promoting the reform of the medical and healthcare system. METHODS: The study data were obtained from the electronic medical record system of Shanghai Hospital. The grey relational analysis was applied to analyze the correlation strength of various expenses with hospitalization costs. The structural equation modeling was constructed to analyze the influences of factors on the hospitalization expenses, as well as the relationship between each factor. RESULTS: A total of 23 335 study subjects were included. The results of grey relational analysis showed that the total cost of drugs had the strongest correlation with hospitalization expenses, followed by material expenses and surgery cost, whereas those of others were lower. The results of the structural equation modeling showed that age had the greatest influence on hospitalization expenses with a path coefficient of 0.618. Other influencing factors included surgery history, length of stay, hospital level, gender, and medical insurance. CONCLUSIONS: The total cost of drugs had the strongest correlation with hospitalization expenses. Factors such as gender, age, and hospital level all affect the hospitalization expenses. In the future, it is necessary to take further measures to control the cost of drugs and constantly optimize the structure of hospitalization costs. Meanwhile, the reform of the medical and healthcare system should be deepened to reasonably regulate the medical behaviors and reduce the financial burden of patients.

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

Carbon emission reduction and hydrogen production maximization from carbon emission-based hydrogen sources.

This study aims to optimize hydrogen (H2) production via ethanol steam reforming (ESR) and water gas shift reaction (WGSR) pathways, focusing on minimizing CO, CO2, and CH4 emissions while maximizing H2 yield. Employing Taguchi grey relational analysis, we investigate the intricate balance between production conditions and multi-response gas generation. Utilizing Origin Pro software, regression modeling forecasts individual and overall gas generation. Our analysis identifies optimal conditions: a feed liquid flow rate of 2 mL/min, water-to-carbon ratio of 3, ESR temperature of 300 °C, and WGSR temperature of 350 °C. These conditions promise clean, efficient H2 production. Key results show the water-to-carbon ratio and ESR temperature contributing 59.22% and 32.69% to production conditions' impact, respectively. Graphical and mathematical models validate these findings. Moving forward, further experimental validation of optimal conditions for multi-response gas generation is recommended. This study pioneers a transformative approach towards sustainable, environmentally friendly H2 production.

Evaluation of the applicability of hospital-affiliated green spaces to patient recovery using the entropy weight method and grey relational analysis.

Introduction: Hospital affiliated green spaces can help patients recover and recover their physical functions, promote physical and mental relaxation, enhance health awareness, and improve overall health. However, there are still significant questions about how to scientifically construct hospital affiliated green spaces. This study examines the impact of hospital green spaces on patient rehabilitation through scientific evaluation methods, providing reference for the scientific construction of hospital affiliated green spaces. Applicability evaluation was conducted on the affiliated green spaces of three hospitals in Harbin. An evaluation system covering plants, space, accessibility, rehabilitation functions, and promotional and educational functions has been constructed. The entropy weight method is used to determine the weight of indicators, and the grey correlation analysis method is used to evaluate the suitability of green space for patient rehabilitation. Methods: The experimental results showed that the landscape accessibility index had the highest weight (0.3005) and the plant index had the lowest weight (0.1628), indicating that caring for special needs is the foundation of hospital landscapes, and plants have subtle and long-term effects on physical and mental health. In the evaluation of the rehabilitation applicability of the affiliated green spaces of various hospitals, the second hospital has the highest grey correlation degree (0.8525), followed by the tumor hospital (0.5306) and the fifth hospital (0.4846). It can be seen that the green space of the second hospital has high applicability for patient rehabilitation, but the green space of the tumor hospital and the fifth hospital needs to be improved and developed. Results and discussion: The evaluation criteria used in this study are comprehensive. The landscaping at the Third Hospital is well-planned with good plant configuration and reasonable spatial layout. However, there is insufficient consideration for accessibility in the landscape design, and the details are lacking. The rehabilitation and educational functions of the landscape are inadequate, with limited outdoor activities and low road safety. The hospital's affiliated green spaces should adhere to the principle of "appropriate scale, comprehensive functionality, and educational leisure," integrating rehabilitation and educational functions while increasing the variety of outdoor activities. In the future, emphasis should be placed on exploring the integration of landscape and rehabilitation to provide a functional site that is convenient for visiting, with improved rehabilitation facilities and an educational and enjoyable environment. The design should incorporate elements that contribute to a sense of well-being, including roads and.

Sustainability assessment of machining Al 6061-T6 using Taguchi-grey relation integrated approach.

Modern machining requires reduction in energy usage, surface roughness, and burr width to produce finished or near-finished parts. To ensure high surface quality in machining processes, it is crucial to minimize surface finish and minimize burr width, which are considered as significant parameters as specific cutting energy. The objective of this study was to identify the optimal machining parameters for milling in order to minimize surface roughness, burr width, and specific cutting energy. To achieve this, the research investigated the impact of feed per tooth, cutting speed, depth of cut, and number of inserts on the responses across three intervals using Taguchi L9 array. Observing the responses by varying these parameters, underlined the need for multi objective optimisation. Machining conditions of 0.14 mm/tooth f z , 350 m/min V c and 2 mm ap using 1 cutting insert (exp no 9) was identified as the best machining run using grey relational analysis owing to its highest grey relational grade of 0.936. ANOVA examination identified cutting speed as the leading factor impacting the grey relational grade with 31.07 % contribution ratio, with the number of inserts, depth of cut, and feed per tooth also making notable contributions. Conclusively, machining parameters identified through response surface optimisation resulted in 21.69 % improvement in surface finish, 11.39 % reduction in specific energy consumption, and 6.2 % decrease in burr width on the down milling side albeit with an increase of 9 % in burr width on the up-milling side.

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO's ability to discover refined solutions, reducing average surface roughness to 4.63 µm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM.

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Multi-response optimization of reinforcement parameters of aluminum alloy composites by Taguchi method and grey relational analysis.

The present work describes the optimization of reinforcement parameters for hardness, thermal conductivity, and coefficient of thermal expansion while developing LM6 alloy/soda-lime glass particulate composite through Taguchi-based Grey Relational Analysis (GRA). Soda-lime glass particle weight % (1.5, 3.0 and 4.5 %), particle size (100, 150 and 300 µm) and pre-heat temperature (260, 380 and 500oC) are varied accordingly to explore the effect of reinforcement parameters on LM6 alloy/soda-lime glass composite properties. Composites are developed through stir casting based on the L9 Taguchi orthogonal array approach. The properties such as hardness, thermal conductivity and coefficient of thermal expansion of developed composites are assessed. Signal to Noise Ratios (S/N ratios) are calculated and used for the optimization of parameters. GRA is employed for multi-response optimization to find the levels of parameters that affect the desirable properties of the composite. Thus, the reinforcement parameters are optimized for attaining the combined objectives of higher hardness, higher thermal conductivity and lower coefficient of thermal expansion values considered in this investigation. The analysis shows that 4.5 wt %, particle size of 200 µm and pre-heat temperature of 380oC are optimal parameter levels. A confirmation test is carried out with the optimal parameter levels and the GRG value of 0.7778 is obtained. The GRG with the initial parameter settings is 0.4711, and the improvement of GRG is found to be 65.1 %. ANOVA is performed on GRG to find out significant parameters and the contribution of each parameter is identified. The wt.% of soda-lime glass is the most significant parameter and its contribution is 92.6 %.

Combined effect of freshwater salinization and harmful algae on the benthic invertebrate Chironomus pallidivittatus.

Global climate change as well as human activities have been reported to increase the frequency and severity of both salinization and harmful algal blooms (HABs) in many freshwater systems, but their co-effect on benthic invertebrates has rarely been studied. This study simultaneously examined the joint toxicity of salinity and different cyanobacterial diets on the behavior, development, select biomarkers, and partial life cycle of Chironomus pallidivittatus (Diptera). High concentrations of salts (e.g., 1 g/L Ca2+ and Mg2+) and toxic Microcystis had synergistic toxicity, inhibiting development, burrowing ability and causing high mortality of C. pallidivittatus, especially for the Mg2+ treatment, which caused around 90% death. Low Ca2+ concentration (e.g., 0.01 g/L) promoted larval burrowing ability and inhibited toxin accumulation, which increased the tolerance of Chironomus to toxic Microcystis. However, low Mg2+ concentration (e.g., 0.01 g/L) was shown to inhibit the behavior, development and increase algal toxicity to Chironomus. Toxic Microcystis resulted in microcystin (MC) accumulation, inhibited the burrowing ability of larvae, and increased the proportion of male adults (>50%). The combined toxicity level from low to high was verified by the weight of evidence and the grey TOPSIS model, which integrated five lines of evidence to increase the risk assessment accuracy and efficiency. This is the first study that provided insights into ecological risk arising from the joint effect of salinity and harmful algae on benthic organisms. We suggest that freshwater salinization and HABs should be considered together when assessing ecological threats that arise from external stress.

Optimization of Environment-Friendly and Sustainable Polylactic Acid (PLA)-Constructed Triply Periodic Minimal Surface (TPMS)-Based Gyroid Structures.

The demand for robust yet lightweight materials has exponentially increased in several engineering applications. Additive manufacturing and 3D printing technology have the ability to meet this demand at a fraction of the cost compared with traditional manufacturing techniques. By using the fused deposition modeling (FDM) or fused filament fabrication (FFF) technique, objects can be 3D-printed with complex designs and patterns using cost-effective, biodegradable, and sustainable thermoplastic polymer filaments such as polylactic acid (PLA). This study aims to provide results to guide users in selecting the optimal printing and testing parameters for additively manufactured/3D-printed components. This study was designed using the Taguchi method and grey relational analysis. Compressive test results on nine similarly patterned samples suggest that cuboid gyroid-structured samples perform the best under compression and retain more mechanical strength than the other tested triply periodic minimal surface (TPMS) structures. A printing speed of 40 mm/s, relative density of 60%, and cell size of 3.17 mm were the best choice of input parameters within the tested ranges to provide the optimal performance of a sample that experiences greater force or energy to compress until failure. The ninth experiment on the above-mentioned conditions improved the yield strength by 16.9%, the compression modulus by 34.8%, and energy absorption by 29.5% when compared with the second-best performance, which was obtained in the third experiment.

A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems.

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach's superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems.

Integrated analytical hierarchy process-grey relational analysis approach for mechanical recycling scenarios of plastics waste in India.

Mechanical recycling is an indispensable tool for plastic waste (PW) recycling and has the highest share in the PW recycling sector in India. The transition to the circular economy of plastics (CEoP) needs a systemic perspective on the mechanical recycling processes. Nevertheless, the assessment of multiple parameters influencing the mechanical recycling of PW is a complex decision-making problem for the development of triple-bottom-line mechanical recycling. A systemic perspective of various mechanical recycling scenarios was performed by employing a multi-criteria decision-making approach to examine the complexity of interlinked factors in the present investigation. Analytical hierarchy process (AHP) integrated with grey relational analysis (GRA) was used to evaluate the criteria that directly influence quality-oriented mechanical recycling. Data were collected by conducting semi-structured interviews using a framed questionnaire in stakeholder engagement with mechanical recyclers of PW. The first level hierarchy included economy, technical, resource consumption and environmental criteria. These criteria were further categorized into various significant indices such as quality of recyclate, recyclability, water and energy consumption during recycling. The results of the integrated grey relational analysis indicated that the technical parameters including quality of recyclate, resource efficiency, PW processing rate and recyclability have a significant influence on mechanical recycling. Based on AHP-GRA, scenario MR6, i.e. manufacturing of PET strap from recycled PET flakes, was ranked the optimal mechanical process amongst the various scenarios. MR6 was followed by Straps and Films at the second and third rank. The lowest ranking was observed for polymer blend recycling. These processes with higher ranks produced good quality recyclate with better efficiency and recyclability. Moreover, these processes consumed optimal resources during manufacturing. These processes also exhibited less maintenance cost, high production rate, low chemical consumption and waste generation as well as implemented pollution control practices.

Multi-Attribute Decision Making: Parametric Optimization and Modeling of the FDM Manufacturing Process Using PLA/Wood Biocomposites.

The low carbon footprint, biodegradability, interesting mechanical properties, and relatively low price are considered some of the reasons for the increased interest in polylactic acid-based (PLA-based) filaments supplied with natural fillers. However, it is essential to recognize that incorporating natural fillers into virgin PLA significantly impacts the printability of the resulting blends. The complex inter-relationship between process, structure, and properties in the context of fused deposition modeling (FDM)-manufactured biocomposites is still not fully understood, which thus often results in decreased reliability of this technology in the context of biocomposites, decreased accuracy, and the increased presence of defects in the manufactured biocomposite samples. In light of these considerations, this study aims to identify the optimal processing parameters for the FDM manufacturing process involving wood-filled PLA biocomposites. This study presents an optimization approach consisting of Grey Relational Analysis in conjunction with the Taguchi orthogonal array. The optimization process has identified the combination of a scanning speed of 70 mm/s, a layer height of 0.1 mm, and a printing temperature of 220 °C as the most optimal, resulting in the highly satisfactory combination of good dimensional accuracy (Dx = 20.115 mm, Dy = 20.556 mm, and Dz = 20.220 mm) and low presence of voids (1.673%). The experimentally determined Grey Relational Grade of the specimen manufactured with the optimized set of process parameters (0.782) was in good agreement with the predicted value (0. 754), substantiating the validity of the optimization process. Additionally, the research compared the efficacy of optimization between the integrated multiparametric method and the conventional monoparametric strategy. The multiparametric method, which combines Grey Relational Analysis with the Taguchi orthogonal array, exhibited superior performance. Although the monoparametric optimization strategy yielded specimens with favorable values for the targeted properties, the analysis of the remaining characteristics uncovered unsatisfactory results. This highlights the potential drawbacks of relying on a singular optimization approach.

Synergy of hybridization of bauhinia vahlii and kenaf fiber on mechanical and sliding wear properties of epoxy composites: A Grey Taguchi Optimization Study.

The present research work aims to develop Bauhinia vahlii fibre epoxy composites with incorporation of different weight percentage (wt%) of kenaf fiber as secondary reinforcement to elevate the mechanical and wear properties of prepared composites (through hand layup method). Higher value of mechanical properties like tensile strength-114.85 MPa, flexural strength- 64.64 MPa, and hardness- 57.2 Hv are achieved for bauhina vahlii-epoxy composites. In case of hybrid composites, tensile strength-161.92 MPa; flexural strength- 93.28 MPa; and hardness- 76.0Hv for bauhinia vahlii/kenaf-epoxy composites at 10 wt% of fiber reinforcement. The design of experiment is developed by Taguchi L9 orthogonal array to optimize the experimental run with three control factors; sliding velocity, fiber wt%, and normal load. In order to assess the multiple responses, the fabricated composite is analysed by Grey-Taguchi method with optimal factor setting to improve the output responses i.e. specific wear rate, tensile strength, flexural strength, and hardness. The optimal parameters which highly affect the properties of composites are sliding velocity (2.5 m/s), fiber wt% (10 wt %), and normal load (15 N). In wear mechanism analysis of composites by scanning electron microscopy (SEM), it is demonstrated that the synergy of hybridization of bauhinia vahlii and kenaf fiber improved the mechanical and wear properties of composites.

Evaluation of crude watermelon oil as lubricant in cylindrical turning of AISI 1525 steel employing Taguchi and grey relational analyses techniques.

Cutting fluids are used for cooling and lubricating the machining area of components used in manufacturing industries such as aerospace, automotive, petroleum, and heavy machinery. Mineral oils derived from petroleum are commonly utilized as cutting fluids. Mineral oil is hazardous to the health of workers and damaging to the environment. There is a need for a substitute for mineral oil. Vegetable oil is increasingly being used as a cutting fluid. Vegetable oils are easily accessible and have benefits including excellent biodegradability, resistance to fire, low humidity rates, and a low coefficient of expansion under heat. This study adopts watermelon oil as a lubricant in MQL machining of AISI 1525 steel using tungsten tools. In the experiment, the feed rate, depth of cut (DC) and spindle speed were varied using the Taguchi L9 orthogonal array. Grey relational analysis was conducted to obtain optimum cutting parameters for surface roughness, machine vibration, and cutting temperature. Hardness and microstructural analysis of the workpiece were also conducted. Results showed that vegetable oil performed much more effectively than mineral oil in most experiments. The DC was shown to be the most efficient cutting parameter after applying ANOVA analysis based on the parameters that were evaluated. Additionally, models for cutting temperature, machine vibration, and surface roughness values have been developed with accuracy between 69.73 % and 99.05 %. The hardness of the workpiece increases with an increase in diameter, which was attributed to the increase in the steel rod (workpiece) cross-sectional area and the likelihood of a more uniform stress distribution. Moreover, finer grain sizes were observed at 70 mm diameter, with the predominant presence of pearlites. These characteristics were reportedly beneficial to the material's toughness and strength.

Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence.

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

Optimization of Simultaneous Nutrients and Chemical Oxygen Demand Removal from Anaerobically Digested Liquid Dairy Manure in a Two-Step Fed Sequencing Batch Reactor System Using Taguchi Method and Grey Relational Analysis.

The technological development for efficient nutrient removal from liquid dairy manure is critical to a sustainable dairy industry. A nutrient removal process using a two-step fed sequencing batch reactor (SBR) system was developed in this study to achieve the applicability of simultaneous removal of phosphorus, nitrogen, and chemical oxygen demand from anaerobically digested liquid dairy manure (ADLDM). Three operating parameters, namely anaerobic time:aerobic time (min), anaerobic DO:aerobic DO (mg L-1), and hydraulic retention time (days), were systematically investigated and optimized using the Taguchi method and grey relational analysis for maximum removal efficiencies of total phosphorus (TP), ortho-phosphate (OP), ammonia-nitrogen (NH3-N), total nitrogen (TN), and chemical oxygen demand (COD) simultaneously. The results demonstrated that the optimal mean removal efficiencies of 91.21%, 92.63%, 91.82%, 88.61%, and 90.21% were achieved for TP, OP, NH3-N, TN, and COD at operating conditions, i.e., anaerobic:aerobic time of 90:90 min, anaerobic DO:aerobic DO of 0.4:2.4 mg L-1, and HRT of 3 days. Based on analysis of variance, the percentage contributions of these operating parameters towards the mean removal efficiencies of TP and COD were ranked in the order of anaerobic DO:aerobic DO > HRT > anaerobic time:aerobic time, while HRT was the most influential parameter for the mean removal efficiencies of OP, NH3-N, and TN followed by anaerobic time:aerobic time and anaerobic DO:aerobic DO. The optimal conditions obtained in this study are beneficial to the development of pilot and full-scale systems for simultaneous biological removal of phosphorus, nitrogen, and COD from ADLDM.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine.

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.

The Influence Mechanism of Interfacial Characteristics between CSH and Montmorillonite on the Strength Properties of Cement-Stabilized Montmorillonite Soil.

To elucidate the impact mechanism of the interfacial characteristics of Calcium Silicate Hydrate gel (CSH)-Montmorillonite (MMT) at the nanoscale on the strength of cement-stabilized montmorillonite soil, this paper begins by examining the interfacial energy. Through Molecular Dynamics (MD) simulation methods, the energy at the MMT and CSH binding interface is quantitatively calculated, and the correlation between the interfacial energy and macroscopic strength is determined in conjunction with grey relational analysis. Finally, based on the characterization results from X-ray diffraction (XRD), the accuracy and sources of deviation in the MD simulation results are discussed. The study shows the CSH-MMT interfacial energy is composed of van der Waals forces, hydrogen bond energy, and electrostatic interactions, which are influenced by the migration of cations; there is a good consistency between the CSH-MMT interfacial energy and the unconfined compressive strength (UCS) of cement-stabilized soil (cemented soil), with the interfacial energy decreasing as the number of water molecules increases and first decreasing then increasing as the number of MMT layers grows; by adjusting the mix proportions, the magnitude of the CSH-MMT interfacial energy can be altered, thereby optimizing the strength of the cemented soil.