Optimal Reference Research Articles

Synthesis of nitrogen-doped amorphous carbon nanotubes from novel cobalt-based MOF precursors for improving potassium-ion storage capability

Amorphous carbon materials with sophisticated morphologies, variable carbon layer structures, abundant defects, and tunable porosities are favorable as anodes for potassium-ion batteries (PIBs). Synthesizing amorphous carbon materials typically involves the pyrolysis of carbonaceous precursors. Nonetheless, there is still a lack of studies focused on achieving multifaceted structural optimizations of amorphous carbon through precursor formulation. Herein, nitrogen-doped amorphous carbon nanotubes (NACNTs) are derived from a novel composite precursor of cobalt-based metal–organic framework (CMOF) and graphitic carbon nitride (g-CN). The addition of g-CN in the precursor optimizes the structure of amorphous carbon such as morphology, interlayer spacing, nitrogen doping, and porosity. As a result, NACNTs demonstrate significantly improved electrochemical performance. The specific capacities of NACNTs after cycling at current densities of 100 mA/g and 1000 mA/g increased by 194 % and 230 %, reaching 346.6 mAh/g and 211.8 mAh/g, respectively. Furthermore, the NACNTs anode is matched with an organic cathode for full-cell evaluation. The full-cell attains a high specific capacity of 106 mAh/gcathode at a current density of 100 mA/g, retaining 90.5 % of the specific capacity of the cathode half-cell. This study provides a valuable reference for multifaceted structural optimization of amorphous carbon to improve potassium-ion storage capability.

Two-dimensional electromagnetic scattering analysis based on the boundary element method

An effective formula for the shape-sensitivity analysis of electromagnetic scattering is presented in this paper. First, based on the boundary element method, a new electromagnetic scattering formula is derived by combining the traditional electromagnetic scattering formula with the non-uniform rational B-spline (NURBS) curve, and the geometric model is represented by NURBS, which ensures the geometric accuracy, avoids the heavy grid division in the optimization process, and realizes the fast calculation of high-fidelity numerical solutions. Second, by deducing the sensitivity variables, the electromagnetic scattering equation of shape optimization is obtained, which can provide reliable data references for shape optimization. Finally, the effectiveness and accuracy of the algorithm are demonstrated by an example, and the sensitivity data of some examples are given.

Leveraging Short Video Platforms for Agricultural E-commerce: Enhancing Purchase Intent through Optimized Content

With the popularity and improvement of short video platforms, short video e-commerce live streaming (ELS) has emerged as a prevalent trend for promoting produce. Despite the widespread adoption of short video platforms and ELS, there remains a notable gap regarding the content of short video ELS, with many researches mainly emphasizing the path of ELS general development. Therefore, this study aims to optimize agricultural short-video ELS content, by identifying factors contributing to purchase intention and leveraging the advantages of short-video platforms. This study employs questionnaires, interviews, and the stability coefficient method to explore pathways for innovating agricultural ELS content. The results show that ELS scenes and anchor identity significantly affect purchase intention. Furthermore, an assessment of the hierarchical order of content elements has been established. Additionally, the interview findings indicate the significance of recognizing synergies between the benefits inherent in short video platforms and the content of agricultural ELS. This study can serve as a reference for ELS content optimization and have implications for agricultural ELS influence improvement.

Dynamic performance of a polygonal thermoelectric generator using sickle-shaped fins for automotive application

About 40 % of automotive fuel energy is lost in the form of heat through exhaust gas, the utilization of thermoelectric power generation technology presents a direct conversion of thermal to electric energy. A thermoelectric generator (TEG) system was designed in this study, comprising a polygonal heat exchanger using sickle-shaped fins to enhance waste heat recycling. Additionally, a transient Computational Fluid Dynamics (CFD)-analysis model was introduced to investigate its dynamic response utilizing the instantaneous variations of exhaust gas and coolant under the China Light-Duty Vehicle Test Cycle (CLTC) as transient inputs. The TEG system exhibits an average power output of 24.48 W and a conversion efficiency of 1.12 % throughout the CLTC. After heating from ambient temperature to normal operating temperature, the power rises to 34.48 W and efficiency to 1.57 %, showing enhanced output performance. A comparison of the transient and steady-state analysis results indicates that the transient analysis reflects the hysteresis effect of heat transfer more accurately. Through experimental verification, the mean absolute percentage errors of the transient model are calculated to be 5.57 % and 8.28 % respectively, proving the credibility of the simulated results. The proposed dynamic model allows for evaluating the actual behavior of the TEG system under intricate driving cycles and offers a robust reference for TEG system optimization and its automotive applications in future.

Insights into two-phase flow dynamics in closed-loop pulsating heat pipes utilizing Fe3O4/water: experimental visualization study

This article discusses a focused study on visualizing the flow patterns in a two-phase pulsating heat pipe (PHP) using Fe3O4/water as the working fluid at 3 V/V% concentration. The research also aims to meticulously examine phase change phenomena in the heating section, particularly focusing on bubble formation and expansion processes. A high-speed video camera was utilized to capture dynamic insights into the behavior of the Fe3O4/water mixture. Based on the findings, a straightforward model was developed to explain bubble generation and growth in the mixture, serving as a useful reference for future PHP designs and optimizations. Visual observations also noted the stable nature of the Fe3O4/water nanofluid over a 4-day period, confirming its consistency throughout the experiments. Moreover, the impact of heat load variation on the evaporator section was assessed using controlled heat inputs ranging from 10 to 80 W. Observations on the arrangement of slugs and plugs at a 50% filling ratio revealed interesting self-adjusting flow patterns in response to increasing heat inputs, providing valuable insights into PHP operational dynamics. Notably, the oscillatory flow behavior of Fe3O4/water, the chosen working fluid, exhibited greater activity in comparison to water. This distinctive flow behavior contributed to achieving heightened thermal performance efficiency for the Fe3O4/water system, attributed to its faster attainment of the annular flow condition.

Experimental and numerical investigation on deck system of a 102 m simply supported prestressed UHPC box-girder highway bridge

To address two main challenges (excessive mid-span deflections and numerous cracks in the main girder) in long-span prestressed concrete (PC) box-girder bridges, this paper proposes a long-span simply-supported UHPC box-girder bridge. In comparison to conventional PC box-girder bridges with three-dimensional prestress, the proposed UHPC box-girder consists only one-dimensional (longitudinal) prestress, and the thickness of the bottom/top plate and web of the UHPC box-girder are relatively thin and are strengthened with dense diaphragms and stiffening ribs. The 4th Beijiang River Bridge adopts this type of bridge with a span of 102 m, which is the longest span simply supported prestressed UHPC box-girder highway bridge in the world. Considering the fact that the thickness of the top plate of the UHPC box-girder is relatively thin (only 20 cm) and the transverse prestress is removed from the top plate, thus, under the direct action of wheel loads, the risk of bridge deck cracking may correspondingly increase. To investigate the mechanical behavior of the deck system of UHPC box-girder bridges, a 1:2 scaled experimental specimen associated with the field bridge is tested. The mechanical behavior of the new deck system is investigated and discussed. It is found that because the support strength of the stiffening rib is significantly weaker than that of the web, the UHPC deck slab should still be regarded as a two-way slab rather than a one-way slab in the elastic stage. In addition, although the test load location is unfavorable to the UHPC deck slab and the first crack appears on the UHPC deck slab, this deck system finally suffers from bending failure of the transverse rib. Furthermore, finite element analysis is carried out to study the mechanical behavior of this new deck system. Parametric analysis considers some factors including the reinforcement ratio in the transverse rib, the height of the transverse rib, and the space between transverse ribs. The results provide a good reference for design and optimization in the new deck system of UHPC box-girder bridges.

Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard

This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops.

Identification and Evaluation of qRT-PCR Reference Genes in Melanaphis sacchari.

Appropriate reference genes must be selected for accurate qRT-PCR data to conduct a thorough gene expression analysis in the sorghum aphid (Melanaphis sacchari, Hemiptera, Aphididae). This approach will establish a foundation for gene expression analysis and determines the efficacy of RNA interference in the sorghum aphid. Nine potential reference genes, including Actin, 18S, GAPDH, RPL7, EF-1α, EF-1β, 28S, HSP70, and TATA, were assessed under various experimental conditions to gauge their suitability based on qRT-PCR Ct values. The stability of these candidate reference genes in diverse experimental setups was analyzed employing several techniques, including the ΔCt comparative method, geNorm, Normfinder, BestKeeper, and RefFinder. The findings revealed that the quantity of ideal reference genes ascertained by the geNorm method for diverse experimental conditions remained consistent. For the developmental stages of the sorghum aphid, RPL7 and 18S proved to be the most dependable reference genes, whereas GAPDH and EF-1β were recommended as the most stable reference genes for different tissues. In experiments involving wing dimorphism, EF-1α and GAPDH were identified as the optimal reference gene pair. Under varying temperatures, EF-1α and EF-1β were found to be the most dependable gene pair. For studies focusing on insecticide susceptibility, 18S and TATA emerged as the most stable candidate reference genes. Across all experimental conditions, EF-1α and EF-1β was the optimal combination of reference genes in the sorghum aphid. This research has pinpointed stable reference genes that can be utilized across various treatments, thereby enhancing gene expression studies and functional genomics research on the sorghum aphid.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

A Study on Effect Analysis and Process Parameter Optimization of Viscous Acid Acidification in a Porous Heterogeneous Carbonate Reservoir

The homogeneous acid etching of conventional acid in porous heterogeneous carbonate reservoirs leads to a large amount of consumption in the near-wellbore area, which makes the acidification effect often not ideal. In order to improve the acidizing effect of porous heterogeneous carbonate reservoirs, viscous acid is used to increase the stimulation of the target block in this paper. Through systematic experiments, the adaptability of the viscous acid in the four layers of the M reservoir in the target block was evaluated, and the MD and ME layers suitable for acidizing stimulation were determined in combination with physical property analysis. Finally, based on the geological characteristics and experimental data of the preferred layers, a two-scale acid wormhole growth radial model was established, and the construction parameters of acidizing stimulation were optimized. The results show that (1) The preferred viscous acid system has a dissolution rate of more than 95% for the rock powder in the four layers. When the matrix permeability is high, the effect of the acid wormhole is obvious and the permeability increase is higher. (2) The steel sheet corrosion and residual acid damage experiments showed that the acid system was not corrosive to the wellbore, and the reservoir damage rate of the residual acid after the reaction was low. (3) Based on the relationship between reservoir porosity and permeability and the position of edge and bottom water, the MD and ME layers with more potential for acidizing stimulation are selected. (4) The results of the numerical simulation show that the optimal acid pump rate of the MD and ME layers is 1.4 bpm and 1.0 bpm, and the acidizing fluid volume is 255 bbl, which can form effective acid wormholes, and the range of reservoir permeability transformation is the largest. The field application results show that the optimization scheme effectively improves the production of oil wells, verifies the practicability of the scheme, and provides a reference for the process optimization of viscous acid in the same type of porous heterogeneous carbonate reservoir stimulation.

18F]-D3FSP β-amyloid PET imaging in older adults and alzheimer's disease.

[18F]-D3FSP is a new β-amyloid (Aβ) PET imaging tracer designed to decrease nonspecific signals in the brain by reducing the formation of the N-demethylated product. However, its optimal reference region for calculating the standardized uptake value ratio (SUVR) and its relation to the well-established biomarkers of Alzheimer's disease (AD) are still unclear. We recruited 203 participants from the Greater Bay Area Healthy Aging Brain Study (GHABS) to undergo [18F]-D3FSP Aβ PET imaging. We analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. We compared the standardized uptake value (SUV) of five reference regions (cerebellum, cerebellum cortex, brainstem/PONs, white matter, composite of the four regions above) and AD typical cortical region (COMPOSITE) SUVR among different clinical groups. The association of D3FSP SUVR with plasma biomarkers, imaging biomarkers, and cognition was also investigated. Brainstem/PONs SUV showed the lowest fluctuation across diagnostic groups, and COMPOSITE D3FSP SUVR had an enormous effect distinguishing cognitively impaired (CI) individuals from cognitively unimpaired (CU) individuals. COMPOSITE SUVR (Referred to brainstem/PONs) was positively correlated with p-Tau181 (p < 0.001), GFAP (p < 0.001), NfL (p = 0.014) in plasma and temporal-metaROI tau deposition (p < 0.001), and negatively related to plasma Aβ42/Aβ40 (p < 0.001), temporal-metaROI cortical thickness (p < 0.01), residual hippocampal volume (p < 0.001) and cognition (p < 0.001). The voxel-wise analysis replicated these findings. This study suggests brainstem/PONs as an optimal reference region for calculating D3FSP SUVR to quantify cortical Aβ plaques in the brain. [18F]-D3FSP could distinguish CI from CU and strongly correlates with well-established plasma biomarkers, tau PET, neurodegeneration, and cognitive decline. However, future head-to-head comparisons of [18F]-D3FSP PET images with other validated Aβ PET tracers or postmortem results are crucial.

Assessment of reference genes for qRT-PCR normalization to elucidate host response to African swine fever infection.

Viral infection disrupts the normal regulation of the host gene's expression. In order to normalise the expression of dysregulated host genes upon virus infection, analysis of stable reference housekeeping genes using quantitative real-time-PCR (qRT-PCR) is necessary. In the present study, healthy and African swine fever virus (ASFV) infected porcine tissues were assessed for the expression stability of five widely used housekeeping genes (HPRT1, B2M, 18S rRNA, PGK1 and H3F3A) as reference genes using standard algorithm. Total RNA from each tissue sample (lymph node, spleen, kidney, heart and liver) from healthy and ASFV-infected pigs was extracted and subsequently cDNA was synthesized, and subjected to qRT-PCR. Stability analysis of reference genes expression was performed using the Comparative delta CT, geNorm, BestKeeper and NormFinder algorithm available at RefFinder for the different groups. Direct Cycle threshold (CT) values of samples were used as an input for the web-based tool RefFinder. HPRT1 in spleen, 18S rRNA in liver and kidney and H3F3A in heart and lymph nodes were found to be stable in the individual healthy tissue group (group A). The majority of the ASFV-infected organs (liver, kidney, heart, lymph node) exhibited H3F3A as stable reference gene with the exception of the ASFV-infected spleen, where HPRT1 was found to be the stable gene (group B). HPRT1 was found to be stable in all combinations of all CT values of both healthy and ASFV-infected porcine tissues (group C). Of five different reference genes investigated for their stability in qPCR analysis, the present study revealed that the 18S rRNA, H3F3A and HPRT1 genes were optimal reference genes in healthy and ASFV-infected different porcine tissue samples. The study revealed the stable reference genes found in healthy as well as ASF-infected pigs and these reference genes identified through this study will form the baseline data which will be very useful in future investigations on gene expression in ASFV-infected pigs.

Multi-Scenario Model Predictive Control for Greenhouse Crop Production Considering Market Price Uncertainty

This paper presents a stochastic model predictive control (SMPC) strategy to maximize the economic profit of a greenhouse crop production. The strategy consists of an optimization problem that is solved with a multi-scenario MPC formulation (MS-MPC). It considers the uncertainty of market price by using its historical evolution per year as multiple price scenarios in the cost function. MS-MPC calculates a single set of dates to harvest and sell the crop production that optimizes profits for all the considered scenarios. In addition, MS-MPC determines the optimal temperature references that should be achieved inside the greenhouse for the growth of the crop. A case study for a Mediterranean tomato crop is simulated to analyze the performance of the developed MS-MPC strategy using a hierarchical control architecture with two layers. In the upper layer, MS-MPC calculations are executed following a receding horizon implementation. In the lower layer, regulatory control techniques are applied to reach the optimal temperature references by using natural ventilation and a heating system. Results show that MS-MPC can improve economic profits compared to the use of an average price scenario for the MPC calculations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the need of greenhouse farmers for strategies that maximize profit considering market prices and crop production dynamics. It is not easy for them to make decisions to achieve such long-term objectives while minimizing economic risks, because market prices are very difficult to predict. As a solution, this work presents a novel control strategy to maximize profits by means of automatic selection of the best possible dates to harvest and sell the crop production. This selection is made thanks to considering the uncertainty of market prices in the control strategy by evaluating different scenarios, which are recorded evolutions of the prices from previous years. Although the strategy was tested in simulation, results suggest that using multiple scenarios of historical prices is better for the optimization of profits than just considering an average yearly trend of prices. The more scenarios are considered, the more protection against possible evolution of market prices is obtained. In future research, this control strategy could be extended to include other uncertainties of factors affecting decision making, such as weather forecasts.

Evaluation of adaptability of software-defined telemetry systems for operational use

Abstract Aimed at solving the key problem of operational adaptability assessment of software-defined telemetry systems, the evaluation method of improved hierarchical method and fuzzy comprehensive evaluation is proposed. In the “general + expand + unique” flexible method to build an index system, the “expert decision matrix” index weight assignment and the fuzzy comprehensive evaluation method using operational adaptability assessment are used. According to the principle of maximum membership, the calculation results tend to be the largest. The proportion of 0.6524 shows that the equipment’s operational performance is good. At the same time, it was found that the proportion of qualified was 0.157, exposing its weaknesses and putting forward targeted suggestions, which provided a reference for the technical optimization and improvement, and forward-looking deployment of new equipment.

Bibliometric Research on Surgical Scheduling Management from the Perspective of Web of Science.

Reasonable surgical scheduling management is crucial to optimize the utilization rate of operating room. This study aims to understand the context, frontier and hot spots of surgical scheduling management research, in order to provide reference for surgical scheduling optimization. Literature on operation scheduling management collected in Web of Science core collection database was searched from the database establishment to June 21, 2023. HisCite Pro 2.1 software was used to analyze the publication time, countries, research institutions, journals, authors, keywords and highly cited papers. A total of 1383 literatures were included, and research institutions in the United States, Canada and other countries played a leading role in this field. Among them, the combination of machine algorithm and system model optimization to improve the accuracy of surgical duration prediction is the future research focus in this field. Improving operation efficiency is one of the key issues in operating room management. Managers should find the best operation scheduling plan from a more detailed and comprehensive perspective to improve operation efficiency.

Performant automatic differentiation of local coupled cluster theories: Response properties and ab initio molecular dynamics

In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNO-CC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer.

Research of integrated spiral maneuvering and guidance based on virtual target

To address the issue of interception, maneuvering, and precise guidance during the final stage of gliding flight, a study was conducted on the design of integrated maneuvering and guidance using a spiral trajectory as the focus. Firstly, the spiral trajectory was decomposed into an optimal reference trajectory considering the glide angle constraint and a relative motion trajectory. Then, virtual circular moving targets were introduced to meet the requirements of spiral maneuvering and virtual trajectory moving targets were introduced to ensure trajectory convergence, based on the task division of different flight stages. Finally, the guidance rate of the reference trajectory was designed based on optimal control theory, and the guidance rate of the spiral maneuver was designed based on pure proportional guidance. Simulation results show that this guidance method can enable the vehicle to perform active spiral maneuvers, and control the landing deviation and impact angle deviation to within 2.5 m and 1 degree, respectively.

디자이너와 생성형 인공지능의 협업을 통한 디자인 이미지 생성에 관한 연구

This study focuses on the collaborative design between designers and generative artificial intelligence, aiming to create images that align with design intentions. In the theoretical background section, the theoretical foundations of the Fourth Industrial Revolution and the development of artificial intelligence are explored. The working principles and functional characteristics of ChatGPT and Midjourney are analyzed in detail, revealing that the reasonable combination of these two tools can significantly enhance the alignment and creativity of image design. From a cognitive perspective, the design thinking process of designers is analyzed, and the design thinking process of this study is summarized, providing experimental guidance for this research. Experimental results indicate that under the guidance of clear design intention prompts, the images generated by ChatGPT and Midjourney align with the designers' expectations. This reveals the advantages and limitations of generative artificial intelligence in image generation, offering references for future optimization. This study suggests that designers should maintain a leading role in the design process, using artificial intelligence as an auxiliary tool to expand creativity rather than replace human creativity. Human-machine collaborative design is feasible and effective, and this study also provides specific recommendations for designers to better utilize generative artificial intelligence.

Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship between Kansei images. First, according to the initial weight of the Kansei images, variable weight theory is used to determine the Kansei image variable weights of the samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the fuzzy relationship between the Kansei images of each sample. Combined with the cobweb grey target decision-making model (CGTDM) for multiple Kansei images, decision coefficients are calculated. The decision coefficients are compared and ranked to determine the relatively optimal design reference sample. Finally, the constructed model is compared with the CGTDM for multiple Kansei images and TOPSIS. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of the designers and improve the discrimination among the decision-making results to a certain extent.

Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables

Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process.