Combination Of Methods Research Articles

Accelerating optimization of terahertz metasurface design using principal component analysis in conjunction with deep learning networks

Metamaterials are a class of artificial materials that have exceptional physical properties that do not exist in nature. They are widely used in various fields, such as electromagnetics, optics, and acoustics. However, designing metamaterials can be a challenging and time-consuming task. Traditional methods rely on simulations and trial-and-error, which are inefficient and often require significant computational resources. Recently, deep learning has emerged as a promising tool to design metamaterials. Deep learning involves training neural networks to learn complex patterns and relationships in data, which can be used to predict the behavior of metamaterials under different conditions. This paper proposes a neural network that maps geometric parameters to frequency domain responses for optimized design. The network utilizes PCA (Principal Component Analysis) to reduce the training time by approximately 5%, and this combination method is far superior to similar algorithms in terms of prediction accuracy and generalization ability. Experimental results demonstrate that the designed network model can be used for optimized design, achieving a remarkably low RMSE (Root Mean Square Error) of 0.0408 and a prediction accuracy of 97.64% in the reverse network, outperforming similar articles. The proposed network model improves the design efficiency of metamaterials, providing a more efficient and effective approach for designing these metamaterials.

Numerical and Experiment Analysis of Sapphire Sandwich-Structure Fabry-Perot Pressure Sensor through Fast Fourier Transform and Mean Square Error Demodulation Algorithm.

Pressure sensors prepared from sapphire exhibit excellent characteristics, including high-temperature resistance, high hardness, and resistance to electromagnetic interference. A Fast Fourier Transform and Mean Square Error (FFT-MSE) demodulation algorithm was employed to demodulate a sapphire sandwich-structure Fabry-Perot (F-P) pressure sensor. Through simulation analysis, the experimental results indicated that the demodulation error of the air cavity length in the range of 206 μm to 216 μm was less than 0.0008%. Compared to single demodulation methods and combined demodulation methods based on FFT or Minimum Mean Square Error (MMSE), the method proposed in this work reduced the demodulation error by more than three times and increased accuracy by more than six times. The algorithm was utilized to demodulate the sapphire sandwich-structure F-P pressure sensor, and the test results indicated that the fitting error of the sensor was less than 0.025% within the pressure range of 0 MPa to 10 MPa. The repeatability error was less than 0.066%, the zero-point deviation was 1.26%, and the maximum stability deviation was 0.0063% per 30 min. The algorithm effectively demodulated the actual cavity length variation in the sapphire sandwich-structure F-P pressure sensor, providing a solution for the performance evaluation of the sapphire sandwich-structure F-P pressure sensor.

Discrimination of tea seed oil adulteration based on near-infrared spectroscopy and combined preprocessing method

Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this study. To address the limitations of a single preprocessing method, nine preprocessing methods from four categories were combined, and the impact of preprocessing method order on model accuracy was assessed. Additionally, variable iterative space shrinkage approach (VISSA), interval combinatorial optimization (ICO), and uninformative variables elimination (UVE) were used to screen characteristic wavelengths. Subsequently, a discriminative model for tea seed oil adulteration was constructed using two strategies. The results indicate that the order of preprocessing methods significantly influences model accuracy, and combining preprocessing methods can effectively enhance model accuracy. All three characteristic wavelength selection methods effectively screened characteristic variables. Both two strategies demonstrate good discriminant capabilities for binary adulteration in tea seed oil. In strategy 1, identification accuracies for the calibration, prediction and external datasets are 98.67 %, 100 % and 94.44 %, respectively. In strategy 2, identification accuracies for the calibration, prediction and external datasets are 100 %, 98 % and 94.44 %, respectively. Therefore, integrating NIRS with combined preprocessing and variable screening can effectively discern the types of adulterated oils in tea seed oil, serving as a potent detection tool.

A comprehensive cutting tool adaptation method for multi-feature part based on evolutionary game algorithm

The adaptation of cutting tools is the key link in machining. Although a large amount of work has been done to achieve tool selection for a single feature, there is relatively little research on tool scheme adaptation for multi-feature parts. Considering the factors of matching, efficiency, cost, and environment, a comprehensive optimization method for a multi-feature part tool scheme is proposed in this work. First, the critical tool of a complex feature is defined, and a tool combination method with complex single features is proposed to generate an efficient tool strategy. Then, a tool scheme optimization model for multi-feature parts is constructed in which each machining feature is mapped to a game player in the model space, and the set of available tools is mapped to the player strategies. In addition, the classical evolutionary game algorithm is improved to adapt to the model, according to the difference in the features number and type of different parts. Finally, a square test piece is taken as a case to verify the proposed method. The result shows that the method presented in this work can efficiently obtain the customer-preferred tool scheme for the part.

Artificial lightweight aggregate made from alternative and waste raw materials, hardened using the hybrid method

Lightweight aggregates are a material used in many industries. A huge amount of this material is used in construction and architecture. For the most part, lightweight construction aggregates are obtained from natural resources such as clay raw materials that have the ability to swell at high temperatures. Resources of these clays are limited and not available everywhere. Therefore, opportunities are being sought to produce lightweight artificial aggregates that have interesting performance characteristics due to their properties. For example, special preparation techniques can reduce or increase the water absorption of such an aggregate depending on the needs and application. The production of artificial lightweight aggregate using various types of waste materials is environmentally friendly as it reduces the depletion of natural resources. Therefore, this article proposes a method of obtaining artificial lightweight aggregate consolidated using two methods: drum and dynamic granulation. Hardening was achieved using combined methods: sintering and hydration, trying to maintain the highest possible porosity. Waste materials were used, such as dust from construction rubble and residues from the processing of PET bottles, as well as clay from the Bełchatów mine as a raw material accompanying the lignite overburden. High open porosity of the aggregates was achieved, above 30%, low apparent density of 1.23 g/cm3, low leachability of approximately 250 µS. The produced lightweight aggregates could ultimately be used in green roofs.

Musician-AI partnership mediated by emotionally-aware smart musical instruments

The integration of emotion recognition capabilities within musical instruments can spur the emergence of novel art formats and services for musicians. This paper proposes the concept of emotionally-aware smart musical instruments, a class of musical devices embedding an artificial intelligence agent able to recognize the emotion contained in the musical signal. This spurs the emergence of novel services for musicians. Two prototypes of emotionally-aware smart piano and smart electric guitar were created, which embedded a recognition method for happiness, sadness, relaxation, aggressiveness and combination thereof. A user study, conducted with eleven pianists and eleven electric guitarists, revealed the strengths and limitations of the developed technology. On average musicians appreciated the proposed concept, who found its value in various musical activities. Most of participants tended to justify the system with respect to erroneous or partially erroneous classifications of the emotions they expressed, reporting to understand the reasons why a given output was produced. Some participants even seemed to trust more the system than their own judgments. Conversely, other participants requested to improve the accuracy, reliability and explainability of the system in order to achieve a higher degree of partnership with it. Our results suggest that, while desirable, perfect prediction of the intended emotion is not an absolute requirement for music emotion recognition to be useful in the construction of smart musical instruments.

Effect of ultrafine grinding on the structure and physical properties of pregelatinized rice starch.

This study used a combination method of ultrafine grinding and pregelatinization to modify rice starch (RS) to delay its retrogradation and provide a rationale for prolonging rice product shelf life. The structure and physicochemical properties of the pregelatinized ultrafine grinding rice starch (PURS) were compared with those of RS, ultrafine grinding rice starch (URS), and pregelatinized rice starch (PRS). The microstructure, molecular weight, branched starch length distribution, short-range order, crystal structure, and physical properties of RS, URS, PRS, and PURS were analyzed, respectively. Results showed that RS, URS, PRS, and PURS granules exhibited similar spherical or polygonal shapes, and the content of amylose and short-branched starch in PURS increased compared with RS, URS, and PRS. Furthermore, the cross-polarization of PRS and PURS disappeared. Long-chain amylopectin and average molecular weight of PURS decreased significantly after ultrafine grinding. Our study suggested reduced breakdown value and setback value and improved gel stability, and PURS was beneficial for delaying retrogradation compared to RS, URS, and PRS. The ultrafine grinding method improved the water swelling capacity (WSC), solubility, pasting properties, and gelation properties of PRS. The hardness of PURS was reduced by ultrafine grinding. These suggest that the combination of ultrafine grinding and pregelatinization could improve the properties of RS. Pearson's correlation analysis showed that the structure of PURS significantly influenced the physicochemical properties. The present study was helpful in better understanding the importance of ultrafine grinding in improving the anti-retrogradation of PURS and provided new insights into extending the shelf life of rice products by ultrafine grinding and pregelatinization.

Performance analysis of the dual-hop mixed RF/UWOC system with fog channel and turbulence based on diversity

ABSTRACT In this paper, the performance of a dual-hop mixed radio frequency (RF)/underwater wireless optical communication (UWOC) system with amplifying and forwarding (AF) relaying is investigated. The diversity combining technology were combined with unifying Fisher-Snedecor ( F ), extended generalized- K (EGK), and α − μ distribution for atmospheric turbulence in foggy conditions to analyze the probability density function (PDF) of RF links. The UWOC link experiences mixed exponential generalized gamma (EGG) fading. By utilizing diversity combination methods and considering the statistical effects of atmospheric turbulence, random fog, and underwater turbulence, we derive the expression for the PDF of the RF/UWOC system. In addition, the tight asymptotic expression for the outage probability and average bit error rate under high signal-to-noise ratio (SNR) is provided. The analytical expressions are verified by Monte Carlo method, and the effects of channel number on RF/UWOC system performance under various atmospheric, underwater and weather conditions are demonstrated.

Journal Club: Prehospital Detection of Large Vessel Occlusion Stroke With Electroencephalography: Results of the ELECTRA-STROKE Study.

The ELECTRA-STROKE study investigated the potential of EEG for prehospital triage of patients with ischemic stroke due to large vessel occlusion (LVO), in which fast triage to stroke centers for endovascular treatment is crucial. The study was conducted in 4 phases, and this Journal Club article focuses on the fourth phase in the prehospital setting with suspected stroke patients. An EEG cap with dry electrodes was used to measure brain activity. The main focus was on the diagnostic accuracy of the theta/alpha ratio, which yielded an area under the receiver operator characteristic curve (AUC) of 0.80. Secondary endpoints, particularly the Brain Symmetry Index (a quantified EEG interhemispheric cortical power asymmetry index) in the delta frequency band, showed an AUC of 0.91. Despite the convenient study design and user-friendly EEG device, limitations include a single-arm design, a relatively small sample size, and exclusions due to data quality issues. The usefulness of EEG in the detection of neuronal changes based on brain ischemia was highlighted, but uncertainties remain regarding its use in certain patient groups. The improvements in the Brain Symmetry Index from phase 3 to 4 of the study indicate the potential for further refinement and investigation of combined methods to improve diagnostic accuracy. The study provides insight into the role of EEG in prehospital stroke detection, recognizing both the strengths and limitations. Overall, the study contributes to understanding the promise of EEG in optimizing LVO stroke triage and urges further refinement and exploration of complementary diagnostic approaches.

Hook-Like DNAzyme-Activated Autocatalytic Biosensor for the Universal Detection of Pathogenic Bacteria.

DNAzyme-based assays have found extensive utility in pathogenic bacteria detection but often suffer from limited sensitivity and specificity. The integration of a signal amplification strategy could address this challenge, while the existing combination methods require extensive modification to accommodate various DNAzymes, limiting the wide-spectrum bacteria detection. We introduced a novel hook-like DNAzyme-activated autocatalytic nucleic acid circuit for universal pathogenic bacteria detection. The hook-like connector DNA was employed to seamlessly integrate the recognition element DNAzyme with the isothermal enzyme-free autocatalytic hybridization chain reaction and catalytic hairpin assembly for robust exponential signal amplification. This innovative autocatalytic circuit substantially amplifies the output signals from the DNAzyme recognition module, effectively overcoming DNAzyme's inherent sensitivity constraints in pathogen identification. The biosensor exhibits a strong linear response within a range of 1.5 × 103 to 3.7 × 107 CFU/mL, achieving a detection limit of 1.3 × 103 CFU/mL. Noted that the sensor's adaptability as a universal detection platform is established by simply modifying the hook-like connector module, enabling the detection of various pathogenic bacteria of considerable public health importance reported by the World Health Organization, including Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Salmonella typhimurium. Additionally, the specificity of DNAzyme in bacterial detection is markedly improved due to the signal amplification process of the autocatalytic circuit. This hook-like DNAzyme-activated autocatalytic platform presents a versatile, sensitive, and specific approach for pathogenic bacteria detection, promising to significantly expand the applications of DNAzyme in bacteria detection.

A new fault location method for high‐voltage transmission lines based on ICEEMDAN‐MSA‐ConvGRU model

AbstractGiven the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.

Functional-structural reorganisation of salivary glands in oncological pathology: A mini-review

. Dysfunctions of salivary glands during the treatment of benign and malignant tumours are a widely diagnosed problem among oncology patients. This study aimed to investigate the disruption of the structure of salivary glands as a result of the treatment of oncological diseases and methods for their correction and elimination. 39 scientific papers were analysed. Methods of search, comparison, systematisation, and combination of the results of the conducted studies were used. The destructive effect of chemo- and radiotherapy and the treatment of malignant tumours by increasing the body’s overall temperature on the structure of salivary glands in the experiment was proven. The influence of low intensity ultrasound on the submandibular gland demonstrates its tissues’ high adaptive and regenerative properties. Data from clinical studies on the correction and elimination of salivary gland dysfunctions in oncological patients showed an ambiguous result. The simultaneous application of photobiomodulation and radiotherapy in patients with head and neck tumours significantly improves organ function: it stimulates salivation, regulates saliva composition, controls the redox mechanism of salivary glands, and locally eliminates inflammation. A slight compensation of the mineralising function of saliva was found with substitution therapy using biologically active components and a remineralising agent in children with malignant tumours undergoing chemotherapy. A positive effect of betanechol and vitamin E in protecting salivary glands during the treatment of head and neck cancer with radiotherapy has been noted, which consists of improving the structure and function of the glands in the long term and is the same in women and men. Studies of vitamin C intake after radioiodine therapy in patients with head and neck cancer have shown its insufficient restorative effect on the quantitative and qualitative composition of saliva. The results of this review can serve as a basis for an accurate assessment by oncologists of salivary gland damage and the pathogenesis of their dysfunctions as a result of cancer therapy, provide an opportunity to reconsider existing views on the problem, develop new methods for preventing and treating these complications, and improve the quality of life and recovery process for patients

Selective Separation of Lithium from Leachate of Spent Lithium-Ion Batteries by Zirconium Phosphate/Polyacrylonitrile Composite: Leaching and Sorption Behavior

This study introduces a straightforward and effective amorphous ZrP/polyacrylonitrile composite ion exchange method for separating Li from the leachate of spent Li-ion batteries (NMC 111). The cathode materials were leached with a series of optimized experiments. The influence of operating variables, including the H2SO4 concentration, temperature, H2O2 concentration, and pulp density, on leaching efficiency was examined to determine the optimal conditions for sorption experiments. The leaching efficiencies of Li, Co, Ni, and Mn were found to be 99.9%, 99.5%, 98.8%, and 99.9%, respectively. Subsequently, batch sorption experiments were performed by using am-ZrP/PAN, including the determination of the effect of pH, sorption kinetics, and the sorption isotherm. The effect of pH on adsorption was examined in 1 mmol/L equimolar solutions of Li, Ni, Mn, and Co. Li was separated from Mn, Co, and Ni in the leaching liquor. The adsorbent for Mn, Co, and Ni sorption better fitted pseudo-second-order kinetics. High selectivity for Li was observed, even at the higher solution concentration of 15 mM Li, Ni, Co and Mn. In addition, the column loading process demonstrated selectivity for Li over Co, Ni, and Mn metal ions. The preliminary evaluation of the whole process with mass flow demonstrated that it would be feasible to achieve full separation and metal recovery by integrating a combined hydrometallurgical method in future studies. However, much work is still needed to develop a practical separation flowsheet.

Monitoring soil nutrients using machine learning based on UAV hyperspectral remote sensing

ABSTRACT Unmanned aerial vehicles (UAV) are rapidly evolving experimental platforms that play an important role in remote sensing. In this study, we investigated a machine learning method for monitoring soil nutrient content using UAV hyperspectral remote sensing. We employed machine learning techniques for feature extraction and soil hyperspectral information modelling. In contrast to traditional mathematical transformation methods, we adopted a combination of random forest and differential evolution algorithms to rank the weights of individual hyperspectral data, thereby obtaining a series of spectral feature subsets for soil organic matter, total nitrogen and available phosphorus and potassium. Furthermore, the analytic hierarchy process was used for weight analysis, and the characteristic bands of the four soil nutrients were successfully extracted. Next, a quantitative inversion model based on a back-propagation (BP) neural network was established to estimate soil nutrient content, with determination coefficients higher than 0.7 and 0.6 for the modelling and verification sets, respectively. The relative percent difference values were greater than 2, among which the highest was for available potassium, with determination coefficients of 0.95 and 0.84 for the modelling and verification sets, respectively. In addition, visualization distribution maps of soil nutrients were obtained by combining the BP model and original reflectance hyperspectral images, and the comparisons of content histograms showed a relatively consistent distribution between the sampling and inversion points. The results verified the effectiveness of the combined machine learning method for large-scale and high-precision monitoring and visualization of soil nutrient contents.

Deformation and failure behavior of 2024-T42 sheet under impact loading

The deformation and failure behavior of 2024-T42 aluminum alloy sheet was investigated through a combined experimental-numerical method. Nine types of specimens were designed to cover a wide range of stress triaxialities and strain rates. Quasi-static and dynamic tests were carried out by electronic testing machine and split Hopkinson tensile bar respectively, and digital image correlation (DIC) method was introduced to measure the deformation. The phenomenological damage model GISSMO (generalized incremental stress state dependent damage model) and Johnson-Cook model were adopted to simulate all of the above tests and the load-displacement curves through numerical simulation were derived. An optimization method was developed to obtain all the model parameters by matching the load-displacement curves from simulation with those from tests by LS-OPT. Finally, drop weight tests and tensile tests of the central-hole plate were conducted. The applicability and accuracy of the damage models were verified by comparing the simulation results with the experiments, which indicates that GISSMO model predicts better the tests than the Johnson-Cook failure model.

Development and characterization of an immortalized nasopharyngeal epithelial cell line to explore airway physiology and pathology in yak (Bos grunniens).

Airway epithelial cells play a crucial role in investigating the physiological and pathological mechanisms of the respiratory tract in yaks, a species whose unique respiratory system has garnered extensive interest. Despite this growing interest, there currently are no available airway epithelial cell lines from yaks, underscoring the crucial need to establish a yak respiratory epithelial cell line. Therefore, our objective was to isolate a population of primary yak nasopharyngeal epithelial cells (pYNE) and transform them into immortalized yak nasopharyngeal epithelial cells (iYNE), assessing their suitability as an in vitro model. Employing a combined method of physical elimination and differential adhesion, we successfully isolated a population of high-purity pYNE, and developed an iYNE line through pCI-neo-hTERT plasmid transfection. Karyotype and transmission electron microscopy analyses confirmed that pYNE and iYNE share identical morphologies and structures. Gel electrophoresis and real-time PCR analyses demonstrated that pYNE and iYNE expressed similar levels of KRT18 and CDH1 genes (p ≥ 0.541). Notably, iYNE expressed a significantly high level of TERT gene expression (p < 0.001). Immunofluorescence analysis demonstrated that both cell types expressed Pan-Cytokeratin, ZO-1, and E-cadherin proteins. Furthermore, immunoblotting analysis indicated significantly higher levels of hTERT and Ki67 proteins in iYNE (p < 0.001), and similar levels of Cluadin-3 and Occludin proteins (p ≥ 0.103). Proliferation curve analysis highlighted iYNE's serum-dependency and significantly enhanced proliferation capacities (p < 0.001). Additionally, pYNE and iYNE cells demonstrated comparable susceptibilities to infectious bovine rhinotracheitis virus (IBRV). These findings collectively suggest that the developed iYNE retains the evaluated physiological characteristics of pYNE, making it an appropriate in vitro model. This advancement will facilitate further investigation into the respiratory physiological and pathological mechanisms in yaks.

Carbon emissions from road transportation in China: from past to the future.

Road transportation is an important contributor to carbon emissions. China's car ownership is rapidly increasing, ranking first worldwide; however, there are limited data about carbon emission inventories. This study assesses carbon emissions from road transportation from the past to the future across China, using market survey, COPERT (Computer Programme to Calculate Emissions from Road Transport) model, and a combination method of principal component analysis and backpropagation neural network. From 2000 to 2020, the national carbon emissions from road transportation grew from 11.9 to 33.8 Mt CO2e, accounting for 0.47% of national total emissions by then. Trucks generally emit a higher proportion (77.3%) of total emissions than passenger cars (18.9%); however, the emission proportion of passenger cars (18.9-31.0%) has increased yearly. The carbon emissions at the prefecture level show an urban agglomeration trend, decreasing from the eastern coastal areas to central China. Future car ownership is expected to grow rapidly at 3.1% during 2021-2049, but only half of that growth rate during 2051-2060. Those vehicles are expected to contribute carbon emissions of 27.2-39.1 Mt CO2e under different scenarios in 2060. Scientifically reducing emissions and innovatively reducing the carbon emission coefficient, combined with a reasonable new energy vehicle growth scenario, are efficient methods for reducing national carbon levels. This study demonstrates that the uncertainty is within an acceptable range. This work details the carbon emission inventories associated with road transportation in China and provides basic data for developing a better carbon reduction policy for China's car industry.

Commercial Tara Protein: Functional properties and use to stabilize sacha inchi oil emulsions obtained by ultrasound

Commercial tara protein (CTP) and sacha inchi oil are promising Peruvian products for forming food emulsions. The present work aimed (1) to characterize the functional properties of CTP as a new protein source (water and oil absorption, foam, and gelling capacity) and (2) to deepen the CTP to form sacha inchi oil emulsions. The CTP (2%, 4%, 6%) and oil concentration (15%, 20% and 25%) were evaluated for rotor-stator (RS) emulsion production. Final emulsions (RS-US) were produced with RS emulsions added with 2% tara gum and ultrasound homogenization at 75% power amplitude for 3 min. Emulsions were analyzed according to gravitational stability, droplet size, and optical microscopy. The results showed that the CTP presented a centesimal composition of 6.03% moisture, 45.16% proteins, 12.32% lipids, 2.49% fiber, 6.04% ashes, and 27.96% carbohydrates. CTP had a greater oil absorption (2.1442 ±0.26 g/g solids) than water absorption (1.8201 ±0.02 g/g solids), did not present foam formation, and the least gelation concentration was 18%. RS-US emulsions prepared with 25% oil and 2% or 4% protein had greater stability against the creaming index and phase separation during 4h, despite emulsion prepared with 15% oil presenting the lowest mean droplet size. In conclusion, the results show that commercial tara protein effectively prepared emulsions with a combined method (rotor-stator, ultrasound, and tara gum).

The NEDC-GTOPSIS Node Influence Evaluation Algorithm Based on Multi-Layer Heterogeneous Classroom Networks

To address the deficiency in the analysis of individual students within existing research on in-classroom social networks and the constraints of traditional centrality metrics in identifying influential nodes, this paper presents the NEDC-GTOPSIS evaluation method for evaluating node influence in multi-layer heterogeneous networks. Initially, students' friendship, interaction, and attribute information are leveraged to compute neighborhood overlap and attribute similarity between nodes, to construct the Composite Relationship Network. Subsequently, the Seat Similarity Network is constructed by applying the Nearest-Neighbor Effective Distance Criterion to compute seat similarity across various class sessions among nodes. Finally, the structure characteristics of two networks serve as influence decision indicators, and the GRA-TOPSIS algorithm, based on the combined weight method, evaluates nodes' influence. Experiments demonstrate that, compared to traditional single-layer relational networks and classical algorithms, this method can more effectively assess influential student nodes.

Amorphous Boron Nitride Nanosheet‐Based Solid State Electrolytes with High Ionic Conductivity

AbstractAmorphous boron nitride nanosheets, with natural electrical insulation, outstanding chemical stability and mechanical properties, are introduced as a matrix to be incorporated with lithium bis (trifluoromethanesulfonyl) imide (LiTFSI) and polymer polyvinylidene fluoride‐hexafluoropropylene copolymer (PVDF‐HFP), for constructing a novel composite solid‐state electrolyte by a simple solution‐casting method. Amorphous boron nitride nanosheets are produced via a combination method of ball milling and pure water exfoliation. The obtained composite solid‐state electrolyte containing 40 % (mass fraction) PVDF‐HFP shows a high lithium ion conductivity (1.936×10−3 S cm−1) and lithium ion transference number (0.47), and a wide electrochemical stability window (5 V) at room temperature. The LiFePO4/Li based all solid‐state battery, consisting of the composite solid electrolyte (40 % PVDF‐HFP), delivers a capacity of 128.1 mAh g−1, a Coulombic efficiency of 99.79 % after 500 cycles and a capacity retention rate of 94.5 % at 1.0 C and 25 °C. The composite solid electrolyte (40 % PVDF‐HFP) can maintain flat and stable stripping/plating plateaus for over 800 hours in symmetrical cells. The amorphous boron nitride nanosheets with high lithium ion conductivity are employed to construct solid‐state electrolytes, which is important significant for the development of high energy density, high safety, and long cycle life all‐solid‐state lithium‐ion batteries.