Quantitative nuclear magnetic resonance （qNMR） technology is a highly versatile and accurate quantitative analysis approach. Due to its merits， such as simple operation， no requirement for reference standards of the analytes， and the capacity to conduct structural analysis of the analytes and impurities simultaneously during quantitative analysis， it has found extensive applications in the quantitative analysis of materials such as pharmaceuticals， food， natural products， and metabolomics. The fundamental principle of quantitative nuclear magnetic resonance is that the number of atoms generating a specific resonance signal in different chemical environments is proportional to the intensity of the nuclear magnetic response signal， and the signal response of the same atomic nucleus in different molecules shows no discrepancy under optimized conditions. In this paper， we review the sample preparation procedures， experimental parameter optimization and data processing methods required in order to obtain accurate signal intensities and， hence， accurate quantitative results. Taking the two currently most typical ¹H qNMR applications， namely hydrogen quantitative NMR （¹H qNMR） analysis of low-abundance （concentration） substances and purity determination of high-purity substances （reference materials， etc.）， as examples， we have highlighted relevant experimental methods and provide a general overview of the use of NMR quantitative analysis in various fields.

In order to accurately predict the variation in wax deposition thickness in crude o il pipelines and alleviate the shortcomings of the traditional grey model （GM） in model structure and data processing， a three⁃parameter grey model （TPGM） （1，1） has been constructed. The bald eagle search algorithm （BES） was improved in terms of initial population and Levy flight strategy. The improved bald eagle search algorithm （IBES） was used to automatically optimize the initial value and background value of the TPGM （1，1） model， and an optimization prediction model of pipeline wax deposition thickness based on IBES-TPGM （1，1，λ，η） was established. The wax deposition thickness data for the indoor loop and field pipeline were used for training and prediction. The prediction accuracies of different models were compared， and the influence of the number of data sets on the accuracy of the model was analyzed. Ablation experiments show that the IBES algorithm is superior to other algorithms in optimization accuracy， convergence speed and global search ability， and the improved strategy is of practical use. The average relative errors of the IBES-TPGM （1，1，λ，η） model on indoor loop experimental data and field pipeline data are the smallest， 0.686 7% and 0.152 7%， respectively. The prediction effect is better than that of GM （1，1）， TPGM （1，1） and BES-TPGM （1，1，λ，η） models. The established model has low requirements for the number of training sets and is suitable for medium and long⁃term prediction of pipeline wax deposition thickness. This work provides a practical reference for the determination of pigging cycles and the safe operation of pipelines.

The shell⁃side fluid heat transfer characteristics of a hairpin heat exchanger have been studied by numerical simulation. In order to improve the comprehensive performance index PEC （the ratio of total heat transfer to total power consumption） and reduce the dimensionless material cost M' （the ratio of heat exchanger material cost to original structure material cost）， a neural network model was established. The non‑dominated sorting genetic algorithm （NSGA-Ⅱ） was used to optimize the four design variables of the key dimensionless parameters： baffle spacing l'， baffle notch height h'， curvature radius r' and Reynolds number Re. The results show that within the scope of this study， the heat transfer of the elbow section accounts for 5.0%-16.3% of the total heat transfer of the heat exchanger， while the power consumption only accounts for 0.5%-1.0% of the total power consumption， indicating that the existence of the elbow section structure significantly improves the heat transfer performance of the hairpin heat exchanger with only a slight increase in power consumption. After parameter optimization， the optimal value of l' is 2.50， and the optimal value ranges of h'， r' and Re are 0.33-0.45， 0.80-1.30 and 8 000-11 000， respectively. Two representative solutions were selected from the optimal solution set. Compared with the original structure， the PEC of the optimized structure 1 is increased by 25.12%， and the M' is essentially unchanged. The PEC of optimized structure 2 increased by 17.93%， and the M' decreased by 6.56%， indicating that multi⁃objective optimization has an obvious advantage in the optimization of the structural parameters of hairpin heat exchangers.

Using 2-ethylhexyl acrylate， butyl acrylate， methyl methacrylate， and acrylic acid as the main monomers， with hydroxyethyl acrylate and isocyanoethyl methacrylate as the functional and grafting monomers， respectively， and dibenzoyl peroxide （BPO）/azo diisobutyronitrile （AIBN）as the complex initiation system， a thermo-photo dual-curing acrylic resin material has been prepared under solution polymerization conditions for use as a tack-reducing adhesives used in wafer processing. The material can be thermally cured into a film in the presence of an isocyanate curing agent to provide the adhesive strength necessary for wafer fixation. Furthermore the double bond introduced by grafting can be cured a second time under UV irradiation， giving UV tack reduction， in order to realize the peeling of the resin from the chip after processing. The structures and peeling properties of the synthetic resin were systematically studied.The BPO/AIBN ratio has a significant effect on the degree of molecular chain branching， which allows for the tailoring of the peeling strength before and after UV curing.

In order to meet the sealing requirements for the joints in concrete panels in severely cold areas， we have developed an organosilicon plastic joint sealing material with excellent low‑temperature resistance， using silicone resin as the raw material. The effect of silica micro‑powder dosage on the performance of the organosilicon plastic joint sealing material was studied. The organosilicon plastic joint sealing material had the best comprehensive performance when the silica micro‑powder dosage was 550 parts（based on 100 parts of silicone rubber）. The material showed a flow‑stopping length of 165 mm and a sagging degree of 1.3 mm. At a temperature of -40 ℃， the material exhibited a pinning degree greater than 200 （0.1 mm） and an elongation at break larger than 1 000%. Importantly， no bond failure at the interface with the concrete was observed during the stretching process. The preparation of this material solves the problem of traditional materials hardening at -40 ℃ and provides -a new joint stopping and sealing material for the construction of concrete panel dams in alpine areas， both domestically and internationally.

An electrolytic potential method has been employed for surface treatment of high‑strength medium‑modulus carbon fibers （CF）. The effect of current intensity on the chemical composition on the surface， wettability of the CF， and the interlayer shear strength （ILSS） was investigated using XPS， dynamic contact angle measurements and ILSS tests. Analysis of the results before and after treatment of CF using the electrolytic potential method showed a deviation of the linear relationship between current and voltage intensity during surface treatment. This indicates the occurrence of oxidation reactions on the surface of CF. With increasing current intensity， the oxygen content and the O/C ratio on the surface of CF increased and the carbon content decreased. The main functional groups on the surface of CF were C—OH and O—C=O， and their amount increased markedly for current intensities above 0.3 A. The dynamic contact angle of CF decreased from 90.2° to 62.4° after treatment at 0.5 A， and the wettability of CF significantly improved after treatment， with an increase in surface activity. The ILSS values of epoxy resin and the CF after treatment at current intensities of 0.3 A and 0.5 A and were 90.35 MPa and 99.41 MPa， respectively， which are 42% and 56% higher than the values for untreated CF. A slight increase in the tensile strength of the treated CF was observed after electrolytic potential treatment.

Urban rail transit stations experience a high passenger flow density， resulting in complex passenger movement within the station premises. Efficient and prompt passenger evacuation can be achieved by strategically planning evacuation routes in alignment with a station’s environmental characteristics and passenger flow patterns. To address the problem of adaptive social force models not being able to plan evacuation routes for pedestrians in real-time based on station exit opening and closing information， a method for the simulation of subway station passenger flow evacuation based on the social force model and improved K-shortest path planning is proposed. Through enhancements to the conventional Yen algorithm， this method can efficiently determine K-shortest paths that guide passengers to multiple evacuation exits， thus providing vital path information during evacuation procedures. A simulation experiment simulating crowd evacuation was devised within a simplified scenario in order to assess the effectiveness of our approach. The outcomes of this experiment clearly demonstrate the superior evacuation performance achieved when combining the adaptive social force model with improved K-shortest path planning. Moreover， the proposed method has been applied to a passenger flow evacuation simulation experiment within a subway station setting. These experimental results conclusively affirm the feasibility and practical applicability of our simulation technique for conducting passenger flow evacuation simulations within subway stations.

The output of photovoltaic new energy generation has complex dynamic features， which leads to inaccurate energy measurements. In order to explore the features of photovoltaic new energy power generation， a new energy measurement signal matrix was first established， and an unequally spaced interval STFT algorithm was then proposed based on the finite coverage theorem. A time-frequency domain data representation model was then constructed to represent the real and imaginary parts of the time-frequency domain information of the three-phase voltage and current of the energy measurement signal. This leads to a feature matrix which represents the important features in the time-frequency domain. Finally， important time-frequency domain features of photovoltaic new energy signals were extracted， providing a theoretical basis for determining the time-frequency domain feature parameters of the experimental signal for energy meter error testing.

The health state of the fuel system of an internal combustion engine has an important impact on the performance of the whole engine， but the current internal combustion engine dispenser monitoring parameters have not yet been fully utilized. For this reason， this paper proposes an intelligent diagnostic method based on the fusion of deep learning and anomalous information， which realizes the utilization of the existing dispenser monitoring parameters and the anomalous phenomenon information， leading to improved reliability of the internal combustion engine operation. Mutual information theory is first introduced to realize the automatic grouping of the dispenser monitoring parameters， and a deep learning diagnostic model is constructed for the dispenser monitoring parameters of the internal combustion engine by using the denoising autoencoder and an attention mechanism in combination with a bidirectional gate recurrent unit. This affords a preliminary intelligent diagnosis of the typical faults of the fuel system. Subseguently， considering the auxiliary value of anomalies acquired during the actual operation of the internal combustion engine for fault diagnosis， a Bayesian network is constructed and a Leaky-Noisy-Or model is used to quantify the correlation between anomalies and specific faults， thus optimizing the results of the intelligent diagnosis of faults. Finally， the fuel system fault sample dataset obtained from GT-Power simulation is substituted into the model， and the diagnostic results verify the effectiveness of the proposed method in improving the accuracy of fuel system fault diagnosis in internal combustion engines. This provides a deep learning intelligent diagnosis model based on the monitoring parameters of the dispenser， and also provides a new information fusion pathway for fuel system fault diagnosis， and has important practical application value for the intelligent diagnosis of internal combustion engines.

In order to enhance the heat transfer efficiency of direct contact heat exchangers， numerical simulation methods have been employed to investigate three different thicknesses of SV hybrid elements. An experimental platform for direct contact heat transfer was established， utilizing THERMINOL62 synthetic heat transfer oil and R141b refrigerant as the working fluids. The maximum relative error between the experimental heat transfer results and numerical simulation results was within 3%， indicating the accuracy of the numerical simulation in reflecting the direct contact heat transfer process. The results showed that the heat transfer effectiveness of the heat exchanger with a thickness of 3 mm （a heat exchanger equipped with SV hybrid elements with a thickness value of 3 mm） was superior， manifested by a higher outlet temperature of the gaseous working fluid. Compared to heat exchangers with thicknesses of 2 mm and 4 mm， the separation intensity decreased to 0.03， representing reductions of 62.5% and 25% respectively. Turbulence intensity values were relatively high， with some exceeding 10%， reaching a peak of 19.57%. Compared to heat exchangers with thicknesses of 2 mm and 4 mm， the volumetric heat transfer coefficients increased by 8.9% and 3.7% respectively. The maximum pressure drop of heat exchangers with a thickness of 3 mm and 4 mm increased by 2.91% and 3.0% compared to those with a thickness of 2 mm.This work shows that appropriately increasing the thickness of SV hybrid elements （3 mm） can afford better heat transfer efficiency without causing excessive pressure drop.

In order to improve the safety and economy of lightweight designs of large pressure vessel tube sheets， the effects of temperature load model， convection load model and fluid-solid coupling load model on the temperature field and stress field of isothermal reactor tube sheets have been compared through numerical simulation. The stress distribution rule at the key position of the tube sheet was obtained， and the equivalent stress of each model was evaluated by means of the pressure vessel standard. The results show that the temperature gradient of the tube sheet of the temperature load model is the largest， and the stress concentration of the tube sheet is the most prominent. The temperature gradient of the tube sheet of the fluid-solid coupling load model is the smallest， and the stress concentration of the tube sheet is greatly alleviated. The local film stress and the primary + secondary stress of the tube sheet increase with the increase of the temperature gradient of the tube sheet. The two stresses of the temperature load model are 36.49 MPa and 155.73 MPa， respectively. The two stresses of the convection load model are 31.40 MPa and 132.74 MPa， respectively. The two stresses of the fluid-solid coupling load model are 27.84 MPa and 112.84 MPa， respectively. Compared with the other two thermal load models， the stress of the tube sheet calculated by the fluid-solid coupling load model corresponded more closely to actual conditions， and the reliability of the optimized design of the large pressure vessel tube sheet was the highest.

This work studies the influence of the cytotoxic T lymphocyte （CTL） death rate on the bifurcation of a 5D melanoma model. The existence of Hopf bifurcation is analyzed using bifurcation theory and higher-order harmonic balance method in the frequency domain. In particular， the fourth-order harmonic balance approximations to the frequency and the amplitude of the periodic solution， and the analytical expressions for the solution are given. All the results verify that the CTL death rate is an important factor leading to the periodic oscillation of melanoma cell number.

In order to solve the statistical inference problem of a class of parametric varying coefficients regional quantile regression， we propose an effective estimation of model parameters based on the idea of a weighted composite quantile regression. A random weighted resampling method was used to construct a rejection domain for significance tests of parameters in the model given limited samples. The numerical simulation results indicate that the proposed test statistic can effectively screen out covariates in the model. Finally， we applied the method to overseas study data and analyzed the impact of various factors on the chance of admission at different quantiles， in order to select the factors that have a significant impact on the chance of admission.