上海工程技术大学（Shanghai University Of Engineering Science），简称“工程大”，教育部“卓越工程师教育培养计划”首批61所试点高校之一，全国12所“卓越工程师计划””试点工作示范性高校之一，中国工程院咨询研究项目依托单位 ，全国地方高校新工科建设的牵头单位，CDIO工程教育联盟成员单位 ，上海市“高水平地方应用型高校”试点建设单位，上海市博士学位授予立项建设单位，入选教育部首批新工科研究与实践项目、教育部产学合作协同育人项目、国家级大学生创新创业训练计划、国家级高校学生科技创业实习基地、上海市创业孵化示范基地，长三角高水平特色地方高校创新联盟发起成员，全国首个5G网络深度覆盖高校，是一所多学科互相渗透、协调发展的上海市属大学。 学校前身为创建于1978年的上海交通大学机电分校、华东化工学院分院（1984年编入上海交通大学机电分校）和华东纺织工学院分院。1985年经教育部批准，在上述院校的基础上，组建上海工程技术大学。2003年，上海市高级技工学校（创建于1951年）整体划入。 据2020年4月学校官网显示，学校拥有松江、长宁、虹口等校区，占地近1400亩，总建筑面积48万平方米；设有2个学部和23个学院（部、中心）；拥有国家大学科技园、国家级实验教学示范中心和国家级虚拟仿真实验教学中心；拥有省级学科科研平台11个；拥有一级学科硕士点11个 ，专业学位硕士点3个，本科专业（含专业方向）63个；全日制在校生近22000名，其中硕士研究生约3200名。
Ground Delay Programs (GDPs) are widely used to mitigate demand-capacity imbalances in air traffic operations. However, existing studies typically address GDP planning from either the perspective of air traffic control or that of individual airlines, with limited attention to their coordination. This study develops a collaborative framework for the airline-driven GDP that integrates proactive slot reduction and airline precancellation. The problem is formulated as a two-stage stochastic program. The first determines slot reductions and flight precancellations in anticipation of various potential capacity scenarios, whereas the second adjusts slot allocations and executes airline recovery actions based on the realized slot capacity. To address the computational challenges of this large-scale problem, we propose an efficient Lagrangian dual decomposition algorithm, which reduces the computation time by 49.06% on average compared with the standard column generation approach. Numerical experiments based on real-world data show that the proposed framework consistently achieves lower expected total recovery costs than rule-based deterministic sequential strategies commonly used in current GDP practice. Moreover, when fairness constraints are imposed, allowing slot exchanges among airlines further reduces total costs by an average of 1.37%. These findings highlight the importance of collaborative decision-making between air traffic managers and airlines, providing a robust and practical tool to enhance system-wide efficiency under capacity disruptions.
Porous structures with tunable mechanical properties are widely required across personalized biomedical devices, orthotic applications, and protective cushioning systems, yet conventional design methods relying on empirical iteration and finite element analysis (FEA) are time-consuming and inefficient for rapid, patient-specific customization. To address this limitation, this study proposes a deep learning–based inverse design framework for Gyroid triply periodic minimal surface (TPMS) porous structures, enabling direct identification of material type and porosity from target stress–strain responses. The framework employs a hierarchical neural network comprising stress-level classification, inverse parameter identification, and forward response reconstruction modules, with stress–strain curves represented by 200 paired data points. Physics-constrained multi-objective loss functions are incorporated to enforce monotonicity, smoothness, and densification behavior, ensuring physically consistent predictions. The model is trained using uniaxial compression datasets generated by finite element simulations, with material parameters calibrated to reported experimental data. Five biocompatible polymeric materials (PA12, PA11, TPE, TPU95A, and TPUA09) and porosities ranging from 50% to 80% are considered. Validation results show a material classification accuracy of 94.8% and a mean porosity prediction error of 3.2%. The reconstructed stress–strain curves closely match target responses across elastic, plateau, and densification regimes, achieving a composite quality score of 94.9/100. Finite element cross-validation further confirms the reliability of the predicted design parameters. This work demonstrates a rapid and data-driven approach for inverse design of mechanically tunable porous structures, with broad applicability to personalized biomedical devices, protective equipment liners, and other engineering applications requiring spatially customized mechanical responses.
The computational efficiency of neural operators has established them as a transformative tool for (PDEs), spurring widespread interest in their integration with mathematical physics frameworks for scientific modeling. This paper introduces the Exponential-enhanced Fourier Neural Operator (E-FNO), a novel architecture that unifies the fast Fourier transform (FFT) and nonlocal kernels operation based on the sum-of-exponentials (SOE) approximation within a dual-channel framework for solving PDEs. Building on integral neural operators, we enhance kernel integral transformations by introducing a layer-independent SOE kernel with distinct decay rates that captures critical spectral components. Moreover, the layer-independent parameters improve inter-layer stability, enabling the training of deeper, more accurate networks via shallow-to-deep initialization. We further demonstrate that E-FNO retains the discretization-invariant learning inherent to the FNO family while generalizing to diverse tasks, including PDE solving and image-to-mapping learning. Numerical experiments on three representative PDEs and the Mechanical MNIST dataset validate the effectiveness of E-FNO, showing consistent improvements over baseline methods in diverse problem settings.
Calibration errors and instrument nonidealities can bias spectroscopic ellipsometric inversion when calibration and sample estimation are treated as independent operations. This work presents a calibration-aware framework that embeds incidence-angle, wavelength-axis, polarization-element, compensator-retardance, and spectral-response errors in the thin-film forward model. Reference-derived calibration covariance is propagated through constrained inversion, while sensitivity, Fisher-information, and cross-correlation metrics quantify identifiability. Eight single-layer films spanning 35–320 nm were evaluated against X-ray reflectometry and an independently calibrated commercial ellipsometer. The laboratory system achieved a mean absolute XRR difference of 0.49 nm, and the mean laboratory–commercial difference was −0.39 nm with 95% limits of agreement from −1.35 to +0.57 nm. Under controlled instrument drift, prior-constrained refinement reduced thickness MAE from 2.25 to 0.78 nm and increased interval coverage from 74% to 93%, approaching the nominal 95% level. Routine inversion required 0.84 s per sample. The results establish a traceable calibration–inversion–uncertainty workflow for thin-film metrology under instrument nonidealities.
In this study, the solubility of (S)-finerenone and (R,S)-finerenone in six solvents over the temperature range of 283.15–323.15 K was determined using a gravimetric procedure. The experimental data were well fitted by the Apelblat, λh, and Yaws models, with ARD
