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Effect of microstructure on lipolysis behavior of milk fat globules during gastrointestinal digestion

Food Science and Human Wellness 2025年第05期 1947-1957页

作者： Xinyun Zhou Ting Guo Chen Chen Hadiatullah Hadiatullah Yunping Yao Changmo Li Xingguo Wang State Key Laboratory of Food Nutrition and Safety Key Laboratory of Food Nutrition and Safety Ministry of Education College of Food Science and Engineering Tianjin University of Science & Technology Tianjin Key Laboratory for Modern Drug Delivery & High-Efficiency Collaborative Innovation Center of Chemical Science and Engineering School of Pharmaceutical Science and Technology Tianjin University Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province National Engineering Research Center for Functional Food School of Food Science and Technology Jiangnan University

Milk fat globules(MFG) are the primary source of energy and functional factor in dairy-based *** evaluate the suitability of commercial MFG substitutes for infant and adult consumers,the lipolysis properties and regulatory factors in digesting bovine,caprine,and human MFG were *** lipolysis kinetic models of bovine and caprine MFG are comparable,with bovine MFG displaying the highest degree of lipolysis(50.21%) during gastric digestion in infants,resulting in the production short-chain saturated fatty *** MFG has a high rate of lipolysis due to its weak phospholipid-glycoprotein layer,which is prominent in both infants' and adults' *** effects of consuming bovine and caprine MFG for adults are comparable in terms of lipolysis properties and nutritional value of fatty *** gastrointestinal digestion,human MFG has the highest digestibility(93.98%).The advantages of human MFG include its phospholipid structure,exogenous lipase,and lipolysis-regulating *** study provides recommendation for the selection of milk fat substitutes and improvements in commercial dairy.

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Evolutionary game analysis of coal industry chain supply chain risk prevention and control from the perspective of resilience

Sustainable Futures

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Sustainable Futures 2025年 9卷

作者： Zhang, Ziming Wang, Xinping School of Management Xi'an Jiaotong University China State Key Laboratory for Manufacturing Systems Engineering China Key Laboratory of the Ministry of Education for Process Control & Efficiency Engineering China School of Management Xi'an University of Science and Technology China

Promoting transformation of energy structure while preventing industry chain supply chain risks has become an important issue facing coal industry chain supply chain (CICSC) under “carbon peaking and carbon neutrality”. This study is based on evolutionary game model to investigate the risk prevention and regulatory issues involving chain master enterprises, other enterprises, and government regulators. Research results indicate that: (1) System evolution of risk prevention and control of CICSC is not constant convergence in theoretical optimal strategy combination, there is a systematic bias in behavioral decision-making. (2) Reference effect, risk preferences and other subjective preferences heterogeneity affects value judgment of decision makers. The effect of cost reference is greater than that of utility reference, maintaining risk avoidance in cost problems and risk pursuit in revenue problems can help to improve the quality of individual decision-making. (3) There is a non-linear effect of fairness preference on risk prevention and control of CICSC, it makes fairness preference have the positive impact to risk prevention and control when intensity of fairness preference is in moderate range and types of fairness preference are arranged and combined in a scientific order, otherwise, fairness preference will become a kind of behavior that is “detrimental to others and not beneficial to oneself”. We propose new insights for risk prevention and control from the aspects of achievement transformation and guarantee mechanisms, building reward and punishment mechanisms and so on, call for further analysis of risk prevention and control according to specific risks in each link of CICSC in future research. © 2025 The Author(s)

关键词： Coal industry chain supply chain Evolutionary game Fairness preference Risk prevention and control

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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

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Chinese Journal of Chemical engineering 2025年 81卷 241-254页

作者： Li, Zhi Xia, Yuchong Long, Jian Liu, Chensheng Zhang, Longfei State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively. © 2025 Chemical Industry and engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Frequency estimation

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Combining decomposition and graph capsule network for multi-objective vehicle routing optimization

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Intelligent Data Analysis 2025年第1期29卷 116-140页

作者： Zhang, Haifei Ge, Hongwei Li, Ting Zhou, Lujie Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education Jiangsu Wuxi China College of Automobile and Transportation Tianjin University of Technology and Education Tianjin China School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan China Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia PA United States

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering. © 2024 – IOS Press. All rights reserved.

关键词： attention mechanism decomposition strategy deep reinforcement learning graph capsule network multi objective optimization Urban logistics distribution

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Error Bounds Revisited, and How to Use Bayesian Statistics While Remaining a Frequentist

Error Bounds Revisited, and How to Use Bayesian Statistics W...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ning Xu Christopher M. Foster Jonathan H. Manton Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi PR China Department of Electrical and Electronic Engineering The University of Melbourne Parkville Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Signal processing makes extensive use of point estimators and accompanying error bounds. These work well up until the likelihood function has two or more high peaks. When it is important for an estimator to remain reliable, it becomes necessary to consider alternatives, such as set estimators. An obvious first choice might be confidence intervals or confidence regions, but there can be difficulties in computing and interpreting them (and sometimes they might still be blind to multiple peaks in the likelihood). Bayesians seize on this to argue for replacing confidence regions with credible regions. Yet Bayesian statistics require a prior, which is not always a natural part of the problem formulation. This paper demonstrates how a reinterpretation of the prior as a weighting function makes an otherwise Bayesian estimator meaningful in the frequentist context. The weighting function interpretation also serves as a reminder that an estimator should always be designed in the context of its intended application; unlike a prior which ostensibly depends on prior knowledge, a weighting function depends on the intended application. This paper uses the time-of-arrival (TOA) problem to illustrate all these points. It also derives a basic theory of region-based estimators distinct from confidence regions.

关键词： Accuracy Buildings Signal processing Acoustics Bayes methods Reliability Speech processing Guidelines

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Interfacial Polarization-Enabled High Anti-Interference for Fiber Pressure Sensors

SSRN

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SSRN 2025年

作者： Liu, Lu Huang, Xin Zhao, Kezheng Jiang, Gaoming Cong, Honglian Ramakrishna, Seeram He, Haijun Engineering Research Center for Knitting Technology Ministry of Education Jiangnan University Wuxi214122 China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi214122 China Faculty of Engineering Department of Mechanical Engineering National University of Singapore Singapore117574 Singapore

Fiber pressure sensors possess the ability to be integrated into intelligent fabrics or garments due to their flexible one-dimensional (1D) size, thus playing a crucial role in wearable electronics and robotics. However, due to the proximity effect, the signal accuracy of the reported capacitive sensors is inevitably degraded by interference from the surrounding environment. Therefore, minimizing interference while maintaining excellent sensing performance is a challenge for fiber capacitive sensors. One straightforward method to enhance anti-interference capability involves raising the initial capacitance. Based on it, this study proposed an anti-interference fiber capacitive sensor by enhancing the interfacial polarization effect between the nanofiller and the matrix. The modified CNTs@PDA with a core-shell structure increased the polarization interface as well as the charge storage capacity of the composite dielectric layer by improving the nanofillers’ dispersion, thereby significantly increasing the sensor’ initial capacitance to 1.6 times. This enables the sensor an ideal signal-to-noise ratio (SNR), reflecting negligible capacitive signal fluctuations when electromagnetic interference and proximity to objects are present. Moreover, with the synergy of interfacial polarization effect and excellent elastic recovery, the sensor achieved a linear response with high sensitivity in the ultra-high pressure range of 3500 kPa and long-term stability over 5000 cycles. With the flexible 1D structure, it was easily knitted into sensing textiles, and a 2D pressure recognization fabric and a traditional Chinese medicine (TCM) foot massage sock system were prepared by circuit integration. This demonstrates the potential application of high anti-interference fiber flexible pressure sensors in wearable electronic textiles. © 2025, The Authors. All rights reserved.

关键词： Flexible electronics

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Structural Evolution of Biomass Pellets of Different Densities During High and Low Convection Pyrolysis

SSRN

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SSRN 2025年

作者： Sadeq, Abdullah Schmitt, Marian Wang, Shen Rothberg, Sophia Pietsch-Braune, Swantje Shen, Laihong Heinrich, Stefan Institute of Solids Process Engineering and Particle Technology Hamburg University of Technology Hamburg21073 Germany Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing210096 China

In this study, wood pellets of different densities were produced from wood shavings of the same batch using a flat die press. Their structure and mechanical properties were investigated and compared to the properties after subsequent pyrolysis with two setups: high convection (HC) pyrolysis in a fluidized bed and low convection (LC) pyrolysis. Various measurement methods were used to investigate the structural properties and the applicability of wood pellets and pellet char. It could be shown that although pellet chars from LC-pyrolysis exhibit a cylindrical shape, their mechanical stability is significantly reduced due to the greatly increased porosity. However, the characteristics in the radial porosity distribution remained as in the untreated pellets. In contrast, HC-pyrolysis in a fluidized bed led to severe structural damage resulting in pellet chars with larger cavities, mainly in their central regions. Consequently, a higher porosity in the center and a lower mechanical stability was determined. Furthermore, it became clear that low-density wood pellets produced with a flat die press equipped with short press channels are especially prone to these structural changes. It is assumed that an open pore network with big cracks and gaps between wood shavings within wood pellets in combination with high heat and mass transfer during pyrolysis can enhance the release of volatiles from the pellet. With micro computed tomography (µCT), it could be shown that cavities in pellet chars are likely formed at wood pellet regions with smaller wood shavings. © 2025, The Authors. All rights reserved.

关键词： Computerized tomography

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Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis

arXiv

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arXiv 2025年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

In this paper, two model-free optimal output tracking frameworks based on policy iteration for discrete-time multiagent systems are proposed. First, we establish a framework of stabilizing policy iteration that can start from any initial feedback control policy, relaxing the dependence of traditional policy iteration on the initial stabilizing control policy. Then, another efficient and equivalent Q-learning policy iteration framework is developed, which is shown to require only less system data to get the same results as the stabilizing policy iteration. Both frameworks obtain stabilizing control policy by iterating the stabilizing virtual closed-loop system step-by-step to the actual closed-loop system. Multiple explicit schemes for the iteration step-size/coefficient are designed and their stability during the above iterations is analyzed. By using the generated closed-loop stabilizing control policy and two frameworks, the optimal feedback control gain is obtained. The approximate solution of the regulator equations is found by model-free iteration, which leads to the optimal feedforward gain. Finally, the cooperative optimal output tracking is realized by a distributed feedforward-feedback controller. The proposed algorithms are validated by simulation. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback control

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

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IEEE Transactions on Automatic control 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. ©1963-2012 IEEE.

关键词： Mixed-integer linear programming

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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