Model Constraints Research Articles

Multibody interactions between protein inclusions in the pointlike curvature model for tense and tensionless membranes.

The pointlike curvature constraint (PCC) model and the disk detachment angle (DDA) model for the deformation-mediated interaction of conical integral protein inclusions in biomembranes are compared in the small deformation regime. Given the radius of membrane proteins, which is comparable to the membrane thickness, it is not obvious which of the two models should be considered the most adequate. For two proteins in a tensionless membranes, the PCC and DDA models coincide at the leading-order in their separation but differ at the next order. Yet, for distances larger than twice the proteins diameter, the difference is less than . Like the DDA model, the PCC model includes all multibody interactions in a non-approximate way. The asymptotic many-body energy of triangular and square protein clusters is exactly the same in both models. Pentagonal clusters, however, behave differently; they have a vanishing energy in the PCC model, while they have a non-vanishing weaker asymptotic power law in the DDA model. We quantify the importance of multibody interactions in small polygonal clusters of three, four and five inclusions with identical or opposite curvatures in tensionless or tense membranes. We find that the pairwise approximation is almost always very poor. At short separation, the three-body interaction is not sufficient to account for the full many-body interaction. This is confirmed by equilibrium Monte Carlo simulations of up to ten inclusions.

Reimei Satellite Observations of Alfvénic Interaction Modulating Inverted‐V Electrons and Filamentary Auroral Forms at the Poleward Edge of a Discrete Arc

AbstractWe present an event based on Reimei satellite observations in the low‐altitude midnight auroral region, showing that intense and clear energy‐dispersed electron precipitations, repetitively generated by field‐aligned accelerations due to dispersive Alfvén waves, were modulating inverted‐V electrons. These Alfvénic electrons had peak energies equal to or slightly larger than those of the inverted‐Vs and were associated with the filamentary auroral forms rapidly streaming at the poleward edge of a broad discrete arc. This arc was caused by the inverted‐V accompanied by ion depletions produced by quasi‐electrostatic parallel potential drop. Assuming instantaneous electron accelerations over a wide energy range in a single location and a simple time‐of‐flight effect for the energy‐time dispersions, the Alfvénic source distances were estimated 1,500 ± 500 km above the satellite altitude of ∼676 km, a lower bound since the interaction locations are realistically distributed in altitudinally extended regions. The electron characteristics in detailed energy‐pitch angle distributions obtained at high time resolution can be categorized into: (a) original inverted‐V fluxes energized by quasi‐electrostatic upward electric field, (b) accelerated and decelerated/reduced inverted‐V fluxes, (c) field‐aligned energy‐dispersed precipitations accelerated by dispersive Alfvén waves, and (d) upwelling secondary components effectively produced by the field‐aligned precipitations particularly at energies of a few tens of eV. This event is useful to reveal the interactions between the inverted‐V and Alfvénic electrons and their related ionospheric effects in the magnetosphere‐ionosphere coupling processes. The detailed energy‐pitch angle distributions presented here provide constraints for models of these interactions and processes.

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1) . BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-kcat (TUST-kcat) module predicts the kcat values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa

Abstract. Wildfires and agricultural burning generate seemingly increasing smoke aerosol emissions, impacting societal and natural ecosystems. To understand smoke's effects on climate and public health, we analyzed the spatiotemporal distribution of smoke aerosols, focusing on two major light-absorbing components, namely black carbon (BC) and brown carbon (BrC) aerosols. Using NASA's Earth Polychromatic Imaging Camera (EPIC) instrument aboard NOAA's Deep Space Climate Observatory (DSCOVR) spacecraft, we inferred BC and BrC volume fractions and particle mass concentrations based on spectral absorption provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm with 1–2 h temporal resolution and ∼ 10 km spatial resolution over North America and central Africa. Our analyses of regional smoke properties reveal distinct characteristics for aerosol optical depth (AOD) at 443 nm, spectral single-scattering albedo (SSA), aerosol layer height (ALH), and BC and BrC amounts. Smoke aerosols in North America showed extremely high AOD up to 6, with elevated ALH (6–7 km) and significant BrC components up to 250 mg m−2 along the transport paths, whereas the smoke aerosols in central Africa exhibited stronger light absorption (i.e., lower SSA) and lower AOD, resulting in higher-BC mass concentrations and similar BrC mass concentrations than the cases in North America. Seasonal burning source locations in central Africa, following the seasonal shift in the Intertropical Convergence Zone and diurnal variations in smoke amounts, were also captured. A comparison of retrieved AOD443, SSA443, SSA680, and ALH with collocated AERONET and CALIOP measurements shows agreement with RMSE values of 0.2, 0.03–0.04, 0.02–0.04, and 0.8–1.3 km, respectively. An analysis of the spatiotemporal average reveals distinct geographical characteristics in smoke properties closely linked to burning types and meteorological conditions. Forest wildfires over western North America generated smoke with a small-BC volume fraction of 0.011 and a high ALH with large variability (2.2 ± 1.2 km), whereas smoke from wildfires and agricultural burning over Mexico region shows more absorption and low ALH. Smoke from savanna fires over central Africa had the most absorption, with a high-BC volume fraction (0.015) and low ALH with a small variation (1.8 ± 0.6 km) among the analyzed regions. Tropical forest smoke was less absorbing and had a high variance in ALH. We also quantify the estimation uncertainties related to the assumptions of BC and BrC refractive indices. The MAIAC EPIC smoke properties with BC and BrC volume and mass fractions and assessment of the layer height provide observational constraints for radiative forcing modeling and air quality and health studies.

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.

Models and P4R asset description for digital twin-based advanced planning and scheduling using cyber-physical integration for resilient production operation

Advanced planning and scheduling (APS) addresses the complex and uncertain nature of production control. A digital twin (DT), which incorporates simulations through cyber-physical integration, provides an advanced functionality for APS. To facilitate efficient design and implementation, a DT-based APS must satisfy three requirements: technical functionalities for resilience, robust models for diverse operational constraints, and efficient interoperability through cyber-physical integration. Although several studies have proposed the use of DT as a primary technology for APS, proposals that address the process, functionality, integration, and information models are lacking. Additionally, the existing asset descriptions cannot adequately capture the sophisticated characteristics of DT and necessary informational elements for APS. Thus, this study designed a process model, functionalities, and integration models for the DT-based APS and asset descriptions for snapshot synchronization. Crucial service-compositions and functionalities were defined using work-center-level lifecycles. Consequently, a process model was developed, which focused on core activities for resilience. Moreover, horizontal integration between DT and control functionalities and vertical integration between DT and standards, were proposed to enhance the DT-based APS. The proposed method effectively managed the product, process, plan, plant, and resource classes by ensuring adherence to asset administration shell principles. To validate the effectiveness of the proposed methods, two work centers with distinctly different characteristics were employed and demonstrated dominant preventive measures compared to static functionality-based methods. The primary contributions encompass the facilitation of integration and interoperability within a DT-based APS. The proposed methods support the advanced characteristics of DT, ensuring robustness and neutrality across heterogeneous operational contexts.

SMALE: Hyperspectral Image Classification via Superpixels and Manifold Learning

As an extremely efficient preprocessing tool, superpixels have become more and more popular in various computer vision tasks. Nevertheless, there are still several drawbacks in the application of hyperspectral image (HSl) processing. Firstly, it is difficult to directly apply superpixels because of the high dimension of HSl information. Secondly, existing superpixel algorithms cannot accurately classify the HSl objects due to multi-scale feature categorization. For the processing of high-dimensional problems, we use the principle of PCA to extract three principal components from numerous bands to form three-channel images. In this paper, a novel superpixel algorithm called Seed Extend by Entropy Density (SEED) is proposed to alleviate the seed point redundancy caused by the diversified content of HSl. It also focuses on breaking the dilemma of manually setting the number of superpixels to overcome the difficulty of classification imprecision caused by multi-scale targets. Next, a space–spectrum constraint model, termed Hyperspectral Image Classification via superpixels and manifold learning (SMALE), is designed, which integrates the proposed SEED to generate a dimensionality reduction framework. By making full use of spatial context information in the process of unsupervised dimension reduction, it could effectively improve the performance of HSl classification. Experimental results show that the proposed SEED could effectively promote the classification accuracy of HSI. Meanwhile, the integrated SMALE model outperforms existing algorithms on public datasets in terms of several quantitative metrics.

Coupled Spatial-Spectral Constrained Convolutional Fusion Network for Hyperspectral and Panchromatic images

ABSTRACT Target monitoring is an important subject in machine vision. Hyperspectral image (HSI) can effectively assist the target detection and recognition effect of traditional optical images because of its rich spectral information. However, limited by pixel mixing, the resolution of HSI is generally lower than that of optical image, which restricts the monitoring distance and accuracy. Therefore, a fusion method of HSI and panchromatic image (PAN) based on coupled spatial-spectral constrained convolution neural network is proposed in this paper to improve the spatial resolution of HSI and reduce the spectral distortion. Through this approach, the linear spectral mixing model and the spatial-spectral transformation constraint model are incorporated into the learning stage of the coupled convolutional neural network, aiming to make full use of the spatial-spectral information of HSI and PAN, and improve the spectral fidelity of fused images. Experiments on several groups of HSI and PAN data sets show that compared with some currently proposed HSI and PAN fusion methods, the proposed approach has better spectral fidelity and lower fusion errors, so as to improve the monitoring distance and accuracy of machine vision in engineering applications and expand the engineering application scenarios.

A comparative review of de- and post-growth modeling studies

In recent years, a small but rapidly growing field of modeling alternatives to growth as represented by the de- (DG) and post-growth (PG) discourses has emerged. We compare selected model characteristics of 75 DG and PG related modeling studies, compiled through a systematic literature review (2000−2023), and link model structures and results to different theoretically contested debates surrounding DG/PG. The reviewed studies cover different geographical and temporal scopes, economic theories, modeling techniques and operationalizations of DG/PG. The majority of studies models DG/PG as intentional transition and does not question its compatibility with a capitalist system, while more radical strands of the DG/PG discourse are excluded. Although DG/PG modeling exercises frequently explore the effects of sustainability policies, they represent only a fraction of theoretical DG policy proposals, with the most frequent being: working time reduction, maximum income caps, carbon taxes and a universal basic income. DG/PG modeling studies have demonstrated the importance of integrating biophysical constraints in economic modeling but also have quantitatively assessed the feasibility of environmental integrity and social well-being without growth. Nonetheless, future modeling could be rendered more realistic by paying more attention to the Global South, introducing heterogeneous agents driving sustainability transitions and including multiple planetary boundaries.

Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.

Inflation and dark matter from the low entropy hypothesis and modeling mechanism of modified gravity

Abstract The hypothesis of low entropy in the initial state of the Universe usually explains the observed entropy increase is in only one time direction: the thermodynamic arrow of time. The Hamiltonian formalism is commonly used in the context of general relativity. The set of Lagrange multipliers are introduced in the formalism, and they are corresponding to the Hamiltonian constraints which are written in terms of ‘weak equality’—the equality is satisfied if the constraints hold. Follow the low-entropy hypothesis, we postulate a modeling mechanism—a weak equality (of modeling) that holds only on the subspace of the theory space of physical models defined by some modeling constraints. By applying the modeling mechanism, we obtain a specific model of modified gravity under specific modeling conditions. We derive a novel equation of modeling from the mechanism, that describes how different gravitational models emerge. The solution of the modeling equation naturally turns out to be the model of R 2-gravity (with additional terms) if ordinary matter is negligible. We also found that this mechanism leads to two models: large-field inflation and wave-like dark matter (DM). Interestingly, the wave-like DM model is supported by the most recent observations of Einstein rings.

Development of improved finite element formulations for pile group behavior analysis under cyclic loading

AbstractThe effect of cyclic loading is an essential factor leading to progressive soil strength degradation. Therefore, a comprehensive analysis of the pile‐soil system behavior under cyclic loading is required to ensure the stability of pile group. There is room for improvement in the inherent constraint of the conventional numerical model in terms of approximating the soil resistance distribution along the pile by point loads at element nodes, necessitating a specific element that integrates considerations of pile group effect and cyclic loading within a unified framework. This study aims to develop a newly specific type of element for efficiently predicting nonlinear behavior within the pile‐soil system, addressing simulations involving nonlinear pile‐soil interaction, pile group effect, and cyclic loading. Modified element formulations based on soil stiffness matrices and soil resistance vectors specifically address pile group effect and consider parameters that influence pile behavior under cyclic lateral loading. The numerical solution procedure with Newton‐Raphson iteration allows the calculation of pile responses in geometric and material nonlinear analyses. The validation of the proposed method includes several examples, comparing it with existing numerical solutions and experimental tests of single piles and pile groups under cyclic loading. These comparisons further support the consistency of the proposed method with measured data and validate its accuracy in considering group effect and cyclic loading. The parametric study illustrates the ability of the proposed method to capture cyclic loading parameters while considering the influence of the number and magnitude of load cycles, the cyclic load direction, and the installation methods.

Deciphering the physiopathology of neurodevelopmental disorders using brain organoids.

Neurodevelopmental disorders (NDD) encompass a range of conditions marked by abnormal brain development in conjunction with impaired cognitive, emotional, and behavioural functions. Transgenic animal models, mainly rodents, traditionally served as key tools for deciphering the molecular mechanisms driving NDD physiopathology, and significantly contributed to the development of pharmacological interventions aimed at treating these disorders. However, the efficacy of these treatments in humans has proven to be limited, due in part to the intrinsic constraint of animal models to recapitulate the complex development and structure of the human brain but also to the phenotypic heterogeneity found between affected individuals. Significant advancements in the field of induced pluripotent stem cells (iPSC) offer a promising avenue for overcoming these challenges. Indeed, the development of advanced differentiation protocols for generating iPSC-derived brain organoids gives the unprecedented opportunity to explore the human neurodevelopment. This review provides an overview of how 3D brain organoids have been used to investigate various NDD (i.e., Fragile X syndrome, Rett syndrome, Angelman syndrome, microlissencephaly, Prader-Willi syndrome, Timothy Syndrome, tuberous sclerosis syndrome), and elucidate their pathophysiology. We also discuss the benefits and limitations of employing such innovative 3D models compared to animal models and 2D cell culture systems, in the realm of personalized medicine.

Constraining the Earth-mass Primordial Black Hole Mergers Model of the Non-repeating FRBs Using the First CHIME/FRB Catalog

In this paper, we upgrade the constraints for the Earth-mass primordial black hole mergers model based on the first Canadian Hydrogen Intensity Mapping Experiment (CHIME)/fast radio burst (FRB) catalog. Assuming the null hypothesis that the observed non-repeating FRBs originate from Earth-mass primordial black hole mergers, we find that how the charges were distributed in the primordial black hole population is well described by a double power-law function with typical charge value of qc/10−5=1.60−0.28+0.28 , where the power-law index α1=2.33−0.18+0.15 for q < q c and α2=4.56−0.26+0.30 for q ≥ q c. Here, q represents the charge of the black hole in units of GM , where M is the mass of the black hole. Furthermore, we infer the local event rate of the bursts is 8.8−2.1+5.7×104Gpc−3yr−1 , which indicates that an abundance of the primordial black hole population f ≳ 10−4 is needed to account for the observed FRBs by CHIME. The results of this paper lay the basis for further research on the electromagnetic radiation background generated by the merger of primordial black hole mergers.

Advancing virology research with a human brain organoid platform

In humans, the capacity of viruses to infiltrate multiple organs and cause extensive damage is well-established. However, our comprehension of viral infection pathogenesis within the central nervous system (CNS) remains limited due to challenges in accessing clinical research specimens and the inherent constraints of animal models. Derived from either human embryonic stem cells (hESCs) or human-induced pluripotent stem cells (hiPSCs), human brain organoids undergo meticulous differentiation in culture media that simulate the developmental environment of the brain, characterized by their structural intricacy and diverse cell types. The evolution of human brain organoids has undoubtedly provided the scientific community with a potent research tool for investigating neurological diseases. We summarizes research findings on viral infection models based on human brain organoids. Furthermore, we discuss the limitations of this model and envision future developments and applications of human brain organoids.

Data driven origin–destination matrix estimation on large networks—A joint origin–destination-path-choice formulation

This paper presents a novel approach to data-driven time-dependent origin–destination (OD) estimation using a joint origin–destination-path choice formulation, inspired by the well-known equivalence of doubly constraint gravity models and multinomial logit models for joint O–D choice. This new formulation provides a theoretical basis and generalizes an earlier contribution. Although including path choice increases the dimensionality of the problem, it also dramatically improves the quality of the data one can directly use to solve it (e.g. measured path travel times versus coarse centroid-to-centroid travel times); and opens up possibilities to combine different assimilation techniques in a single framework: (1) fast shortest path set computation using static (e.g. road type) and dynamic (speed, travel time) link properties; (2) predicting a “prior OD matrix” using the resulting path-shares and (estimated or measured) production and attraction totals; and (3) scaling/constraining this prior using link flows (informative of demand). If the resulting system of equations has insufficient rank, we use principal component analysis to reduce the dimensionality, solve this reduced problem, and transform that solution back to a full OD matrix. Comprehensive tests and sensitivity analysis on 7 networks with different sizes and characteristics give an empirical underpinning of the extended equivalence principle; demonstrate good accuracy and reliability of the OD estimation method overall; and suggest that the method is robust with respect to major assumptions and contributing factors.

Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China

As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.

Optimal scheduling of green heating systems in buildings considering economy and exergy efficiency

Green heating in buildings is of great significance to global energy conservation and emission reduction, and the design of optimal scheduling models is the key to achieve cost reduction and efficiency improvement in heating systems. For the building heating system containing multiple heterogeneous energy sources such as solar energy, air energy and electricity, a multi-objective optimal scheduling model is established with the goal of maximizing the exergy efficiency and minimizing the economic cost of the system, taking into account the quality difference of energy in the conversion process. Regarding the nonconvex and nonlinear constraints in the scheduling model, the incremental linearization method and Big-M method are used to linearize the transformation, and obtain the multi-objective mixed-integer linear programming model. Further, the ε-constraint method is used to solve the Pareto frontier of multi-objective optimization and the technique for order preference by similarity to ideal solution (TOPSIS) method is used to make decision. Finally, the simulation study of the model is carried out, and the results show that the proposed multi-objective optimal scheduling can balance the system operation economy and exergy efficiency compared with the belief rule base and evidential reasoning (BRB-ER) method, and the system’s daily economic cost are reduced by 9.66 %, 9.94 % and 17.24 %, respectively, and the system’s average daily coefficient of performance (COP) are improved by 4.66 %, 14.05 % and 13.22 %, respectively, on three typical days, it is verified that the proposed optimal scheduling model has better economy and exergy efficiency. The proposed multi-objective optimal scheduling model introduces exergy efficiency rather than energy efficiency as one of the optimization objectives of the heating system and reasonably solves the optimization model, which can provide a new idea and reference for the multi-objective optimal scheduling of the green heating system in buildings.

Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning.

The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

A modified bi-objective NSGA-II approach to sustainability in reconfiguration planning of dynamic cellular manufacturing systems

Manufacturing plant layouts are developed to facilitate optimal process flow. Modern manufacturing systems must meet present production demands and be adaptable to changes in process flow in the future. Dynamic Cellular Manufacturing Systems (DCMS) increase the flexibility of layouts by reconfiguring cell structure and equipment distribution, to effectively adjust part routings for optimal process flow. Frequent reconfiguring of plant layout may not always be feasible or economical, however, when new product releases are planned, reconfiguring the plant layout to optimise the work-flow may be extremely beneficial. This paper presents a Non-dominated Sorting Genetic Algorithm (NSGA-II) approach to solving a DCMS problem in a sustainable, and responsible manner. A bi-objective integer programming model was developed over multiple planning horizons with fluctuating product demands. This model aims to achieve sustainability by reducing the cost of production, mitigating the environmental impact of production, and minimise negative social impacts on labourers that work in such environments. A penalty function approach was used to enforce the model constraints during optimisation. This study details trade-offs between the economic factors of a DCMS, the environmental implications of reconfiguring such a system, and the social impacts of reconfigurations on the workforce.