Model Configuration Research Articles

Flexible allocation and optimal configuration of multi-level energy exploitation units for heterogeneous energy systems considering resource distribution

The energy systems are evolving into heterogeneous energy systems (HESs) with complicated integration of sources, networks, loads, and storage towards a decarbonized future of human beings. This paper develops the flexible allocation and optimal configuration of multi-level energy exploitation units (MEEUs) for the HES considering resource distribution including local energy conditions and actual coupled relationship of various energy networks. A bi-level optimal planning model of MEEUs for the HES considering initial investment cost, carbon emission cost, operation cost and line loss is developed to figure out the optimal location, structure, configuration and corresponding operation strategy. To address the non-convex and non-linear model, a novel hybrid solution method considering the mixing characteristics of mixed-integer second-order cone programming (MISOCP) model and quadratic non-convex model is proposed. The solution method adopts MISOCP model for figuring out the optimal location, structure and configuration of MEEUs and quadratic non-convex model for optimal operation strategy of MEEUs, which not only improves the computational efficiency but also ensures the solution accuracy. Finally, the related case studies are conducted to compare the proposed MEEU and distributed energy storage unit (DESU) in terms of energy consumption, renewable energy absorbency, carbon emission and line loss. The results verify the significant improvement of the comprehensive energy efficiency, the investment potentiality and the environmental benefits obtained by the proposed planning model.

Overall survival prediction and risk stratification in patients with neuroblastoma through machine learning in the large multi-institutional PRIMAGE cohort.

10054 Background: Neuroblastoma (NB) is the most prevalent solid cancer in childhood in which imaging plays a pivotal role at every step of the patient's journey. This investigation aimed to develop a machine learning model using clinical, molecular (MycN amplification), and magnetic resonance (MR) radiomics features at diagnosis to predict patient’s overall survival (OS) and improve their risk stratification. Methods: A database comprising clinical, molecular, and International Neuroblastoma Risk Group (INRG) staging on 513 patients with accessible MR imaging (discovery cohort) was employed for model training, validation, and testing. An additional 22 patients from non-discovery cohort hospitals served as an external validation group. Tumor segmentations of NB were manually and semi-automatically performed on corresponding T2-weighted MR images by an experienced radiologist. In total, 107 radiomics features were extracted and harmonized across MR scan manufacturers and magnetic field strengths using the nested ComBat methodology to correct both batch effects. These radiomics features, combined with clinical and molecular data, were utilized as input for the models. A nested cross-validation approach was employed for model development to determine the optimal preprocessing, machine learning algorithm, and model configuration. Results: The discovery cohort yielded a C-index of 0.788 ± 0.049 in the test partitions, with a random survival forest (RSF) exhibiting the best performance. In the validation cohort, a C-index of 0.934 was achieved. Interpretability analysis identified lesion heterogeneity, size, and molecular variables as crucial factors in OS prediction. The model demonstrated superior predictive performance and patient stratification compared to conventional staging systems in both cohorts. Conclusions: The RSF predictive model exhibited high performance, emphasizing the significant contribution of radiomics features and alignment with established clinical and molecular variables. The model stratified NB patients into low-, intermediate-, and high-risk categories and suggests that radiomics features potentially could improve current risk stratification systems. Additional external validation is warranted as this may present new evidence for enhancing patient care and clinical decision-making for NB patients.

Enhancing open stope stability prediction in mining engineering: Optimal configuration of an artificial neural network model

Ensuring the stability of underground excavations is a crucial concern in mining engineering, directly impacting operational safety and efficiency. Given the dynamic nature of the subsurface environment, innovative methods are essential to enhance stability in open stopes and mitigate potential risks. This study focuses on refining predictions of underground excavation stability in mining engineering through the optimization of an artificial neural network (ANN) model. Analyzing Potvin’s database, which includes 175 historical cases, we examined the impact of various ANN model configurations. Our findings indicate that normalizing data using Standard Scaler and employing Swish as the activation function across all layers yielded the most accurate predictions for this specific scenario. Additionally, employing the SHAP (Shapley Additive exPlanations) tool enabled us to assess feature importance and identify the most influential factors. Our results underscore the significant impact of the shape factor on underground opening stability, closely followed by the Q value. This research contributes to enhancing predictive models for underground mining excavation stability, emphasizing the critical role of specific parameters affecting open stope stability.

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG.

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Harnessing AI for solar energy: Emergence of transformer models

This review emphasizes the critical need for accurate integration of solar energy into power grids. It meticulously examines the advancements in transformer models for solar forecasting, representing a confluence of renewable energy research and cutting-edge machine learning. It evaluates the effectiveness of various transformer architectures, including single, hybrid, and specialized models, across different forecasting horizons, from short to medium term. This review unveils substantial improvements in forecasting accuracy and computational efficiency, highlighting the models' proficiency in handling complex and diverse solar data. A key contribution is the emphasis on the crucial role of hyperparameters in refining model performance, balancing precision against computational demands. Importantly, the research also identifies critical challenges, such as the significant computational resources required and the need for expansive, high-quality datasets, which limit the broader application of these models. In response, this review advocates for future research directions focused on standardizing model configurations, venturing into longer-term forecasting, and fostering innovations to enhance computational economy. These proposed pathways aim to surmount current challenges, steering the domain towards more accurate, adaptable, and sustainable solar forecasting solutions that can contribute to achieving global renewable energy and climate objectives. This review not only maps the present landscape of transformer models in solar energy forecasting but also charts a trajectory for future advancements. It serves as a pivotal guide for researchers and practitioners, delineating the current advancements and future directions in navigating the complexities of solar data interpretation and forecasting, thereby significantly contributing to the development of reliable and efficient renewable energy systems.

DETERMINATION OF PHYSICAL AND MECHANICAL PROPERTIES OF POLYETHYLENE AND POLYMER NANOCOMPOSITES USING THE METHODS OF MOLECULAR DYNAMICS

Today, molecular dynamics (MD) simulation become an effective method for investigating and predicting the physical and mechanical properties (PMP) of materials at the atomic scale. In this work, initial configurations of molecular systems of pure polyethylene (PE) and carbon nanotube-reinforced nanocomposites (PE-CNT) are created and described by the united atom model and Dreiding force field. Equilibration of the initial configurations of molecular models carried out using NVT and NPT ensembles in LAMMPS. Simulated complex of PMP of PE and PE-CNT includes elastic modulus, Poisson's ratio, bulk modulus, shear modulus, yield strength, mass isobaric heat capacity, and coefficient of linear thermal expansion (CLTE). The results verification of the PE molecular model showed the obtained MD simulation data either coincide with available literature data or are close to them. The results justify the use of the same algorithms to obtaine reliable data on the PMP of PE-CNT. Comparing the complex PMP of molecular models of pure PE and PE-CNT, it was established the elastic modulus of PE-CNT α=1.4 % increases by 10.4% at 300 K and by 29.3% at 320 K. The yield strength of PE-CNT α=1.4 % also increases by 33.2 % at a strain rate of 109 s−1 and a temperature of 300 K compared to PE. Meanwhile, mass isobaric heat capacity and CLTE decrease with increasing temperature: mass isobaric heat capacity decreases by 1.3 % at 300 K and by 4.4 % at 320 K; CLTE decreases by 20.2 % at 300 K and by 22.6 % at 320 K. Nonlinear two-parameter dependencies of the PMP of PE-CNT obtained in the temperature range (280–320) K and the volume fraction of fillers (0–1.5) %. These results allow to develop of new composite materials without conducting sufficiently complex and time-consuming numerical experiments based on MD simulation. The obtained data on the complex PMP are necessary for modeling the thermo-elastic-plastic state of products made of PE-CNT under operational conditions in a continuum approximation.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

Simulation testing performance of ensemble models when catch data are underreported

Abstract Ensemble model use in stock assessment is increasing, yet guidance on construction and an evaluation of performance relative to single models is lacking. Ensemble models can characterize structural uncertainty and avoid the conundrum of selecting a “best” assessment model when alternative models explain observed data equally well. Through simulation, we explore the importance of identifying candidate models for both assessment and short-term forecasts and the consequences of different ensemble weighting methods on estimated quantities. Ensemble performance exceeded a single best model only when the set of candidate models spanned the true model configuration. Accuracy and precision depended on the model weighting scheme, and varied between two case studies investigating the impact of catch accuracy. Information theoretic weighting methods performed well in the case study with accurate catch, while equal weighting performed best when catch was underreported. In both cases, equal weighting produced multimodality. Ensuring that an ensemble spans the true state of nature will be challenging, but we observed that a change in sign of Mohn’s rho across candidate models coincided with the true OM being bounded. Further development of protocols to select an objective and balanced set of candidate models, and diagnostics to assess adequacy of candidates are recommended.

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

Recycling Krylov subspaces for efficient partitioned solution of aerostructural adjoint systems

Robust and efficient solvers for coupled-adjoint linear systems are crucial to successful aerostructural optimization. Monolithic and partitioned strategies can be applied. The monolithic approach is expected to offer better robustness and efficiency for strong fluid-structure interactions. However, it requires a higher implementation cost and convergence may depend on appropriate scaling and initialization strategies. On the other hand, the modularity of the partitioned method enables a straightforward implementation while its convergence may require relaxation. In addition, a partitioned solver often leads to a higher number of iterations to get the same level of convergence as the monolithic one.The objective of this paper is to accelerate the fluid-structure coupled-adjoint partitioned solver by considering techniques borrowed from approximate invariant subspace recycling strategies adapted to sequences of linear systems with varying right-hand sides. Indeed, in a partitioned framework, the structural source term attached to the fluid block of equations affects the right-hand side with the nice property of quickly converging to a constant value. For the fluid block of equations, we also consider deflation of approximate eigenvectors in conjunction with advanced inner-outer Krylov solvers.To demonstrate the effectiveness of our proposed recycling strategy, we compute the coupled derivatives for two emblematic problems in transonic viscous flow. The first one is an aeroelastic configuration of the ONERA-M6 fixed wing. For this exercise, the fluid grid was coupled to a structural model specifically designed to exhibit a high flexibility. The second one is a wing-body aeroelastic configuration of the Common Research Model. All computations are performed using a fully linearized one-equation Spalart-Allmaras turbulence model. Numerical simulations show up to 39% reduction in matrix-vector products for GCRO-DR and up to 25% for the nested FGCRO-DR solver.

Evaluation of WRF model configurations for dynamic downscaling of tropical cyclones activity over the North Atlantic basin for Lagrangian moisture tracking analysis in future climate

This study assessed five well-established physics suites of the Weather Research and Forecasting (WRF) model in operational forecasting systems in the North Atlantic (NATL) basin or from previous sensitive experiments for dynamic downscaling tropical cyclone (TC) activity. We performed long-term simulations for the 2020 TC season in the NATL and compared the WRF tracks against the HURDAT2 dataset from the US National Hurricane Center. Among the tested configurations, the analysis revealed that the Kain-Fritsch, Purdue Lin, BouLac and revised MM5 schemes for cumulus, microphysics, planetary boundary layer and surface layer, respectively (hereafter WT), outperformed all four others in terms of TC frequency, track density and intensity and showed good performance in the cyclone accumulated energy and TC landfalling locations. In addition, WT well-captured the spatial distribution of accumulated TC precipitation and moisture uptake patterns, although it overestimated the precipitation maxima. Likewise, it agreed on the relative moisture contribution from fixed moisture sources (i.e., Gulf of Mexico, Caribbean Sea, tropical NATL, and western NATL) in the NATL basin. Overall, this study highlighted the high potential of using the WT physics suite in WRF for downscaling TC activity over the NATL basin, which will be useful for TC Lagrangian moisture sources analysis in future climate.

An alternate representation of the geomagnetic core field obtained using machine learning

Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Prevailing methods currently used in inferring the characteristic properties and the probable time evolution of the geodynamo are mostly based on reduced representations of magnetohydrodynamics (MHD). This study introduces a new inference method, referred to as Current Loop-based UNet Model Segmentation Inference (CLUMSI). Its long-term goal focuses on uncovering concentrations of electric current densities inside the core as the direct sources of the magnetic field itself, rather than computing the fluid motion using MHD. CLUMSI relies on simplified models in which equivalent current loops represent electric current systems emerging in turbulent geodynamo simulations. Various configurations of such loop models are utilized to produce synthetic magnetic field and secular variation (SV) maps computed at the core–mantle boundary (CMB). The resulting maps are then presented as training samples to an image-processing neural network designed specifically for solving image segmentation problems. This network essentially learns to infer the parameters and configuration of the loops in each model based on the corresponding CMB maps. In addition, with the help of the Domain Adversarial Training of Neural Networks (DANN) method during training, historical geomagnetic field data could also be considered alongside the synthetic samples. This implementation can increase the likelihood that a network trained primarily on synthetic data will appropriately handle real inputs. Our results focus mainly on the method's feasibility when applied to synthetic data and the quality of these inferences. A single evaluation of the trained network can recover the overall distribution of loop parameters with reasonable accuracy. To better represent conditions in the outer core, the study also proposes a computationally feasible process to account for magnetic diffusion and the corresponding induced currents in the loop models. However, the quality of the reconstruction of magnetic field properties is compromised by occasional poor inferences, and an inability to recover realistic SV.Graphical

Valuing drinking water quality after a PFAS contamination event: Results from a meta-analysis benefit transfer

Drawing upon an extensive body of valuation literature focused on water quality, this paper performs a meta-analysis benefit transfer exercise aimed at quantifying willingness to pay (WTP) for an enhancement in drinking water quality for households that have been directly exposed to Perfluoroalkyl Substances (PFAS) over recent decades in Italy. The analysis compiles metadata of 72 WTP estimates extracted from 40 previous valuation studies conducted in advanced economies. The benefit transfer is realized estimating a meta regression model (MRM) which includes both study design and socio-economic explanatory variables, according to the Weak Structural Utility Theoretic approach. To determine the most suitable MRM specification, a comparative evaluation of various model configurations is developed exploiting the Least Absolute Shrinkage and Selection Operator (LASSO) selection criterion, and assessing their predictive performances in terms of transfer error and explanatory power. The mean transfer error (MTE) and the adjusted R-squared of the preferred MRM are in line with past published meta-analyses (0.665 and 0.607, respectively). The parameters estimated in the model align with both economic theory and intuition. The benefit transfer process results in an estimated annual WTP of € 250.80 per household for improved drinking water quality in the PFAS-affected area and an aggregated value of social benefits from PFAS decontamination of around € 12 million.

Numerical Modeling on Ballistic Impact Analysis of the Segmented Sandwich Composite Armor System

This research delves into the design, modeling, and finite element impact analysis of the segmented sandwich composite armor system subjected to impact loading, considering different parameters such as materials to be used, armor height, and armor design configuration. Initial studies were performed to select the ideal model that will provide the best impact resistance at the least weight and with minimal fabrication requirements. Material type, thickness, and overall model configuration were defined during the initial model development period. Once the final design was defined, finite element analysis was performed using 2017 ABAQUS software to observe the performance of the model and to validate the efficiency of the chosen armor. Based on the results from the material selection and thickness validation, the optimal design with the best impact resistance was noted as 1.2 mm thick rectangular segmented silicon carbide tiles, serving as the top layer that covers the three-level gradient core composed of a titanium metal honeycomb frame filled with silicon carbide inserts, and finally a 2 mm thick glass epoxy composite layer made from four laminas in a 0/45/90/-45-degree configuration serving as the last layer of the armor.

Speckle Plethysmograph-Based Blood Pressure Assessment

Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring.

Design of Software Reliability Prediction using Radial Basis Function Networks with Nonlinear Analysis and Topological Considerations

This study presents a thorough methodology for predicting software reliability by employing Radial Basis Function Networks (RBFNs). By using machine learning methodologies such as Radial Basis Function Networks (RBFNs), software development teams are enabled to make well-informed decisions, optimize resource allocation, and proactively mitigate potential dependability concerns. Consequently, this results in improved software quality and heightened customer happiness. This methodology involves multiple stages, starting with data collection and preprocessing. We assemble a comprehensive dataset comprising historical software performance metrics, defect reports, and relevant development process information. After data cleansing and feature engineering, we split the dataset into training, validation, and testing subsets. The core of this approach lies in the construction and training of RBFNs. These neural networks consist of an input layer, a hidden layer with radial basis functions, and an output layer. The architecture parameters, particularly the number of hidden neurons and the spread parameter of the radial basis functions, are optimized through a systematic hyperparameter tuning process using the validation dataset. Upon achieving an optimal model configuration, we rigorously evaluate the RBFN predictive capabilities using the testing dataset.

Numerical study of aerodynamics drag and flow characteristics of the high-speed train head design

A native design of the Indonesian high-speed train has been developed as an improvement of the previous medium-speed train model. As the design speed is proposed to be increased from 160 km/h to 250 km/h, the nose shape and nose length of the train head were modified into a sharper and longer nose. In this study, numerical simulation of computational fluid dynamics was used to investigate the aerodynamic phenomena and flow behavior around the modified train head and carbodies. The CFD simulation was used as the verification of the shape design and a tool for rapid design changes and optimization. The model configuration is three cars on a real scale by ignoring other parts like the train window and door, pantograph, and train connecting. A k-ε turbulence model was used in the simulation. From this investigation, it is found that the coefficient of drag on the model is calculated to be 0.34. Meanwhile, an experimental wind tunnel test validates the result with a coefficient drag is 0.38 or 10% divergence from the numerical method.

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Abstract. Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.

Toward intelligent food drying: Integrating artificial intelligence into drying systems

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies.

A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system

Objective:Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model. Methods:Data from twenty-nine subjects under normo and hypercapnic (5% CO2 in air) conditions was used. Cerebrovascular resistance and vessel compliance models with ABP as input and CF velocity as output were fitted using a MO approach, considering fitting Pearson’s correlation and error. Results:MO approach finds better model configurations than the single-objective (SO) approach, especially for hypercapnic conditions. In addition, the Pareto-optimal front from the multi-objective approach enables new information on dCA, reflecting a higher contribution of myogenic mechanism for explaining dCA impairment.