Optimization Step Research Articles

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.

Multi-modal fusion network guided by prior knowledge for 3D CAD model recognition

Currently, the 3D CAD model has found extensive use in various applications. The more accurate recognition and more reliable reuse of 3D computer-aided design (CAD) models could significantly save labor costs and time in the production and design of 3D products. However, existing 3D model recognition methods are constrained by a single representation form that lacks supplementary information from multiple sources, and there is a lack of corresponding guidance for relationships between different data. In this paper, we focus on multi-modal information for 3D model representation and propose a Multi-modal Fusion Network (MMFN) guided by prior knowledge for 3D CAD model recognition. In particular, we consider prior knowledge about the class distribution of 3D models: The designed labeling information is utilized as the prior knowledge to guide the multi-modal information fusion via the cross-attention structure. Then, the contrastive learning method is utilized in the optimization step, further increasing the aggregation of similar samples and contributing to enhancing the discriminative capability of features. Finally, we conduct extensive experiments on the public datasets, ModelNet, ShapeNet, and a challenging industrial 3D CAD dataset built by ourselves. Compared to state-of-the-art approaches, our MMFN provides competitive results. The source code has been published on Github: https://github.com/WhiteTJU/MMFN.

Infrared spectroscopy across scales in length and time at BESSY II.

The infrared beamline at BESSY II storage ring was upgraded recently to extend the capabilities of infrared microscopy. The endstations available at the beamline are now facilitating improved characterization of molecules and materials at different length scales and time resolutions. Here, the current outline of the beamline is reported and an overview of the endstations available is given. In particular, the first results obtained by using a new microscope for nano-spectroscopy that was implemented are presented. The capabilities of the scattering-type near-field optical microscope (s-SNOM) are demonstrated by investigating cellulose microfibrils, representing nanoscopic objects of a hierarchical structure. It is shown that the s-SNOM coupled to the beamline allows imaging to be performed with a spatial resolution of less than 30 nm and infrared spectra to be collected from an effective volume of less than 30 nm × 30 nm × 12 nm. Potential steps for further optimization of the beamline performance are discussed.

Cost minimization of multi-class demands groomed over multi-rate OTN interfaces considering WDM-layer constraints

This paper introduces a practical comprehensive approach to optimize the cost of installed Optical Transport Network (OTN) interfaces, as well as the required number of wavelengths in the substrate Wavelength Division Multiplexing (WDM) optical network. The approach assumes grooming variable-bit-rate traffic demands on multi-rate OTN interfaces. We propose an Integer Linear Programming (ILP) formulation to address the combined problem of OTN interface deployment, virtual-network formation, traffic grooming on the established distinct-capacity virtual paths, and virtual network embedding on the WDM physical substrate. The presented model focuses on mitigating the capital expenditure required to deploy OTN interfaces, usually the most costly element in the network, as well as minimizing the required number of wavelengths in the Routing and Wavelength Assignment (RWA) problem, contributing to the overall reduction in network costs and ensuring open capacity for future expansions. As the RWA problem is NP-hard and involves significant computational costs, we also propose a multi-step heuristic approach to tackle larger network instances more efficiently. In this approach, the optimization steps for the OTN design and the RWA solutions are conducted independently. For the OTN-layer planning, the optimum ILP formulation (OTN-ILP) is maintained, so that the optimum solution for the cost of installed interfaces on the OTN layer is guaranteed. The RWA problem is proposed through either an ILP formulation (RWA-ILP) or a heuristic approach. The RWA-ILP model can find the same or slightly superior number of wavelengths compared to the optimum solution of the Integrated ILP formulation, but it can provide results in reasonable times only for small networks. On the other hand, the heuristic RWA is able to run any network size, but it requires more wavelengths compared to the RWA-ILP model.

Less inappropriate medication: first steps in medication optimization to improve post-intensive care patient recovery.

Less inappropriate medication: first steps in medication optimization to improve post-intensive care patient recovery.

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

Rapid preconcentration and determination of Co (II) ions in aqueous medium using ultrasound assisted cloud point extraction method

A flame atomic absorption spectrophotometric technique was studied for the preconcentration and determination of of Co2+ ions in trace amounts after ultrasound enhanced enrichment of its 1-(2-pyridylazo)-2-naphthol (PAN) complexes by cloud point extraction. The Co2+ ions were firstly complexed with PAN molecules at pH 4 and the complex transferred to a surfactant (Tergitol NP-7) rich phase with a very small volume under ultrasound waves. The surfactant rich phase was separated and dissolved by 1 mL, 1.0 M HNO3 in ethanol prior to determination of Co2+ concentration by flame atomic absorption spectrophotometry. The optimization steps of ultrasound interaction time, solution pH and surfactant volumes were achieved by response surface methodology (RSM) which is a combination of statistical approaches. Sample volume, PAN concentration and and possible interfering ions were optimized conventionally. The Co2+ ions were extracted only in 150 s at pH 4 quantitatively under optimum conditions (˃95%). The accuracy and validity of developed UA-CPE was granted by employing a certified reference material wastewater UME CRM 1204. The technique was applied to wastewater and natural water with satisfactory results.

Computation cost reduction in 3D shape optimization of nanophotonic components

Inverse design methodologies effectively optimize many design parameters of a photonic device with respect to a primary objective, uncovering locally optimal designs in a typically non-convex parameter space. Often, a variety of secondary objectives (performance metrics) also need to be considered before fabrication takes place. Hence, a large collection of optimized designs is useful, as their performance on secondary objectives often varies. For certain classes of components such as shape-optimized devices, the most efficient optimization approach is to begin with 2D optimization from random parameter initialization and then follow up with 3D re-optimization. Nevertheless, the latter stage is substantially time- and resource-intensive. Thus, obtaining a desired collection of optimized designs through repeated 3D optimizations is a computational challenge. To address this issue, a machine learning-based regression model is proposed to reduce the computation cost involved in the 3D optimization stage. The regression model correlates the 2D and 3D optimized structural parameters based on a small dataset. Using the predicted design parameters from this model as the initial condition for 3D optimization, the same optima are reached faster. The effectiveness of this approach is demonstrated in the shape optimization-based inverse design of TE0-TE1 mode converters, an important component in mode-division multiplexing applications. The final optimized designs are identical in both approaches, but leveraging a machine learning-based regression model offers a 35% reduction in computation load for the 3D optimization step. The approach provides a more effective means for sampling larger numbers of 3D optimized designs.

Coupled cluster-inspired geminal wavefunctions.

Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes to the promise of serving as building blocks of quantitative descriptions of the electronic structure of complex molecules and materials. However, traditionally, two-electron wavefunctions (geminals) have not enjoyed the popularity and widespread use of the more standard single-particle methods. This has changed recently, with a renewed interest in the development of geminal wavefunctions as an alternative to describing strongly correlated phenomena. Hence, there is a need to find geminal methods that are accurate, computationally tractable, and do not demand significant input from the user (particularly via cumbersome and often ill-behaved orbital optimization steps). Here, we propose new families of geminal wavefunctions inspired by the pair coupled cluster doubles ansatz. We present a new hierarchy of two-electron wavefunctions that extends the one-reference orbital idea to other geminals. Moreover, we show how to incorporate single-like excitations in this framework without leaving the quasiparticle picture. We explore the role of imposing seniority restrictions on these wavefunctions and benchmark these new methods on model strongly correlated systems.

Panoptic segmentation of 3D point clouds with Gaussian mixture model in outdoor scenes

Accurate panoptic segmentation of 3D point clouds in outdoor scenes is critical for the success of applications such as autonomous driving and robot navigation. Existing methods in this area typically assume that the differences between instances are greater than the differences between points belonging to the same instance and use heuristic techniques for segmentation. However, this assumption may not hold in real scenes with occlusion and noise. In addition, most of the previous methods formulate point-wise embedding learning and instance clustering as two decoupled steps for separate optimization, making it a challenging task to learn discriminative embeddings. To address these issues, we introduce a framework for modeling points belonging to the same instance using learnable Gaussian distributions and formulate the point cloud as a Gaussian mixture model. Based on this formulation, we introduce a unified loss function that links the embedding learning and instance clustering in an end-to-end manner. Our framework is generic and can be seamlessly incorporated with existing panoptic segmentation networks. By explicitly modeling intra-instance variance and leveraging end-to-end optimization, our framework improves the discrimination capability of point embeddings with higher accuracy and robustness. Extensive experiments on two large-scale benchmarks demonstrate the effectiveness of the proposed method.

Concordance of judgement: A tool to foster the development of professionalism in dentistry.

Developing professionalism notably involves learning how to make professional judgements in ambiguous situations. The Concordance of Judgement Test (CJT) is a learning tool that was proposed to develop professionalism competencies, but it was never performed in dentistry or used with a synchronous methodology. The present study evaluated the feasibility of the use of CJT in the context of dental education, to foster professionalism and stimulate reflexivity and discussion. After different steps of optimization, a questionnaire presenting 12 vignettes was submitted to 33 Canadian students. Second, after an additional optimization, a questionnaire of 7 vignettes was submitted to 87 French students. An immediate educational feedback was proposed after each vignette to promote reflexivity and discussions during the experience. The overall experience of the students was reported as good, thanks to the feedback of real-life situations. This promoted reflexivity and stimulated discussion between students and educators regarding professionalism issues. The students considered CJT as a relevant and well-adapted tool, and reported positive feelings regarding the inter-university aspect of the activity. The mean score of the panel members was close to 80/100 and the mean score of the students was 5 to 10 points lower, which is in agreement with docimological performance. The results suggested that the use of CJT in a synchronous way was a feasible and relevant tool to motivate the students to improve their professionalism, and to stimulate their reflexivity and discussion. The students reported positive experience with CJT, and we believe that this tool can be integrated in the dental curriculum.

Simultaneous Optimization of Work and Heat Exchange Networks

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network’s total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies.

Construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms.

In power electronics systems, system design and operation often involve multiple time and space scales, ranging from nanosecond switching dynamics to hour-level system operation behavior. Due to the complexity of these systems and the rise of wide-gap semiconductor technology, a series of multi-scale phenomena have emerged that are difficult to ignore. The high frequency of switching operations makes multi-scale effects particularly significant, including the fast dynamic response of the power loop, EMI, and heat conduction problems. They are key factors that must be considered in the design to ensure the efficient and reliable operation of power electronic devices. This study proposes the construction and simulation of a joint scale model for power electronic converters based on wavelet decomposition and reconstruction algorithms to address the multi-scale phenomenon and limitations of single-scale power electronic converters. Firstly, a joint scale model for power electronic converters at both macro and micro-scales was established, targeting both single-scale models and simple combinations of multiple scale models for power electronic converters. The traditional single-scale model is sufficient to describe the average behavior of the converter, but it has serious limitations in capturing fast transient processes and high-frequency switching behavior in power electronic systems. These limitations often manifest themselves when there is a need to capture fine timescales of detail. By transforming between the time domain and the frequency domain, wavelet decomposition enables the model to capture both macroscopic average characteristics and microscopic transient dynamics. The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. Secondly, this algorithm is used to analyze BC in short time scale. Finally, the short time scale characteristics of power electronic converters are analyzed. Experimental results show that the fusion of wavelet decomposition and reconstruction algorithm enhances the accuracy of the power electronic converter model and improves the performance of the system. The model achieves an error reduction of nearly 3% in the calculation step size of 10-7s, which has a significant impact on the high precision requirements of high-frequency operations. In addition, the optimal calculation step size of 8×10-8s achieves an error reduction of more than 14%, making an important contribution to the transient analysis and fine structure simulation. The wavelet algorithm can improve the accuracy of multi-scale modeling in power electronic system and reduce the simulation time. The reduction of error not only shows the improvement of the accuracy of the model, but also shows its practical significance in the design and test of the actual power electronic system. The reduction in error reveals the ability to more accurately predict and mitigate potential performance problems in matching tests with actual hardware, as well as its ability to adapt to emerging wide bandgap semiconductor materials and structures.

CTAB DNA Extraction Method Optimization for Quantification of Lard Content in Cheese and Butter Using Real-Time PCR

ABSTRACT Porcine adulteration in dairy products for economic purposes has been tackled mostly by protein- and lipid-based porcine detection methods. In this study, a DNA-based approach was used to determine the presence of porcine DNA in dairy products, especially butter and cheese. DNA was extracted by optimizing the conventional CTAB extraction method followed by an assessment of DNA fragmentation by real-time PCR assays targeting different sizes of amplicon. Butter and cheese DNA samples were analyzed using both endogenous and porcine-specific real-time PCR assays and the lard content was estimated based on the linear regression analysis of a series of different percentages of lard-adulterated butter and cheese DNA samples. Even though the optimization steps did not improve the DNA yield, the successful amplification of the 200 bp amplicon depicted high DNA quality. Approximately 1% lard was able to be quantified exhibiting high potential of the assay in quantifying porcine content in lard-adulterated highly processed food products.

Diflubenzuron Mass Concentration Determination Method in Pesticides Using High-Performance Liquid Chromatography.

Pesticides are actively used in agriculture to protect crops from pests, and accurate determination of the concentration of their active ingredient is critical to ensuring their efficacy and safety. The research aims to develop a methodology that will enable the accurate determination of the mass concentration of diflubenzuron, a widely used active ingredient in pesticide formulations. This is achieved through the use of high-performance liquid chromatography (HPLC). The proposed method for determining the mass concentration of diflubenzuron using HPLC was developed through a series of experiments and optimisation steps. The sample preparation procedure was optimised to extract the maximum amount of diflubenzuron from the pesticide formulation. The chromatography conditions were optimised to separate diflubenzuron from other components in the sample matrix. A calibration approach was established for accurate quantification of diflubenzuron in the sample based on peak area measurements. The developed method can be widely used in agriculture, where accurate determination of diflubenzuron concentrations in pesticide formulations is critical for proper application and regulatory compliance. Both regulatory organizations and pesticide manufacturers can use it for batch-release testing and quality control to evaluate the efficacy and safety of pesticide products. In addition, the method can serve as a valuable tool for researchers and analysts working with pesticide residues in crops and environmental monitoring studies. The optimised sample preparation, chromatography conditions, and calibration approach provide reliable and reproducible results.

Optimization of stepped revetment configuration on minimizing wave run-up and overtopping

A stepped revetment has proven to be able to attenuate wave run-up height and wave overtopping discharge more effectively than a dike or a smooth-slope revetment does. However, the investigation into the optimal design of a stepped revetment, tailored to different wave and coastal conditions, is notably lacking. This paper introduces a computational model developed from the non-linear shallow water equations and the Genetic Algorithm. The aim is to optimize the width of a stepped revetment's step and crest, considering wave run-up and overtopping as key parameters. Multiple benchmark tests were conducted to validate the model's accuracy in estimating wave run-up and overtopping for specific setups. The results demonstrate a strong correlation between the numerical findings and experimental data. The Genetic Algorithm was then employed to predict the optimal step width for minimizing wave run-up height and determining the optimal step and crest widths to reduce wave overtopping discharge to an acceptable level. The findings reveal that for a gently sloping stepped revetment, the optimal step widths, based on wave run-up, remain relatively consistent across different wave heights. In contrast, when optimizing for wave overtopping, the optimal step width tends to fluctuate in most cases, although it shows some consistency in certain scenarios.

Monitoring, Modeling Techniques Help Optimize Eagle Ford Completions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212367, “Sealed Wellbore Pressure Monitoring and Calibrated Fracture Modeling: The Next Step in Unconventional Completions Optimization,” by Karen Olson, SPE, Joshua Merritt, and Rair Barraez, SPE, Well Data Labs, et al. The paper has not been peer reviewed. _ Sealed wellbore pressure monitoring (SWPM) has been used across North and South America, with more than 16,000 stages monitored. A recent development is the added capability of a fracture model that can automatically history-match the volume to first response (VFR) determined from SWPM. The complete paper’s focus is a case study of the Department of Energy Eagle Ford refracturing project, where a range of completion designs were trialed while monitoring offset SWPM and fiber-optic strain. Introduction of the Hydraulic Fracture Test Site 1 (HFTS1), Phase 3 Background. The HFTS1 Phase 3 was conducted at the Zgabay unit in northwest DeWitt County, Texas. This unit was initially developed with horizontal multistage completions and has been producing since 2013. The 10 original wells in the unit, and the four new drilled wells, are landed in the approximately 100-ft-thick Lower Eagle Ford Shale. Of the four new drills, Well 14H is a dedicated observation well instrumented with various diagnostics, including a permanent fiber-optic cable. Well 12H, another new drill, was monitored using a wireline-deployable fiber. Both of these wells featured downhole gauges, while Wells 11H and 13H featured surface gauges. The focus of the refracturing strain diagnostics— SWPM and fiber—was to characterize new fracture growth and interaction with pre-existing fractures during the liner refracturing of parent Wells 5H and 3H. Completion Designs. Multiple stage and cluster architectures were tested on the Zgabay Wells 3H and 5H. Four single-cluster stages were performed per well. Both a high (2,000-psi target) and low (500-psi target) perforation friction design was tested on seven-, 12-, and 22-cluster stages. Well 3H maintained a slightly tighter cluster spacing (10–15 ft) compared with the 12–20 ft spacing of Well 5H. The main method of measuring the effect of different designs is strain monitoring using SWPM and fiber optics. Technology Background SWPM. SWPM uses an uncompleted wellbore to monitor fracture intersections from offset-well stimulations. The monitor well collecting the SWPM data cannot be connected to a formation through perforations or other types of access points. The wellbore should be filled with low-compressibility fluid, with pressure added to the monitor well. Fractures intersecting the sealed wellbore cause local deformation, which results in a small volume reduction in the closed system and generates a discernable and distinct pressure response. The total slurry injected into the stage of the active well when the first fracture arrival is identified using SWPM is the VFR. VFRs are used as a proxy for cluster efficiency and to calculate fracture-growth rates. The field execution of SWPM does not require any tools to enter the wellbore. Multiple sealed wellbores can be used as monitor wells for a single treatment well.

Investigation of torque and sensitivity analysis of a two-phase hybrid stepper motor

Abstract Stepper motors are extensively used in various automated systems, notably hybrid stepper motors (HSMs), renowned for their high torque density. Optimizing and improving their performance and torque density is highly beneficial. The first step in motor optimization is to perform a sensitivity analysis to identify the parameters most significantly affecting motor performance and torque density. Using Ansys Maxwell, a finite element method (FEM) software, we modeled and analyzed a commercially available HSM. After validating the computer model’s accuracy, we investigated the motor’s sensitivity to various parameters and discussed their effects on performance. Our findings highlight the significance of parameters such as air gap, magnet diameter, wire-turn numbers in the coils, and the shapes of the rotor and stator teeth in influencing HSM sensitivity—conversely, factors like magnet height and stator pole thickness exhibit negligible impact on motor performance.

Silicate Mineralogy from Vis–NIR Reflectance Spectra

The asteroid composition is the key to understanding the origin and evolution of the solar system. The composition is imprinted at specific wavelengths of the asteroid reflectance spectra. We wish to find the optimal wavelength range and step of reflectance spectra that contain sufficient information about S-complex asteroids while keeping the data volume as low as possible. We especially aim for the ASPECT instrument on board the Milani/Hera CubeSat that will observe the S-complex binary asteroid (65803) Didymos–Dimorphos. We use labeled reflectance spectra of the most common silicate found in meteorites, namely olivine, orthopyroxene, clinopyroxene, and their mixtures. The spectra are interpolated to various wavelength grids. We use convolutional neural networks and train them with the labeled interpolated reflectance spectra. The reliability of the network outputs is evaluated using standard regression metrics. We do not find any significant dependence between the error of the model predictions and normalization position, fineness of coverage within the 1 μm band, and wavelength step up to 50 nm. High-precision predictions of the olivine and orthopyroxene modal abundances are obtained using spectra that cover wavelengths from 750 to 1050 nm and from 750 to 1250 nm, respectively. For high-precision predictions of the olivine chemical composition, the spectra should cover wavelengths from 750 to 1550 nm. The orthopyroxene chemical composition can be estimated from spectra that cover wavelengths from 750 to 1350 nm. We design a simple web interface through which everybody can use the pretrained models.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.