Dynamic Integration Research Articles

A step towards the integration of spatial dynamics in population dynamics models: Eastern Bering Sea snow crab as a case study

Considering spatial processes in population dynamics models can be difficult because of data limitations and computational costs. We adapted a high-resolution spatiotemporal assessment framework to better address fine-scale spatial heterogeneities based on theories of fish population dynamics and spatiotemporal statistics. Specifically, we developed a size-based state-space model for the snow crab (Chionoecetes opilio) population in the Eastern Bering Sea (EBS) to refine the representation of spatial processes in integrated population models, facilitate understanding of the drivers of spatiotemporal population dynamics, and provide new insights for management advice. The model fits to spatial survey and fishery-dependent catch data. It implicitly accounts for seasonal movement between the time of the survey and that of fishery to estimate fine-scale spatial population dynamic and fishing impacts, including potential environmental drivers. We quantify, for the first time, spatiotemporal variation in exploitable abundance, fishing mortality, recruitment, and mature and immature abundance. The model estimated declines in exploitable abundance and in fishing mortality with variable spatial distributions, and sporadic recruitment, spatially concentrated in the northeast EBS. Few spatial assessments have been used as the basis for management advice and we consider this study as a step towards the integration of spatial dynamics in stock assessment.

Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions.

The human gut is home to a complex array of microorganisms interacting with the host and each other, forming a community known as the microbiome. This community has been linked to human health and disease, but understanding the underlying interactions is still challenging for researchers. Standard studies typically use high-throughput sequencing to analyze microbiome distribution in patient samples. Recent advancements in meta-omic data analysis have enabled computational modeling strategies to integrate this information into an in silico model. However, there is a need for improved parameter fitting and data integration features in microbial community modeling. This study proposes a novel alternative strategy utilizing state-of-the-art dynamic flux balance analysis (dFBA) to provide a simple protocol enabling accurate replication of abundance data composition through dynamic parameter estimation and integration of metagenomic data. We used a recurrent optimization algorithm to replicate community distributions from three different sources: mock, in vitro, and clinical microbiome. Our results show an accuracy of 98% and 96% when using in vitro and clinical bacterial abundance distributions, respectively. The proposed modeling scheme allowed us to observe the evolution of metabolites. It could provide a deeper understanding of metabolic interactions while taking advantage of the high contextualization features of GEM schemes to fit the study case. The proposed modeling scheme could improve the approach in cases where external factors determine specific bacterial distributions, such as drug intake.

Safety analysis of proton exchange membrane water electrolysis system

This study presents a novel causality model for the safety analysis of proton exchange membrane water electrolysis (PEMWE). The model is based on the integration of computational fluid dynamics (CFD)-based and Bayesian network (BN)-based modeling approaches. The model is aimed at analyzing evolving hazard scenarios, such as gas permeation/crossover (GP) during PEMWE based on fluid dynamics and electrochemistry of electrolysis. The probabilistic domain is employed to model these scenarios using a BN-based technique to assess the likelihood of hazardous scenario occurrence for the first time. A sensitivity analysis was performed, and it was found that operating pressure is the most significant contributor to GP. Operating cell voltage and pressure are two dominant parameters that can trigger GP with the largest likelihood of 87%. Therefore, gas combinators serve as the most effective safety barrier in avoiding the formation of hazardous H2/O2 gas mixtures. This work provides a robust framework for analyzing accident scenarios and developing safety management strategies. It is expected that this model will help in the development of safer PEMWE systems, which is of great importance for various industrial applications.

The impact of orthodontic-surgical treatment on facial expressions—a four-dimensional clinical trial

ObjectiveThe objective of this clinical trial was to compare facial expressions (magnitude, shape change, time, and symmetry) before (T0) and after (T1) orthognathic surgery by implementing a novel method of four-dimensional (4D) motion capture analysis, known as videostereophotogrammetry, in orthodontics.MethodsThis prospective, single-centre, single-arm trial included a total of 26 adult patients (mean age 28.4 years; skeletal class II: n = 13, skeletal class III: n = 13) with indication for orthodontic-surgical treatment. Two reproducible facial expressions (maximum smile, lip purse) were captured at T0 and T1 by videostereophotogrammetry as 4D face scan. The magnitude, shape change, symmetry, and time of the facial movements were analysed. The motion changes were analysed in dependence of skeletal class and surgical movements.Results4D motion capture analysis was feasible in all cases. The magnitude of the expression maximum smile increased from 15.24 to 17.27 mm (p = 0.002), while that of the expression lip purse decreased from 9.34 to 8.31 mm (p = 0.01). Shape change, symmetry, and time of the facial movements did not differ significantly pre- and postsurgical. The changes in facial movements following orthodontic-surgical treatment were observed independently of skeletal class and surgical movements.ConclusionsOrthodontic-surgical treatment not only affects static soft tissue but also soft tissue dynamics while smiling or lip pursing.Clinical relevanceTo achieve comprehensive orthodontic treatment plans, the integration of facial dynamics via videostereophotogrammetry provides a promising approach in diagnostics.Trial registration numberDRKS00017206.

Engineering-Scale Integrated Energy System Data Projection Demonstration via the Dynamic Energy Transport and Integration Laboratory

The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.

Evolution of the Spatial Patterns of Global Egg Trading Networks in the 21 Century

Global agriculture and food system is faced with increasing uncertainty from natural disasters, epidemics, financial crises, and wars. Agricultural trade can provide a powerful supplement to global food security. Eggs have been recognized as the most important protein source for many years as they contain the highest quality protein naturally available while the price is competitive. Egg trading plays an important role in enhancing the resilience of the global food system under uncertainty. We empirically investigate the evolution of global egg trading networks by social network analysis. And, the quadratic assignment procedure (QAP) is applied to detect factors that impact global egg trading networks. The results show that in the 21st century, global egg trading networks are becoming more complex and the clusters are undergoing dynamic differentiation and integration. Additionally, compared to cultural difference among countries, factors including geographical distances, natural endowments, economic developments, trade policies, and political stability have a more significant effect on the evolution of egg trading networks. Our work provides suggestions for participating countries to develop more resilient egg trading networks to resist external shocks.

Integration of discrete-event dynamics and machining dynamics for machine tool: Modeling, analysis and algorithms

Machining dynamics research lays a solid foundation for machining operations by providing stable combinations of spindle speed and depth of cut. Furthermore, machine learning has been applied to predict tool life as a function of cutting speed. However, the existing research does not consider the discrete-event dynamics in machine shop, i.e., the machine tool needs to process a series of parts in queue under various practical production requirements. This paper addresses the integration of discrete-event dynamics and machining dynamics to achieve cost savings in machining. A learning-based cost function is first proposed for the studied integrated optimization problem of machine tool. The proposed cost function utilizes the predicted tool life under different stable cutting speeds for further optimizing speed selection of machine tool to deal with the discrete-event dynamics in machine shop. Then, according to the practical production requirements, effective mathematical optimization models are developed for the related integrated optimization problems with the consideration of cost, makespan and due date, respectively. Numerical results show the effectiveness of our proposed methods and also the potential to be used in practice.

Static and dynamic nonlinear behavior of a multistable structural system consisting of two coupled von Mises trusses

Recent years have witnessed a growing interest in multistable structures. In most cases it is attained by a sequence of bistable elements. However, little is known on their nonlinear static and dynamic responses. In this work a detailed static and dynamic nonlinear analysis of a multistable structural system consisting of two coupled bistable von Mises trusses is conducted to enlighten the complex behavior of these systems. For this, the exact nonlinear equilibrium equations and equations of motion in their dimensionless forms are obtained through the principle of stationary potential energy and Hamilton’s principle, respectively, considering a linear elastic material. Using continuation algorithms, the nonlinear equilibrium paths are obtained. The stability of each configuration is analyzed using the principle of minimum potential energy. Multiple equilibrium paths of the coupled system are identified, leading to several coexisting stable and unstable solutions, bifurcations and potential wells which are closely connected to the symmetries of the system. It is shown that the number of coexisting solutions increases exponentially with the number of bistable units. The effect of unavoidable imperfections is also clarified. The nonlinear dynamics and bifurcations of the system under harmonic forcing and static pre-load are then studied. Bifurcation diagrams, Poincaré maps and cross-sections of the basins of attraction are used to study the influence of coexisting attractors due to multiple potential wells, resonances and bifurcations. The coexisting responses can merge giving rise to several types of cross-well motions, the threshold value being closely connected with the limit point load of the coupled system. The increase in coexisting solutions leads to increasingly complex basins of attraction with broad fractal regions. On the one hand, the complexity of the bifurcation scenarios seems valuable in several applications to encode information, to respond to external forcing and to switch between different states. On the other hand, multiple attractors and their fractal basins can lead to the loss of global stability and dynamic integrity due to the small uncorrupted basins surrounding each attractor. Thus, the knowledge of the static and dynamic behavior of multistable systems is of great significance to either inducing or avoiding this phenomenon or making use of it.

Dynamic viral integration patterns actively participate in the progression of BK polyomavirus-associated diseases after renal transplantation

The classical lytic infection theory along with large T antigen-mediated oncogenesis cannot explain the BK polyomavirus (BKPyV)-associated tumor secondary to BKPyV-associated nephropathy (BKVAN), viremia/DNAemia, and viruria after renal transplantation. This study performed virome capture sequencing and pathological examination on regularly collected urine sediment and peripheral blood samples, and BKVAN and tumor biopsy tissues of 20 patients with BKPyV-associated diseases of different stages. In the early noncancerous stages, well-amplified integration sites were visualized by in situ polymerase chain reaction, simultaneously with BKPyV inclusion bodies and capsid protein expression. The integration intensity, the proportion of microhomology-mediated end-joining integration, and host PARP-1 and POLQ gene expression levels increased with disease progression. Furthermore, multiomics analysis was performed on BKPyV-associated urothelial carcinoma tissues, identifying tandem-like structures of BKPyV integration using long-read genome sequencing. The carcinogenicity of BKPyV integration was proven to disturb host gene expression and increase viral oncoprotein expression. Fallible DNA double-strand break repair pathways were significantly activated in the parenchyma of BKPyV-associated tumors. Olaparib showed an antitumor activity dose-response effect in the tumor organoids without BRCA1/2 genes mutation. In conclusion, the dynamic viral integration patterns actively participate in the progression of BKPyV-associated diseases and thus could be a potential target for disease monitoring and intervention.

Evaluation and development strategy of urban-rural integration under ecological protection in the Yellow River Basin, China.

The problem of imbalanced urban-rural development in China is becoming increasingly serious. Urban-rural integration (URI) is an inevitable way to narrow the urban-rural gap and promote rural development. The Yellow River Basin (YRB) is an important barrier to China's ecological security and plays a crucial role in promoting ecological protection and in high-quality development strategies. Therefore, this article constructs an evaluation index system for URI from five dimensions: economic integration, social integration, ecological integration, element integration, and spatial integration. The simulated annealing-projection pursuit model and time degree are used to analyze the dynamic urban-rural integration level (URIL) of 373 counties from 2000 to 2019. Building upon this foundation, this study classifies four types of URI zones and proposes development strategies tailored to each zone. The results indicate (1) that the URIL in the YRB continues to increase, and that there has been a significant increase in high-level integration areas since 2010. (2) The URIL exhibits a distinct spatial clustering pattern, characterized by lower levels in the upper reaches, and higher levels in the middle and lower reaches. (3) There exists a spatial disparity between economic development and the ecological environment, and it is particularly noticeable in the lower reaches regions. These results contribute to a better understanding of URI in the YRB and provide a reference for the sustainable development of URI in various regions.

Probability flow solution of the Fokker–Planck equation

The method of choice for integrating the time-dependent Fokker–Planck equation (FPE) in high-dimension is to generate samples from the solution via integration of the associated stochastic differential equation (SDE). Here, we study an alternative scheme based on integrating an ordinary differential equation that describes the flow of probability. Acting as a transport map, this equation deterministically pushes samples from the initial density onto samples from the solution at any later time. Unlike integration of the stochastic dynamics, the method has the advantage of giving direct access to quantities that are challenging to estimate from trajectories alone, such as the probability current, the density itself, and its entropy. The probability flow equation depends on the gradient of the logarithm of the solution (its ‘score’), and so is a-priori unknown. To resolve this dependence, we model the score with a deep neural network that is learned on-the-fly by propagating a set of samples according to the instantaneous probability current. We show theoretically that the proposed approach controls the Kullback–Leibler (KL) divergence from the learned solution to the target, while learning on external samples from the SDE does not control either direction of the KL divergence. Empirically, we consider several high-dimensional FPEs from the physics of interacting particle systems. We find that the method accurately matches analytical solutions when they are available as well as moments computed via Monte-Carlo when they are not. Moreover, the method offers compelling predictions for the global entropy production rate that out-perform those obtained from learning on stochastic trajectories, and can effectively capture non-equilibrium steady-state probability currents over long time intervals.

Linear Kalman Filtering with Persistence Predictive Model for Vehicle Slip Angle Estimation Based on Inverse Trigonometric Function

Slip angle estimation is extensively employed in mobile robotics. Specifically, the estimation method predicated on the acceleration sensor can compute the slip angle without the necessity for a dynamical model and integration calculations. However, wave distortion of the estimated slip angle emerges due to the inclusion of the inverse trigonometric function in the estimation computation. In this study, we utilized a linear Kalman filter predicated on a persistent predictive model for slip angle estimation. The proposed method modulates the Kalman gain according to the Mahalanobis distance, thereby eliminating the wave distortion of the estimated slip angle. The efficacy of the proposed method is corroborated by a nonlinear vehicle simulation. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Placement of Multiple Virtual Objects in Physical Space in Augmented Reality Applications

Introduction. The challenges of placing virtual objects in a real-world environment limit the potential of augmented reality (AR) technology. This situation identifies a gap in scientific knowledge that requires additional research. Therefore, the main task of this study was to develop a method for optimal placement of virtual objects, in which the objective function of comfort was minimized. This approach is aimed at improving AR systems and developing the corresponding theory.Materials and Methods. The conducted research was based on the analysis of the placement of virtual objects in AR/VR applications with particular emphasis on optimization. The concept of comfort of placement was proposed, taking into account the size of the object and the distance to the boundaries of free space in X, Y, Z coordinates.Results. As part of the study, formulas were obtained for the optimal placement of objects with an arbitrary comfort function. The basic criterion was to minimize the difference between comfort levels from different sides of the object. It was found that a successful placement of objects required taking into account their size and comfort zones, as well as solving a system of n linear equations.Discussion and Conclusion. The results obtained make an important contribution to the study of the problem of placing virtual objects in AR/VR/MR. They open up new opportunities for improving user interaction and conducting further research in the field of spatial computing. Possible directions for further development are dynamic adjustments and integration of the results into various XR scenarios.

Augmented Reality and 3-Dimensional Dynamic Navigation System Integration for Osteotomy and Root-end Resection

IntroductionAugmented reality (AR) superimposes high-definition computer-generated virtual content onto the existing environment, providing users with an enhanced perception of reality. This study investigates the feasibility of integrating an AR head-mounted device into a 3-dimensional dynamic navigation system (3D-DNS) for osteotomy and root-end resection (RER). It compares the accuracy and efficiency of AR + 3D-DNS to 3D-DNS for osteotomy and RER. MethodsSeventy-two tooth roots of 3D-printed surgical jaw models were divided into two groups: AR + 3D-DNS (n = 36) and 3D-DNS (n = 36). Cone-beam computed tomography scans were taken pre and postoperatively. The osteotomy and RER were virtually planned on X-guide software and delivered under 3D-DNS guidance. For the AR + 3D-DNS group, an AR head-mounted device (Microsoft HoloLens 2) was integrated into the 3D-DNS. The 2D- and 3D-deviations were calculated. The osteotomy and RER time and the number of procedural mishaps were recorded. ResultsOsteotomy and RER were completed in all samples (72/72). AR + 3D-DNS was more accurate than 3D-DNS, showing lower 2D- and 3D-deviation values (P < .05). The AR + 3D-DNS was more efficient in time than 3D-DNS (P < .05). There was no significant difference in the number of mishaps (P > .05). ConclusionsWithin the limitations of this in vitro study, the integration of an AR head-mounted device to 3D-DNS is feasible for osteotomy and RER. AR improved the accuracy and time efficiency of 3D-DNS in osteotomy and RER. Head-mounted AR has the potential to be safely and reliably integrated into 3D-DNS for endodontic microsurgery.

Dual disentanglement of user–item interaction for recommendation with causal embedding

To achieve personalized recommendations, the recommender system selects the items that users may like by learning the collected user–item interaction data. However, the acquisition and use of data usually form a feedback loop, which leads to recommender systems suffering from popularity bias. To solve this problem, we propose a novel dual disentanglement of user–item interaction for recommendation with causal embedding (DDCE). Different from the existing work, our innovation is we take into account double-end popularity bias from the user-side and the item-side. Firstly, we perform a causal analysis of the reasons for user–item interaction and obtain the causal embedding representation of each part according to the analysis results. Secondly, on the item-side, we consider the influence of item attributes on popularity to improve the reliability of the item popularity. Then, on the user-side, we consider the effect of the time series when obtaining users’ interest. We model the contrastive learning task to disentangle users’ long–short-term interests, which avoids the bias of long–short-term interests overlapping, and use the attention mechanism to realize the dynamic integration of users’ long–short-term interests. Finally, we realize the disentanglement of user–item interaction reasons by decoupling user interest and item popularity. We experiment on two real-world datasets (Douban Movie and KuaiRec) to verify the significance of DDCE, the average improvement of DDCE in three evaluation metrics (NDCG, HR, and Recall) compared to the state-of-the-art model are 5.1106% and 4.1277% (MF as the backbone), 3.8256% and 3.2790% (LightGCN as the backbone), respectively.

Deconstructing Organizational Capabilities of Megaproject Owners: Dimensions and Levels

Given that a strong owner has been identified as an important factor in the success of megaprojects, there has been a growing recognition of the importance of megaproject owner organizational capabilities. However, the questions of what organizational capabilities do owners need to foster successful megaprojects, and whether these capabilities are the stronger the better, have yet to be addressed. To answer these questions, this study identified the dimensions of owner organizational capabilities based on literature and interviews, and developed several sets of parallel hypotheses. Based on the 229 valid questionnaire data collected, the relationships between owner organizational capabilities and megaproject success were analyzed through hierarchical multiple regression, and the results were discussed through expert interviews. The findings show that owner coordination, dynamic, and system integration capabilities have positive impacts on megaproject success, with coordination capabilities being the most significant. While the impact of motivational capabilities on megaproject success is inverted U-shaped. This study provides an actionable dimensional framework for megaproject owner capabilities, moving the field of owner organizational capabilities beyond the conceptual level. This study provides empirical evidence for the importance of megaproject owner organizational capabilities and takes a more dialectical view of a strong owner. The empirical results can provide insights and guidance on the configuration and development of megaproject owner capabilities.

Dynamic interactions between anterior insula and anterior cingulate cortex link perceptual features and heart rate variability during movie viewing.

The dynamic integration of sensory and bodily signals is central to adaptive behaviour. Although the anterior cingulate cortex (ACC) and the anterior insular cortex (AIC) play key roles in this process, their context-dependent dynamic interactions remain unclear. Here, we studied the spectral features and interplay of these two brain regions using high-fidelity intracranial-EEG recordings from five patients (ACC: 13 contacts, AIC: 14 contacts) acquired during movie viewing with validation analyses performed on an independent resting intracranial-EEG dataset. ACC and AIC both showed a power peak and positive functional connectivity in the gamma (30-35 Hz) frequency while this power peak was absent in the resting data. We then used a neurobiologically informed computational model investigating dynamic effective connectivity asking how it linked to the movie's perceptual (visual, audio) features and the viewer's heart rate variability (HRV). Exteroceptive features related to effective connectivity of ACC highlighting its crucial role in processing ongoing sensory information. AIC connectivity was related to HRV and audio emphasising its core role in dynamically linking sensory and bodily signals. Our findings provide new evidence for complementary, yet dissociable, roles of neural dynamics between the ACC and the AIC in supporting brain-body interactions during an emotional experience.

Fish feces reveal diverse nutrient sources for coral reefs.

Consumers mediate nutrient cycling through excretion and egestion across most ecosystems. In nutrient-poor tropical waters such as coral reefs, nutrient cycling is critical for maintaining productivity. While the cycling of fish-derived inorganic nutrients via excretion has been extensively investigated, the role of egestion for nutrient cycling has remained poorly explored. We sampled the fecal contents of 570 individual fishes across 40 species, representing six dominant trophic guilds of coral reef fishes in Moorea, French Polynesia. We measured fecal macro- (proteins, carbohydrates, lipids) and micro- (calcium, copper, iron, magnesium, manganese, zinc) nutrients and compared the fecal nutrient quantity and quality across trophic guilds, taxa, and body size. Macro- and micronutrient concentrations in fish feces varied markedly across species. Genera and trophic guild best predicted fecal nutrient concentrations. In addition, nutrient composition in feces was unique among species within both trophic guilds (herbivores and corallivores) and genera (Acanthurus and Chaetodon). Particularly, certain coral reef fishes (e.g., Thalassoma hardwicke, Chromis xanthura, Chaetodon pelewensis and Acanthurus pyroferus) harbored relatively high concentrations of micronutrients (e.g., Mn, Mg, Zn and Fe, respectively) that are known to contribute to ocean productivity and positively impact coral physiological performances. Given the nutrient-rich profiles across reef fish feces, conserving holistic reef fish communities ensures the availability of nutritional pools on coral reefs. We therefore suggest that better integration of consumer egestion dynamics into food web models and ecosystem-scale processes will facilitate an improved understanding of coral reef functioning.

Direct multiple shooting and direct collocation perform similarly in biomechanical predictive simulations

Direct multiple shooting (DMS) and direct collocation (DC) are two standard transcription methods for solving optimal control problems (OCP) in biomechanics and robotics. They have yet to be compared in terms of solution and speed. Through five examples of predictive simulations solved using five transcription methods and 100 initial guesses in the Bioptim software, we showed that no single method outperformed the others systematically. All methods converged to almost the same solution (cost, states, and controls) in all but one OCP, with several local minima found in the latter. Nevertheless, DC based on fourth-order Legendre polynomials provided overall better results, especially regarding dynamics integration accuracy compared to DMS based on a fourth-order Runge–Kutta method. Furthermore, expressing the rigid-body constraints using inverse dynamics was usually faster than forward dynamics. DC with dynamic constraints based on inverse dynamics converged to better and less variable solutions. We recommend starting with this transcription to solve OCPs but keep testing other methods.

On the use of an high order perturbation method for numerical time integration in structural dynamics.

On the use of an high order perturbation method for numerical time integration in structural dynamics.