Variable Trajectories Research Articles

Surface area reduction during the sintering of alumina

AbstractSurface area reduction trajectories of two well characterized calcined alumina powders were analyzed over a range of thermal profiles. Surface area reduction trajectories were confirmed to be independent of initial green density. Variations in trajectory were explained through experimental demonstrations of blended coarse and fine particle alumina systems. This work demonstrates that (1) nanoparticles do not appear to assist conventional sintering, because the nanoparticle surface area drops precipitously with minimal increase in density; and (2) the initial compact density does not contribute, nor control, surface area reduction during alumina sintering.

Analysis of the drag anchor behaviour at shallow depths

Understanding the effects of shallow behaviour is necessary for accurate kinematic predictions of drag anchors since original methods are mostly based only on deep failure mechanisms. Therefore, shallow flow channel-soil interactions characterized by varying tangential and normal bearing factors are used in plasticity analysis (PA) to capture the distinct shallow behaviour of the anchor. To provide a comparison, using the coupled Eulerian-Lagrangian (CEL) finite element method combined with the VUAMP subroutine to incorporate the mooring chain effect, the embedment of the drag anchor under the tension of the mooring chain is simulated. With a constant soil strength profile, the CEL results on the variation of drag force, rotation angle, and anchor trajectory agree well with calculations of PA that are mature for deeply embedded anchors. However, for shallow depths, similar results to the CEL can only be gained from PA considering shallow fluke-soil interactions. The effects of soil strength flow during installation, soil stability, soil sensitivity and fluke width are also investigated.

System-Information and Cognitive Technologies of Man-Made Infrastructure Cyber Security

The complication of technological processes due to the modernization of complex technological processes aggregates with a distributed spatial infrastructure requires the use of new control systems and computer and information network technologies. Accordingly, this poses the problem of revising the basic concepts of information-measuring systems building, developing software and hardware for the implementation of the target management process. This requires development of new approaches to presentation, processing, and display of data about the aggregates state of energy-active objects and the entire information structure. For management under threat conditions, it is necessary to take into account the features of the information infrastructure, data selection and processing methods, methods and algorithms for classifying the situation, which are formed from blocks of data obtained from each unit and the technological process as a whole. Information about the current state of system and infrastructure is necessary for the formation, adoption and implementation of management decisions which is the basis for coordination strategies synthesis. Appropriate target orientation, reasonable indicators of real process trajectories divergence from the target state determine the probability of object attack. Knowledge and decision-making procedures for the coordination of managerial actions is based on the strategic target orientation of the structure, their professionalism and the level of intellectual, cognitive and scientific training which is the basis for correct situation interpretation of countermeasures against threats formation. In the event of active threats complex on man-made systems in a certain region (resource, cognitive, system, information) and natural disasters or military operations, the threats lead to active destruction or failure of the production process. In order to functionally withstand related production structures, when loyal to the industrial relations concept, they need to integrate at the strategic management level on common goal basis to reduce risks. If necessary, to increase sustainability, they can be integrated at operational management level using interconnections at the production and resource levels. To do this, it is necessary to develop a strategic and goal-oriented management system behaviour line, which requires informational and intelligent data processing at the highest level using cognitive creative methods. For each level of the infrastructure hierarchy, oriented towards strategic goals in the global infrastructure dynamic environment, methods of assessing the situation to detect failures and the actions of attacks have been developed, based on which countermeasures are formed depending on the type of threats.

Hurricanes affect diversification among individual life courses of a primate population.

Extreme climatic events may influence individual-level variability in phenotypes, survival and reproduction, and thereby drive the pace of evolution. Climate models predict increases in the frequency of intense hurricanes, but no study has measured their impact on individual life courses within animal populations. We used 45 years of demographic data of rhesus macaques to quantify the influence of major hurricanes on reproductive life courses using multiple metrics of dynamic heterogeneity accounting for life course variability and life-history trait variances. To reduce intraspecific competition, individuals may explore new reproductive stages during years of major hurricanes, resulting in higher temporal variation in reproductive trajectories. Alternatively, individuals may opt for a single optimal life-history strategy due to trade-offs between survival and reproduction. Our results show that heterogeneity in reproductive life courses increased by 4% during years of major hurricanes, despite a 2% reduction in the asymptotic growth rate due to an average decrease in mean fertility and survival by that is, shortened life courses and reduced reproductive output. In agreement with this, the population is expected to achieve stable population dynamics faster after being perturbed by a hurricane ( ; 95% CI: 1.488, 1.538), relative to ordinary years . Our work suggests that natural disasters force individuals into new demographic roles to potentially reduce competition during unfavourable environments where mean reproduction and survival are compromised. Variance in lifetime reproductive success and longevity are differently affected by hurricanes, and such variability is mostly driven by survival.

A toolbox to explore the composition of species dynamics behind multi‐species indices

Abstract In the light of declining biodiversity, monitoring its fate is essential for conservation strategies. Aggregation of temporal change of different species into multi‐species indices such as geometric means makes it possible to identify species groups that are at risk as well as those that are doing well. However, aggregated indices mask the between‐species variability in the temporal trajectories, which could be of high relevance for conservation actions. We propose a toolbox, available as an r package, to investigate compositions of species dynamics in geometric mean multi‐species indices. The toolbox is based on a dynamic factor analysis which uses species dynamics and their uncertainty to (1) identify common latent trends in those species dynamics, (2) display the variability of species dynamics and (3) extract clusters of species with similar dynamics within the species groups used for the indices. We apply the toolbox to common breeding birds in Sweden and explore the variability in dynamics among species included in EU‐official indices for farmland and woodland species, highlighting clusters of species with related dynamics previously hidden by averaging. The toolbox is designed to be applicable to a wide range of ecological monitoring data. By enabling a deeper exploration of the structure behind existing indices, we may refine our understanding of biodiversity change to better inform subsequent conservation policies.

Archaeological Research in the Canary Islands: Island Archaeology off Africa’s Atlantic Coast

Island archaeology is a well-established field within the wider discipline, but African contributions to it remain scarce. The Canary Islands are unusual in the broader African context for their relatively long history of occupation (~2000 years) and the intensity with which archaeological research has been, and is, undertaken there. Much of that research, however, has focused on specifically Canarian issues, including efforts to demonstrate connections between the islands’ initial settlement and the Classical Mediterranean world. Relatively little of it has been conducted within the broader comparative framework that an island archaeology perspective provides. Additionally, much of the Canarian literature is not directly accessible to non-Hispanophones. In response, I synthesize what is currently known about the archaeology of the Canary Islands, focusing on determining when, how, and by whom they were first settled; the impacts of human settlement on their environments; inter-island variability in precolonial subsistence, social, and political trajectories; and the record left by European contact and subsequent colonization, which began in the 14th century AD. As well as pointing to further opportunities for research within the archipelago, I simultaneously map out several areas where archaeological work there could contribute to wider debates in island archaeology as a whole.

Variations of labour aristocracy and union trajectories across South Asia

Variations of labour aristocracy and union trajectories across South Asia

What drives greenhouse gas emissions? An international scoping review of academic studies in 2010–2019

AbstractGreenhouse gas (GHG) emissions have increased globally 10% in the last decade, but there is a large variation in emissions trajectories by country. Understanding the main drivers of recent changes in GHG emissions is important to guide effective climate action. Using a narrative scoping review of academic literature, we access 648 abstracts and review 30 studies to identify statistically significant independent variables that were associated with GHG emissions nationally and multinationally (i.e., in country groupings) during or overlapping the period 2010–2019. We describe the findings in terms of potential reasons for the positive or negative associations, outline the strength of associations relative to other variables within the same study, and compare the associations to findings in other studies. We find that population, energy consumption, and gross domestic product (GDP) per capita are the most common independent variables associated with increases in GHG emissions, whereas the square of GDP per capita and renewable energy production are associated with GHG reductions. We assign GHG drivers to seven categories: economic, energy, demographic, technology innovation, transportation, policy, and others. We conclude by discussing implications for future research and climate policy.

Effects of oil market sentiment on macroeconomic variables

This paper aims to evaluate the effects of oil price shocks on macroeconomic variables, for the economies of the United States and Brazil. We develop a variable that measures the volatility of oil prices, from a textual sentiment analysis. We evaluate oil price shocks using the Local Projection method. Our results suggest that changes in oil prices cause larger impacts on the US economy, compared to the effects on the Brazilian economy. The responses of the US and Brazilian variables were similar when using the sentiment indicator or the VIX volatility index. Finally, we find that decreasing the frequency of the variables, together with changing the method, does not change the response trajectories of the macroeconomic variables.

The influence of foreperiod duration on the preparation and control of sequential aiming movements.

Reaction time (RT) and movement times (MTs) to the first target are typically longer for two-target sequential movements compared to one-target movements. While this one-target advantage has been shown to be dependent on the availability of advance information about the numbers of targets, there has been no systematic investigation of how foreperiod duration (i.e., interval between presentation of the target(s) and stimulus) influences the planning and execution of sequential movements. Two experiments were performed to examine how the one-target advantage is influenced by the availability and timing of advance target information. In Experiment 1, participants performed one- and two-target movements in two separate blocks. In Experiment 2, target conditions were randomised from trial to trial. The interval between target(s) appearing and stimulus tone (i.e., foreperiod) was varied randomly (0, 500, 1,000, 1,500, and 2,000 ms). The results of Experiment 1 revealed that while the one-target advantage in RT was not influenced by foreperiod duration, the one-target advantage in MT increased as foreperiod duration increased. The variability of endpoints at the first target was greater in the two- compared to one-target condition. In Experiment 2, the one-target advantage in both RT and MT increased as the length of the foreperiod increased. However, there was no difference in limb trajectory variability between target conditions. The implication of these findings for theories of motor planning and execution of multiple segment movements is discussed.

Energy efficient robots based on structures with tensegrity features and cable-driven mechanisms

The paper presents concept of energy efficient motion control of robots and other machines based on structures with tensegrity features and/or cable-driven mechanisms. The essence of concept is generalization of so called eigenmotion idea for these multi-DOF complex mechanisms. The term eigenmotion here refers to a motion of a mechanism in which the constant sum of kinetic and potential energy is maximally preserved. The operation of the drives is ideally used only to eliminate passive resistances and to minimize deviations of motion from the required trajectory. The main advantage of mechanisms with tensegrity features and cable-driven ones is a relatively high number of elements such as springs, active cables or variable bodies, whose energy absorbing properties can be suitably adjusted during design and some also continuously during operation. The variability of attainable eigenmotion trajectories of these types of mechanisms can be further extended thanks to number of drives higher than number of end-effector degrees of freedom. The concept is demonstrated on two planar systems, one structure with tensegrity features and one serial–parallel cable-driven robot. The examples show the optimization of the parameters to achieve the eigenmotion properties on the given trajectories, as well as the change of the eigenmotion trajectory by changing the adjustable parameters. The final control of mechanical models along energy efficient trajectories is realized by computed torque control method.

Variation in ontogenetic trajectories of limb dimensions in humans is attributable to both climatic effects and neutral evolution

Previous studies showed that there is variation in ontogenetic trajectories of human limb dimensions and proportions. However, little is known about the evolutionary significance of this variation. This study used a global sample of modern human immature long bone measurements and a multivariate linear mixed-effects model to study 1) whether the variation in ontogenetic trajectories of limb dimensions is consistent with ecogeographic predictions and 2) the effects of different evolutionary forces on the variation in ontogenetic trajectories. We found that genetic relatedness arising from neutral (nonselective) evolution, allometric variation associated with the change in size, and directional effects from climate all contributed to the variation in ontogenetic trajectories of all major long bone dimensions in modern humans. After accounting for the effects of neutral evolution and holding other effects considered in the current study constant, extreme temperatures have weak, positive associations with diaphyseal length and breadth measurements, while mean temperature shows negative associations with diaphyseal dimensions. The association with extreme temperatures fits the expectations of ecogeographic rules, while the association with mean temperature may explain the observed among-group variation in intralimb indices. The association with climate is present throughout ontogeny, suggesting an explanation of adaptation by natural selection as the most likely cause. On the other hand, genetic relatedness among groups, as structured by neutral evolutionary factors, is an important consideration when interpreting skeletal morphology, even for nonadult individuals.

Stress Trajectory Variations During Occlusal Loading in Human Skull with a Maxillofacial Defect: A Finite Element Analysis.

Biting forces from the teeth are distributed to the facial bones and to the skull through the stress trajectories. The presence of a bony defect in either the maxilla or mandible might lead to variations in the stress distribution. The aim of this study was to evaluate the stress distribution and variations in stress trajectories from biting forces in a human skull with maxillofacial defect using a finite element (FE) model. In this study, a cone beam computed tomography (CBCT) of an adult male patient with a maxillectomy defect consequent to surgical treatment of mucormycosis is evaluated for the stress distribution from the biting forces of the remaining posterior teeth. Finite element model without the mandible was constructed from the patient's CBCT data. Occlusal loading forces of 600N were applied on each side of the maxillary teeth. Stress trajectories were visualised through the stress distribution pattern. The results showed deviations in the normal stress distribution during occlusal loading and variations in zygomatic and pterygoid stress trajectories in the maxillofacial and skull regions in our FE model due to the bony defect. We conclude that a skeletal maxillofacial defect should be reconstructed to resume proper stress distribution during functional forces to maintain a healthy craniofacial skeleton.

Slope synchronous chirplet transform and its application to tacho-less order tracking of rotating machineries

Chirplet transform (CT) has been widely used in time–frequency analysis (TFA) of rotating machinery vibration signals. However, due to the smear effect and time–frequency resolution limitation, CT and most of its improved algorithms cannot achieve sufficient refined time–frequency representation (TFR) for the instantaneous frequency (IF) close-spaced and IF trajectory slope mutation signals. Aiming at the aforementioned shortcomings, a novel TFA method named slope synchronous chirplet transform (SSCT) is proposed to address this problem. The proposed SSCT algorithm can adaptively adjust the window lengths according to the slope variation of the IF trajectory, thereby higher time–frequency resolution can be obtained for the IF close-spaced components. Besides, the proposed method utilizes a high-order phase operator to accurately capture the trend of IF trajectory slope, so that the time–frequency (TF) energy of the IF trajectory can be better concentrated, thereby the weak TF energy of the IF trajectory can be uncovered. The effectiveness of the proposed method is verified by simulated signals and an aero-engine vibration signal measured under a transient operating mode, the validation results indicate the proposed method outperforms some latest reported TFA algorithms and is available for accurate estimation of instantaneous angular speed for tacho-less order tracking of rotating machineries.

Influence of two-dimensional expiratory airflow variations on respiratory particle propagation during pronunciation of the fricative [f

Propagation of respiratory particles, potentially containing viable viruses, plays a significant role in the transmission of respiratory diseases (e.g., COVID-19) from infected people. Particles are produced in the upper respiratory system and exit the mouth during expiratory events such as sneezing, coughing, talking, and singing. The importance of considering speaking and singing as vectors of particle transmission has been recognized by researchers. Recently, in a companion paper, dynamics of expiratory flow during fricative utterances were explored, and significant variations of airflow jet trajectories were reported. This study focuses on respiratory particle propagation during fricative productions and the effect of airflow variations on particle transport and dispersion as a function of particle size. The commercial ANSYS-Fluent computational fluid dynamics (CFD) software was employed to quantify the fluid flow and particle dispersion from a two-dimensional mouth model of sustained fricative [f] utterance as well as a horizontal jet flow model. The fluid velocity field and particle distributions estimated from the mouth model were compared with those of the horizontal jet flow model. The significant effects of the airflow jet trajectory variations on the pattern of particle transport and dispersion during fricative utterances were studied. Distinct differences between the estimations of the horizontal jet model for particle propagation with those of the mouth model were observed. The importance of considering the vocal tract geometry and the failure of a horizontal jet model to properly estimate the expiratory airflow and respiratory particle propagation during the production of fricative utterances were emphasized.

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

AbstractThe trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.

Longitudinal Canonical Correlation Analysis.

This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis (LCCA) effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.

A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization

Discovering the underlying dynamics of complex systems from data is an important practical topic. Constrained optimization algorithms are widely utilized and lead to many successes. Yet, such purely data-driven methods may bring about incorrect physics in the presence of random noise and cannot easily handle the situation with incomplete data. In this paper, a new iterative learning algorithm for complex turbulent systems with partial observations is developed that alternates between identifying model structures, recovering unobserved variables, and estimating parameters. First, a causality-based learning approach is utilized for the sparse identification of model structures, which takes into account certain physics knowledge that is pre-learned from data. It has unique advantages in coping with indirect coupling between features and is robust to stochastic noise. A practical algorithm is designed to facilitate causal inference for high-dimensional systems. Next, a systematic nonlinear stochastic parameterization is built to characterize the time evolution of the unobserved variables. Closed analytic formula via efficient nonlinear data assimilation is exploited to sample the trajectories of the unobserved variables, which are then treated as synthetic observations to advance a rapid parameter estimation. Furthermore, the localization of the state variable dependence and the physics constraints are incorporated into the learning procedure. This mitigates the curse of dimensionality and prevents the finite time blow-up issue. Numerical experiments show that the new algorithm identifies the model structure and provides suitable stochastic parameterizations for many complex nonlinear systems with chaotic dynamics, spatiotemporal multiscale structures, intermittency, and extreme events.

A dynamic model of tablet film coating processes for control system design

Aqueous film coating is a common and important step in the production of most pharmaceutical tablets. Controlling this process is beneficial to maintain stable and optimal operation and the use of a model capable of describing its main physical phenomena is fundamental for control system design. This paper presents the development of a dynamic macro scale model of a pan coater based on energy and mass transfer equations. Model complexity is kept low to simplify parameterization and allow for real-time applications, such as the development of system observers to estimate critical unmeasured variables. Pilot plant data from seven batches, ran according to designed experiments, are used for model calibration and validation. The final model adequately captures the global trajectories of the main process variables; mean differences between experimental and simulated values are below 2 °C for the outlet gas and tablet bed temperatures, and 2% for the outlet gas relative humidity.

5613510 EFFECTS OF REGIONAL BRAIN VOLUME ON COGNITION IN SICKLE CELL ANAEMIA: A DEVELOPMENTAL PERSPECTIVE

Cognitive decline, related at least in part to reduced processing speed and lower working memory, is a major problem in paediatric and adult patients with sickle cell anaemia (SCA)1,2 and affects quality of life. Multiple studies investigating the association between quantitative and qualitative neuroimaging findings and cognition have had mixed results. Hence, the aetiology of cognitive decline in this population is not clearly understood. Cerebral atrophy on magnetic resonance imaging is noted in the literature but few studies have investigated the association between brain volumes and cognition. White matter microstructural integrity and white matter hyperintensities have been associated with reduced processing speed index (PSI).3 However no study has investigated the association between white matter volumes and PSI in the SCA population. Total subcortical volumes have also been noted to be lower in SCA children and adults. Only one study has investigated the association between total subcortical volumes and working memory index (WMI) in adults.4 Moreover, several studies in the past have noted age related decline in total brain volume as well neurodevelopmental delay in grey matter volume growth.5,6 Most of the developmental trajectory data in the literature comes from longitudinal data sets with limited samples and without control groups. Hence, for the purpose of this study, we investigated the association between regional brain volumes (white matter volumes and total subcortical volumes) and cognition (processing speed index and working memory index) in 129 SCA patients aged 8 – 64 years (66 male) and 50 controls (21 males). Participants were enrolled in two studies - the Sleep Asthma Cohort (SAC) and the Prevention of Morbidity in SCA (POMS) trial. We hypothesized that white matter volumes would predict processing speed index and total subcortical volumes would predict working memory index in SCA patients as well as controls. We also hypothesized that the developmental trajectories for regional brain volumes and cognitive variables would differ between patients and controls. Separate multiple linear regression models for males and females revealed that WMV predicted processing speed index(p=.019) and total subcortical volumes predicted working memory index (p=.025) in male patients only. Age (p=.003) and socioeconomic status (p=.03) also independently predicted PSI in male patients. Individual subcortical regions predicting WMI were statistically significant for left and right thalamus only in male patients. Haemoglobin and age emerged as the best predictors of cognitive decline in patients. Comparison of developmental trajectories between patients and controls revealed that only processing speed was significantly delayed at 8 years. The rate of development for all variables was lower in SCA patients and age-related decline was accelerated in patients as compared to controls. We speculate that early cerebral haemodynamic compromise may contribute to long term cognitive decline in patients. We also highlight that male SCA patients are particularly vulnerable to SCA disease severity. Hence, timely intervention to stabilise haemoglobin levels, especially in males, should be considered. Limitations include lack of stratification by SCI and age (adults and children separately) and the possibility that we were underpowered to detect effects in females.