Adaptive Integration Research Articles

A New Control Strategy for Greenhouse Environment Control System Based on Inverse Model

The greenhouse environment control system is a type of non-linear system since the temperature and humidity of the system are highly coupled. Besides, the time lag of the temperature and humidity control process is large, so it’s quite difficult to linearize and decouple the temperature and humidity of the system. To cope with this issue, this paper proposed a novel control strategy for greenhouse environment control system based on Back Propagation Neural Network (BPNN) and inverse model, the proposed method can perform inverse identification on the temperature and humidity control system to attain higher accuracy. Then, the inverse model and the original system were connected in series to form a pseudo linear system to realize the decoupled control of temperature and humidity. After that, aiming at the impact of some non-linear factors on the greenhouse environment system, this paper adopted the adaptive fuzzy Proportion Integration Differentiation (PID) controller to enhance the adaptability of the system, thereby reducing control error and the interference caused by non-linear factors of the temperature and humidity control system. At last, the experimental results showed that, the temperature error of the system could be controlled within 1.2℃ and the error of relative humidity was less than 2.5%. The proposed method can improve the control effect of the greenhouse environment to a certain extent, and it provides a novel approach of greenhouse control.

Lamellar carbon nitride membrane for enhanced ion sieving and water desalination

Membrane-based water treatment processes offer possibility to alleviate the water scarcity dilemma in energy-efficient and sustainable ways, this has been exemplified in filtration membranes assembled from two-dimensional (2D) materials for water desalination purposes. Most representatives however tend to swell or disintegrate in a hydrated state, making precise ionic or molecular sieving a tough challenge. Here we report that the chemically robust 2D carbon nitride can be activated using aluminum polycations as pillars to modulate the interlayer spacing of the conjugated framework, the noncovalent interaction concomitantly affords a well-interlinked lamellar structure, to be carefully distinguished from random stacking patterns in conventional carbon nitride membranes. The conformally packed membrane is characterized by adaptive subnanochannel and structure integrity to allow excellent swelling resistance, and breaks permeability-selectivity trade-off limit in forward osmosis due to progressively regulated transport passage, achieving high salt rejection (>99.5%) and water flux (6 L m−2 h−1), along with tunable permeation behavior that enables water gating in acidic and alkaline environments. These findings position carbon nitride a rising building block to functionally expand the 2D membrane library for applications in water desalination and purification scenarios.

AdaFI-FCN: an adaptive feature integration fully convolutional network for predicting driver’s visual attention

ABSTRACT Visual Attention Prediction (VAP) is widely applied in GIS research, such as navigation task identification and driver assistance systems. Previous studies commonly took color information to detect the visual saliency of natural scene images. However, these studies rarely considered adaptively feature integration to different geospatial scenes in specific tasks. To better predict visual attention while driving tasks, in this paper, we firstly propose an Adaptive Feature Integration Fully Convolutional Network (AdaFI-FCN) using Scene-Adaptive Weights (SAW) to integrate RGB-D, motion and semantic features. The quantitative comparison results on the DR(eye)VE dataset show that the proposed framework achieved the best accuracy and robustness performance compared with state-of-the-art models (AUC-Judd = 0.971, CC = 0.767, KL = 1.046, SIM = 0.579). In addition, the experimental results of the ablation study demonstrated the positive effect of the SAW method on the prediction robustness in response to scene changes. The proposed model has the potential to benefit adaptive VAP research in universal geospatial scenes, such as AR-aided navigation, indoor navigation, and street-view image reading.

Direct and Spillover Effects of Urban Land Expansion on Habitat Quality in Chengdu-Chongqing Urban Agglomeration

Urban land expansion has dramatically changed the spatial distribution patterns and functional structure of habitats. Previous studies on the spatial externality effect of urban land expansion on the habitat quality of urban agglomerations are still insufficient. With the use of remote sensing and statistical data from 2000 to 2018, this study explored the evolutionary relationship between urban land expansion and habitat quality in the Chengdu-Chongqing urban agglomeration (CUA) using the bivariate local autocorrelation method and spatial Durbin model. Partial differential equation decomposition of the local and spatial spillover effects was implemented to investigate the marginal effects of the influencing factors. The highlights of the results are as follows: CUA’s urban land increased by 2890.42 km2 from 2000 to 2018, mainly caused by urban encroachment over farmland and grassland. New urban lands were situated primarily in the main urban districts of Chengdu and Chongqing; urban expansion intensity slowed to 7.64% in 2010–2018, declining by 53.95% from 2000 to 2010. The average habitat quality decreased to 0.905, and two “ring-shaped decline areas” were formed around the main urban areas of Chengdu and Chongqing. “Low-High” and “Low-Low” clusters were the main associations between urban land expansion and habitat quality changes. The impact of urban land expansion on local habitat quality changed from insignificant to negative, while its spatial spillover effects over adjacent areas have increased the negative environmental externalities to habitat quality in adjacent areas through spatial spillovers. Our findings provide evidence for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint protection strategies of habitat quality, also proving that habitat quality protection should be an integration of urban expansion regulation, natural adaptation and socioeconomic adjustment.

Experimental Validation of Adaptive Augmented LQI Control for a 2 DoF Helicopter

This paper presents a novel adaptive augmented linear quadratic integral (LQI) controller for a 2 degrees of freedom (DoF) helicopter to improve the closed-loop performance of the baseline controller even when the system contains unmodeled dynamics and exogenous disturbances. Although the LQI controller is widely used in aircraft control applications for its excellent reference-tracking capability and inherent robustness, the performance of the baseline LQI controller degrades when the aircraft model has unmodeled dynamics and external disturbances, which are inevitable in aircraft applications. Hence, to deal with the parameter uncertainty and unmeasured disturbances, we present an adaptive control framework using a model reference adaptive control (MRAC) scheme combined with the baseline LQI controller. The key aspect of the proposed control scheme is that the robustness of the closed-loop control system to deal with the matched uncertainty is enhanced by tracking error augmentation with the control law. Moreover, this work proves the asymptotic stability of the closed-loop system using the inverse Lyapunov function and Barbalat’s lemma. The efficacy of the adaptive augmented LQI is experimentally validated through hardware in loop (HIL) implementation on a laboratory scale 2 DoF helicopter workstation for several real-time test cases. Experimental results accentuate that the adaptive augmented control scheme can significantly improve not only the reference-tracking capability but also the robustness of the closed-loop system.

Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis

Recent progress on intelligent fault diagnosis is mainly attributed to the explosive development of convolutional neural networks (CNNs). Many existing CNN-based fault diagnosis models can extract abundant features from the measured vibration signals but cannot explore enough discriminative features under strong noise conditions. This poses a challenge for industrial applications. To address this problem, we develop a new deep CNN model, called a multireceptive field denoising residual convolutional network (MF-DRCN). The major contributions are: a multireceptive field denoising (MFD) block is designed to enhance the deep features extracted by the CNN model and filter out the interference feature information; an adaptive feature integration (AFI) module is embedded in the CNN model to adaptively integrate features, so as to make better use of the extracted information; and an end-to-end CNN model called MF-DRCN is developed based on MFD and AFI. The experimental results demonstrate that the MF-DRCN has better feature extraction and antiinterference capabilities than the other seven competitive methods. Specifically, under strong noise conditions with SNR = −6 dB, the MF-DRCN achieves 84.51% and 86.45% diagnostic accuracy, respectively, on the planetary gearbox dataset and the industrial pump dataset, which suggests the MF-DRCN is a promising intelligent fault diagnosis approach.

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

The problem of improving the methodological and algorithmic support of the complexing process by developing models of adaptive redundant complexing of weighted interval data has been solved. The object of this study is the process of complexing interval data obtained from several independent sources; the subject is the algebraic methods of excessive complexing of weighted interval data. The relevance of the task is due to the severity of the problem of consolidating homogeneous data in order to obtain more accurate and relevant information about the object or process under study. Models have been developed that, unlike the existing ones, make it possible a posteriori to take into account the accuracy of experts at the preliminary stage of expert evaluation. A single analytical form of the model for processing weighted interval and point estimates with the possibility of structural and parametric tuning is proposed. It allows one to increase the degree of automation of processing expert assessments under conditions of interval uncertainty. Recommendations for the practical application of the proposed models have been formulated. Options for parametric configuration of preference functions were indicated depending on the characteristics of weighted interval estimates. The commonality of the limiting cases of the proposed models with previously known ones is proved. The example shows the shift of the integrated assessment to the side of more accurate assessments at the previous stage of source assessment. The adaptability of the proposed models is illustrated. At the same time, a slight, on average, about 10 %, expansion of the complexed interval relative to the primary ones was registered. The built models and algorithms can be used in automated expert systems, as well as in cascade models of information processing and compression.

Horticultural Therapy for Improving the Work Performance and Interpersonal Relationships of Persons with Intellectual Disabilities.

For the occupational adaptation and social integration of the intellectually disabled, it is helpful to improve their work performance and interpersonal skills. The purpose of the study was to evaluate the effectiveness of horticultural therapy (HT) programs to improve work performance and interpersonal relationships of persons with intellectual disabilities. Based on observations and analyses of how people with intellectual disabilities work, we have developed a 12-session HT program that includes upper limb movements and physical activities to improve hand function. We recruited, with the consent of their legal guardians, 14 (6 males, 8 females) participants who had intellectual disabilities and were working at a sheltered workshop in K-gu, Seoul, South Korea. The program consisted of twelve sixty-minute sessions that were conducted twice a week at a rooftop garden. For pre- and post-evaluation of the program, the survey of functional adaptive behavior (SFAB), interpersonal negotiation strategies, a horticultural job evaluation (self), hand function tests (pegboard, pinch gauge, fingertips), and blood sample tests for physiological indicators of exercise were conducted. Interpersonal negotiation strategies, functional adaptive behaviors, and physical abilities for job behaviors, including agility and grasping of the hand, improved significantly from before to after the program (p < 0.05). A positive result of VEGF (vascular endothermic growth factor) in blood sample tests implies the need for further research on cognitive changes caused by horticultural activities. This study has limitations due to the small number of participants, but the results suggest that low- to medium-intensity horticultural treatment programs using the upper body and hands could be effective for vocational rehabilitation of the intellectually disabled.

Ecological migrants' socio-spatial integration in Yinchuan City, China.

Promoting the social integration of ecological migrants and identifying the key constraints to their integration are significant issues for social governance and transformation. Most previous studies have not systematically analyzed the level of social integration of migrants from the perspective of spatial ternary dialectics or systematically analyzed physical, social, and spiritual spaces. Based on space production theory, this study used principal component analyses to evaluate the ternary spatial integration level of physical, social, and spiritual spaces of ecological migrants in a specific resettlement area in Yinchuan City, China, and the Tobit regression model was used to identify the key constraint factors. The results demonstrate that the overall socio-spatial integration levels of ecological migrants in Yinchuan City are lower than that of the overall migrant population nationwide, and the levels of their spatial adaptation and spatial practice integration lag behind that of spatial belonging and spatial identity integration. Meanwhile, length of residency, occupation type, monthly income, and household type have facilitating effects on the ecological migrants’ social integration, while gender, age, ethnicity, and education levels have insignificant effects. In particular, occupation type is a key factor in promoting social integration and improving employment among ecological migrants. It is concluded that upgrading production skills and raising employment for ecological migrants can foster sustainable social space production patterns, facilitate virtuous cycles, and eliminate inhibiting factors such as lagging spatial practices, regional cultural differences, and socio-spatial deprivation.

Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.

Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.

Nutritional entrainment of circadian rhythms under alignment and misalignment: A mechanistic review.

The rising prevalence of obesity is a major international concern and is associated with a substantial burden of disease. Disrupted circadian behaviours, including late and extended eating patterns, are identified as risk factors for obesity. The circadian rhythm synchronises metabolic functions between and within tissues, optimising physiology to integrate with environmental and behavioural cycles. Cellular circadian rhythms also separate poorly compatible processes and enable adaptive integration of energy metabolism with autophagy. The timing of nutritional input is a key and easily controllable variable that influences circadian function. Misalignment of nutritional input with the centrally generated circadian rhythm may dampen and disrupt circadian metabolic function. This review seeks to provide a mechanistic overview of nutritional circadian entrainment and its downstream metabolic effects. The aims are: to characterise the key cellular and physiological mechanisms involved in the nutritional entrainment of circadian rhythms; and to explore the perturbation of these pathways by misaligned nutritional inputs, with relevance to obesity-associated dysmetabolism. A systematic two-tranche search strategy was employed. Searches were conducted within PubMed between March and December 2020. Included studies were formally evaluated for quality. Evidence was extracted and coded into key themes. 142 records were screened and 50 accepted. The evidence analysed was moderate-to-high quality and enabled the detailed characterisation of cellular pathways involved in nutritional circadian entrainment. Results indicated that diverse nutritional input pathways converge upon key nutrient/redox sensors and nutritionally sensitive core clock genes, which integrate with circadian metabolic pathways, allowing bidirectional communication between circadian clock function and metabolism. Versus alignment, nutritional misalignment was causally associated with dampening and alteration of core clock rhythms, between-tissue rhythmic decoupling, dysmetabolism, and obesity. Signalling through key circadian nodes, such as NAD+/SIRT, was indicated to have importance in these metabolic changes. Misaligned nutritional inputs were associated with altered core circadian temporal dynamics of metabolism and autophagy, and different time division between insulin-sensitive and insulin-resistant metabolic states. Time-restricted feeding protocols aligned with the natural circadian rhythm (light-dark cycle) relatively strengthened circadian oscillatory patterns and protected against diet-induced obesity. This review suggests potential value in further investigating circadian-normalising nutritional interventions for obesity, such as circadian-aligned time-restricted feeding.

Development of time-space adaptive SPH for large deformation and impact

<p indent="0mm">A spatially adaptive smoothed particle hydrodynamics (ASPH) method, where the particles can split and merge according to the adaptive criteria, was developed to improve the computational efficiency of the classical smoothed particle hydrodynamics (SPH) method. Recently, the ASPH algorithm has been widely applied to solve complex problems in mechanics. However, the splitting pattern in the current ASPH algorithm may lead to particle disorder when simulating large deformation and impact problems, and a smaller time step should be performed after particle splitting. An adaptive splitting pattern that is based on a relative position vector and velocity interpolation is introduced in this study. Subsequently, this numerical method is combined with the Runge-Kutta Chebyshev method to achieve adaptive time integration. Thus, the time-space adaptive smoothed particle hydrodynamics method (T-S ASPH) is proposed in this study. This algorithm can increase the computational accuracy by eliminating the particle disorder when simulating the large deformation and impact problems while improving the computational efficiency and time integration accuracy. Typical examples are simulated by the classical SPH, current ASPH and developed T-S ASPH methods. The results indicate that, compared with the other two methods, the proposed T-S ASPH method exhibits higher accuracy, efficiency, stability, and order of convergence for the large deformation and impact problems.

Dynamic protein hydrogel with supramolecularly enveloped kartogenin promotes cartilage regeneration through mitochondrial activation

Limited self-renewal potency of cartilage impedes the repair and regeneration at the site of damage. Investigating small-molecule compounds to boost chondrogenesis has yielded the dawn. However, the durability and targeting of current drug delivery and the adaptive integration for cartilage defects are not addressed. Herein, we fabricated a dynamic hydrogel based on the coordination between metal ions and collagen and then supramolecularly enveloped with a chondro-inductive factor kartogenin (KGN) via cyclodextrin (CD). The self-recovery and injectability of dynamic hydrogel enabled minimally invasive and precise treatment of cartilage injury assisted by continuous KGN administration. The hydrogel could significantly promote the expression of cartilage matrix components (type II collagen and aggrecan) in articular chondrocytes without affecting cell proliferation or migration in vitro. Mechanistically, the protective effects depended on enhancing chondrocyte mitochondrial function and intracellular antioxidant properties. In situ hydrogel injection strengthened cartilage regenerative potential and facilitated the reconstruction of subchondral bone following full-thickness defects in vivo. This study revealed that the dynamic chondrogenic hydrogel could benefit cartilage repair via mitochondrial reinforcement, acting as a novel therapeutic strategy for refractory cartilage damage.

A comprehensive assessment of accuracy of adaptive integration of cut cells for laminar fluid-structure interaction problems

Finite element methods based on cut-cells are becoming increasingly popular because of their advantages over formulations based on body-fitted meshes for problems with moving interfaces. In such methods, the cells (or elements) which are cut by the interface between two different domains need to be integrated using special techniques in order to obtain optimal convergence rates and accurate fluxes across the interface. The adaptive integration technique in which the cells are recursively subdivided is one of the popular techniques for the numerical integration of cut-cells due to its advantages over tessellation, particularly for problems involving complex geometries in three dimensions. Although adaptive integration does not impose any limitations on the representation of the geometry of immersed solids as it requires only point location algorithms, it becomes computationally expensive for recovering optimal convergence rates. This paper presents a comprehensive assessment of the adaptive integration of cut-cells for applications in computational fluid dynamics and fluid-structure interaction. We assess the effect of the accuracy of integration of cut cells on convergence rates in velocity and pressure fields, and then on forces and displacements for fluid-structure interaction problems by studying several examples in two and three dimensions. By taking the computational cost and the accuracy of forces and displacements into account, we demonstrate that numerical results of acceptable accuracy for FSI problems involving laminar flows can be obtained with only fewer levels of refinement. In particular, we show that three levels of adaptive refinement are sufficient for obtaining force and displacement values of acceptable accuracy for laminar fluid-structure interaction problems.

Arquitetura responsiva: uma análise bibliométrica da produção científica

Abstract Responsive architecture deals with the integration of data and physical adaptations between building and environment. The importance of the building envelope as a responsive construction system is one of the mostly investigated issues in the research field of architecture. Thisliterature review and bibliometric analysis took into account type, quantity and year of publication, co-occurrence of terms, authors and affiliations. Output regarding conferences and journals, authors and higher-education institutions, and knowledge fields related to responsive architecture were highlighted. The participation of conference papers is more evident from a quantitative point of view; its percentage of the sample was around 55%. Automation in Construction is the most significant journal reaching 17% from the total journal output. The Netherlands and United Kingdom are the most cited countries on the subject matter. The study confirmed that responsive architecture has relevant interactions between different areas of knowledge, from computational science, robotics, civil engineering, urban design, product design to sustainable architecture, hence exhibiting a pronounced collaborative and multidisciplinary approach. Results from the systematic review and bibliometric analysis allow us to stress the need for a continued assessment of scientific production in the area and also for further research initiatives aimed at database analyses.

Discrete scaling and criticality in a chain of adaptive excitable integrators

We describe a chain of unidirectionally coupled adaptive excitable elements slowly driven by a stochastic process from one end and open at the other end, as a minimal toy model of unresolved irreducible uncertainty in a system performing inference through a hierarchical model. Threshold potentials adapt slowly to ensure sensitivity without being wasteful. Activity and energy are released as intermittent avalanches of pulses with a discrete scaling distribution largely independent of the exogenous input form. Subthreshold activities and threshold potentials exhibit Lorentzian temporal spectra, with a power-law range determined by position in the chain. Subthreshold bistability closely resembles empirical measurements of intracellular membrane potential. We suggest that critical cortical cascades emerge from a trade-off between metabolic power consumption and performance requirements in a critical world, and that the temporal scaling patterns of brain electrophysiological recordings ensue from weighted linear combinations of subthreshold activities and pulses from different hierarchy levels.

Disaster risk reduction for whom? The gap between centrally planned Disaster Management Program and people's risk perception and adaptation

Integration of Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) is gaining popularity to address the increasing threat of climate change. People living in high disaster-prone impoverished areas can benefit from risk reduction interventions and subsequent adaptation of new technologies and behavior if those interventions diminish the perceived risk of disaster. Perceived risk of disaster plays an important role in influencing adaptive behavior. Disaster risk reduction interventions therefore can be benefitted from inclusive local consultation during the planning and implementation of the risk reduction interventions by taking local people's perceived risk of disaster into account. We studied a national-level disaster risk reduction program called ‘The comprehensive Disaster Management Program (CDMP)’ in Bangladesh to understand the linkage between risk reduction interventions, perceived risk of disaster, and adaptation. Following a mixed-method approach, we collected quantitative administrative data, interviewed local people, and conducted Focus Group Discussions in two disaster-prone neighboring coastal unions of Bangladesh. We have found that if risk reduction interventions designed by the central government are not aligned with the perceived risk of the local people, the implementation of these interventions does not reduce perceived risk significantly, and consequently, people do not adopt new technologies and behavior to increase their resilience to climate change shocks. Furthermore, people's valuation of a risk reduction intervention in reducing the perceived risk of disasters does not change after an actual disaster takes place insinuating the relative accuracy and robustness of local people's assessment of such interventions. We also found that risk reduction interventions implemented in a holistic way addressing multiple sources of risks can reduce perceived risk significantly. Lastly, we demonstrated that reducing perceived risk is a necessary condition but is not sufficient to encourage adaptation. Our study can contribute to improving the design and implementation of large-scale risk-reduction interventions implemented at the community level.

Parallel adaptation prompted core-periphery divergence of Ammopiptanthus mongolicus.

Range expansion requires peripheral populations to shift adaptive optima to breach range boundaries. Opportunities for range expansion can be assessed by investigating the associations of core-periphery environmental and genetic differences. This study investigates differences in the core-periphery adaptation of Ammopiptanthus mongolicus, a broad-leaved evergreen shrub species in a relatively homogeneous temperate Asian desert environment, to explore the environmental factors that limit the expansion of desert plants. Temperate deserts are characterized by severe drought, a large diurnal temperature range, and seasonality. Long-standing adaptation to the harsh desert environment may confine the genetic diversity of A. mongolicus, despite its distribution over a wide range of longitude, latitude, and altitude. Since range edges defined by climate niches may have different genetic responses to environmental extremes, we compared genome-wide polymorphisms between nine environmental core populations and ten fragmented peripheral populations to determine the “adaptive peripheral” populations. At least four adaptive peripheral populations had similar genetic-environmental association patterns. High elevations, summer drought, and winter cold were the three main determinants of converging these four adaptive peripheral populations. Elevation mainly caused similar local climates among different geographic regions. Altitudinal adaptation resulting from integrated environmental-genetic responses was a breakthrough in breaching niche boundaries. These peripheral populations are also located in relatively humid and warmer environments. Relaxation of the drought and cold constraints facilitated the genetic divergence of these peripheral populations from the core population’s adaptive legacy. We conclude that pleiotropic selection synchronized adaptative divergence to cold and drought vs. warm and humid environments between the core and peripheral populations. Such parallel adaptation of peripheral populations relies on selection under a background of abundant new variants derived from the core population’s standing genetic variation, i.e., integration of genetic surfing and local adaptation.

Digital Outcrop Model Generation from Hybrid UAV and Panoramic Imaging Systems

The study of outcrops in geosciences is being significantly improved by the enhancement of technologies that aims to build digital outcrop models (DOMs). Usually, the virtual environment is built by a collection of partially overlapped photographs taken from diverse perspectives, frequently using unmanned aerial vehicles (UAV). However, in situations including very steep features or even sub-vertical patterns, incomplete coverage of objects is expected. This work proposes an integration framework that uses terrestrial spherical panoramic images (SPI), acquired by omnidirectional fusion camera, and a UAV survey to overcome gaps left by traditional mapping in complex natural structures, such as outcrops. The omnidirectional fusion camera produces wider field of view images from different perspectives, which are able to considerably improve the representation of the DOM, mainly where the UAV has geometric view restrictions. We designed controlled experiments to guarantee the equivalent performance of SPI compared with UAV. The adaptive integration is accomplished through an optimized selective strategy based on an octree framework. The quality of the 3D model generated using this approach was assessed by quantitative and qualitative indicators. The results show the potential of generating a more reliable 3D model using SPI allied with UAV image data while reducing field survey time and complexity.

Accelerating Real-Time Coupled Cluster Methods with Single-Precision Arithmetic and Adaptive Numerical Integration.

We explore the framework of a real-time coupled cluster method with a focus on improving its computational efficiency. Propagation of the wave function via the time-dependent Schrödinger equation places high demands on computing resources, particularly for high level theories such as coupled cluster with polynomial scaling. Similar to earlier investigations of coupled cluster properties, we demonstrate that the use of single-precision arithmetic reduces both the storage and multiplicative costs of the real-time simulation by approximately a factor of 2 with no significant impact on the resulting UV/vis absorption spectrum computed via the Fourier transform of the time-dependent dipole moment. Additional speedups─of up to a factor of 14 in test simulations of water clusters─are obtained via a straightforward GPU-based implementation as compared to conventional CPU calculations. We also find that further performance optimization is accessible through sagacious selection of numerical integration algorithms, and the adaptive methods, such as the Cash-Karp integrator, provide an effective balance between computing costs and numerical stability. Finally, we demonstrate that a simple mixed-step integrator based on the conventional fourth-order Runge-Kutta approach is capable of stable propagations even for strong external fields, provided the time step is appropriately adapted to the duration of the laser pulse with only minimal computational overhead.