Models Of Natural Systems Research Articles

Ultrafast energy transfer in biomimetic multistrand nanorings.

We report the synthesis of LH2-like supramolecular double- and triple-stranded complexes based upon porphyrin nanorings. Energy transfer from the antenna dimers to the π-conjugated nanoring occurs on a subpicosecond time scale, rivaling transfer rates in natural light-harvesting systems. The presence of a second nanoring acceptor doubles the transfer rate, providing strong evidence for multidirectional energy funneling. The behavior of these systems is particularly intriguing because the local nature of the interaction may allow energy transfer into states that are, for cyclic nanorings, symmetry-forbidden in the far field. These complexes are versatile synthetic models for natural light-harvesting systems.

Challenges and Opportunities for Design, Simulation, and Fabrication of Soft Robots

This article describes new opportunities in soft robotics and some potential avenues to overcome challenges associated with the realization of these opportunities. New opportunities include new applications that exploit novel amorphous nonrigid dynamics, a new design space due to the elimination of traditional manufacturing constraints, more opportunities for modeling and mimicry of natural systems, and increased safety and mechanical compatibility with humans. Challenges include limited simulation and design automation tools, lack of soft actuation methods, and difficulty in manufacturing and component standardization. Both computational (e.g., evolutionary design tools) and mechanical (porous and jamming materials) approaches are proposed to alleviate these needs.

Mathematical algorithm development and parametric studies with the GEOFRAC three-dimensional stochastic model of natural rock fracture systems

This paper presents results from research conducted at MIT during 2010–2012 on modeling of natural rock fracture systems with the GEOFRAC three-dimensional stochastic model. Following a background summary of discrete fracture network models and a brief introduction of GEOFRAC, the paper provides a thorough description of the newly developed mathematical and computer algorithms for fracture intensity, aperture, and intersection representation, which have been implemented in MATLAB. The new methods optimize, in particular, the representation of fracture intensity in terms of cumulative fracture area per unit volume, P32, via the Poisson-Voronoi Tessellation of planes into polygonal fracture shapes. In addition, fracture apertures now can be represented probabilistically or deterministically whereas the newly implemented intersection algorithms allow for computing discrete pathways of interconnected fractures. In conclusion, results from a statistical parametric study, which was conducted with the enhanced GEOFRAC model and the new MATLAB-based Monte Carlo simulation program FRACSIM, demonstrate how fracture intensity, size, and orientations influence fracture connectivity.

An information theoretic alternative to model a natural system using observational information alone

[1] How to define a system? This is a problem faced routinely in science and engineering, with solutions developed from our understanding of the processes inherent, to assessing the underlying structure based on observational evidence alone. In general, system specification involves identifying a few meaningful predictors (from a large enough set that is plausibly related to the response) and formulating a relation between them and the system response being modeled. For systems where physical relationships are less apparent, and sufficient observational records exist, a range of statistical alternatives have been investigated as a possible way of specifying the underlying form. Here we introduce partial information (PI) as a new means for specifying the system, its key advantage being the relative lack of major assumptions about the processes being modeled in order to characterize the complete system. In addition to PI which offers a means of identifying the system predictors of interest, we also introduce the concept of partial weights (PWs) which uses the identified predictors to formulate a predictive model that acknowledges the relative contributions, predictor variables make to the prediction of the response. We assess the utility of the PI-PW framework using synthetically generated data sets from known linear, nonlinear, and high-dimensional dynamic yet chaotic systems and demonstrate the efficacy of the procedure in ascertaining the underlying true system with varying extents of observational evidence available. We highlight how this framework can be invaluable in formulating prediction models for natural systems which are modeled using empirical or semiempirical alternatives and discuss current limitations that still need to be overcome.

On Invertibility of Functional Operators with Shift in Weighted Hölder Spaces

In this paper, we consider functional operators with shift in weighted H?lder spaces. We present the main idea and the scheme of proof of the conditions of invertibility for these operators. As an application, we propose to use these results for solution of equations with shift which arise in the study of cyclic models for natural systems with renewable resources.

Modelling Atmospheric Concentrations of Particulate Matter Model Climate Sensitivity of Ecosystems and Feedbacks for Region South Algeria

Atmospheric dispersion modelling refers to the mathematical description of contaminant transport in the atmosphere. Each climate system component operates on a range of characteristic temporal and spatial scales. The knowledge of these scales is necessary for a correct formulation of climate models; the present work emphasis is given on the modelling of the horizontal transport of pollutant by taking basic assumptions for the wind velocity, mass burning rate of pollutant from the source. The primary cause of environmental degradation is human disturbance. The degree of the environmental impact varies with the cause, the habitat, and the plants and animals that inhabit it. This present study is desired to calculate the concentration in horizontal direction of flow of pollutant. As any mathematical models of natural systems, a climate model is a simplification. The degree of accepted simplification determines the complexity of the model and restricts the applicability of the model to certain questions. Hence, the complexity of a chosen model sets the limitations to its application. The quality of a climate model is not judged by the mere number of processes considered, but rather by the quality of how chosen processes and their couplings are reproduced.

THE EMERGING CHALLENGE: THE VALIDITY OF COMPUTATIONAL ORGANIZATION MODEL AND METHODOLOGY

The validity of the computational organization model is a necessary condition and also poses a bottleneck in computational organization theory development. Established on the frontline of computational organization theory, this paper provides an overall discussion on the difficulties in validation and validation methodology of the computational organization model. First, different from natural engineering system model, and also exceeding the traditional empirical method, the computational organization model is faced with various subjective and objective difficulties during the validation progress; second, in developing the situational validation methodology based on relationships among problem field, modeling purpose and referents, it is important for the computational organization model that the validation is conducted according to certain degree; third, the establishment of verification, validation and accreditation approach (VV&A), which is different from the natural engineering system, is an irresistible trend for future development of the computational organization model. Model validation should be focused on concept validation, operation validation, and data validation, as well as the principle of iterative validation approach and such validation should be conducted throughout the modeling process; finally, the validation of computational organization model should be the process to enhance people's confidence in the model. From the perspective of review, if the model is able to pass through all the validation tests, it is helpful for a better understanding of the model's ability, limitation and applicability. In this case, research from interdisciplinary experts is required urgently.

Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos

Comprehensive analysis of parameter and driver sensitivity is key to establishing the credibility of models representing complex systems. This is especially so for models of natural systems where experimental manipulation of the real-world to provide controlled validation data is not possible. End-to-end ecosystem models (nutrients to birds and mammals) of marine ecosystems fall into this category with applications for evaluating the effects of climate change and fishing on nutrient fluxes and the abundances of flora and fauna. Here we present results of both ‘one-at-a-time’ (OAT) and variance based global sensitivity analyses (GSA) of the fish and fishery aspects of StrathE2E, an end-to-end ecosystem model of the North Sea. The sensitivity of the model was examined with respect to internal biological parameters, and external drivers related to climate and human activity. The OAT Morris method was first used to screen for factors most influential on model outputs. The Sobol GSA method was then used to calculate quantitative sensitivity indices. The results indicated that the fish and shellfish components of the model (demersal and pelagic fish, filter/deposit and scavenge/carnivore feeding benthos) were influenced by different sets of factors. Harvesting rates were highly influential on demersal and pelagic fish biomasses. Suspension/deposit feeding benthos were directly sensitive to changes in temperature, while the temperature acted indirectly on pelagic fish through the connectivity between model components of the food web. Biomass conversion efficiency was the most important factor for scavenge/carnivorous feeding benthos. The results indicate the primacy of fishing as the most important process affecting total fish biomass, together with varying responses to environmental factors which may be relevant in the context of climate change. The non-linear responses and parameter interactions identified by the analysis also highlight the necessity to use global rather than local methods for the sensitivity analysis of ecosystem models.

MATHEMATICAL MODELING REGIME STEADY STATE FOR DOMESTIC WASTEWATER TREATMENT FACULTATIVE STABILIZATION PONDS

This paper presents a model for natural systems used in Wastewater Treatment Plant (WWTP) Sewon Bantul. The model is modeling development, derived from the physical and biochemical phenomena involved in the biological treatment process. The numerical solution of the resulting on 13 simultaneous systems of nonlinear equations by the Quasi_Newton. Data validation is measured by facultative pond at the inlet and outlet of the pond to the concentration of bacteria, algae, zooplankton, organic matter, detritus, organic nitrogen, NH3, organic phosphor, dissolved phosphorus, Dissolved Oxygen (DO), total coliform, faecal coliform and Biochemical Oxygen Demand (BOD). A simulation model is presented to predict ke performance regime steady state of domestic wastewater treatment facultative stabilization pond. The high degree of significant of at least 10% indicates that the effluent parameters can be reasonably accurately predicted.

MATHEMATICAL MODELING REGIME STEADY STATE FOR DOMESTIC WASTEWATER TREATMENT FACULTATIVE STABILIZATION PONDS

This paper presents a model for natural systems used in Wastewater Treatment Plant (WWTP) Sewon Bantul. The model is modeling development, derived from the physical and biochemical phenomena involved in the biological treatment process. The numerical solution of the resulting on 13 simultaneous systems of nonlinear equations by the Quasi_Newton. Data validation is measured by facultative pond at the inlet and outlet of the pond to the concentration of bacteria, algae, zooplankton, organic matter, detritus, organic nitrogen, NH3, organic phosphor, dissolved phosphorus, Dissolved Oxygen (DO), total coliform, faecal coliform and Biochemical Oxygen Demand (BOD). A simulation model is presented to predict ke performance regime steady state of domestic wastewater treatment facultative stabilization pond. The high degree of significant of at least 10% indicates that the effluent parameters can be reasonably accurately predicted.

Integration of multicriteria evaluation and cellular automata methods for landslide simulation modelling

The representation and modelling of physical and natural systems are mostly implemented using partial differential equations. Physical processes such as flooding, tsunamis, avalanches, earthquakes and landslides undeniably possess complex systems behaviour. Modelling such dynamic phenomena can be adequately addressed by using geocomplexity – complex systems theory and cellular automata (CA). This study develops a novel approach that couples geographic information systems (GIS), multicriteria evaluation (MCE) and CA to simulate shallow landslide flows occurring in urban areas. The landslide susceptibility map produced from the MCE model was used as one input for the CA model. The high-resolution digital elevation model (DEM) is used to calculate topographic variables such as slope gradient, aspect gradient, stream power index, topographic wetness index, and flow direction which were all used in model design. The developed MCE-CA simulation model was tested on historical landslide data in Metro Vancouver, Canada. The spatial extents of the landslide simulations were compared with actual data to test the model simulation outcomes. The developed model has a potential to become useful tool that can aid urban planners and emergency workers in identifying and mitigating threats due to landslides.

Spatial Control of Condensation and Freezing on Superhydrophobic Surfaces with Hydrophilic Patches

AbstractCertain natural organisms use micro‐patterned surface chemistry, or ice‐nucleating species, to control water condensation and ice nucleation for survival under extreme conditions. As an analogy to these biological approaches, it is shown that functionalized, hydrophilic polymers and particles deposited on the tips of superhydrophobic posts induce precise topographical control over water condensation and freezing at the micrometer scale. A bottom‐up deposition process is used to take advantage of the limited contact area of a non‐wetting aqueous solution on a superhydrophobic surface. Hydrophilic polymer deposition on the tips of these geometrical structures allows spatial control over the nucleation, growth, and coalescence of micrometer‐scale water droplets. The hydrophilic tips nucleate water droplets with extremely uniform nucleation and growth rates, uniform sizes, an increased stability against coalescence, and asymmetric droplet morphologies. Control of freezing behavior is also demonstrated via deposition of ice‐nucleating AgI nanoparticles on the tips of these structures. This combination of the hydrophilic polymer and AgI particles on the tips was used to achieve templating of ice nucleation at the micrometer scale. Preliminary results indicate that control over ice crystal size, spatial symmetry, and position might be possible with this method. This type of approach can serve as a platform for systematically analyzing micrometer‐scale condensation and freezing phenomena, and as a model for natural systems.

An agile FCM for real-time modeling of dynamic and real-life systems

Fuzzy cognitive map (FCM) is a well-established model of control and decision making based on neural network and fuzzy logic methodologies. It also serves as a powerful systematic way for analyzing real-life problems where tens of known, partially known, and even unknown factors contribute to complexity of a system. FCM-based inference requires a neural activation function much like other neural network systems. In modeling, in addition to an activation function, FCM involves with weight training to learn about relationships as they exist among contributing factors. Therefore, numerous contributing factors could be analyzed to understand the behaviors of factors within a real-life system and to represent it in form of tangible matrices of weights. This article discusses a new incremental FCM activation function, named cumulative activation, and introduces a new weight training technique using simulated annealing (SA) known as agile FCM. Smooth variation of FCM nodes that is due to cumulative nature of inference results into faster convergence, while a unique minimum cost solution is guaranteed using the SA training module that is entirely expert-independent. A combination of these two techniques suits time-related applications where inclusion of temporal features is necessary. The resulted system is examined through numerical example datasets where the candidate FCM shows sensitivity to dynamic variables over time. A real-life example case is included as well to further support the effectiveness of the developed FCM in modeling of natural and complex systems.

A CO2-PENS model of methods and costs for treatment of water extracted during geologic carbon sequestration

Extraction of water during subsurface carbon sequestration may be useful for the control of CO2 placement, reducing pressure risks, and mitigating environmental risks. Desalination of this water may be possible if costs are kept low, in order to minimize the quantity that must be reinjected or otherwise disposed. Added value may be recovered in the form of treated water that can be reused by carbon capture, sequestration, and other industrial processes. Total dissolved solids will range from 10,000 mg/L up to over 100,000 mg/L, and temperatures may range up to 120°C, once the water is brought to the surface. We have developed a system-level, mesoscale analysis module for the CO2-Predicting engineered natural system model to analyze the feasibility of treatment, the costs of treatment, the value of energy recovery, and the costs of concentrate disposal. Costs are derived from a database of reported literature values. The model allows the user to select the most economic options for treatment, to compare costs, and to understand the trade-off of risks and costs. Results of preliminary modeling indicate that while reverse osmosis is feasible within certain temperature and salinity ranges, nanofiltration and thermal methods may be more cost-effective or otherwise feasible.

Modeling and Simulation Agent-based of Natural Disaster Complex Systems

A number of modeling and simulation tools have been developed in the domain of Natural Disasters. In these situations, several research teams may make an intervention and that have to coordinate their activities in order to save the maximum number of lives. To this end, they have to define an organizational structure and adopt management policies to improve their performance. The organizational structure and the policies are important elements that have to be taken into account to simulate a real emergency activity. To facilitate the design of these simulations, an agent-based methodological framework for complex system (Supply Chain, Natural Disaster) is proposed. The main contribution of the framework is that it will reflect the organizational structure and policies within the simulation, and which involves the integration truly dynamic dimension of this organization. We propose also a specification of the translation process to ensure the transition between various models that are proposed in the methodological framework.

“Making the green one red”: Dynamic Ecologies in Macbeth, Edward Barlow’s Journal, and Robinson Crusoe

We need a more colorful eco-palette. As ecological interpretations have become increasingly central to twenty-first-century literary studies, calls have emerged to move “beyond the green” toward a more variegated spectrum of environmental alternatives. What Jeffrey Jerome Cohen calls “ecology’s rainbow” refers to a current goal of the environmental humanities—to pluralize thinking about the relationship between human beings and nonhuman nature. My work in this area has flowed out of oceanic or “blue” ecologies, but the logic of dynamic ecological thinking cannot stop at the water’s edge. The need to multiply ecocritical models responds to an increasing recognition, which began in the ecological sciences and has emerged in the humanities and social sciences more recently, that natural systems are more dynamic and less stable than once believed. The logic that moves from stasis and sustainability to dynamic “post-equilibrium” models requires that we match the constant innovations of natural systems with flexible interpretive practices. With this pressure toward dynamism in mind, this essay reconsiders green—but not the old green. Remembering that green is an oceanic as well as terrestrial color, and using a famously opaque phrase from Macbeth as a linguistic cue to re-introduce complexity into our literary models of natural systems, this essay offers immersion in hostile waters as a structure within which to think about the human encounter with nonhuman nature. In this model, it is no longer a question of “being green,” but of enduring, with effort and difficulty, inside the “green one.” By analyzing a key line from Macbeth , an episode of near-shipwreck in a seventeenth-century sailor’s diary, and the moment when the hero washes ashore in Robinson Crusoe , this article discovers the myriad meanings of oceanic green.

MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity

MENS is a bio-inspired model for higher level cognitive systems; it is an application of the Memory Evolutive Systems developed with Vanbremeersch to model complex multi-scale, multi-agent self-organized systems, such as biological or social systems. Its development resorts to an info-computationalism: first we characterize the properties of the human brain/mind at the origin of higher order cognitive processes up to consciousness and creativity, then we ‘abstract’ them in a MENS mathematical model for natural or artificial cognitive systems. The model, based on a ‘dynamic’ Category Theory incorporating Time, emphasizes the computability problems which are raised.

Computer Simulations of Textile Non-Woven Structures

The nonwoven fabric has been used widely due to its wide uses in the textile and paper industry or as well as medical field. The properties of the nonwoven can be very accurately predicted with the help of simulation and computer imaging interface process. Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviors of a selected physical or abstract system. Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. A computer simulation (or 'sim') is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables, predictions may be made about the behavior of the system.

GEM-Selektor geochemical modeling package: revised algorithm and GEMS3K numerical kernel for coupled simulation codes

Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase–multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid–rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov’s convex programming approach are described, as implemented in the GEMS3K C/C+ + code, which is also the numerical kernel of GEM-Selektor v.3 package ( http://gems.web.psi.ch ). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing.

Reaction path modeling of enhanced in situ CO2 mineralization for carbon sequestration in the peridotite of the Samail Ophiolite, Sultanate of Oman

The peridotite section of the Samail Ophiolite in the Sultanate of Oman offers insight into the feasibility of mineral carbonation for engineered, in situ geological CO2 storage in mantle peridotites. Naturally occurring CO2 sequestration via mineral carbonation is well-developed in the peridotite; however, the natural process captures and sequesters CO2 too slowly to significantly impact the concentration of CO2 in the atmosphere. A reaction path model was developed to simulate in situ CO2 mineralization through carbonation of fresh peridotite, with its composition based on that of mantle peridotite in the Samail Ophiolite and including dissolution kinetics for primary minerals. The model employs a two-stage technique, beginning with an open system and progressing to three different closed system scenarios- a natural system at 30°C, an engineered CO2 injection scenario at 30°C, and an engineered CO2 injection scenario at 90°C. The natural system model reproduces measured aqueous solute concentrations in the target water, signifying the model is a close approximation of the natural process. Natural system model results suggest that the open system achieves steady state within a few decades, while the closed system may take up to 6,500years to reach observed fluid compositions. The model also identifies the supply of dissolved inorganic carbon as the limiting factor for natural CO2 mineralization in the deep subsurface. Engineered system models indicate that injecting CO2 at depth could enhance the rate of CO2 mineralization by a factor of over 16,000. CO2 injection could also increase mineralization efficiency – kilograms of CO2 sequestered per kilogram of peridotite – by a factor of over 350. These model estimates do not include the effects of precipitation kinetics or changes in permeability and reactive surface area due to secondary mineral precipitation. Nonetheless, the faster rate of mineralization in the CO2 injection models implies that enhanced in situ peridotite carbonation could be a significant sink for atmospheric CO2.