Models Of Natural Systems Research Articles

Оценка антропологического воздействия интерфейсов на этапе проектирования программно-аппаратного обеспечения

In the information society, as the scope of human activity expands and deepens, continuous formalisation occurs, underpinned by ontological models of technical and natural systems. The user's perception of comprehensive information about a complex subject area leads to information overload. This situation encourages the use of algorithms that present information in a compressed form. The paper introduces modified indicators and qualitative criteria to estimate the potential for errors in compressing subject area information, as well as evaluate the anthropometric nature of the interface using threshold values set by experts, both for artificial intelligence algorithms and users. A method is proposed for quantitatively assessing the anthropological impact of application-level interfaces during the design stage of hardware and software. The method consists of eight stages, which assess the comprehensibility of the hardware and software interface for both artificial intelligence algorithms and users. This approach reduces the likelihood of developing destructive hardware and software systems.

Оптимизация региональных планов адаптации к изменению климата в Арктической зоне России на основе прогностического моделирования

Based on the Russian Arctic example, an attempt was made to update the developed regional plans for adaptation to climate change, taking into account currently available scientific knowledge, observational data, climate scenarios and the results of mathematical modeling of natural and socio-economic systems — recipients of climate risks. The authors using the developed methods have calculated the geocryological risk index for the macro-regions of the Russian permafrost zone, as well as the vulnerability index of economic systems for municipalities of the Russian Arctic.

Architecture characterization of orchard trees for mechanical behavior investigations

Characterizing the architecture of tree root systems is essential to advance the development of root-inspired anchorage in engineered systems. This study explores the structural root architectures of orchard trees to understand the interplays between the mechanical behavior of roots and the root architecture. Full three-dimensional (3D) models of natural tree root systems, Lovell, Marianna, and Myrobalan, that were extracted from the ground by vertical pullout are reconstructed through photogrammetry and later skeletonized as nodes and root branch segments. Combined analyses of the full 3D models and skeletonized models enable a detailed examination of basic bulk properties and quantification of architectural parameters. While the root segments are divided into three categories, trunk root, main lateral root, and remaining roots, the patterns in branching and diameter distributions show significant differences between the trunk and main laterals versus the remaining lateral roots. In general, the branching angle decreases over the sequence of bifurcations. The main lateral roots near the trunk show significant spreading while the lateral roots near the ends grow roughly parallel to the parent root. For branch length, the roots bifurcate more frequently near the trunk and later they grow longer. Local thickness analysis confirms that the root diameter decays at a higher rate near the trunk than in the remaining lateral roots, while the total cross-sectional area across a bifurcation node remains mostly conserved. The histograms of branching angle, and branch length and thickness gradient can be described using lognormal and exponential distributions, respectively. This unique study presents data to characterize mechanically important structural roots, which may help link root architecture to the mechanical behaviors of root structures.

Ghost Channels and Ghost Cycles Guiding Long Transients in Dynamical Systems.

Dynamical descriptions and modeling of natural systems have generally focused on fixed points, with saddles and saddle-based phase-space objects such as heteroclinic channels or cycles being central concepts behind the emergence of quasistable long transients. Reliable and robust transient dynamics observed for real, inherently noisy systems is, however, not met by saddle-based dynamics, as demonstrated here. Generalizing the notion of ghost states, we provide a complementary framework that does not rely on the precise knowledge or existence of (un)stable fixed points, but rather on slow directed flows organized by ghost sets in ghost channels and ghost cycles. Moreover, we show that the appearance of these novel objects is an emergent property of a broad class of models typically used for description of natural systems.

Improving building natural ventilation simulation accuracy: calibration of standard k − ε closure coefficients using design of experiment

Abstract The objective of this study is to improve the accuracy of the standard k − ε (SKE) turbulence model for natural ventilation simulation, a critical component in the analysis and design of energy-efficient buildings. The methodology involves generating 17 design points for the four closure coefficients (C ɛ 1,C ɛ 2,C µ ,σ k ) for the SKE model using 2k full-factorial design of experiment (DOE) with a centre point. A sheltered single storey-building with windward and leeward openings is modelled and simulated using computational Fluid Dynamics (CFD) to obtain the Root Mean Square Error (RMSE) and Factor of two observation (FAC2) for each design point. Response models for RMSE and FAC2 are then generated, and optimization is employed to maximize FAC2, minimize RMSE, and the combination of both. The results are validated and compared with experimental and simulation data reported in existing literature. The findings indicate that the indoor velocity profiles with closure coefficients obtained by maximizing FAC2 are better aligned with the actual experimental results. Compared with the existing calibration method, the proposed calibration method can yield superior results with fewer sampling points, highlighting the efficiency of the proposed approach. The calibration framework developed in this study can be extended to other simulation scenarios, contributing to the robust and efficient modelling of natural ventilation systems in various contexts. This research presents a novel calibration method for the SKE model in natural ventilation simulation, providing a more accurate and efficient approach compared to existing methods. The results have important implications for researchers and practitioners working in the field of building energy simulation and sustainability.

A High‐Performance Coupled Human And Natural Systems (CHANS) Model for Flood Risk Assessment and Reduction

AbstractIn recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high‐performance hydrodynamic model with an agent‐based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real‐time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood‐human dynamics and assess flood impact at the household‐level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short‐term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.

Experimental (ρ,P,T) data of H2 + CH4 mixtures at temperatures from 278 to 398 K and pressures up to 56 MPa

The blending of hydrogen and natural gas (H2-NG) has emerged as a crucial and cost-effective means for processing hydrogen fluids through natural gas networks thereby aiding transition to net-zero energy. Nonetheless, the effective adaptation of H2-NG mixtures, especially up to 20% hydrogen concentrations, to existing natural gas distribution and storage facilities relies heavily on a comprehensive understanding of their thermophysical properties. We measured densities of three binary mixtures of hydrogen and methane (xH2 = 0.0532, 0.0858, and 0.2031) at temperatures between 278 and 398 K, and pressures up to 56 MPa using a vibrating tube densimeter (VTD). The VTD was calibrated using H2 and H2O. The measured densities were in reasonable agreements with the predictions of two thermodynamic equations of states (EoSs), Multi-Fluid Helmholtz Energy Approximation (MFHEA) and Peng-Robinson (PR). The average absolute relative deviation (AARD) for the measured density against the predictions of the PR EoS are 1.07%, 1.09%, and 0.89% for xH2 = 0.0532, 0.0858, and 0.2031 mol fractions respectively. While the AARD for the measured density against the predictions of MFHEA EoS are 0.16%, 0.16%, and 0.32% for xH2 = 0.0532, 0.0858, and 0.2031 mol fractions respective. The MFHEA EoS, which has the structure of GERG-2008, has better agreements with our density data than the PR EoS. The relative high deviations with PR EoS results from the PR's simple structure making it deficient in calculating thermal properties of natural gas in saturated-liquid and homogeneous states. In addition, the uncertainty of the measured density was determined, and the experimental data were compared with available literature data. Finally, the compressibility factor (Z), speed of sound (w), specific heat capacity at constant volume (CV), and specific heat capacity at constant pressure (Cp) were obtained from the experimental results and were compared with the predictions of MFHEA EoS. This work provides accurate density and other thermophysical data useful for engineering/process designs and modelling of H2 and natural gas systems thereby enhancing energy transition strategy.

Modeling and analysis method for carbon emission flow in integrated energy systems considering energy quality

AbstractWhile proposing dual carbon strategy goals, research on carbon reduction in integrated energy systems (IESs) has become a focus of attention. Traditional IES carbon emission flow is mostly based on energy flow, without considering the differences in energy quality. Exergy can be used as a reasonable measure of energy quality. Combining the theory of exergy flow and carbon emission flow, a modeling and analysis method for IES carbon emission flow considering energy quality is proposed. First, exergy flow models for electric and natural gas systems, equivalent exergy flow models for heat systems, and exergy hub models are introduced. Afterward, the exergy branch losses are allocated to the nodes at both ends. A networked modeling method for the carbon emission flow of energy stations is proposed. A unified matrix calculation model for IES carbon emission flow considering energy quality is established. On this basis, an IES carbon emission flow calculation method considering energy quality is proposed. Finally, this method is applied to the test IES, and the results show that the carbon emission flow calculation results reflect the distribution of carbon emissions borne by the effective energy supplied by the source and consumed by the load in the system, taking into account both the quantity and quality of energy. The reasonable allocation of new energy is conducive to synchronously improving the energy efficiency, exergy efficiency, and cleanliness of the system.

Breaking reflection symmetry: evolving long dynamical cycles in Boolean systems

In interacting dynamical systems, specific local interaction rules for system components give rise to diverse and complex global dynamics. Long dynamical cycles are a key feature of many natural interacting systems, especially in biology. Examples of dynamical cycles range from circadian rhythms regulating sleep to cell cycles regulating reproductive behavior. Despite the crucial role of cycles in nature, the properties of network structure that give rise to cycles still need to be better understood. Here, we use a Boolean interaction network model to study the relationships between network structure and cyclic dynamics. We identify particular structural motifs that support cycles, and other motifs that suppress them. More generally, we show that the presence of dynamical reflection symmetry in the interaction network enhances cyclic behavior. In simulating an artificial evolutionary process, we find that motifs that break reflection symmetry are discarded. We further show that dynamical reflection symmetries are over-represented in Boolean models of natural biological systems. Altogether, our results demonstrate a link between symmetry and functionality for interacting dynamical systems, and they provide evidence for symmetry’s causal role in evolving dynamical functionality.

Long-term planning and coupling optimization of multi-regional natural gas and hydrogen supply systems: A case study of China

Low-carbon transition of energy systems features more natural gas and hydrogen consumption to replace coal and oil. Planning of natural gas and hydrogen supply systems with multiple supply sources, end-consumers, large infrastructures and spatio-temporal mismatches are challenging tasks. In this study, two long-term, multi-regional and monthly time-scale optimization models for natural gas and hydrogen supply systems are developed respectively. China is taken as a case. The natural gas demand will initially increase and then decrease, leading to a premature decommissioning of infrastructure. A coupling model is established to explore the retrofitting of decommissioned natural gas infrastructure for hydrogen supply. Results show that 44.14 % decommissioned pipelines and 34.17 % decommissioned storage facilities of natural gas supply system can be retrofitted, with an annual increase of 63.98 % and 62.80 % in hydrogen pipelines and storage facilities, and a 9.15 % reduction in transition costs.

Ethorobotic rats for rodent behavioral research: design considerations.

The development of robots as tools for biological research, sometimes termed "biorobotics", has grown rapidly in recent years, fueled by the proliferation of miniaturized computation and advanced manufacturing techniques. Much of this work is focused on the use of robots as biomechanical models for natural systems. But, increasingly, biomimetic robots are being employed to interact directly with animals, as component parts of ethology studies in the field and behavioral neuroscience studies in the laboratory. While it has been possible to mechanize and automate animal behavior experiments for decades, only recently has there been the prospect of creating at-scale robotic animals containing the sensing, autonomy and actuation necessary for complex, life-like interaction. This not only opens up new avenues of enquiry, but also provides important ways to improve animal welfare, both by reducing or replacing the use of animal subjects, and by minimizing animal distress (if robots are used judiciously). This article will discuss the current state of the art in robotic lab rats, providing perspective on where research could be directed to enable the safe and effective use of biorobotic animals.

How greedy is too greedy? A network toy model for evaluating the sustainability of biased evolutionary dynamics.

Modern humanity has changed the biosphere at a global scale, threatening its own sustainability. It is claimed that through technology humans maximize the extraction of energy from the natural system towards their own benefit, with rates of appropriation that surpass the time-scales for systemic adaptation. This time-decoupled coevolutionary dynamic is at the core of human societal unsustainability. Here, we developed in silico experiments of an open energy-based flowing network toy model of natural systems and study the effects that greedy evolutionary strategies, resembling human societal demands, have upon the performance and scarcity tolerance of the system. We aim to determine the flexibility that those biased evolutionary dynamics have for matching or surpassing natural evolution outcomes. We studied four different indexes of system's growth and development (total system throughflow (TST), average mutual information, ascendency and entropy difference) and compare their scarcity tolerance and performance outcomes with respect to four different greedy scenarios. The results showed that greedy strategies rarely surpassed the tolerance and performance achieved by natural systemic evolution. The nature of the greedy scenarios developed were closely related to increases in TST and therefore, we emphasized this comparison. Here, the maximum percentage of greedy networks capable of surpassing natural dynamics was around one-third (approx. [Formula: see text]). However, results suggest the existence of a space parameter where local increases of energy flow can outperform the outcomes of natural systemic evolution, but no evident network property seems to characterize those greedy networks. A mild inverse relationship was found between the number of links that greedy nodes have towards the output and their capacity to outpass the control evolution. As many of the human societal effect upon biospheric processes have dissipative byproducts, knowing that such dynamics might diminish the systems tolerance and performance suggest care in their (ab)use. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

High-pressure vapor-liquid equilibrium measurements of methane + water mixtures by nuclear magnetic resonance spectroscopy

The vapor-liquid equilibrium (VLE) of methane + water mixtures has been studied with nuclear magnetic resonance (NMR) spectroscopy. This work had two primary goals. The first goal was to develop methods that broaden the utility of NMR spectroscopy for VLE measurements. In this regard, we report a method by which the liquid-phase and vapor-phase compositions are measured in separate experiments by adjusting the height of the liquid phase in the sample tube. We also report a method for hastening phase equilibration by adding glass beads to the sample and repeatedly inverting the sample tube. The second goal of this work was to collect VLE data on a challenging mixture with real-world importance. Mixtures of methane + water are a useful test case because of their challenging characteristics, including the widely differing vapor pressures of the two components. One use for accurate VLE data on methane + water mixtures is to better predict the formation of harmful liquid phases in natural gas pipelines. Herein we utilize 1H NMR spectroscopy to measure the VLE of methane + water mixtures at temperatures of 299.73, 307.98, and 323.25 K, and pressures ranging from 0.69 MPa to 13.89 MPa. Experiments were carried out with a 600 MHz spectrometer. Mixtures were prepared and equilibrated in a high-pressure zirconia sample tube with an integrated needle valve. NMR-based VLE measurements on the liquid phase are in good agreement with available literature data and with Henry's Law predictions at low pressures. However, the commonly used GERG-2008 model for natural gas systems deviates dramatically from the experimental data for the liquid phase. NMR-based VLE measurements on the vapor-phase resulted in measured water concentrations that are systematically lower than available literature data and models. This systematic offset is likely caused by peak overlap in the NMR spectra.

DEEP PROCESS-DATA MINING FOR BUILDING OF ANALYTICAL MODELS: 1. MEDIUM-TERM FORECAST OF SPRING FLOOD EXTREMES FOR MOUNTAIN RIVERS

A standard methodology of deep process-data mining for building high-performance process-driven (analytical) models of complex natural systems was proposed. The method- ology (called as system-analytical modeling) is based on a system-hierarchical approach and deep mining of large datasets providing both extraction of the information hidden in such datasets and quantitative characterization of real processes occurring in natural systems. With its help, deep process-data mining of data (1951–2020) on spring flood discharge peaks and troughs (with ice motion) on 34 mountain rivers of the Altai-Sayan mountain country was performed. An analytical hydrological model of high performance (Nash-Sutcliffe criterion NSE = 0.78) was developed for the annual medium-term forecasting of discharge peaks and troughs in April using the data on meteorological conditions of the recent autumn and current winter periods. Flood peaks depend on autumn-winter precipitation (which determines 29% of the peak variance), landscape structure of river basins (14%), and winter air temperatures (0.8%). Spring floods on mountain rivers often threaten the life of local population that makes the developed model topical.

Exploring Interactions between Awqāf and Digital Data

The aim of the study is to investigate the interactions between the phenomena of awqāf and digital data through exploratory research that is conducted in this occasion on an unaddressed topic. The study sheds light on the structural phenomena that digital transformation imposes on life in all its aspects beyond the prevailing approach that deals with technique in terms of advantages and disadvantages. The study is inspired by the modeling of natural systems, an analytical framework to provide a basis for the interaction between awqāf and digital data in terms of the balance between efficiency and resilience, in addition to a prospective model based on the distinction between open and closed systems: reliance on knowledge generates open systems through a multitude of bifurcations, while reliance on technique alone creates closed systems that lead to self-destruction. Despite the huge media hype in social networks, digital data should not replace interactions in the natural ecosystems that awqāf have sustained over the centuries by fulfilling the rights of God and those of living creatures

Exploring Interactions between Awqāf and Digital Data

The aim of the study is to investigate the interactions between the phenomena of awqāf and digital data through exploratory research that is conducted in this occasion on an unaddressed topic. The study sheds light on the structural phenomena that digital transformation imposes on life in all its aspects beyond the prevailing approach that deals with technique in terms of advantages and disadvantages. The study is inspired by the modeling of natural systems, an analytical framework to provide a basis for the interaction between awqāf and digital data in terms of the balance between efficiency and resilience, in addition to a prospective model based on the distinction between open and closed systems: reliance on knowledge generates open systems through a multitude of bifurcations, while reliance on technique alone creates closed systems that lead to self-destruction. Despite the huge media hype in social networks, digital data should not replace interactions in the natural ecosystems that awqāf have sustained over the centuries by fulfilling the rights of God and those of living creatures. Keywords: Awqāf, Data, Digital Transformation, Efficiency and Resilience, Open and Closed Systems

Hydrothermal fluorite solubility experiments and mobility of REE in acidic to alkaline solutions from 100 to 250 °C

Fluorite is a common vein mineral in critical mineral deposits that are overprinted by hydrothermal processes. Its chemistry potentially reflects the mobilization conditions of rare earth elements (REE) in these hydrothermal fluids. However, the controls of temperature and fluid chemistry (i.e., pH and salinity) on fluorite solubility and coupled REE mobilization is still uncertain. In this study, batch-type experiments were conducted to investigate REE mobility in aqueous fluids controlled by the solubility of fluorite at temperatures between 100 and 250 °C. Natural fluorite crystals from Cooke’s Peak (New Mexico) were equilibrated with aqueous solutions of variable starting pH (2–10), salinities (0 and 15 wt.% NaCl), and initial REE concentrations (0 and 0.5 ppm). Reacted fluorite crystals display dissolution-controlled reaction textures (i.e., etch pits and dissolution channels along fluorite cleavage planes), which are most pronounced in experiments conducted in acidic fluids and at elevated temperature. Addition of NaCl considerably enhances fluorite dissolution, in both acidic and alkaline solutions, which is also reflected by the compositions of the reacted solutions displaying an increase in Ca, F, and REE concentrations. The overall fluorite solubility is controlled by the association behavior of HF0 at acidic conditions, and by the formation of Ca chloride, Ca hydroxyl, and Na fluoride complexes, which become enhanced by addition of NaCl, including in mildly acidic and alkaline solutions. Mass balance calculations were used to compare the REE stoichiometry of unreacted fluorite crystals with measured REE concentrations in the reacted solutions, which reveal several possible controls on REE mobility: i) stoichiometric release of REE upon fluorite dissolution; ii) precipitation of secondary REE-depleted fluorite; and/or iii) precipitation of secondary light REE fluorides, which were observed to form on fluorite crystals surfaces in the REE-doped experiments. Fluorite solubility constants (Ksp) were regressed with available low temperature solubility data using the equation logKsp = A + BT + C/T + Dlog(T) (T, temperature in K) valid between 25–250 °C. Fitted coefficients yield: A = 171.180, B = 0.01477, C = −7794.10, D = −64.6634. The experimental results have important implications for geochemical modeling of natural systems, where fluorine controls the mobility of REE, and can be enhanced by addition of NaCl in both acidic and alkaline solutions. Therefore, the salinity of magmatic-hydrothermal fluids plays a crucial role in mobilizing REE during the formation of hydrothermal fluorite veins.

Nitrogen budgets and nitrogen use efficiency as agricultural performance indicators in Lake Victoria basin

Too little nitrogen (N) is a threat to crop productivity and soil fertility in sub-Saharan Africa (SSA). Nitrogen budgets (NB) and nitrogen use efficiency (NUE) are critical tools for assessing N dynamics in agriculture and have received little or no attention in the region. Data were collected from smallholder farmers clustered into two categories, farmers applying and farmers not applying N fertilizers. NB were calculated using the Coupled Human and Natural Systems (CHANS) model approach for field and farm spatial scales. The results showed spatial variabilities in NB and NUE at the field level (maize and rice) across all the catchments. At the field level, N balances were negative for the two crops in all the catchments. Similarly, at the farm gate, a deficit of −78.37 kg N ha−1was observed, an indicator of soil N mining. NUE values at the field scale varied across the catchments for both crops, with values for maize grown without N ranging from 25.76 to 140.18%. Even with the application of mineral N at higher levels in rice fields compared to maize fields, NUE values ranged between 81.92 and 224.6%. Our study revealed that the Lake Victoria region suffers from inefficient N cycling due to depleted soil N pools and low synchrony between N input and N removal. Therefore, a challenge lies in exploiting more sustainable N sources for farmers in the region for sustainable farming systems. The NB and NUE provide critical information to agriculture stakeholders to develop environmental, agronomic, and economically viable N management solutions.

Nanopore-based technologies beyond DNA sequencing.

Inspired by the biological processes of molecular recognition and transportation across membranes, nanopore techniques have evolved in recent decades as ultrasensitive analytical tools for individual molecules. In particular, nanopore-based single-molecule DNA/RNA sequencing has advanced genomic and transcriptomic research due to the portability, lower costs and long reads of these methods. Nanopore applications, however, extend far beyond nucleic acid sequencing. In this Review, we present an overview of the broad applications of nanopores in molecular sensing and sequencing, chemical catalysis and biophysical characterization. We highlight the prospects of applying nanopores for single-protein analysis and sequencing, single-molecule covalent chemistry, clinical sensing applications for single-molecule liquid biopsy, and the use of synthetic biomimetic nanopores as experimental models for natural systems. We suggest that nanopore technologies will continue to be explored to address a number of scientific challenges as control over pore design improves.

Interval neutrosophic stochastic dynamical systems driven by Brownian motion

Stochastic and neutrosophic theory are effective tools for modeling real-world phenomena and natural dynamical systems, where inputs are often affected by stochastic noises and outputs often contain both randomness and indeterminacy. In this work, we present a new type of stochastic differential equations (SDE) driven by an one-dimensional Brownian motion that can be considered as an efficient tool to describe the uncertain behavior of dynamical systems operating in interval neutrosophic environments with stochastic noises. After introducing some basic foundations on neutrosophic arithmetic, neutrosophic calculus and neutrosophic stochastic process, we define the new form of interval neutrosophic stochastic differential equations taking values in neutrosophic environment. Under some suitable conditions, the unique existence result of stochastic solution is obtained based on the use of Picard successive approximation. We also introduce an efficient numerical algorithm, namely Euler–Maruyama method, to solve the numerical solution of proposed problem and further demonstrate the effectiveness of the numerical method by solving some examples in stochastic biological systems such as stochastic logistic growth model, stochastic Lotka–Volterra predator–prey model, and stochastic SARS model, respectively.