This paper presents the nonlinear dynamic analysis of energy-based models arisen from the applied systems characterized by the energy transport in the presence of fractional order derivative and time delay. The studied model is the fractional version of Bianca-Ferrara-Dalgaard-Strulik model of economy which is viewed as a transport network for energy in which the law of motion of capital occurs. By considering the time delay as bifurcation parameter, a proof to investigate the existence of Hopf bifurcation and the phase (...) lock solutions using the Poincare-Linstedt and the harmonic balance methods is given. At definite values of time delay, period-doubling bifurcations followed up by the consequences of chaotic states are detected. Simulation results assure that the BFDS model can generate new chaotic attractors beyond half order derivatives through the effect of the time delay on that system. In accordance with the literatures related to the problem of chaos, the concluded results confirm the proposed theorem by El-Borhamy in which the time delay possesses the ability to change the dynamic state of nonlinear systems from regular to chaotic within the fractional order derivative domain. (shrink)

In recent years, the green aviation technology draws more attention, and more hybrid power units have been applied to the aerial vehicles. To achieve the high performance and long lifetime of components during varied working conditions, the effective regulation of the energy management is necessary for the vehicles with hybrid power unit. In this paper, power prediction-based model predictive control for energy management strategy is proposed for the vehicle equipped with HPU based on turboshaft engine in order to maintain proper (...) battery’s state of charge and decrease turboshaft engine’s exhaust gas temperature. First, a modeling approach based on data-driven method is adopted to obtain the mathematical model of turboshaft engine considering time delay and inertial of states. An integrated power predictor consisting of the classification of input status and the subpredictors are developed based on the deep learning method to improve the accuracy of the prediction model of the model predictive control. Subsequently, an EMS based on MPC using the proposed power predictor is introduced to regulate the SOC of battery and the EGT of turboshaft engine. The comparison with experimental results shows the high accuracy of mathematical model of turboshaft engine. The simulation results show the effectiveness of the proposed EMS for the vehicle, and the effects of different weight coefficients of objective function on the proposed EMS are discussed. (shrink)

Surface flattening plays an important role in the whole process of garment design. We proposed a novel method by using three-dimensional triangle mesh flattening in this study. First, the three-dimensional triangle mesh is flattened to a two-dimensional plane to approximate the original surface. The initial flattening results are then used as preliminary guesses for subsequent optimizations. Considering that the deformation energy in the real woven fabric is related to tensile or shear deformation, a simplified fabric deformation model based on energy (...) is proposed to update the energy distribution to determine the best two-dimensional pattern. An innovative unified axis system process is proposed to obtain the deformation energy, and energy relaxation in local flattening is proposed to release the distortion of flattening. Finally, the experimental results show that complex surfaces such as garments could achieve better flattening results. Compared with other energy-based methods in garment design, our proposed methods are more flexible and practical. (shrink)

Sports energy consumption is a quantitative reflection of physical exercise effect. Combined with different sports modes and students’ physical characteristics, the calculation model of sports energy consumption is put forward. Firstly, the relationship between students’ age, height, weight, gender, and energy consumption is analyzed by using multiple linear regression method, and a linear acceleration model is proposed by combining different exercise methods. The relationship between the integral value of acceleration and energy consumption is analyzed, and a linear integral model based (...) on different motion modes is proposed. Based on the kinetic energy theorem, the student movement energy expenditure is estimated. This paper proposes a human movement recognition method based on hybrid features, which mostly can represent the curve of the second generation wavelet transform edge thinning, and from the edge and texture features of the optimal said human posture, the statistical characteristic of the second generation wavelet transform is subtly trained as image characteristics, learning and recognition of human movement. Then, the motion recognition algorithm is tested, which can effectively identify the common movement patterns of primary and middle school students. Finally, the linear relationship between the estimation results of the model and the calculation results of Meijer is analyzed. The analysis results show that the linear acceleration model proposed in this paper can estimate the energy consumption of primary and middle school students’ motion relatively accurately. (shrink)

Signature-based model-independent searches for new physics form an explorative component of High Energy Physics experiments that aims at avoiding biases toward beyond-the-standard-model theo...

This paper is a summary of the research developed in the author’s Ph.D. programme. The case study deals with the implementation of Energy Efficiency policies in the Building sector of the Lazio Region and carries out an ABM analysis of the impacts of training on the social actors involved. Its purpose is to reproduce the social mechanisms through the study of the actors’ actions with ABM in order to evaluate socio-economic impacts by interconnecting the social and economic variables by means (...) of the Social Accounting Matrix. (shrink)

Undoubtedly the Penrose-Hameroff Orch OR model may be considered as a good theory for describing information processing mechanisms and holistic phenomena in the human brain, but it doesn’t give us satisfactory explanation of human perception. In this work a new approach explaining our perception is introduced, which is in good agreement with Orch OR model and other mainstream science theories such as string theory, loop quantum gravity and holographic principle. It is shown that human perception cannot be explained in the (...) terms of elementary particles and we should introduce new indivisible holistic objects with geometry based on smooth infinitesimal analysis - elastic membranes. The example of such a membrane is our Universe which is an indivisible whole. It is shown that our perception may be considered as the result of elastic oscillations of two dimensional (2D) elastic membranes with closed topology embedded in our bodies. Only one elastic membrane responsible for its perceptions will correspond to the selected organism, but there may be other membranes, even at the cell level. In other words, reality may be considered as the process of time evolution of holistic energetically very weak macro objects - elastic membranes with the geometry based on smooth infinitesimal analysis. An embedded membrane in this multidimensional world will look different for the external and internal observers: from the outside it will look like a material object with smooth infinitesimal geometry, while from the inside our Universe-like space-time fabric. When interacting with elementary particles and other membranes, a membrane will transform their energy into its elastic energy (a new form of energy) - the energy of stretching of the infinitesimal segments. The theory postulates that these elastic deformations will not be observable from the point of view of the internal observer. Heisenberg’s uncertainty principle will work in this physics only from the point of view of the internal observer. For the external observer each embedded elastic membrane may be stretched and even a very small region will become observable. For example, living organisms play the role of internal observers of the Universe, and at the same time they serve as external observers for 2D membranes embedded into our Universe. We can observe our 2D self-membranes through our perceptions, which are encoded in elastic oscillations of the elastic membrane. According to the theory, elastic membranes occupy energetically favorable positions around microtubules involved into Orch OR. The theory not only gives us a really multidimensional holistic picture of reality, but it also provides us with a new method for understanding such phenomena as perception, self-awareness and will. (shrink)

Disagreement about how best to think of the relation between theories and the realities they represent has a longstanding and venerable history. We take up this debate in relation to the free energy principle (FEP) - a contemporary framework in computational neuroscience, theoretical biology and the philosophy of cognitive science. The FEP is very ambitious, extending from the brain sciences to the biology of self-organisation. In this context, some find apparent discrepancies between the map (the FEP) and the territory (target (...) systems) a compelling reason to defend instrumentalism about the FEP. We take this to be misguided. We identify an important fallacy made by those defending instrumentalism about the FEP. We call it the literalist fallacy: this is the fallacy of inferring the truth of instrumentalism based on the claim that the properties of FEP models do not literally map onto real-world, target systems. We conclude that scientific realism about the FEP is a live and tenable option. (shrink)

The paper proposes a process-based model for an ontology that encompasses the emergence of process systems generated by increasingly complex levels of organization. Starting with a division of processes into those that are persistent and those that are fleeting, the model builds through a series of exclusive and exhaustive disjunctions. The crucial distinction is between those persistent and cohesive systems that are energy wells, and those that are far-from-equilibrium. The latter are necessarily open; they can persist only by interaction with (...) their environments. Further distinctions, developed by means of the notions of self-maintenance and error detection, lead to the identification of complex biological organisms that are flexible learners, some of which are self-conscious and form themselves into social institutions. This model provides a non-reductive model for understanding human beings as both embodied and yet emergent. In particular, it provides a way of characterizing action as ‘metaphysically deep’, not an ontological embarrassment within an otherwise physicalist world. (shrink)

The energy domain is still dominated by equilibrium models that underestimate both the dangers and opportunities related to climate change. In reality, climate and energy systems contain tipping points, feedback loops, and exponential developments. This paper describes how to create realistic energy transition management models: quantitative models that can discover profitable pathways from fossil fuels to renewable energy. We review the literature regarding agent-based economics, disruptive innovation, and transition management and determine the following requirements. Actors must be (...) detailed, heterogeneous, interacting, learning, and strategizing. Technology should be represented as a detailed and heterogeneous portfolio that can develop in a bottom-up manner, using endogenous feedback loops. Assumptions about discount rates and the social cost of carbon should be configurable. The model should contain interactions between the global, national, local, and individual level. A review of modelling techniques shows that equilibrium models are unsuitable and that system dynamics and discrete event simulation are too limited. The agent-based approach is found to be uniquely suited for the complex adaptive sociotechnical systems that must be modelled. But the choice for agent-based models does not mean a rejection of other approaches because they can be accommodated within the agent-based framework. We conclude with practical guidelines. (shrink)

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production, heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and (...) sustainability in the industry. This paper presents an economic model of a microgrid system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression model was optimized by the gravitational search algorithm. The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic, CCHP system, fuel cell, and energy storage devices are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model. (shrink)

A model of a three-dimensional quantum vacuum based on Planck energy density as a universal property of a granular space is suggested. The possibility to provide an unifying explanation of dark matter and dark energy as phenomena linked with the fluctuations of the three-dimensional quantum vacuum is explored. The changes and fluctuations of the quantum vacuum energy density generate a curvature of space–time similar to the curvature produced by a “dark energy” density. The formation of large scale structures in the (...) universe associated to the flattening of the orbital speeds of the spiral galaxies can be explained in terms of primary fluctuations of the quantum vacuum energy density without attracting the idea of dark matter. (shrink)

The purpose of this article is to present a summary of a consistent European Union (EU) energy and energy-related emissions outlook for the period to 2020. The material presented here is based on quantitative analysis and on a process of communication with and feedback from a number of energy experts and organizations. All the results presented for EU energy trends are based on the PRIMES partial equilibrium model for the European energy system, version 2. The PRIMES model provides simulation of (...) the energy system and the decisions of the agents and the markets, covering in detail several sectors, uses, and technologies. The results of this analysis indicate that the EU will not meet the obligations for greenhouse gas emissions reductions it undertook at Kyoto unless it introduces policy initiatives for the abatement of energy-related emissions. Although the industrial, tertiary, household, and transportation sectors can all make significant contributions to CO2 emissions reductions, the electricity- and steam-generation sector appears to offer the greatest potential because of its relative flexibility in terms of technology and fuel choice. Finally, when the only criterion for emissions reductions is economic efficiency, our analysis indicates that reduction levels differ significantly among EU countries. (shrink)

The prediction of protein–protein interactions based on independently obtained structural information for each interacting partner remains an important challenge in computational chemistry. Procedures where hypothetical interaction models (or decoys) are generated, then ranked using a biochemically relevant scoring function have been garnering interest as an avenue for addressing such challenges. The program PatchDock has been shown to produce reasonable decoys for modeling the association between pig alpha-amylase and the VH-domains of camelide antibody raised against it. We designed a biochemically (...) relevant method by which PatchDock decoys could be ranked in order to separate near-native structures from false positives. Several thousand steps of energy minimization were used to simulate induced fit within the otherwise rigid decoys and to rank them. We applied a partial free energy function to rank each of the binding modes, improving discrimination between near-native structures and false positives. Sorting decoys according to strain energy increased the proportion of near-native decoys near the bottom of the ranked list. Additionally, we propose a novel method which utilizes regression analysis for the selection of minimization convergence criteria and provides approximation of the partial free energy function as the number of algorithmic steps approaches infinity. (shrink)

Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance (...) costs related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators. (shrink)

The present paper aims to broaden the current understanding of students’ misconception of scientific terminology by identifying the gaps between Arabic and English scientific terminologies and between everyday language and scientific language. The paper compares the polysemy, prototypes, and motivating factors of English energy with those of Arabic طَاقَة (ṭāqa), with more focus on students’ prior knowledge. The study employs Lakoff’s (1987) idealized cognitive models and Rosch’s (1975) prototype theory to reveal the radial members of both categories, i.e., energy (...) and طَاقَة (ṭāqa), and to explain the kinds of cognitive mechanisms that motivate the extension as well as understanding of the meanings of these terms. To this end, the study uses several English and Arabic dictionaries, lexical databases and corpora. This is to explore all the meanings, prototypes and motivating factors of the terms under investigation. The results show that the terms energy and طَاقَة (ṭāqa) overlap in prototypical meanings and motivating factors but differ in less prototypical and peripheral meanings. English and Arabic learners may then face similar issues in learning scientific concepts due to the difference between their pre-existing knowledge and scientific language. (shrink)

Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the (...) curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world’s different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

The issue of the relationship between predictive processing (PP) and the free energy principle (FEP) remains a subject of debate and controversy within the research community. Many researchers have expressed doubts regarding the actual integration of PP with the FEP, questioning whether the FEP can truly contribute significantly to the mechanistic understanding of PP or even undermine such integration altogether. In this paper, I present an alternative perspective. I argue that, from the viewpoint of the constraint-based mechanisms approach, the FEP (...) imposes an important constraint, namely variational free energy, on the mechanistic architecture proposed by PP. According to the constraint-based mechanisms approach, high-level cognitive mechanisms are integral parts of extensive heterarchical networks that govern the physiology and behavior of agents. Consequently, mechanistic explanations of cognitive phenomena should incorporate constraints and flows of free energy as relevant components, given that the implemented constraints operate as long as free energy is available. Within this framework, I contend that the FEP provides a relevant constraint for explaining at least some biological cognitive mechanisms described in terms of Bayesian generative models that minimize prediction errors. (shrink)

With renewable energy being increasingly connected to power grids, pumped storage plants play a very important role in restraining the fluctuation of power grids. However, conventional control strategy could not adapt well to the different control tasks. This paper proposes an intelligent nonlinear model predictive control strategy, in which hydraulic-mechanical and electrical subsystems are combined in a synchronous control framework. A newly proposed online sequential extreme learning machine algorithm with forgetting factor is introduced to learn the dynamic behaviors of the (...) coupling system. Specifically, the initial learning parameters are optimized by prior-knowledge learning and a new self-adaptive adjustment strategy is also put forward. Subsequently, the stair-like control strategy and artificial sheep algorithm are used in rolling the optimization mechanism to replace the existing complex differential geometric solutions. Comparative experiments are carried out under different working conditions based on a PSP in China. The results show that the influence from coupling factors can be considerable and the proposed MPC strategy indicates superiority in voltage and load adjustment as well as the frequency oscillation suppression. (shrink)

In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in-depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and (...) introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low-carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction. (shrink)

Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, (...) a long short-term memory neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform. (shrink)

In this paper, by means of a novel use of insights from the literature on scientific modelling, I will argue in favour of an instrumentalist approach to the models that are crucially involved in the study of adaptive systems within the Free-Energy Principle (FEP) framework. I will begin (§2) by offering a general, informal characterisation of FEP. Then (§3), I will argue that the models involved in FEP-theorising are plausibly intended to be isomorphic to their targets. This will (...) allow (§4) to turn the criticisms moved against isomorphism-based accounts of representation towards the FEP modelling practice. Since failure to establish an isomorphism between model and target would result in the former’s failure to represent the latter, and given that it is highly unlikely that FEP-models are ever isomorphic to their targets, maintaining that FEP-models represent their targets as they are, in a realist sense, is unwarranted. Finally (§5), I will consider what implications my argument in favour of an instrumentalist reading of FEP-models has for attempts at making use of the FEP to elaborate an account of what cognition exactly is. My conclusion is that we should not dismiss FEP-based accounts of cognition, as they would still be informative and would further our understanding of the nature of cognition. Nonetheless, the prospects of settling the philosophical debates that sparked the interest in having a “mark of the cognitive” are not good. (shrink)

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we (...) propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace values that are latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents. (shrink)

Highlights: -/- • Breastfeeding and breastfeeding support can contribute to mitigating climate change. • Achieving global nutrition targets will save more emissions than fuel-switching. • Breastfeeding support programmes support a just transition. • This work can support the expansion of mitigation options in energy system models. -/- Abstract: -/- Renewable gas has been proposed as a solution to decarbonise industrial processes, specifically heat demand. As part of this effort, the breast-milk substitutes industry is proposing to use renewable gas as (...) a substitute for fossil natural gas. However, decarbonising the industrial processing of breast-milk substitutes can increase social license for these products, potentially undermining breastfeeding. World Health Organisation nutrition targets aim to increase exclusive breastfeeding to at least 50% globally by 2025 to improve maternal, infant, and young child health and nutrition. This target will have implications for the energy transition. A weakness of existing energy models is that demands for end-use products such as breast-milk substitutes are typically not considered explicitly. This paper develops an analytical framework for explicitly representing infant feeding methods in energy systems models. We compare the emissions saved in Ireland from decarbonising the industrial processing of breast-milk substitutes with renewable gas with the emissions saved by an increase in exclusive breastfeeding to 50% in both Ireland and a key export market, China. We demonstrate that the emissions saved from achieving the minimum global breastfeeding target are greater than when renewable gas is used to displace natural gas in the production of breast-milk substitutes in Ireland. We discuss the decarbonisation of breast-milk substitutes in relation to the principle of justice as non-maleficence, a principle based on the commitment to avoid harm, a novel application of a principle of justice. We conclude that breastfeeding support can be considered a demand-side measure for mitigating climate change by reducing the demand for energy services to produce breast-milk substitutes. A key recommendation is to position breastfeeding support as both a public health and a climate justice issue that is relevant for a just transition. The framework developed for this paper could be applied to support the inclusion of a wider range of mitigation options with social justice outcomes in energy system models. [Open access]. (shrink)

The energy conversion units and energy storage equipment connected to the multi-energy system are becoming diversified, and the uncertain factors brought by distributed wind power and photovoltaic power generation make the system energy flow structure more complex, which brings great difficulties to the modeling and application of traditional energy hub modeling methods. This study deeply analyzes the multi-energy flow coupling structure and operation mechanism of multi-energy systems, and carries out the power flow calculation and analysis of multi-energy systems based on (...) an energy hub, so as to ensure the safe and stable operation of regional energy. Based on the physical characteristics of energy systems such as power systems, thermal systems, and gas systems, this article studies the comprehensive power flow model including the electric-gas-thermal multi-energy coupling network and proposes the power flow decomposition of the energy supply subsystem and its applicable equation based on Newton–Raphson method. The effectiveness of the proposed method under different operation modes is verified by case studies. The calculation results show that under constant load, the energy hub running in fixing thermal by electricity and fixing electricity by thermal mode has little influence on the voltage of each node in the power sub-network. Within the constraint range, the natural gas flow obtained from the natural gas subsystem is coupled with the power subsystem to meet the load demand. The influence on the power flow at each node of the heat network is not obvious. (shrink)

This study focuses on implementing electric vehicles (EVs) in developing countries where energy production is mainly based on fossil fuels. Although for these countries the environmental short-run benefits of the EVs cannot offset the short-run costs, it may still be the best option to implement the EVs as soon as possible. Hence, it is necessary to evaluate the alternatives to introducing EVs to the market due to the environmental concerns that created an opportunity for some developing countries to catch up (...) with the international competition. Therefore, we develop a case scenario to explore the decision-making process in implementing the EVs with three alter natives and twelve criteria. We solve the decision-making problem by using Type-2 neutrosophic numbers (T2NNs) based on the RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) method. The proposed model combines the advantages of the RAFSI technique, and it applies T2NNs to address the uncertainties. The results show that the alternatives that may suspend the implementation of the EVs are inferior. Direct implementation of EVs is prioritized. The policy implications of the results are discussed in the study. (shrink)

This paper presents a realistic, stochastic, and local model that reproduces nonrelativistic quantum mechanics results without using its mathematical formulation. The proposed model only uses integer-valued quantities and operations on probabilities, in particular assuming a discrete spacetime under the form of a Euclidean lattice. Individual particle trajectories are described as random walks. Transition probabilities are simple functions of a few quantities that are either randomly associated to the particles during their preparation, or stored in the lattice nodes they visit during (...) the walk. QM predictions are retrieved as probability distributions of similarly-prepared ensembles of particles. The scenarios considered to assess the model comprise of free particle, constant external force, harmonic oscillator, particle in a box, the Delta potential, particle on a ring, particle on a sphere and include quantization of energy levels and angular momentum, as well as momentum entanglement. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...) truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Collision-free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six-DOF serial manipulator was constructed by using the Denavit-Hartenberg method. The model of obstacles was defined by (...) axis-aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot. (shrink)

Implementing Poincaré’s geometric conventionalism a scalar Lorentz-covariant gravity model is obtained based on gravitationally modified Lorentz transformations (or GMLT). The modification essentially consists of an appropriate space-time and momentum-energy scaling (“normalization”) relative to a nondynamical flat background geometry according to an isotropic, nonsingular gravitational affecting function Φ(r). Elimination of the gravitationally unaffected S 0 perspective by local composition of space–time GMLT recovers the local Minkowskian metric and thus preserves the invariance of the locally observed velocity of light. The associated energy-momentum (...) GMLT provides a covariant Hamiltonian description for test particles and photons which, in a static gravitational field configuration, endorses the four ‘basic’ experiments for testing General Relativity Theory: gravitational (i) deflection of light, (ii) precession of perihelia, (iii) delay of radar echo, (iv) shift of spectral lines. The model recovers the Lagrangian of the Lorentz–Poincaré gravity model by Torgny Sjödin and integrates elements of the precursor gravitational theories, with spatially Variable Speed of Light (VSL) by Einstein and Abraham, and gravitationally variable mass by Nordström. (shrink)

This study applied cognitive diagnostic models to assess students’ learning progressions in energy. A Q-matrix was proposed based on existing literature about learning progressions of energy in the physical science domain and the Trends in International Mathematics and Science Study assessment framework. The Q-matrix was validated by expert review and real data analysis. Then, the deterministic inputs, noisy ‘and’ gate model with hierarchical relations was applied to data from three jurisdictions that had stable, defined science curricula. The results suggested (...) that the hypothesized learning progression was consistent with the observed progression in understanding the energy concept. We also found similarities in students’ attribute mastery across the three jurisdictions. In addition, we examined the instructional sensitivity of the selected item. We discuss several curriculum-related issues and student misconceptions that may affect students’ learning progressions and mastery patterns in different regions of the world. (shrink)

With the rapid development of Internet of Things technology, the energy consumption of service composition in the IoT environment is a key problem to be studied. At present, the problems of service composition in the IoT environment mostly focus on the evaluation research based on quality of service, ignoring the overall energy consumption in the process of dynamic configuration of service composition. Therefore, we construct the service composition structure for the IoT and propose the QoS evaluation model and energy evaluation (...) model for the service composition in the IoT environment. Considering that the service composition in the Internet of things environment is NP hard, moth algorithm is successfully applied to the QoS evaluation model and energy evaluation model. The simulation results reveal that MFO has good optimization effect in the abovementioned models, and the optimization effect of MFO is improved by 8% and 6% compared with the genetic algorithm and particle swarm optimization, so as to realize the green energy strategic management of QoS composition in the environment of IoT. (shrink)

According to the hierarchy of models (HoM) account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics (HEP) experiments. I argue that even though the HoM account aims (...) to characterize experimentation as a model-based activity, it does not involve a modeling concept for the process of data acquisition, and it thus fails to provide a model-based characterization of the theory-experiment relationship underlying this process. In order to characterize the foregoing relationship, I propose the concept of a model of data acquisition and illustrate it in the case of the ATLAS experiment at CERN’s Large Hadron Collider, where the Higgs boson was discovered in 2012. I show that the process of data acquisition in the ATLAS experiment is performed according to a model of data acquisition that specifies and organizes the experimental procedures necessary to select the data according to a predetermined set of selection criteria. I also point out that this data acquisition model is theory-laden, in the sense that the underlying data selection criteria are determined by considering the testable predictions of the theoretical models that the ATLAS experiment is aimed to test. I take this sense of theory-ladenness to indicate that the relationship between the procedures of the ATLAS experiment and the theoretical models of the phenomena of interest is first established, prior to the formation of data models, through the data acquisition model of the experiment, thus not requiring the intermediary of other types of models as suggested by the HoM account. I therefore conclude that in the context of present-day HEP experiments, the HoM account does not consistently extend to the process of data acquisition so as to include models of data acquisition. (shrink)

According to the hierarchy of models account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics experiments. I argue that even though the HoM account aims to characterize (...) experimentation as a model-based activity, it does not involve a modeling concept for the process of data acquisition, and it thus fails to provide a model-based characterization of the theory-experiment relationship underlying this process. In order to characterize the foregoing relationship, I propose the concept of a model of data acquisition and illustrate it in the case of the ATLAS experiment at CERN’s Large Hadron Collider, where the Higgs boson was discovered in 2012. I show that the process of data acquisition in the ATLAS experiment is performed according to a model of data acquisition that specifies and organizes the experimental procedures necessary to select the data according to a predetermined set of selection criteria. I also point out that this data acquisition model is theory-laden, in the sense that the underlying data selection criteria are determined by considering the testable predictions of the theoretical models that the ATLAS experiment is aimed to test. I take this sense of theory-ladenness to indicate that the relationship between the procedures of the ATLAS experiment and the theoretical models of the phenomena of interest is first established, prior to the formation of data models, through the data acquisition model of the experiment, thus not requiring the intermediary of other types of models as suggested by the HoM account. I therefore conclude that in the context of present-day HEP experiments, the HoM account does not consistently extend to the process of data acquisition so as to include models of data acquisition. (shrink)

The disciplinary enterprises engaged in the study of consciousness now extend beyond their original paradigms providing additional knowledge toward an overall understanding of the fundamental meaning and scope of consciousness. A new transdisciplinary domain has resulted from the syncretism of several approaches bringing about a new paradigm. The background for this overarching enterprise draws from a variety of traditions. In this paper however elaboration is restricted to the quantum-mechanical account in David Bohm’s theoretical work in relation to his ideas about (...) “active information”, “protointelligence”, and “non-locality”. This leads to an adapted version of Bohm’s thesis concerning the implicate order and explicate order of the Universe - the Impression Order and the Expression Order, respectively. On this view, the Universe is formed on an actual-material level, the apparent properties of things, and a potential-material level, a constant process of becoming that exerts an attractive force on the present. The central thesis emerges from a radical reformulation of certain core concepts that transforms many ontological assumptions about the material basis of consciousness. That is, the above platform inclusively connects to a hypothesis that the concepts of ‘space’, ‘time’, ‘event’, and ‘motion’ can be unified to capture the notion of simultaneous activity at reducing levels to the Impression order. This notion coheres with a physical-theoretical model of Signature-Energy-Frequency which has been well demonstrated in the atomic, chemical, and information fields. This study embraces a holistic and creative worldview based on a triadic model wherein consciousness itself is postulated as the most basic, primordial stratum. Importantly, both orders are constantly conjoined since the Impression Order exists as potential energy of the quantum vacuum. The physical realm is formed by the proposed Triangulate-Three conditions as a principle of animation and ‘being’: Consciousness, Body-of-Experience and Intellect-Reflective in the Expression Order, one aspect of which emerge as lifelike properties. This Triangulate-Three principle inheres in every particle and organism to which it guides its development, adaptation, and survival. The conscious being thus possesses all three conditions in Expression Order through STEM-interactions via SEF transmissions, as a self-organising organism. (shrink)

This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters and (...) a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of |$10$| minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. (shrink)

Energy has an ambivalent status in social theory, variously figuring as a driver or an outcome of social and institutional change, or as something that is woven into the fabric of society itself. In this article the authors consider the underlying models on which different approaches depend. One common strategy is to view energy as a resource base, the management and organization of which depends on various intersecting systems: political, economic and technological. This is not the only route to (...) take. The authors develop an alternative approach, viewing energy supply and energy demand as part of the ongoing reproduction of bundles and complexes of social practice. In articulating and comparing these two positions they show how social-theoretical commitments influence the ways in which problems like those of reducing carbon emissions are framed and addressed. Whereas theories of practice highlight basic questions about what energy is for, these issues are routinely and perhaps necessarily obscured by those who see energy as an abstract resource that structures or that is structured by a range of interlocking social systems. (shrink)

To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles, hybrid vehicles, and electric vehicles are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China’s auto industry show that the upgrading of the auto industry (...) is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; China’s auto industry upgrade process should adhere to “problem-oriented” and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization. (shrink)

Although China’s wind industry has made great progress in recent years, the wind abandonment phenomenon caused by the unbalanced development of regional wind power is still prominent. It is particularly important for the scientific development of wind power to accurately measure the utilization efficiency of wind power and understand its regional differences in China. This study establishes the improved super-efficiency slack-based measure model and long short-term memory network models, systematically and comprehensively measures and predicts the wind power utilization efficiency (...) of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. Our results show the following: China’s overall wind power utilization efficiency is relatively low but has been on a steady upward trend since 2013. Regional differences are obvious, showing that the spatial distribution pattern of wind power utilization efficiency is greatest in Northeast China, followed by North China, East China, South China, Northwest China, and Central China. The “Three-North” region with abundant wind energy resources has relatively high wind power utilization efficiency and exhibits a good development trend. East China, South China, and Central China, where wind energy resources are relatively poor, have low wind power utilization efficiency, and their development trends are not stable and are more prone to change. The utilization efficiency of wind power in coastal areas is generally better than that in inland areas. There are also differences among the thirty Chinese regions studied. Inner Mongolia and Shandong have achieved real efficiency in wind power utilization efficiency, with optimal allocation of input and output, and a good development trend. The other 28 regions have varying degrees of inefficiency, and there is still room for improvement. (shrink)

In this article, we draw upon the Conceptual Profile Theory to discuss the negotiation of meanings related to the energy concept in an 11th grade physics classroom. This theory is based on the heterogeneity of verbal thinking, that is, on the idea that any individual or society does not represent concepts in a single way. According to this perspective, the processes of conceptualization consist of the use of a repertoire of different socially stabilized signifiers, adjusted to the context in which (...) they occur. We start by proposing zones of a conceptual profile model for energy, each zone being characterized by its own commitments and identifiable by certain modes of talking about energy. Based on classroom evidence, we claim that teachers and students negotiate meanings that interpenetrate the domains of everyday and scientific knowledge. Being inevitable and necessary, this heterogeneity of conceptual thinking needs to be considered in teaching design in order to allow its awareness on the part of the students. We argue that students’ conceptual development goals should be considered in terms of general goals of science education, which points to the need of overcoming the encapsulation of scientific school knowledge. We show that the Conceptual Profile Theory provides a basis for science education that can promote the crossing of cultural boundaries, seeking relations between science and the spheres of everyday life. (shrink)

The paper draws on scientific resources formed around the notion of Free Energy Principle to reconstruct two well-known defences of panpsychism. I reconstruct the argument from continuity by expanding the mind-life continuity thesis under the rubric of the Free Energy Principle (FEP), by showing that FEP does not provide an objective criterion for demarcating the living from the inanimate. Then I will reconstruct the argument from intrinsic nature. The FEP-based account of consciousness is centred on the notion of ‘temporal depth’ (...) of generative models. I argue that even evolution can be modelled as a temporally deep system. Since we have no access to the intrinsic perspective of evolution to see whether or not it models itself as a temporally deep system, we must draw an analogy from our own case and conclude that evolution is consciousness involving. Thus the paper reconstructs two main arguments for panpsychism. (shrink)

Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena (...) in this domain, to explain them and to gain nderstanding. Based on these models, which typically isolate a single feature of QCD and disregard many others, scientists attempt to get a better understanding of the physics of strong interactions. But does this practice make sense? Is it justified to use these models for the purposes at hand? Interestingly, these models do not even provide an accurate description of the mass spectrum of protons, neutrons and pions and their lowest lying excitations well - despite several adjustable parameters. And yet, the models are heavily used. I'll argue that a qualitative story, which establishes an explanatory link between the fundamental theory and a model, plays an important role in model acceptance in these cases. (shrink)

Typical cosmological models are based on the postulate that space is homogeneous. Space however contains overdense regions in which matter is concentrating, leaving underdense regions of almost void. The evolution of the scale factor of the universe has been established from measurements on SNIa. Since such events occur in regions were matter is present, we may expect that most of the SNIa are located in overdense regions. This means that the evolution of the scale factor has been established in (...) a biased manner, by considering only information coming from overdense regions, excluding the one from the underdense regions. We develop a simple model to analyze the effect of this bias, and show that it leads to the appearance of a new tensor in the Einstein equation of general relativity, which can account for the apparent acceleration of the expansion of the universe. We further show that this tensor tends to be proportional to the FLRW metric tensor, and that the constant of proportionality quantitatively corresponds to the measured cosmological constant. We finally discuss the applicability of the bias to other probes used in determining the dynamics of the universe. (shrink)

Much has been written about the free energy principle (FEP), and much misunderstood. The principle has traditionally been put forth as a theory of brain function or biological self-organisation. Critiques of the framework have focused on its lack of empirical support and a failure to generate concrete, falsifiable predictions. I take both positive and negative evaluations of the FEP thus far to have been largely in error, and appeal to a robust literature on scientific modelling to rectify the situation. A (...) prominent account of scientific modelling distinguishes between model structure and model construal. I propose that the FEP be reserved to designate a model structure, to which philosophers and scientists add various construals, leading to a plethora of models based on the formal structure of the FEP. An entailment of this position is that demands placed on the FEP that it be falsifiable or that it conform to some degree of biological realism rest on a category error. To this end, I deliver first an account of the phenomenon of model transfer and the breakdown between model structure and model construal. In the second section, I offer an overview of the formal elements of the framework, tracing their history of model transfer and illustrating how the formalism comes apart from any interpretation thereof. Next, I evaluate existing comprehensive critical assessments of the FEP, and hypothesise as to potential sources of existing confusions in the literature. In the final section, I distinguish between what I hold to be the FEP—taken to be a modelling language or modelling framework—and what I term “FEP models.”. (shrink)

This paper studies the state of charge estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square method and Kalman filtering algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented (...) Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability. (shrink)