Sustainability has been one of architecture’s most significant trends over the last twenty years. The environmental consciousness of professionals has put sustainability at the heart of the architectural profession and has contributed to adopting and implementing sustainable designs on the scale of urban landscapes. A green roof or living roof, which is a sustainable solution in architecture, is a roof on the surface of which plants are grown. The roof is covered by plants, covering the waterproof layer beneath the vegetation. (...) However, various types of plants can be used in this scheme. Understanding the influencing factors in choosing the right plant species and the impact that utilizing green roofs has on the overall energy consumption of the building can tremendously help scientists and clarify the possible future research topics in this field. Hence, this article investigates energy optimization in the construction process of a green roof in sustainable architecture and its advantages and challenges. The results of this study show that budget limitations, managerial and organizational policies, legal issues, technical and scientific infrastructure, and cultural and geographical aspects are all affecting the widespread use of green roofs currently and need to be considered in future studies. (shrink)

Sentiment analysis is also known as opinion mining. It has an important role in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment. With the advancement of technology, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. This research aims to optimize the marketing strategy of energy products in SOE subsidiaries through sentiment analysis using the VADER and LSTM methods on (...) social media.The researcher analyzed data from twitter to identify consumer sentiment towards energy products. The results of the analysis show that the combination of VADER and LSTM is effective in identifying the nuances of sentiment, with the accuracy of sentiment classification reaching 85%. Based on the analysis, the researchers developed recommendations for marketing strategies that are more focused and responsive to public perception. This study also highlights the importance of two-way interaction between companies and consumers as part of an effective marketing strategy. The findings of this study are expected to be used as a reference by state-owned companies in the energy sector to improve the effectiveness of their marketing strategies through a sentiment analysis approach on social media. (shrink)

In the metabolic control theory, the control coefficient is a key parameter in quantifying the sensitivity of the flux towards an infinitesimal variation of enzyme activity. This concept does not apply just as it is for variations of enzyme concentrations whenever there is spatial, energy or resources limitations in the cell. Due to constraint on total enzyme concentration, the variation of concentration of any given enzyme may affect the concentrations of other enzymes. To take into account these correlations between (...) enzyme concentrations, we propose the concept of "combined response coefficient". Its definition is similar to that of the control coefficient, but its mathematical expression is different. Its range of variation is from – + 1, the null value corresponding to optimum enzyme concentration, i.e. to concentrations that maximise the flux, and the negative values to concentrations beyond the optimum value. A summation property could be derived using a simple weighting of the combined response coefficients, the sum of the weighed coefficient being 0. (shrink)

Model predictive control technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal (...) utility, which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method. (shrink)

Hybrid renewable energy system arises regularly in real life. By optimizing the capacity and running status of the microgrid, HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, (...) we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems. (shrink)

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management (...) scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines into two pools: a hot pool and a warm pool. Each PM is configured with a resource search engine that finds an available virtual machine for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm is presented to minimize the system cost and optimize the sleep parameter. (shrink)

Mobile ad hoc network is a network composed of mobile terminals without infrastructure. Due to its fast-networking ability, it is widely used in smart cities, car networking, military, agriculture, medicine, and other fields. Routing technology is a key technology in the field of MANET. The energy consumption is optimized through the technical network simulator NS-3, and the nodes in the MANET routing protocol can accurately simulate the problem, thereby optimizing the MAC layer of important research tools. This article combines (...) the advantages of the lithium-ion battery model. Compared with the KiBaM battery model, the effective charge in the KiBaM model simulates the output voltage well, which can reflect the battery capacity effect rate and the recovery effect of the lithium-ion-KiBaM battery model. The model can respond to the characteristics of the output voltage as the society gets lower and lower. In order to solve the problem that the analytic solution of differential equations cannot be obtained in the model, a numerical solution of the optimization algorithm is proposed based on the implicit Runge–Kutta method. The MAC layer energy consumption and routing protocol optimization algorithm proposed in this paper can be applied well in various fields. (shrink)

Offshore micro integrated energy systems are the basis of offshore oil and gas engineering and play an important role in developing and utilizing marine resources. By introducing offshore wind power generation, the carbon emissions of offshore micro integrated energy systems can be effectively reduced; however, greater challenges have been posted to the reliable operation due to the uncertainty. To reduce the influence brought by the uncertainty, a multiobjective optimization model was proposed based on the chance-constrained programming ; (...) the operating cost and penalty cost of natural gas emission were selected as objectives. Then, the improved hybrid constraints handling strategy based on nondominated sorting genetic algorithm II was introduced to solve the model efficiently. Finally, the numerical studies verified the efficiency of the proposed algorithm, as well as the validity and feasibility of the proposed model in improving the economy of OMIES under uncertainty. (shrink)

This article uses a research method that combines theoretical research and empirical analysis. It first introduces the relevant theories of energy service city distribution sites in the context of e-commerce and then the types of energy service city distribution sites and the composition of energy service city distribution systems. The network layout of the service city distribution site and the location objectives, principles, and processes of the model is studied to determine the network layout plan of the (...) energy service city distribution system in this paper. This paper fully considers the characteristics of the network operation mode of the energy service city distribution site and establishes an optimization model for the location selection and vehicle routing of the distribution center with the lowest total system cost under the simultaneous delivery service mode; based on the hierarchical solution strategy, a combination of deep learning is designed. Algorithms mainly include two-stage hybrid heuristic algorithm of cluster analysis, maximum coverage and genetic algorithm; simulation analysis is conducted to verify the effectiveness of the model and algorithm by data simulation, finally get the integrated optimization plan of distribution center location and routing, and put forward the operation strategy through the result expansion analysis. This paper studies the planning model based on the network layout planning of the energy service city distribution system under the e-commerce environment, aiming to promote the breakthrough development of urban smart logistics and prove the importance of the energy service city distribution station network layout planning. The purpose and results of the research are to reduce traffic and environmental pressures, achieve joint direct distribution, improve the efficiency of urban logistics and distribution, and solve the problem of the last mile of the city. (shrink)

With the contradiction between supply and demand being intensified, the unreasonable crude oil trade structure has become increasingly prominent. Therefore, optimizing the supply and demand structure of the global crude oil trade is an urgent problem, which has become one of the crucial factors affecting the energy strategy and economic development of each country. This paper builds an optimization model that aims at minimizing crude oil trade costs based on the complex network theory. Meanwhile, an optimal solution generation (...) approach is proposed and developed in this paper. Compared with the preoptimized trade network, the proposed model can effectively reduce the trade cost. By topological analysis of the trade network and its optimal configuration, we obtain that both the preoptimized and optimized crude oil trade networks follow power-law distribution. By using the minimum spanning tree, we find that the major crude oil net exporting countries have the most significant influence and are at the core in the optimized trade structure. This work focuses on the sustainable development of the global crude oil trade and provides a fresh perspective for the optimal crude oil trade system. Moreover, the methodology and model may be applied in the investigation of optimization for other energy system structures. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social epistemology (...) of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

The economical/environmental scheduling problem of the ship integrated energy system has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the (...) economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS. (shrink)

The gray prediction model, based on the GM method, is an important branch of gray theory with the most active research and the most fruitful results, and it is the most widely used because of its small sample size, simple modeling process, and easy to use. Such advantages have been successfully applied in many fields such as transportation, agriculture, energy, medicine, and environment and have been gradually developed into a mainstream predictive modeling method. This study combines the Three-parameter Whitenization (...) Grey Model ), which fits the inhomogeneous exponential law sequence, and the Particle Swarm Algorithm to optimize the order and background value coefficients under the condition of the minimum sum of squares of simulation errors, and hence, to solve the problem that the cumulative order is fixed to “1” and the background value coefficient value is fixed to “0.5.” As a result, a parameter-optimized gray system model with flexibility, adaptability, and dynamic adjustment is designed to simulate and predict China’s higher education gross enrollment rate. The application shows that the model has better overall simulation and prediction performance than others. On the one hand, the parametric optimization model significantly improves its own performance, and on the other hand, its intelligent and adjustable adaptivity improves the accuracy and further extends its application. (shrink)

With the increasing number of electric vehicles, the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging (...) stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy. (shrink)

A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem (...) is the operating cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method. (shrink)

Under the requirements of the national carbon reduction target, the recycling and remanufacturing of scrap iron and steel are taken by major iron and steel enterprises as requirements for implementing industrial carbon emission reduction and are incorporated into their production strategies. The scheduling optimization of scrap iron and steel remanufacturing processes plays an important role in the energy saving and emission reduction of iron and steel enterprises. In this paper, a remanufacturing production decision model for scrap iron and (...) steel considering both the product delivery time and carbon emissions was established, and a discrete krill swarm algorithm was designed to solve the multiobjective production decision model. The effectiveness of the model and the algorithm was verified by an example, demonstrating a good decision-making reference for the implementation of energy conservation and emission reduction in iron and steel enterprises. (shrink)

Economic dispatch plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeography-based learning particle swarm optimization for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeography-based learning strategy is employed in which particles learn from each other based on (...) the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems. (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)

Resource allocation has always been a key technology in wireless sensor networks, but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource (...) allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm, it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes. (shrink)

A comparison of nineteen taxa of teleost fishes suggests the gradual acquisition of systems of upper jaw protrusion in the course of fish evolution. However, in view of the loss of protrusion in several groups of advanced teleosts the biomechanicsof protrusile jaws are analysed based on the hydrodynamics of suction feeding. Calculations show that protrusion may reduce the energy otherwise spent in a feeding act to get the predator's mouth as near to the prey in the same time with (...) about 80%. Other generalized advantages and disadvantages of upper jaw protrusion are explained. Detailed ecological data to calculate the balance between pros and cons of jaw protrusion of a particular species in its habitat are still lacking. There is no incompatibility between presence and disappearance of protrusile jaws in fish and current neo-darwinistic theories of evolution. (shrink)

The energy sector contributes approximately two-thirds of global greenhouse gas emissions. In this context, the sector must adapt to new supply and demand networks for all future energy sources. The ongoing transformation in the European energy field is driven by the ambition of the European Union to reach the climate objectives set for 2030. The main actions are increasing renewable energy production, adapting transition fuels like natural gas to reduce emissions, improving energy efficiency across all (...) economic sectors, prioritizing building, transportation, and industry, developing Carbon Capture and Storage technologies, and ensuring universal access to clean and affordable energy. The significant changes envisaged in the energy sector to increase renewable energy production and consumption require improved integration and more use of predictive tools to support stakeholders' decision-making processes. This article presents a case study to assess the performance of predictive models based on a Feed-Forward Neural Network architecture that employ Root Mean Squared Propagation as their optimization function in terms of choosing the most appropriate activation function for this kind of data input and output. The training and testing phases of the models use data about building energy consumption. The lowest training time and Root Mean Square Error values were similar for Rectified Linear Unit and Tanh activation functions. The model with the Rectified linear Unit activation function performed best. (shrink)

A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network model. It includes a linear matrix, a sine function, and a chaotic neural network composed of three chaotic neurons. One (...) of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem. (shrink)

The harmful consequences of fossil fuels use has resulted in the promotion of clean and renewable energies. During the past decades, green technologies have experienced a strong development, paying especial attention to wind energy, that covers a significant share of the electric energy demand. In this context, the main efforts are focused on the optimization of wind generator facilities, not only in the mechanic design but also in the energy management. Then, the present work deals with (...) the prediction of the energy generated in a small wind turbine placed in a bioclimatic house located on the north west region of Spain. This includes an analysis of the characteristics of the atmospheric variables registered during the turbine operation for a period of one year and an exploratory examination of a range of regression techniques in order to assess the suitability of using the registered information to predict the installation’s power generation levels on the short term. The study detailed in this work proves that this objective is an attainable one with a good degree of accuracy. (shrink)

With the stricter environmental regulations and diminishing fossil-fuel reserves, there is now higher emphasis on exploiting various renewable sources of energy. These alternative sources of energy are usually environmentally friendly and emit no pollutants. However, the capital investments for those renewable sources of energy are normally high, and there are also maintenance cost differences to be considered. Furthermore, due to the intermittency of some of these power sources, reliability issues should be addressed when integrating different power sources. (...) In this article, as an example, the design of hybrid generating systems comprising wind turbine generators, photovoltaic panels, and storage batteries is discussed based on multidisciplinary optimization. In this multisource generation system design, three design objectives are considered, that is, system cost, reliability, and pollutant emissions. (shrink)

In an earlier article [Found. Phys. 30, 1191 (2000)], a quasiclassical phase space approximation for quantum projection operators was presented, whose accuracy increases in the limit of large basis size (projection subspace dimensionality). In a second paper [J. Chem. Phys. 111, 4869 (1999)], this approximation was used to generate a nearly optimal direct-product basis for representing an arbitrary (Cartesian) quantum Hamiltonian, within a given energy range of interest. From a few reduced-dimensional integrals, the method determines the optimal 1D marginal (...) Hamiltonians, whose eigenstates comprise the direct-product basis. In the present paper, this phase space optimized direct-product basis method is generalized to incorporate non-Cartesian coordinate spaces, composed of radii and angles, that arise in molecular applications. Analytical results are presented for certain standard systems, including rigid rotors, and three-body vibrators. (shrink)

Much of the literature on the free energy principle has focused on how organisms maintain homeostasis amidst a constantly changing environment. A fundamental feature of the FEP is that biological entities are “hard-wired” towards self-preservation.However, contrary to this notion, there do exist organisms that appear to seek out rather than avoid conditions that pose an elevated risk of serious injury or death, thereby jeopardizing their physiological integrity. Borrowing a term used in 1990s popular culture to refer to stunt performers (...) like Evel Knievel, these organisms that exhibit such behavioural characteristics can be referred to as daredevils.This paper presents the case of daredevils as a challenge to the FEP’s homeostasis- and optimization-based construal of biological systems. It also introduces three possible explanatory strategies by which the FEP can account for daredevils. The broader objective of the paper is to enhance the FEP’s ability to account for a diverse range of complex behaviour. (shrink)

The world I grew up in believed that change and development in life are part of a continuous process of cause and effect, minutely and patiently sustained throughout the millenniums. With the exception of the initial act of creation ..., the evolution of life on earth was considered to be a slow, steady and ultimately demonstrable process. No sooner did I begin to read history, however, than I began to have my doubts. Human society and living beings, it seemed to (...) me, ought to be excluded from so calm and rational a view. The whole of human development, far from having been a product of steady evolution, seemed subject to only partially explicable and almost invariably violent mutations. Entire cultures and groups of individuals appeared imprisoned for centuries in a static shape which they endured with long-suffering indifference, and then suddenly, for no demonstrable cause, became susceptible to drastic changes and wild surges of development. It was as if the movement of life throughout the ages was not a Darwinian caterpillar but a startled kangaroo, going out towards the future in a series of unpredictable hops, stops, skips and bounds. Indeed, when I came to study physics I had a feeling that the modern concept of energy could perhaps throw more light on the process than any of the more conventional approaches to the subject. It seemed that species, society and individuals behaved more like thunder-clouds than scrubbed, neatly clothed and well-behaved children of reason. Throughout the ages life appeared to build up great invisible charges, like clouds and earth of electricity, until suddenly in a sultry hour the spirit moved, the wind rose, a drop of rain fell acid in the dust, fire flared in the nerve, and drums rolled to produce what we call thunder and lightening in the heavens and chance and change in human society and personality.LAURENS VAN DER POST, The Lost World of the Kalahari. (shrink)

In association with the development of intermittent renewable energy generation, dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day-ahead, optimal dispatch problem incorporating energy storage is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in (...) the space-time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization, is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO-TLBO method in solving the proposed dispatch problems. (shrink)

Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. (...) The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance. (shrink)

Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based (...) on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs. (shrink)

Combinatorial network optimization appears to fit well as a model of brain structure: connections in the brain are a critically constrained resource, hence their deployment in a wide range of cases is finely optimized to “‘save wire". This review focuses on minimization of large-scale costs, such as total volume for mammal dendrite and axon arbors and total wirelength for positioning of connected neural components such as roundworm ganglia (and also mammal cortex areas). Phenomena of good optimization raise questions (...) about mechanisms for their achievement: the examples of optimized neuroanatomy here turn out to include candidates for some of the most complex biological structures known to be derivable purely from simple physical energy minimization processes. Part of the functional role of such fine-tuned wiring optimization may be as a compact strategy for generating self-organizing complex neuroanatomical.. (shrink)

Biological order provided by α-helical secondary protein structures is an important resource exploitable by living organisms for increasing the efficiency of energy transport. In particular, self-trapping of amide I energy quanta by the induced phonon deformation of the hydrogen-bonded lattice of peptide groups is capable of generating either pinned or moving solitary waves following the Davydov quasiparticle/soliton model. The effect of applied in-phase Gaussian pulses of amide I energy, however, was found to be strongly dependent on the (...) site of application. Moving solitons were only launched when the amide I energy was applied at one of the α-helix ends, whereas pinned solitons were produced in the α-helix interior. In this paper, we describe a general mechanism that launches moving solitons in the interior of the α-helix through phase-modulated Gaussian pulses of amide I energy. We also compare the predicted soliton velocity based on effective soliton mass and the observed soliton velocity in computer simulations for different parameter values of the isotropy of the exciton-phonon interaction. The presented results demonstrate the capacity for explicit control of soliton velocity in protein α-helices, and further support the plausibility of gradual optimization of quantum dynamics for achieving specialized protein functions through natural selection. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From (...) the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (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)

Synthetic biology is a field of research that concentrates on the design, construction, and modification of new biomolecular parts and metabolic pathways using engineering techniques and computational models. By employing knowledge of operational pathways from engineering and mathematics such as circuits, oscillators, and digital logic gates, it uses these to understand, model, rewire, and reprogram biological networks and modules. Standard biological parts with known functions are catalogued in a number of registries (e.g. Massachusetts Institute of Technology Registry of Standard Biological (...) Parts). Biological parts can then be selected from the catalogue and assembled in a variety of combinations to construct a system or pathway in a microbe. Through the innovative re-engineering of biological circuits and the optimization of certain metabolic pathways, biological modules can be designed to reprogram organisms to produce products or behaviors. Synthetic biology is what is known as a “platform technology”. That is, it generates highly transferrable theoretical models, engineering principles, and know-how that can be applied to create potential products in a wide variety of industries. Proponents suggest that applications of synthetic biology may be able to provide scientific and engineered solutions to a multitude of worldwide problems from health to energy. Synthetic biology research has already been successful in constructing microbial products which promise to offer cheaper pharmaceuticals such as the antimalarial synthetic drug artemisinin, engineered microbes capable of cleaning up oil spills, and the engineering of biosensors that can detect the presence of high concentrations of arsenic in drinking water. One of the potential benefits of synthetic biology research is in its application to biofuel production. It is this application which is the focus of this entry. The term “biofuel” has referred generally to all liquid fuels that are sourced from plant or plant byproducts and are used for energy necessary for transportation vehicles (Thompson 2012). Biofuels that are produced using synthetic biological techniques re-engineer microbes into biofuel factories are a subset of these. (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)

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect (...) with tools internal to PP theory: Miller and Clark’s (2018) embodied inference; Seth’s (2013) interoceptive inference; and Joffily and Coricelli’s (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain ‘s cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Hopeful and Just Futures Across ScaleIsabelle Boucher, Alex Custodio, Hanine El Mir, Janna Frenzel, and Robert MarinovSituated Solar Relations: Rethinking Scale for the Renewable Energy Age/ Solar Media Collective, Concordia University, Tio'tia:Ke (Montréal), Canada, 05 11, 2023In the face of global climate destruction and ecological collapse, many have witnessed—and perhaps grown numb to—the repeated failures of governments and industries to organize a meaningful transition toward more sustainable social (...) and economic formations. Against the troubling concern that the "novelty" and "impact" of techno-solutions have lost all meaning, the Solar Media Collective asks who or what dictates the scales at which change is made visible, meaningful, useful, and sustainable. In other words, we ask: where should we look for the hopeful gestures that define our future worlds, and how can we resourcefully resist and model alternative pathways to Big Tech's ever-grander "solutions" to the climate crisis? 1On May 11, 2023, the Solar Media Collective—a student-run research-creation group affiliated with Concordia University in Tio'tia:ke (Montréal)—ran a day-long symposium on the multiscalar dimensions of just and hopeful energy transitions. 2Taking inspiration from the transnational Solar Protocol network's exploration into the techniques and affordances of solar-powered computer networking, we asked how, when, and where social, political, [End Page 304]technical, and cultural protocols surrounding renewable energy and the production and disposal of digital technologies can be developed to encourage diverse designs and scales of deployment for sustainable innovations and practices. By exploring these themes through a series of roundtable discussions with invited guests, we attempted to imbue the word "sustainability" with new meaning and reclaim it from corporate optimization rhetoric.Situated Solar Relations: Rethinking Scale for the Renewable Energy Agechallenged the sitelessness of large-scale initiatives by asking what might happen if situatedsolar relations were to become the primary protagonist of research, production, and outreach. Across three roundtables, an interactive workshop, and a showcase of some of our functional prototypes, the symposium addressed creative practices as a mode of future building. Roundtable discussions approached the question of how to build a just solar future through multiple angles: art, literature, and aesthetics; ludic interactions; and social, political, and economic transformations. Rather than taking for granted the principle of "scaling up" that requires sustainability to be replicable and expandable into universal and precise sets of standardized models, we argue instead for the necessity of having (non)scalable, situated, and evolving relationships with the sun. 3In other words, we turn to process rather than product, hope rather than despair, and careful research rather than extractive data practices.Relations of scale are at the heart of these dichotomies. Max Liboiron and Josh Lepawsky have recently argued that power lies in the way things appear to be "true, natural and good," which is determined and reproduced on a homogeneous scale by specific interest groups. 4Intervening against extractive, energy-intensive, and polluting practices therefore requires multiple contextualized and situated knowledges of the processes at play, which in turn requires an understanding of the scales within which energy and waste-defining relationships occur. 5They write:Problems arise when one situated way of knowing disavows its context, becomes dominant to the exclusion of other ways of knowing, or thinks it accounts for all forms of discards. The effect of such monolithic approaches to knowledge can be termed "scalar mismatch," where one instance is taken to be the whole phenomenon, or where one perspective is assumed to work in all cases. 6Scale here is about the specific processes and relationships that matter in a given context. It is not about relative size, nor continuity, but the disjuncture [End Page 305]between different sets of relationalities, whether it be at the planetary, governmental, industrial, or molecular levels. 7Power, they explain, is the reproduction of order in one scale (planetary industrial production, for example), while it disrupts order at others (community or molecular processes). 8In order to disentangle these overlapping but incommensurable scales and to counteract the epistemic grip some have over the others, the authors suggest that it is important to find creative ways... (shrink)

Recent years have seen an increasing interest in the study of continuous-time computational models. However, not so much has been done with respect to setting up a complexity theoretic framework for such models. The present paper intends to go a step into this direction. We consider problems over the real numbers which we try to relate to Lyapunov theory for dynamical systems: The global minimizers of particular energy functions are supposed to give solutions of the problem. The structure of (...) such energy functions leads to the introduction of problem classes U and NU; for the systems we are considering they parallel the classical complexity classes P and NP. We then introduce a notion of reducibility among problems and show existence of complete problems for NU and for PU, a polynomial hierarchy of continuous-time problems.For previous work on the computational capabilities of continuous-time systems see the surveys by Cris Moore [9] and by Pekka Orponen [10]. Our paper presents a step into the direction of creating a general framework for a complexity theory of continuous-time systems as outlined in [10]. It is closely related to work done by A. Ben-Hur, H. Siegelmann, and S. Fishman [12, 11]. (shrink)

Control of the population diversity in genetic algorithms applied to the protein structure prediction problem. Genetic Algorithms (GAs), a successful approach for optimization problems, usually fail when employed in the standard configuration in the protein structure prediction problem, since the solution space is very large and the population converges before a reasonable percentage of the possible solutions is explored. Thus, this work investigates the effect of increasing the diversity of the population on this problem by using Hypermutation and Random (...) Immigrants, two traditional population diversity control schemes, in the structure prediction of the proteins Crambin (PDB 1CRN), Met-Enkephalin (PDB 1PLW), and DNA-Ligand (PDB 1ENH). Results show a significant reduction of the minimal energy found, thanks to the diversity, but this does not necessarily means a higher similarity to the original structure.(Portuguese). (English) Copyright of Scientia is the property of Universidade do Vale do Rio dos Sinos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (shrink)

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function neural model to estimate the state-of-charge of lithium battery packs. Firstly, a suitable input set strongly correlated with the package (...) SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm. Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method. (shrink)

The last two centuries have been the centuries of the discovery of the cell evolution: in the XIX century of the germinal cells and in the XX century of two groups of somatic cells, namely those of the brain-mind and of the immune systems. Since most cells do not behave in this way, the evolutionary character of the brain-mind and of the immune systems renders human beings formed by two different groups of somatic cells, one with a deterministic and another (...) with an indeterministic (say Darwinian) behavior. An inherent consequence is that of the generation, during ontogenesis, of a dual biological identity. The concept of the dual biological identity may be used to explain the Kantian concept of the two metaphysical worlds, namely of the causal necessity and of the free will (Azzone, 2001). Two concepts, namely those of complex adaptive systems (CAS) and of emergence (Holland, 2002), are useful tools for understanding the mechanisms of adaptation and of evolution. The concept of complex adaptive systems indicates that living organisms contain series of stratified components, denoted as building blocks, forming stratified layers of increasing complexity. The concept of emergence implies the use of repeating patterns and of building blocks for the generation of structures of increasing levels of complexity, structures capable of exchanging communications both in the top-down and in the bottom-up direction. Against the concept of emergence it has been argued that nothing can produce something which is really new and endowed of causal efficacy. The defence of the concept of emergence is based on two arguments. The first is the interpretation of the variation-selection mechanism as a process of generation of information and of optimization of free energy dissipation in accord with the second principle of thermodynamics. The second is the objective evidence of the cosmological evolution from the Big Bang to the human mind and its products. Darwin has defended the concept of the continuity of evolution. However evolution should be considered as continuous when there is no increase of information and as discontinuous when there is generation of new information. Examples of such generation of information are the acquisition of the innate structures for language and the transition from absence to presence of morality. There are several discontinuity thresholds during both phylogenesis and ontogenesis. Morality is a relational property dependent on the interactions of human beings with the environment. Piaget and Kohlberg have shown that the generation of morality during childhood occurs through several stages and is accompanied by reorganization of the child mental organization. The children respect the conventions in the first stage and gradually generate their autonomous morality. The transition from absence to presence of morality, a major adaptive process, then, not only has occurred during phylogenesis but it occurs again in every human being during ontogenesis. The religious faith does not provide a logical justification of the moral rules (Ayala, 1987) but rather a psychological and anthropological justification of two fundamental needs of human beings: that of rendering Nature an understandable entity, and that of increasing the cooperation among members of the human societies. The positive effects of the altruistic genes in the animal societies are in accord with the positive effects of morality for the survival and development of the human societies. (shrink)

The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures (...) generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units. (shrink)

The notion that the idea of nature isnot quite the unbiased rule to designsustainable futures is obvious. But,nevertheless, questions about nature, how itfunctions and what it might aim at, is leadingthe controversial debates about bothsustainability and biotechnology. These tworesearch areas hardly have the same theorybackground. Whereas in the first concept, theidea of eternal cyclical processes is basic,the latter focuses on optimization. However,both concepts can work together, but only undera narrow range of public acceptance in Europe.The plausibility of arguments for (...) usingbiotechnology within sustainable technologiesvaries according to the assumed part natureitself plays for reaching optimized states. Theculture related vision of nature's functionshas impact on agricultural biotechnology,dealing not only with food crops but also withnon-food plants like renewable resources thatare used for energy or fiber production. Theseplants are grown to reach sustainabledevelopment. However, there is a fundamentaldifference between regarding biofuels as``renewable'' and ``regenerative,'' due to thetension between the concepts of ``the natural''and ``the sustainable.'' Arguments ofoptimization, efficiency, and efficacy arecritically discussed in order to take thepresent need for sustainable technologies forserious. (shrink)