Entropy considerations in improved circuits for a biologically-inspired random pulse computer

We present five novel or modified circuits intended for building a universal computer based on random pulse computing (RPC) paradigm, a biologically-inspired way of computation in which variable is represented by a frequency of a random pulse train (RPT) rather than by a logic state. For the first time we investigate operation of RPC circuits from the point of entropy. In particular, we introduce entropy budget criterion (EBC) to reliably predict whether it is even possible to create a deterministic circuit for a given mathematical operation and show its relevance to numerical precision of calculations. Based on insights gained from the EBC, unlike in the previous art, where randomness is obtained from electronics noise or a pseudorandom shift register while processing circuitry is deterministic, in our approach both variable generation and signal processing rely on the random flip-flop (RFF) whose randomness is derived from a fundamentally random quantum process. This approach offers an advantage in higher precision, better randomness of the output and conceptual simplicity of circuits.

Participative Renewable Energy Community—How Blockchain-Based Governance Enables a German Interpretation of RED II

Distributed solar photovoltaic generation is less expensive than the retail price of electricity from the grid in most parts of Europe. Growing shares of variable generation place a focus on Renewable Energy Communities (REC) to increase the economic benefits of local energy systems. Civil society could play an influential and necessary role in the development of such communities, the expansion of renewable energy capacity and the provision of flexibility. However, current RECs models still confine tenants to their role as non-participating consumers. This article provides a concept to enable participative RECs within the German regulatory framework through collective self-consumption by including consumers for a fair allocation of renewable electricity using the blockchain technology.

The Modeling of Interval-Valued Time Series Using Possibility Measure-Based Encoding-Decoding Mechanism

Interval-valued time series (ITS) is a collection of interval-valued data whose entires are ordered by time. The modeling of ITS is an ongoing issue pursued by many researchers. There are diverse ITS models showing better performance. This paper proposes a new ITS model using possibility measure-based encoding-decoding mechanism involved in fuzzy theory. The proposed model consists of four modules, say, linguistic variable generation module, encoding module, inference module and decoding module. The linguistic variable generation module can provide a series of linguistic variables expressed in fuzzy sets used to described dynamic characteristics of ITS. The encoding module encodes ITS into some embedding vectors with semantics with the aid of possibility measure and linguistic variables formed by linguistic variable generation module. The inference module uses artificial neural network to capture relationship implied in those embedding vectors with semantic. The decoding module decodes for the outputs of the inference module to produce the output of linguistic and interval formats by using the possibility measure-based encoding-decoding mechanism. In comparison with existing ITS models, the proposed model can not only produce the output of linguistic format, but also exhibit better numeric performance.

Development of a Five-Parameter Model to Facilitate the Estimation of Additive, Dominance, and Epistatic Effects with a Mediating Using Bootstrapping in Advanced Generations of Wheat (Triticum aestivum L.)

As a result of two crosses among three local varieties of wheat, five populations (P1, P2, F5, F6 and F7) were used as parents and grown during two successive seasons; 2016/2017 and 2017/2018. To estimate five types of gene action (e.g., mean effects, additive, dominance, additive × additive, and dominance × dominance), five formulas were developed from with algebraic solution, algebraic proof, and mathematical proof. Besides, to test adequate of a simple additive-dominance model, three formulas A, B, and C scaling test were developed. The path analysis method by PROCESS Macro, AMOS, and Bootstrapping was employed to assess the relationships between grain yield/plant (GYP) as the dependent variable and each one of the number of spikes (NS) and 1000-grain weight (TW) as the independent variables. The results show that there are eight validated equations used to estimate the scaling test (A, B and C) and five types of gene effects (m, a, D, I and L), respectively. Confidence interval using Bootstrapping results indicate that TW was played as the partial mediator between NS as an exogenous variable and GYP as an endogenous variable. Generation means analysis is a relatively simple and statistically reliable tool suitable for the fundamental estimation of different genetic influences.

Capacity Value from Wind and Solar Sources in Systems with Variable Dispatchable Capacity—An Application in the Brazilian Hydrothermal System

The most robust methods to determine the capacity contribution from intermittent sources combine load curve, variable generation profile, and dispatchable generators’ data to calculate any new inserted variable source’s capacity value in the power system. However, these methods invariably adopt the premise that the system’s dispatchable generators’ capacity is constant. That is an unacceptable limitation when the energy mix has a large share of hydroelectric sources. Hydroelectric plants are dispatchable sources with variable maximum power output over time, varying mainly according to the reservoirs’ level. This article develops a method that makes it possible to calculate the capacity value from renewable resources when the dispatchable generation units of an electric system have variable capacity. The authors apply the method to calculate the capacity value from solar and wind sources in Brazil as an exercise. By abandoning the hypothesis of constant dispatchable capacity, the developed approach is in principle extensible for other energy-limited resources, such as batteries and concentrating solar power (CSP). This can be a strategy to incorporate energy-limited capacity sources into the planning and operation models as reliable capacity sources.