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Challenging problems arise in all segments of energy industries—generation, transmission, distribution and consumption. Optimization models and methods play a key role in offering decision/policy makers better information to assist them in making sounder decisions at different levels, ranging from operational to strategic planning.
mixed integer linear programming --- fuzzy set theory --- stochastic programming --- mixed integer linear programing --- variable renewable power --- generation efficiency --- optimization --- flexibility option --- portfolio analysis --- firefighting --- semi-mean-absolute deviation model --- component outage --- energy network --- predicted mean vote (PMV) --- generation expansion planning --- building microgrid --- demand side management --- stochastic robust optimization --- oil storage plants --- long-term forecasting --- multi-criteria decision making (MCDM) --- life cycle cost --- graph theory --- scenario-based multistage stochastic programming --- optimal power generation mix --- heating ventilation and air-conditioning (HVAC) --- intermittent sources --- electric-power structure adjustment --- technique for the order of preference by similarity to the ideal solution (TOPSIS) --- integrated energy system --- Markov chain Monte Carlo --- nondominated sorting genetic algorithm (NSGA) --- domino effect --- energy system management model --- electrical distribution systems --- microgrid operation --- influence diagram --- net demand --- wind power forecasting --- energy conservation and emissions reduction --- feasible operation region --- meshed topology --- occupancy-based control --- islanded microgrids --- combined heat and power --- multi-objective optimization --- re-optimization and rescheduling
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Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book.
fuzzy logic --- complexity --- chemical artificial intelligence --- human nervous system --- fuzzy proteins --- conformations --- photochromic compounds --- qubit --- protein dynamics --- conformational heterogeneity --- promiscuity --- fuzzy complexes --- higher-order structures --- protein evolution --- fuzzy set theory --- artificial intelligence --- GCN4 mimetic --- peptides–DNA --- E:Z photoisomerization --- conformational fuzziness --- photoelectrochemistry --- wide bandgap semiconductor --- artificial neuron --- in materio computing --- neuromorphic computing --- intrinsically disordered protein --- intrinsically disordered protein region --- liquid–liquid phase transition --- protein–protein interaction --- protein–nucleic acid interaction --- proteinaceous membrane-less organelle --- fuzzy complex. --- d-TST --- activation energy --- Transitivity plot --- solution kinetic --- Maxwell–Boltzmann path --- Euler’s formula for the exponential --- activation --- transitivity --- transport phenomena --- moonlighting proteins --- intrinsically disordered proteins --- metamorphic proteins --- morpheeins --- n/a --- peptides-DNA --- liquid-liquid phase transition --- protein-protein interaction --- protein-nucleic acid interaction --- Maxwell-Boltzmann path --- Euler's formula for the exponential
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