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A newer nanoscale technology called quantum-dot cellular automata (QCA) has been used by researchers to design digital circuits in place of the more traditional complementary metal–oxide semiconductor (CMOS) technology. This recent development in the technology change is due to the problems faced by CMOS technology in terms of power consumption and physical limitations. The advantages of QCA technology over CMOS technology are high density, low power consumption, high-speed operation, and less footprint area. This research provides a novel circuit for D-latch and static random access memory (SRAM) cells based on QCA technology. Initially, a D-latch circuit is proposed with a layout area of 0.01 μm2, a 0.5 clock cycle delay (latency), and a cell count of 18 QCA cells. Furthermore, an SRAM cell is proposed using the same D-latch circuit, which uses cell counts of 26 QCA cells and contributes to a layout area of 0.02 μm2 with a 0.75 clock cycle delay (latency). It is observed that our proposed circuits have a smaller layout area, fewer QCA cell counts, and a lower clock cycle delay (latency) than existing circuits. [ABSTRACT FROM AUTHOR]

Molecular quantum-dot cellular automata (QCA) may provide high speed, low-power, classical information processing in the post-CMOS era. The readout of molecular QCA bits is challenging because the molecules may be much smaller than transistors and even single-electron transistors. This paper builds on a recent proposal for the spectroscopic readout of bits from asymmetric QCA molecules. Here, we propose circuits for fanning out a bit onto a large QCA circuit to increase the spectroscopic signal-to-noise ratio. As the number of molecules in a fanout circuit grows, the internal bias of each asymmetric cell accumulates, and the circuit may become stuck, tolerating only a very small internal bias. We also propose the use of an applied electric field to compensate for a candidate molecule's internal bias, thereby restoring switchability, even when the internal bias is significant. [ABSTRACT FROM AUTHOR]

Quantum-dot Cellular Automata (QCA) has become one of the promising studies for nano-scale computing. QCA is one of the candidate technologies to be replaced with CMOS technology. QCA technology not only reduces power consumption and delay but also increases operating frequency and speed. The arithmetic logic unit is the essential component in a processor that performs arithmetic and logical operations. This paper presents a novel 5 × 5 reversible logic gate called the NRRG (Norouzi_Rasouli Reversible Gate) which can be used as the basic building block of 4:1 and 8:1 reversible multiplexers. Then, we have designed a RALU (reversible arithmetic and logic unit) using this gate. Our design can perform 20 operations such as AND, NAND, OR, XOR, XNOR, COPY, addition, and increment. The proposed QCA RALU requires 0.44 μm2 area, 480 QCA cells, and 10 clock phases. The proposed design needs less cell count, delay, and cost of QCA compared to previous works. The structure is implemented without any rotated cells and only uses one layer which improves the manufacturability of the design. The architectures are designed and simulated using QCA Designer 2.0.3. [ABSTRACT FROM AUTHOR]

Quantum Dot Cellular Automata (QCA) is an emerging trend in the field of nanoelectronics, and computing can be regarded as an alternative to the traditional complementary metal–oxide–semiconductor technology. The paper is devoted to the study of the key functional properties of the cells for molecular QCA based on mixed valence molecules. The theoretical results for the heat dissipation under the conditions of the fast nonadiabatic switching event and cell–cell response function are obtained in the framework of the quantum-mechanical vibronic approach. These results are parameterized using the previous reliable ab initio calculations performed for oxidized norbornadiene and its polycyclic derivatives with variable lengths of the bridge. The comparative analysis of the dimeric and bidimeric molecular cells composed of these compounds is given. It is underlined that the conditions of a strong non-linear response and a low heat release are contradictory. However, despite this problem, a parametric regime is proposed, which provides a low heat release in combination with a strong nonlinear response of the working cell to the electric field induced by the polarized driver cell. [ABSTRACT FROM AUTHOR]

In this article, we develop a vibronic theory of clocking in molecular quantum cellular automata (QCA). The clocking mechanism is considered for a trigonal trimeric mixed-valence (MV) system with one mobile electron, which is shown to act as the dimeric unit encoding binary information (Boolean states 0 or 1) coupled to a third redox center (Null state). The model includes the electron transfer between the three centers; vibronic coupling of the mobile charge with the "breathing" modes, forming a double degenerate Jahn–Teller vibration of the molecular triangle; and two electric fields, one collinear to the dimeric unit, which controls the binary states, and the other perpendicular to this unit, performing clocking. In the framework of the adiabatic approximation, the potential surface of the trimeric system has been studied and the condition determining switching and clocking has been analyzed in terms of the two controlling fields and the vibronic and transfer parameters. A thorough understanding of the site populations is achieved through the quantum-mechanical solution of the vibronic problem, maintaining the adiabatic condition for the controlling fields. It is shown that a MV trimer can act as a molecular clocked QCA cell, with favorable conditions being a positive electron transfer parameter and sufficiently strong vibronic coupling. [ABSTRACT FROM AUTHOR]

[Objective] To evaluate the ecological sensitivity of the Weihe River Basin, reveal the impact of human activities on its ecosystem, and predict future changes in ecological sensitivity. [Method]A comprehensive ecological sensitivity analysis was conducted in the Weihe River Basin, selecting landscape ecological risk sensitivity index, biodiversity sensitivity index and soil erosion sensitivity index as research indicators, a comprehensive ecological sensitivity index (CESI) was constructed to evaluate the ecological status of the Weihe River Basin over the past 20 years, and GeoSoS-FLUS were used for simulation and prediction. [Result]The ecological sensitivity of the Weihe River Basin had undergone significant changes from 2000 to 2020, with the total area of extremely sensitive areas was decreased. The areas with high ecological sensitivity in the Weihe River Basin were mostly concentrated in the west, north and small areas in east. The simulation and prediction of cellular automaton showed that the changes in low sensitivity areas and lower sensitivity areas will be relatively small by 2030, and most of these areas belong to nature reserves, which are far away from human activity areas and have not undergone significant changes;The area of moderately and severely sensitive areas may decrease compared to 2020, while the area of extremely sensitive areas may increase. [Conclusion] The ecological situation in the Weihe River Basin has greatly improved over the past 20 years. [ABSTRACT FROM AUTHOR]

In this paper, a novel method was presented for forecasting mixed grains within large-scale forgings of 12%Cr heat-resistant steel during the single-pass hot deformation process. The proposed approach integrates a dynamic recrystallization cellular automaton (DRX-CA) model with finite element (FE) simulations and is complemented by evaluation criteria for mixed grains microstructure. Firstly, through the single-pass hot compression experiment, the necessary data for constructing the DRX-CA model were obtained, leading to its establishment. Based on the DRX-CA model, the stress–strain curves, grain microstructure, and DRX percentage observed in the experiment were simulated and reproduced, which verified the model's effectiveness in describing 12%Cr heat-resistant steel's DRX behavior; Secondly, grain microstructure evolution in different deformation zones during 12%Cr heat-resistant steel's single-pass hot deformation (upsetting process) was studied through the integration of DRX-CA model with FE simulation. The mixed grains degree of the resulting microstructure was assessed based on evaluation criteria for mixed grains microstructure; Finally, the grain structure obtained by the above method was validated through upsetting experiments. The results revealed that the average grain size and mixed grain degree obtained from both the experiments and simulations were not significantly different, indicating that the combination of the DRX-CA model and FE simulation can effectively predict the mixed grains during the upsetting process. This novel method for predicting mixed grains shifts the evaluation point of mixed grains forward, integrating the characteristics of defect prediction and process optimization, and provides a reference for controlling mixed grains during the hot forging process of large forgings. [ABSTRACT FROM AUTHOR]

The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). Two approaches are considered: one that preserves the number density and one that performs majority voting. For number-preserving DC, two QCAs are introduced that reach the fixed-point solution in a time scaling quadratically with the system size. One of the QCAs is based on a known classical probabilistic cellular automaton which has been studied in the context of DC. The second is a new quantum model that is designed to demonstrate additional quantum features and is restricted to only two-body interactions. Both can be generated by continuous-time Lindblad dynamics. A third QCA is a hybrid rule defined by both discrete-time and continuous-time three-body interactions that is shown to solve the majority voting problem within a time that scales linearly with the system size. [ABSTRACT FROM AUTHOR]

Cellular automaton (CA) is a computing model which is emerging rapidly. It is largely used in different types of scientific applications and simulations due to its ability to solve complex problems using simple rule(s). Cellular automata (CAs) are used in different types of applications like cryptography, VLSI systems, fault detection, etc. Typically, most of these applications utilize one-dimensional, 2-state, 3-neighborhood CAs. This paper explores the concept of Next State RMT Transition Diagram (NSRTD) for characterization of all the Elementary Cellular Automata (ECA) rules in periodic boundary condition leading to the identification of all ECA rules forming more than two fixed points (referred to as Single Length Cycle Multi-Attractor CA (MACA)) for an arbitrary CA length (n). For this, the 88 Wolfram classification rules and their equivalent rules have been utilized to reduce the search complexity by avoiding exhaustive searching on all the 256 ECA rules. [ABSTRACT FROM AUTHOR]

Elementary Cellular Automata (ECA) are well-studied computational universes capable of impressive computational variety, but harnessing their potential has been challenging. When combined with Reservoir Computing (RC), harnessing this computation becomes feasible, and furthermore enables energy efficient AI. This study compares ECA reservoirs to topological heterogeneous and more biological plausible counterparts of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). Using the 5-bit memory benchmark, Temporal Derrida plots and collapse rate, finding are that more disordered topology does not equate to more disordered computation and moreover the evidence suggest this heterogeneity shrinks the critical range. [ABSTRACT FROM AUTHOR]

The extraction and propagation of caving are complex phenomena involving the breaking of the rock mass, the formation of a column of broken material, and the extraction from the column base. Geomechanical modeling in cave mining commonly uses approaches to model the rock mass as a continuous material, while discontinuous modeling is frequently used for the column of broken material. However, it remains complex to include all mechanisms in a single model. Therefore, to achieve a better representation of ore breakage and extraction in caving mining, this work couples FLAC3D, a continuous finite volume tool, with FlowSim, a discrete tool based on cellular automata, to determine the air gap volume. The methodology first defines the height of caving propagation and the cave back with a tool that models solid rock mass in a continuous manner, which are used to constrain the cellular automata tool that simulates the flow of broken material. The results show that unidirectional FLAC3D‐FlowSim coupling reproduces the generation of cave backs and air gaps in the propagation of caving, rendering the methodology valuable for preliminary estimation of air volumes over fragmented material and the generation of supportive data to control the caving process. [ABSTRACT FROM AUTHOR]

Projecting global urban expansion is crucial for environmental assessment under climate change scenarios. However, existing global future urban land products are typically provided at coarse resolutions (1 km) due to data and computing limitations. This hinders the accurate assessment of the impacts of global urban development at finer scales. Thus, we develop the first Cellular Automata (CA) online tool for simulating future global urban expansion in Google Earth Engine (GEE-CA), which can simulate future urban land change at a 30 m resolution under different SSP scenarios. GEE-CA enables seamless simulations of future urban land at high resolution through a partitioned parallel strategy. Seven large urban agglomerations are simulated under shared socioeconomic pathways as representative regions to present our fine-scale results. Comparatively, our datasets preserve significantly more spatial details than existing global urban land products. The improvement in resolution from 1 km to 30 m reduces errors by a range from 7.11% to 21.27% in the estimation of future urban area. So far, the proposed GEE-CA tool allows users to generate urban land projection maps for any defined region with a resolution as high as 30 m. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work. [ABSTRACT FROM AUTHOR]