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Emerging Industry 5.0 pushes advanced automation towards resilient solutions with enhanced human role. Resilience as an ability to sustain processes in the face of disruptions and adversarial attacks requires careful modelling and simulation. Cellular automata are efficient mathematical models used to simulate the behavior of complex systems, which change their state based on a set of predefined rules. In this paper, we suggest several updates to cellular automata (particularly Conway's "Game of Life") to address resilience. These include "Life and Creation", "War and Peace", and their hybrid "War and Creation" capable of addressing the important components of resilience, such as controllable creativity and adversarial interactions. Inherited in these updates and known advantages of cellular automata, such as simplicity, emergent behavior, parallelism, and adaptability, makes it a powerful simulation tool for a wide range of Industry 5.0 systems that involve humans, smart infrastructure, their complex and adversarial interactions, safety, and resilience. [ABSTRACT FROM AUTHOR]

• The model treats the frost layer as a heterogeneous porous medium. • This model was validated with some experimental data in the literature. • A multistep ascending growth pattern is found for both frost thickness and porosity. • The higher the frost porosity is, the lower the effective frost thermal conductivity is. The frost formation process is involved with many factors which lead to complex morphology. A two-dimensional model to simulate the early-stage frost formation on a cold flat plate is proposed in this paper based on the finite difference and cellular automata method. The concentration diffusion equation is used to constrain the diffusion of water molecules and the mass transfer equation is applied to relate the frost growth to the water vapor concentration and temperature. Furthermore, the temperature distribution during the growth process is obtained by solving the energy conservation equation. With the simulation, a heterogeneous porous medium consisting of ice crystals and air can be obtained and treated as a whole. Then, the physical properties of the frost layer like the frost thickness, the frost porosity and the effective thermal conductivity are calculated and analyzed. Results show that temperature is relatively uniform in the region where ice crystals are concentrated, and the frost surface temperature increases with the frost thickness as the frost grows. A multiple-step ascending growth pattern is observed both for the frost thickness and the frost porosity. The effective thermal conductivity of the frost layer correlates closely with the frost porosity and a higher frost porosity value results in lower thermal conductivity. Besides, the initial crystal nuclei distribution has a great influence on the morphologies of the frost layer, while the initial water vapor supersaturation has more impact on the growth speed of the frost thickness. [ABSTRACT FROM AUTHOR]

This paper presents a very fast numerical approach to simulate microstructures resulting from melt pool solidification including growth competition of columnar dendritic grains, and equiaxed grains nucleated from the melt. To reduce computation time, the key contribution is the development of an upscaling strategy, which instead of considering each dendrite individually consists in defining an average solidification front based on physically-informed dendritic growth velocity. The proposed approach also relies on dendritic preferred growth direction, and favorably oriented grain criterion to determine which grain survives the competition. To significantly reduce the total number of degrees of freedom Voronoi tessellations are used instead of regular grids for numerical implementation. Indeed, 3D regular grids typically leads to N 3 degrees of freedom while Voronoi tessellations lead to only 3 N , which dramatically reduces computation cost. This work is therefore a high-throughput approach enabling large data set generation to explore statistical features of microstructures with respect to melt pool properties. Results have been compared to experimental data, and to phase field and cellular automaton simulations in 2D only. Simulated microstructures are similar as those obtained with cellular automaton. Comparisons in 3D are left for future work. In addition, a convergence analysis is provided for 3D simulations, with thermal conditions corresponding to metal additive manufacturing to demonstrate how the present work can be used in practice. [Display omitted] [ABSTRACT FROM AUTHOR]

Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments. • Propose an LUCC model concerning spatiotemporal dependency and landscape evolution. • Control the spatial evolution of landscape using a novel CA algorithm. • Promote accuracy by employing ST-CNN to assimilate the latent spatiotemporal dependency. • Provide a reproducible tool for policy-makers to achieve sustainable development. [ABSTRACT FROM AUTHOR]

A significant problem facing cybersecurity is that most client/server identity authentication schemes depend on computational complexity which is recently challenged by the super exponential computational abilities of quantum computers. Quantum cryptography with its quantum mechanics principles solves this problem and provides mathematically un-hackable secure communication for the quantum computers epoch. Biometric authentication has a big challenge related to its protection as it has to be submitted and stored in encrypted form. Unfortunately, most existing biometric cryptosystems rely on classical cryptography, so they are insecure in quantum era. This problem is solved in this paper by introducing a novel three-factor biometric quantum identity authentication scheme combining something you know (password), something you produce (client's voice), and something you are (client's face image) is proposed. The scheme consists of two phases: client enrollment and client login/authentication. The methodology is to use in the enrollment phase, two layers of quantum encryption for the client's voice signal. Each layer consists of two simple quantum circuits of SWAP and C-NOT quantum gates respectively using a different pseudorandom key obtained from a cellular automata (CA) pseudorandom generator. The proposed scheme has many merits. Firstly, it introduces a new design of muti-factors quantum biometric authentication scheme as the biometric voice data is processed, transmitted, and stored at the server database in an encrypted unconditionally secure quantum form which provides parallel processing, real-time communications, inherent level of security against attacks, tamper-proof security and exponential powerful storage capability due to the quantum properties. Secondly, the proposed scheme achieves double-layers security by combing quantum voice encryption and quantum secure direct communication in one process such that the quantum channel eavesdropper cannot obtain any valuable information in case of information leakage. Thirdly , the distribution and management of encryption key problem is solved as there is no need for key exchange prior to communication. Fourthly, it achieves high communication efficiency as although it is a three-factor biometric identity authentication, only one biometric data is sent in quantum encrypted form to the server. Fifthly , the numerical simulations showed that the proposed scheme has large key space, high key sensitivity and more robust performance against different types of attacks compared to other existing classical and quantum voice encryption schemes. Finally, as an identity authentication method, the proposed scheme achieves identification accuracy of 100% with zero False Rejection Rate (FRR) and zero False acceptance rate (FAR) which is perfect. [ABSTRACT FROM AUTHOR]

Quantum dot cellular automata (QCA) stands as a highly promising nanotechnology, capable of outperforming conventional CMOS technology. Leveraging its advantages, this paper addresses the challenges in adder circuits related to size, delay, and cost by integrating QCA technology. A novel layout for a co-planar full adder in QCA is introduced, featuring a half-adder design with 23 cells in a single layer without any crossover, achieving a delay of 0.25 clock cycles. By utilizing inter-cellular effects inherent in QCA technology, the presented designs offer significant improvements in a variety of design metrics, and a cost efficiency of 64.3%, distinguishing them from traditional logic design approaches. Additionally, in order to ensure an efficient routing channel and a consistent structure of the adder design, a Universal, Scalable, and Efficient (USE) clocking scheme has also been employed. The adder design is further used to build 4-bit and 8-bit Ripple Carry Adders. An extensive study on energy dissipation using QCADesigner-E and QCAPro tools is conducted for three tunneling energies (0.5 E k , 1 E k , and 1.5 E k). The full adder design showcases substantial improvement in energy to delay cost when compared to the closest reported design, achieving improvements of 91.66%, 89.03%, and 88% at 0.5 E k , 1 E k , and 1.5 E k , respectively. The results highlight enhanced circuit efficiency and streamlined layouts, outperforming existing models and offering substantial benefits to high-performance and energy-efficient computing systems. • To demonstrate simple and streamlined layouts, ensuring ease of implementation. • To designs consider the direction of signal flow, enhancing their efficiency. • To take the advantage of the inter-cellular effects' capability of QCA technology, leading to the observed advancements in the adder circuits. • Detailed energy dissipation calculations performed using QCADesigner-E and QCAPro. • Comprehensive Parameter Analysis: The analysis covers a wide range of important parameters, offering a holistic assessment of the adder designs. [ABSTRACT FROM AUTHOR]

[Display omitted] Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ± 2 σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina's extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters' mitigation efforts. [ABSTRACT FROM AUTHOR]

With the advent and advancement of connected and automated vehicle technologies, it is now possible to control connected and automated vehicles (CAVs) in mixed traffic flows to reduce traffic oscillations and enhance traffic flow performance. Recently, a control strategy for autonomous vehicles (AVs) named "The Follower Stopper controller" (FS) has been proposed. However, the existing studies for the FS strategy only considered the situation that all CAVs execute the FS strategy individually. And the impact on the mixed traffic flow when CAVs in the mixed traffic flow execute FS strategies as platoons is not investigated. To address this limitation, this paper proposes a control strategy for CAVs that considers the driving behavior of CAVs platoons based on the FS strategy, which allows CAVs to execute the FS strategy as a platoon. Then, a cellular automaton model of mixed traffic flow is developed based on the proposed strategy. Finally, a numerical simulation is conducted to analyze the impacts of the proposed strategy on traffic efficiency, traffic congestion, traffic oscillation, vehicle fuel consumption, and pollutant emissions under different penetration rates (PRs) of CAVs and traffic densities. Moreover, a comparative analysis with the FS strategy was conducted to verify the effectiveness and superiority of the proposed strategy. The result shows that (1) the proposed strategy can effectively mitigate traffic oscillations and reduce congestion. The improvement effect of the proposed control strategy on the mixed traffic flow is significantly affected by the penetration rate of CAVs as well as the traffic density. (2) Compared with the FS strategy, the proposed strategy has better performance in improving traffic efficiency, alleviating traffic oscillations, relieving congestion, and reducing fuel consumption and pollutant emissions under different traffic conditions. (3) The effectiveness of the FS strategy in improving the performance of mixed traffic flow is greatly limited by traffic density. There is a significant marginal utility of the FS strategy in improving traffic flow performance under high traffic density. However, the proposed strategy overcomes the limitations imposed by high traffic densities. The proposed strategy is highly adaptable to diverse traffic densities and can continuously enhance traffic flow performance as the penetration rate of CAVs rises. [ABSTRACT FROM AUTHOR]

Nano-communication networks have critical roles in future worldwide communications, which provide efficient data transmission at ultra-high speeds employing nano-circuits that operate with ultra-low power. The quantum cellular automata (QCA) paradigm enables designing molecular-scale devices operating at terahertz frequencies with negligible power consumption. Moreover, reversible logic aids in reducing thermodynamic power dissipation significantly. In this paper, we design and implement a single-layer Fredkin gate and use it to implement reversible QCA devices, including the crossbar switch, multiplexer, demultiplexer, transmitter, and receiver. These devices are then used to implement a reversible switched network in QCA technology. The proposed circuits employ standard 90-degree QCA cells and coplanar crossovers. The results show that the area-time complexity and power dissipation of the designed reversible switched network have been reduced significantly compared to previous designs. [ABSTRACT FROM AUTHOR]