Recently, there has been a lot of research in Quantum Cellular Automata (QCA) technology because it promises low power consumption, low complexity, low latency, and compact space. Simultaneously, approximate arithmetic, a new paradigm in computing, streamlines the computational process and emerges as a low-power, high-performance design approach for arithmetic circuits. Furthermore, the XOR gate has been widely used in digital design and is a basic building block that can be used in many upcoming technologies. The full adder (FA) circuit is a key component of QCA technology and is utilized in arithmetic logic operations including subtraction, multiplication, and division. A great deal of research has been done on the design of approximate FA, full subtractor (FS), full adder/subtractor (FA/S), and 4-bit ripple carry adder (RCA) based on XOR logic, establishing them as essential components in the creation of QCA-based arithmetic circuits. This study presents three new and effective QCA-based circuits, based on XOR logic: an approximate FA, an approximate FS, an approximate FA/S, and an approximate 4-bit ripple carry adder (RCA). Interestingly, some designs have inputs on one side and outputs on the other, making it easier to reach the components without being encircled by other cells and leading to a more effective circuit design. In particular, a delay of 0.5 clock phases, an area of 0.01 μm2, and implementation utilizing just 11 cells was accomplished in the approximate FA and subtractor designs. In a similar vein, the estimated FA/S designs showed 0.5 clock phase delay, 0.01 μm2 area, and 12 cells used for implementation. An approximate 4-bit RCA is proposed using 64 QCA cells. The effectiveness of these designs is evaluated through functional verification with the QCADesigner program. According to simulation results, these proposed solutions not only function well but significantly outperform previous ideas in terms of speed and space. The proposed FA, FS, and RCA designs surpassed the previous best designs by 21%, 21%, and 43%, respectively, in terms of cell count.

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable \"content effects\"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.

Children grow up surrounded by opportunities to learn (the language of their community, the movements of their body, other people\'s preferences and mental lives, games, social norms, etc.). Here, we find that toddlers (N = 36; age range 2.3-3.2 years) rely on a logical reasoning strategy, Disjunctive Inference (i.e., A OR B, A is ruled out, THEREFORE, B), across a variety of situations, all before they have any formal education or extensive experience with words for expressing logical meanings. In learning new words, learning new facts about a person, and finding the winner of a race, toddlers systematically consider and reject competitors before deciding who must be the winner. This suggests that toddlers may have a general-purpose logical reasoning tool that they can use in any situation.

Analyzing the evolutionary features and internal logic of the one-vote veto system in China over the past two decades is highly significant when considering reform and standardization. In order to conduct this analysis, the Nvivo 12 software was used to examine policy texts related to the one-vote veto issued by Fujian, Hubei, and Gansu provinces. Through a comparative analysis of keyword frequency statistics, policy text form, and content characteristics across the three provinces, it was discovered that governmental departments have experienced fundamental changes in their utilization of the one-vote veto system after 20 years of development. These changes are primarily seen in the refinement of the description of the one-vote veto in policy texts, the gradual reduction in the withdrawal mechanism of the one-vote veto, and an expanded application field for the one-vote veto.

The CRISPR-Cas system provides a versatile RNA-guided approach for a broad range of applications. Thanks to advances in RNA synthetic biology, the engineering of guide RNAs (gRNAs) has enabled the conditional control of the CRISPR-Cas system. However, achieving precise regulation of the CRISPR-Cas system for efficient modulation of internal metabolic processes remains challenging. In this work, we developed a robust dCas9 regulator with engineered conditional gRNAs to enable tight control of endogenous genes. Our conditional gRNAs in Escherichia coli can control gene expression upon specific interaction with trigger RNAs with a dynamic range as high as 130-fold, evaluating up to a three-input logic A OR (B AND C). The conditional gRNA-mediated targeting of endogenous metabolic genes, lacZ, malT and poxB, caused differential regulation of growth in Escherichia coli via metabolic flux control. Further, conditional gRNAs could regulate essential cytoskeleton genes, ftsZ and mreB, to control cell filamentation and division. Finally, three types of two-input logic gates could be applied for the conditional control of ftsZ regulation, resulting in morphological changes. The successful operation and application of conditional gRNAs based on programmable RNA interactions suggests that our system could be compatible with other Cas-effectors and implemented in other host organisms.

Spintronics, utilizing both the charge and spin of electrons, benefits from the nonvolatility, low switching energy, and collective behavior of magnetization. These properties allow the development of magnetoresistive random access memories, with magnetic tunnel junctions (MTJs) playing a central role. Various spin logic concepts are also extensively explored. Among these, spin logic devices based on the motion of magnetic domain walls (DWs) enable the implementation of compact and energy-efficient logic circuits. In these devices, DW motion within a magnetic track enables spin information processing, while MTJs at the input and output serve as electrical writing and reading elements. DW logic holds promise for simplifying logic circuit complexity by performing multiple functions within a single device. Nevertheless, the demonstration of DW logic circuits with electrical writing and reading at the nanoscale is still needed to unveil their practical application potential. In this review, we discuss material advancements for high-speed DW motion, progress in DW logic devices, groundbreaking demonstrations of current-driven DW logic, and its potential for practical applications. Additionally, we discuss alternative approaches for current-free information propagation, along with challenges and prospects for the development of DW logic.

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BACKGROUND: Socialism with Chinese characteristics has entered a new stage. The principal social contradiction is the uneven development of urban and rural areas. The rural revitalization strategy has emerged as time has required. The realization of rural revitalization not only requires development to lay the foundation of the countryside but also requires governance to lead the development of the countryside. Development and governance are two indispensable aspects of rural revitalization. However, China\'s rural areas have long been in a state of development without governance, and this situation must change. Therefore, systematically exploring the relationship between development and governance is the key to solving the current shortcomings in rural areas.
METHODS: Based on the data from the statistical yearbook, the study constructed a set of evaluation indicators for rural development governance and revitalization and verified the model\'s effectiveness.The entropy method and the assessment model were used to calculate the comprehensive score of rural development, governance, and revitalization. The relationship between rural development and governance was analyzed using a coupled coordination model. The regression analysis model was used to explore the relationship between the coupling results of rural development, governance, and rural revitalization.
RESULTS: From the comprehensive results, both development and governance show an upward trend, but the upward trend of development is better. From the analysis of coupling coordination between development and governance, the C value is in good condition, the T value fluctuates wildly, and the D fluctuates with the fluctuation of T. Judging from the comprehensive score of rural revitalization, it also shows an upward trend year by year. Judging from the regression analysis results of coupling coordination degree and rural revitalization comprehensive score, coupling coordination degree will significantly impact the rural revitalization evaluation value.
CONCLUSIONS: The study found that current rural development and governance present a spiral coupling coordination relationship, and the degree of coupling coordination significantly correlates with rural revitalization. Based on the research conclusions, the study further proposes three paths to promote the coupling and coordination of development and governance. The first is an organizational isomorphism, which builds a coupled coordination system for rural development and governance. The second is to tilt resources and improve the supply of connected and coordinated factors for rural development and governance. The third is the operating mechanism to optimize rural development and governance\'s coupling and coordination path.

Information, as the most elusive subject, is central to all forms of thought, governance, economic structure, science, and society. Regulation of information, especially within the healthcare field, is proving to be a difficult task globally, given the lack of a qualitative framework and understanding of the concept and properties of information (or data) itself. The presentation of the overall qualitative framework, comprising a qualitative analysis of information, data, and knowledge, will be valuable and of great assistance in delineating regulatory, ethical, and strategic trajectories. In addition, this framework provides insights (and answers) regarding (1) data privacy and protection; (2) delineations between information, data, and knowledge based on the important notion of trust; (3) a structured approach to establishing the necessary conditions for an open society and system, and the maintenance of said openness, based on the work of Karl Popper and Georg Wilhelm Friedrich Hegel; (4) an active agent approach that promotes autonomy and freedom and protects the open society; and (5) a data governance mechanism based on the work of Friedrich Hayek, which structures the current legal-ethical-financial and social society. This is insightful for questions relating to the extent of rights and duties, the extent of biological bodies and freedom, and the structure of relations in distributed networked systems. There is great value offered in this framework; furthermore, it provides critical insights and thoughts about (and uncovers the interplay between) academic culture, politics, science, society, and societal decay. Note that, in line with the ideas expressed in this manuscript, such as incorporation of personal experience (thereby mending the Kantian and Cartesian gap), a first-person perspective will be used, where relevant.

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.