Logic System Research Articles

Global Realism with Bipolar Strings: From Bell Test to Real-World Causal-Logical Quantum Gravity and Brain-Universe Similarity for Entangled Machine Thinking and Imagination

Following Einstein’s prediction that “Physics constitutes a logical system of thought” and “Nature is the realization of the simplest conceivable mathematical ideas”, this topical review outlines a formal extension of local realism limited by the speed of light to global realism with bipolar strings (GRBS) that unifies the principle of locality with quantum nonlocality. The related literature is critically reviewed to justify GRBS which is shown as a necessary and inevitable consequence of the Bell test and an equilibrium-based axiomatization of physics and quantum information science for brain–universe similarity and human-level intelligence. With definable causality in regularity and mind–light–matter unity for quantum superposition/entanglement, bipolar universal modus ponens (BUMP) in GRBS makes quantum emergence and submergence of spacetime logically ubiquitous in both the physical and mental worlds—an unexpected but long-sought simplification of quantum gravity with complete background independence. It is shown that GRBS forms a basis for quantum intelligence (QI)—a spacetime transcendent, quantum–digital compatible, analytical quantum computing paradigm where bipolar strings lead to bipolar entropy as a nonlinear bipolar dynamic and set–theoretic unification of order and disorder as well as linearity and nonlinearity for energy/information conservation, regeneration, and degeneration toward quantum cognition and quantum biology (QCQB) as well as information-conservational blackhole keypad compression and big bang data recovery. Subsequently, GRBS is justified as a real-world quantum gravity (RWQG) theory—a bipolar relativistic causal–logical reconceptualization and unification of string theory, loop quantum gravity, and M-theory—the three roads to quantum gravity. Based on GRBS, the following is posited: (1) life is a living bipolar superstring regulated by bipolar entropy; (2) thinking with consciousness and memory growth as a prerequisite for human-level intelligence is fundamentally mind–light–matter unitary QI logically equivalent to quantum emergence (entanglement) and submergence (collapse) of spacetime. These two posits lead to a positive answer to the question “If AI machine cannot think, can QI machine think?”. Causal–logical brain modeling (CLBM) for entangled machine thinking and imagination (EMTI) is proposed and graphically illustrated. The testability and falsifiability of GRBS are discussed.

Nonsingular Fixed-Time Asymptotic Consensus for Nonlinear Nonstrict-Feedback MASs.

In this article, a fuzzy adaptive fixed-time asymptotic consistent control scheme is developed for a class of nonlinear multiagent systems (NMASs) with a nonstrict-feedback (NSF) structure. In the control process, a fixed-time consistency control method without control singularity is proposed by combining fuzzy logic systems (FLSs) with good approximation capability, fixed-time stability theory, and plus power integration techniques. Then, by using Barbalat's Lemma, the asymptotic stability of tracking errors and the boundedness of the controlled systems are successfully achieved, which means that the tracking errors can converge to zero in a fixed time. Finally, the effectiveness of the designed control scheme is demonstrated by a simulation example.

Adaptive fixed‐time fault‐tolerant fuzzy control of AUVs with asymmetric output constraints

AbstractThe fast and safe formation tracking problem for multiple autonomous underwater vehicle (AUV) systems (MAUVS) with asymmetric output constraints and actuator faults is studied in this article. Actuator faults are composed of loss of effectiveness and time‐varying unknown bias faults, an adaptive fixed‐time fault‐tolerant controller (AFFTC) is designed by employing fixed‐time stable theory and fuzzy logic systems. Under the designed control algorithm, the MAUVS can be practically fixed‐time stable, the tracking errors among the follower AUVs and the virtual leader AUV can converge to a small area near the origin within the fixed time, and the settling time is unaffected by the initial state of the system. To enhance the safety of MAUVS, a novel asymmetric barrier function is utilized to constrain the trajectory tracking errors within the prescribed range. Finally, the simulation results demonstrate the effectiveness of the proposed control algorithm.

Rancang Bangun SPK Kualitas Air Sungai Metode Fuzzy Tsukamoto (Studi Kasus: 4 Kecamatan Karawang)

Research reveals that the Citarum River in West Java is seriously polluted, causing problems for humans and the environment, so monitoring of river water quality is necessary. This research proposes the development of a web-based Decision Support System with the SDLC Waterfall model, using the Fuzzy Tsukamoto method based on seven water parameters (EC, TDS, Salinity, pH, ORP, SG, Temperature) to simplify the determination of water quality. This research uses the Research and Development methodology with the SDLC Waterfall model software development approach, which includes the stages of analyzing data requirements and fuzzy logic systems, designing system architecture and features, implementing technology and process results, functional manual testing and model accuracy, and maintenance in the form of documentation and storage. The results showed that the developed DSS can classify water quality based on 7 parameters with a value scale divided into five categories, namely Good, Poor, Medium, Good, and Very Good. Testing was conducted using data from 8 river points in 4 sub-districts in Karawang Regency on April 1, 2024. The accuracy of the DSS model reached 80%. The development of this DSS is expected to provide an initial overview of water quality in a location without requiring an in-depth technical understanding of the water parameters used. This data can also be an initial indicator to determine whether further action or further investigation is required. Suggestions for future research are to integrate the system with IoT to improve its performance and benefits.

Optoelectronic Neuromorphic Logic Memory Device Based on Ga2O3/MoS2 Van der Waals Heterostructure with High Rectification and On/Off Ratios

AbstractIt is crucial to develop advanced optoelectronic devices that incorporate multiple functions, including sensing, storage, and computing, which is considered at the forefront of semiconductor optoelectronics to meet emerging functional diversification. In this study, by stacking the n‐type Ga2O3 with the n‐type MoS2 flakes, a Ga2O3/MoS2 heterostructure optoelectronic device with high rectification ratio of ≈105 and on/off ratio of ≈108 is fabricated, which achieves high detectivity of 1.34 × 109 Jones and high responsivity of 28.92 mA/W. More importantly, the Ga2O3/MoS2 heterostructure device shows potential ability to integrate sensing and memorizing, simultaneously, which can be used as artificial neuromorphic synaptic. The device exhibits excellent photo‐induced synaptic functions including short‐term plasticity, long‐term plasticity, and paired‐pulse facilitation, realizing the ability to couple light and electrical signals by Pavlovian associative learning. At last, the device also demonstrates the information processing ability to act as optoelectronic logic gate AND by synergistically regulating the light on/off states and gate voltage. The research introduces an innovative strategy for the development of next‐generation optoelectronic devices which are highly integrated with sensing, memory, and logic processing functions, demonstrating great application prospects in constructing an efficient artificial neuromorphic visual and logic systems.

Survey on Knowledge Representation Models in Healthcare

Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction.

Adaptive fuzzy quantized prescribed performance synchronization of uncertain non-strict feedback chaotic systems with time-varying actuator failure

This paper addresses an adaptive fuzzy prescribed performance synchronization for a class of uncertain non-strict feedback chaotic systems subject to input quantization and time-varying actuator faults. The proposed approach utilizes fuzzy logic systems to estimate uncertainties. In addition, multiplicative and additive faults are considered simultaneously, which may occur either separately or simultaneously. To address the challenge, based on the backstepping scheme and a filter, an intermediate controller is conducted to compensate for the interference between actuator faults and quantification, in which a damping term and a positive time-varying function are introduced. Moreover, treating the error system as a constrained system, a simplified nonlinear mapping is employed to achieve asymmetric prescribed performance synchronization. Unlike traditional methods that rely on barrier Lyapunov functions and switch functions, the proposed approach eliminates the need for a switch function and extends the action scope to cover all synchronization errors. The analysis demonstrates that even if actuator faults and input quantization coexist, all signals in the closed-loop system remain bounded, and synchronization errors remain within the prescribed performance range. Finally, synchronization simulations are conducted on a nonlinear gyroscope system and a Non-autonomous chaotic Micro-Electro-Mechanical-System to reveal the validity of the proposed scheme.

Quantum computation with logical gates between hot systems

We consider quantum computer architectures where interactions are mediated between hot qubits that are not in their mechanical ground state. Such situations occur, e.g., when not cooling ideally, or when moving ions or atoms around. We introduce quantum gates between logically encoded systems that consist of multiple physical ones and show how the encoding can be used to make these gates resilient against such imperfections. We demonstrate that, in this way, one can improve gate fidelities by enlarging the logical system, and counteract the effect of unknown positions or position fluctuations of involved particles. We consider both a classical treatment of positions, in terms of probability distributions, and a quantum treatment using mechanical eigenmodes. We analyze different settings including a cool logical system mediating interactions between two hot systems, as well as two logical systems consisting of hot physical systems whose positions fluctuate collectively or individually. In all cases, assuming ideal local control to logical systems, we demonstrate a significant improvement in gate fidelities, which provides a platform-independent tool to mitigate thermal noise in the context of trapped-particle-based architectures. Published by the American Physical Society 2024

Adaptive finite-time optimal fuzzy control for novel constrained uncertain nonstrict feedback mixed multiagent systems via modified dynamic surface control

In this article, the finite-time stability is discussed for the novel nonlinear mixed multiagent systems (MASs) with unmodeled dynamics and constraints. Each agent is characterized as a state or output feedback system structured in the nonstrict form. The improved finite-time dynamic surface control (DSC) and the fuzzy actor-critic networks collectively constitute a novel optimal input. The fuzzy logic systems (FLSs) is introduced to approximate the unknown parts in the derivation process. By using the nonlinear transformation rules (NTRs), all the states or output are made to operate strictly within the predefined boundary conditions. The unmodeled dynamics can be solved by the constructed dynamic signals. By the aid of the compensating signals, the filtering errors can be countervailed in the DSC. The stability analysis demonstrates that all the signals are semi-globally practical finite-time stable (SGPFS). The feasibility of this scheme is explained intuitively by two simulations.

Dynamic superiorities in Defeasible Logic

Abstract In many logical rule-based systems, it often happens that opposite statements can be concluded, hence superiorities (or priorities) among rules are needed to solve such conflicts. Since the specific circumstances describing the scenario at hand determine which rules may apply, we thus need a mechanism able to compute such superiorities dynamically, case-by-case. We formalise a non-monotonic logic able to dynamically compute superiorities over rules. Finally, we advance efficient algorithms to compute the (positive and negative) conclusions of such theories, and prove their computational properties.

Design and Simulation of Balanced Ternary Priority Encoder

The priority encoder is a frequently used circuit in binary logic and is mostly used for interrupt handling and other priority resolving tasks. On the other hand, Ternary computing has tremendous potential for handling a wide variety of functions involving large range of numbers, whereas, the literature is confined to very basic functions. The proposed balanced priority encoder circuit that uses three logic symbols i.e.−1,0 and 1. In this study, we develop the design and architecture of a Ternary Priority Encoder circuit with an estimation of its time complexity. The intricacy of the circuit under consideration is supposed to highlight the capabilities of the ternary logic system. The flexibility of the circuit lies in its implementation using simple binary counterparts. As there is no simulator available for Ternary Logic, we have developed a Balanced Ternary Logic Simulator which is freely available from https://github.com/Aggtur11/Ternary-Logic-Simulator. The logic behaviour of the proposed priority encoder circuits is verified using the developed simulator.

Using fuzzy logic based-modeling and simulated annealing approaches to optimize the hardness distribution of 2024 aluminum alloy during precipitation hardening heat treatment cycles

The present study assesses the impact of age hardening parameters, including aging temperature, aging time, and solution time, on the ultimate hardness of heat-treated 2024 aluminum alloys. Using a numerical approach, fuzzy logic systems were utilized as a robust tool to forecast the mechanical characteristics of high copper aluminum solid solutions throughout the age hardening process. In addition, an attempt was made to use a novel simulated annealing technique to determine the optimum hardness and its corresponding process parameters to achieve the highest mechanical properties. Comparing a fuzzy logic model with experimental results obtained from the Brinell hardness test showed the accuracy and confidence of the fuzzy model in representing such properties. The optimization results indicated that the maximum hardness can be obtained with a solution aging temperature of 173.5 °C, an aging time of 19 hours, and a solution time of 58 minutes. Overall, the variation in the experimental peak hardness obtained using the optimized process parameters was the deciding factor in believing the model.

Energy-efficient design of quaternary logic gates and arithmetic circuits using hybrid CNTFET-RRAM technology

Multi-valued logic (MVL) extends binary logic by providing a framework to represent complex systems with more than two truth values. MVL was introduced to confront the enormous interconnect issue associated with the binary logic in implementing the presnt day complex nanoelectronic architectures. This paper delves into the circuit design, computational aspects, and practical applications of the quaternary logic system, which is a type of MVL with four truth values. The multi-threshold property of carbon nanotube field-effect-transistors (CNTFETs), combined with the ability of resistive random-access memory (RRAM) to store multiple resistance values, has enabled the design of quaternary logic gates and arithmetic circuits. A new CNTFET-based design architecture has been proposed to implement the quaternary logic compatible with the existing technologies. Quaternary logic gates such as inverter, NAND, and NOR, and quaternary arithmetic circuits including decoder, half adder, and multiplier have been designed. The power-delay-product (PDP) of the proposed quaternary inverter, NAND, NOR, half adder, and multiplier is 62.38%, 93.4%, 80.29%, 14.79%, and 20% less than the least PDP of the quaternary designs under consideration. The static power reduction due to the effecciency of the design architecture and high OFF state resistance offered by integrating RRAM into the logic design was explored.The proposed circuits have been subject to various types of parameter variations to validate thir proper functionality in presence of these variations.

An adaptive approximation‐free control for leader‐follower tracking of multi‐agent systems with unknown nonlinearity and disturbance

AbstractDue to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

A logic of trust-based beliefs

Traditionally, knowledge and beliefs are attributed to agents. The article explores an alternative approach where knowledge is informed by data and belief comes from trust in, not necessarily reliable, data. At the core of the article is the modality “if one dataset is trusted, then another dataset informs a belief”. The main technical result is a sound and complete logical system capturing the properties of this modality and its connection with functional dependency between datasets.

Application of Adaptive Fuzzy Algorithm in Single-Chip Microcomputer Control System

Objective: In practical application, an SCM control system needs to respond to user instructions quickly and accurately, and at the same time needs to have robustness and adaptability to adapt to complex and changeable environments and working states. Therefore, how to improve the control accuracy and stability of SCM control systems, and how to adapt to the change of different working environments and working states, has become one of the important issues in the design and research of SCM control systems. Methods: Adaptive fuzzy algorithm, as a new control algorithm, has the advantages of simple structure, easy implementation and high precision, and has been widely used in many fields. On the basis of three kinds of fuzzy logic system framework, this paper analyzes the self-adaptive fuzzy algorithm single-chip microcomputer control system and carries on the corresponding performance test of the system by constructing an intelligent car. The testability test of single-chip microcomputer detection, hardware detection, algorithm recognition rate and system control rate is tested, respectively, to ensure the normal operation of the system. Conclusion: The microcontroller control system of fuzzy algorithm system is compared with that of traditional algorithm system in many aspects. The average error rate of the traditional algorithm system is 2%, while the average error rate of the fuzzy algorithm system is 0.8%. In other aspects, the fuzzy algorithm system is superior to the traditional system. For example, the fuzzy algorithm system has a 100% instruction recognition rate and a data processing speed of up to 1 second. According to the characteristics and requirements of the single-chip microcomputer control system, applying the adaptive fuzzy algorithm to the design of a single-chip microcomputer control system can effectively improve the control precision and stability of thesystem.

Diaminonaphthalene functionalized LUS-1 as a fluorescence probe for simultaneous detection of Hg2+ and Fe3+ in Vetiver grass and Spinach

One of the important problems in the environment is heavy metal pollution, and fluorescence is one of the best methods for their detection due to its sensitivity, selectivity, and relatively rapid and easy operation. In this study, 1,8-diaminonaphthalene functionalized super-stable mesoporous silica (DAN-LUS-1) was synthesized and used as a fluorescence probe to identify Hg2+ and Fe3+ in food samples. The TGA and FT-IR spectra illustrated that 1,8-diaminonaphthalene was grafted into LUS-1. XRD patterns verified that the LUS-1 and functionalized mesoporous silica have a hexagonal symmetrical array of nano-channels. SEM images showed that the rod-like morphology of LUS-1 was preserved in DAN-LUS-1. Also, surface area and pore diameter decreased from 824 m2 g⁻1 and 3.61 nm for the pure LUS-1 to 748 m2 g⁻1 and 3.43 nm for the DAN-LUS-1, as determined by N₂ adsorption–desorption isotherms. This reduction demonstrated that 1,8-diaminonaphthalene immobilized into the pore of LUS-1. The DAN-LUS-1 fluorescence properties as a chemical sensor were studied with a 340/407 nm excitation/emission wavelength that was quenched by Hg2+ and Fe3+ ions. Hg2+ and Fe3+ were quantified using the fluorescence response in the working range 8.25–13.79 × 10–6 and 3.84–10.71 × 10–6 mol/L, with detection limits of 8.5 × 10–8 M and 1.3 × 10–7 M, respectively. Hg2+ and Fe3+ were measured in vetiver grass and spinach. Since the Fe3+ quenching can move in the opposite direction with sodium hexametaphosphate (SHMP) as a hiding compound for Fe3+, consequently, the circuit logic system was established with Fe3+, Hg2+, and SHMP as inputs and the fluorescent quench as the output.

Prescribed-Time Output Feedback Control for Cyber-Physical Systems Under Output Constraints and Malicious Attacks.

This article presents a prescribed-time output feedback control (PTOFC) algorithm for cyber-physical systems (CPSs) under output constraint occurring in any finite time interval (OC-AFT) and malicious attacks. The OC-AFT meaning that the output constraint only occurs during a finite number of time periods while being absent in others, which is more general and complex than traditional infinite-time/deferred output constraints. A stretch model-based nonlinear mapping function is constructed to handle the OC-AFT, and a salient advantage is that the proposed algorithm is also suit for CPSs with infinite-time/deferred output (or funnel) constraints, as well as those that are constraint-free, without necessitating changes to the control structure. The uncertain terms (including system model uncertainties, malicious attacks, and external disturbances) are compensated by fuzzy logic systems. Furthermore, a novel practical prescribed-time stability criterion is proposed, under which a novel PTOFC scheme is given. The results demonstrate that the proposed scheme can ensure that both tracking error and observation error converge to a neighborhood centered on zero within a prescribed time, while accommodating the OC-AFT and malicious attacks. Additionally, the settling time remains unaffected by control parameters and initial states, and the limitations of excessive initial control inputs and singularity problems in existing prescribed-time control algorithms are eliminated. The developed algorithm is exemplified through simulation instances.

A Multienzyme Logic H+ and Na+ Biotransducer.

Sodium ions and protons regulate various fundamental processes at the cell and tissue levels across all biological kingdoms. It is therefore pivotal for bioelectronic devices, such as biosensors and biotransducers, to control the transport of these ions through biological membranes. Our study explores the regulation of proton and sodium concentrations by integrating an Na+-type ATP synthase, a glucose dehydrogenase (GDH), and a urease into a multienzyme logic system. This system is designed to operate using various chemical control input signals, while the output current corresponds to the local change in proton or sodium concentrations. Therein, a H+ and Na+ biotransducer was integrated to fulfill the roles of signal transducers for the monitoring and simultaneous control of Na+ and H+ levels, respectively. To increase the proton concentration at the output, we utilized GDH driven by the inputs of glucose and nicotinamide adenine dinucleotide (NAD+), while recorded the signal change from the biotransducer, together acting as an AND enzyme logic gate. On the contrary, we introduced urease enzyme which hydrolyzed urea to control the decrease in proton concentration, serving as a NOT gate and reset. By integrating these two enzyme logic gates we formed a simple multienzyme logic system for the control of proton concentrations. Furthermore, we also demonstrate a more complex, Na+-type ATP synthase-urease multienzyme logic system, controlled by the two different inputs of ADP and urea. By monitoring the voltage of the peak current as the output signal, this logic system acts as an AND enzyme logic gate. This study explores how multienzyme logic systems can modulate biologically important ion concentrations, opening the door toward advanced biological on-demand control of a variety of bioelectronic enzyme-based devices, such as biosensors and biotransducers.