Dynamic Logic Research Articles

Dynamic Nanostructure-Based DNA Logic Gates for Cancer Diagnosis and Therapy.

DNA logic gates with dynamic nanostructures have made a profound impact on cancer diagnosis and treatment. Through programming the dynamic structure changes of DNA nanodevices, precise molecular recognition with signal amplification and smart therapeutic strategies have been reported. This enhances the specificity and sensitivity of cancer theranostics, and improves diagnosis precision and treatment outcomes. This review explores the basic components of dynamic DNA nanostructures and corresponding DNA logic gates, as well as their applications for cancer diagnosis and therapies. The dynamic DNA nanostructures would contribute to cancer early detection and personalized treatment.

Enhancing Traffic Management in Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach

Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.

Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

AbstractNeuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (> 107), large storage windows (equivalent to ≈95 V), excellent charge retention capability (> 104 s), good endurance (> 103 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.

Inferential knowledge and epistemic dimensions

Abstract Knowledge representation is one way to exploit expertise in a given domain by logical means. But, what kind of knowledge does one acquire from an inference (or inference on a query result over a knowledge base)? Such a question may appear awkward since the answer seems so obvious: from an inference, one simply acquires knowledge. This is undoubtedly the case when only one type of knowledge (for instance, expert knowledge) is involved in an inference. What if several types of knowledge are involved? What type of knowledge can one deduce from a plurality of knowledge types? I claim that reasoning with different knowledge concepts requires a fine-grained representation of knowledge in which every knowledge type finds a singular expression in order to avoid some epistemic equivocity associated with a coarse-grained representation of knowledge. In the first part of the paper, I revisit the Muddy Children Puzzle, which usually serves to illustrate common knowledge in dynamic epistemic logic. I try to show that this problem also shows some sort of epistemic equivocity between concepts of knowledge and, consequently, that the problem calls for some epistemological refinements concerning the representation of the types of knowledge at play in an inference. In the second part, I address this issue from a semantic point of view, and I develop a fragment of epistemic logic capable of providing a solution to the problem of epistemic equivocity.

Topic-Based Communication Between Agents

AbstractCommunication within groups of agents has been lately the focus of research in dynamic epistemic logic. This paper studies a recently introduced form of partial (more precisely, topic-based) communication. This type of communication allows for modelling scenarios of multi-agent collaboration and negotiation, and it is particularly well-suited for situations in which sharing all information is not feasible/advisable. The paper can be divided into two parts. In the first part, we present results on invariance and complexity of model checking. Moreover, we compare partial communication with the public announcement and arrow update settings in terms of both language-expressivity and update-expressivity. Regarding the former, the three settings are equivalent, their languages being equally expressive. Regarding the latter, all three modes of communication are incomparable in terms of update-expressivity. In the second part, we shift our attention to strategic topic-based communication. We do so by extending the language with a modality that quantifies over the topics the agents can ‘talk about’, thus allowing a form of arbitrary partial communication. For this new framework, we provide a complete axiomatisation, showing also that the new language’s model checking problem is PSPACE-complete. Finally, we argue that, in terms of expressivity, this new language of arbitrary partial communication is incomparable to that of arbitrary public announcements and also to that of arbitrary arrow updates.

Hybrid dynamical systems logic and its refinements

Hybrid dynamical systems describe the mixed discrete dynamics and continuous dynamics of cyber-physical systems such as aircraft, cars, trains, and robots. To justify correctness properties of the safety-critical control algorithms for their physical models, differential dynamic logic (▪) provides deductive specification and verification techniques implemented in the theorem prover ▪. The logic ▪ is useful for proving, e.g., that all runs of a hybrid dynamical system α satisfy safety property φ (i.e., ▪), or that there is a run of the hybrid dynamical system α ultimately reaching the desired goal φ (i.e., ▪). Logical combinations of ▪'s operators naturally represent safety, liveness, stability and other properties. Variations of ▪ serve additional purposes. Differential refinement logic (▪) adds an operator α≤β expressing that hybrid system α refines hybrid system β, which is useful, e.g., for relating concrete system implementations α to their abstract verification models β. Just like ▪, ▪ is a logic closed under all operators, which opens up systematic ways of simultaneously relating systems and their properties, of reducing system properties to system relations or, vice versa, reducing system relations to system properties. A second variant of ▪, differential game logic (▪), adds the ability of referring to winning strategies of players in hybrid games, which is useful for establishing correctness properties where the actions of different agents may interfere either because they literally compete with one another or because they may interact accidentally. In the theorem prover ▪, ▪ and its variations have been used for verifying ground robot obstacle avoidance, the Federal Aviation Administration's Next-Generation Airborne Collision Avoidance System ACAS X, and the Federal Railroad Administration's train control model.

Solar powered high gain DC-DC converter with FPGA controller for portable devices

A solar-powered converter design is necessary for portable devices with increased gain and reduced ripple in overall operation. This research presents a photovoltaic-powered Field Programmable Gate Array (FPGA) based on increased gain multiple output converter design and analysis. The dynamic panel input makes it difficult to operate the load. To control and make the input static to satisfy the demand, perturb and observe with a load balancing algorithm technique are used. The converter gained output is high, and batteries are used to balance the loads when solar irradiance is low. Simulation results were carried out with the presented converter circuit that can be operated with a 12 V supply as input from a renewable power source. The output gain ratio of 14 to a minimum of 40 % duty cycle operating at a switching frequency of 20 kHz has been achieved. The proposed methodology is transferred to hardware by connecting with the reconfigurable FPGA Spartan-6 development board to manage the dynamic load control system's complex logic and high processing ability. The overall performance is compared with various pre-existing systems, tabulations are made with the analysis, and experimental results are also illustrated at the end.

Truth diagrams for some non-classical and modal logics

This paper examines truth diagrams for some non-classical, modal and dynamic logics. Truth diagrams are diagrammatic and visual ways to represent logical truth akin to truth tables, developed by Peter C.-H. Cheng. Currently, it is only given for classical propositional logic. In this paper, we establish truth diagrams for Priest's Logic of Paradox, Belnap–Dunn's Four-Valued Logic, MacColl's Connexive Logic, Bochvar–Halldén's Logic of Non-Sense, Carnielli–Coniglio's logic of formal inconsistency as well as classical modal logic and its dynamic extension to shed light on the semantic behaviour of some non-classical and modal logics.

Digital sand patch: Using laser scanning and discrete element simulation for rapider pavement texture depth measurement

The sand patch method is one of the most basic methods for measuring pavement texture depth (TD), which is an important reference index for pavement anti-skid performance evaluation and road maintenance. However, manual sand patch method heavily relies on the experience of the operators. The emergence of laser 3D scanning and simulation has made it possible to conduct digital sand patch. This study introduces a digital sand patch framework based on 3D scanning and discrete element simulation for precise TD measurement. We developed a 3D scanner with an accuracy of 0.04 mm to obtain the pavement texture data, along with algorithms for outlier removal and slope correction in point cloud data. The method of 3D reconstruction of triangular meshes was adopted to transform discrete point clouds into continuous 3D models to build 3D pavement models. In addition, the virtual process of rotation-spreading-measurement of the digital sand patch method was constructed by discrete element simulation with reference to the physical principles of the manual sand patch method. At the same time, an adaptive dynamic feedback control logic was proposed to accurately determine the termination conditions of the sand patching process. To validate our method, we collected 126 samples from over 20 km of pavement surfaces with various gradations. The results demonstrated an average relative error of 2.92 % compared to the manual sand patch method, and a correlation coefficient of 0.9926 with the laser scanning Mean Profile Depth (MPD) algorithm. Stability and accuracy tests, including scanning downsampling and volatility assessments, confirmed the robustness of our method. This innovative technique for rapid and precise TD measurement not only offers immediate practical benefits but also sets the stage for future research on pavement skid resistance.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

INDIDO: A novel low-power approach for domino logic circuits

Power dissipation in Nanoelectronic circuits at advanced technology nodes is dominated by leakage power dissipation. This is due to an increase in short channel effects in the scaled transistors.The VLSI industry is seeking alternative options to enhance the performance of portable electronic systems by ensuring higher speed and reduced power dissipation. In the ultra-deep submicron (DSM) regime, a new device called the fin-shaped field effect transistor (FinFET) was developed to replace CMOS technology. FinFET, a multi-gate device, significantly reduces power dissipation compared to planer MOS (Metal Oxide Semiconductor ) transistor, but it doesn’t entirely resolve the issue. To further reduce power dissipation in the ultra DSM regime, low power approaches are required. Domino logic, a widely-used dynamic logic, is commonly utilized in high-speed VLSI architectures. In this research work, a novel INput Dependent Inverter DOmino (INDIDO) logic approach for low-power domino logic circuits using FinFET devices is proposed. A comparative analysis between the proposed INDIDO method and the existing approaches is performed for domino logic circuits for various performance metrics at the 16 nm technology node. In this research, various circuits like domino buffer, domino OR, domino AND, domino XOR and domino half adder are designed using the proposed INDIDO approach.The proposed INDIDO FinFET buffer circuit offers significant improvement in energy efficiency and FOM (Figure of Merit) by 53.18% and 82% respectively as compared to conventional FinFET footed domino buffer circuit. Beside this, proposed circuits like INDIDO OR, INDIDO AND, INDIDO XOR and INDIDO Half adder circuit follow the same trend as INDIDO buffer circuit and show better performance parametres in comparison to the existing low power domino approaches as well. In addition to this, the proposed INDIDO buffer circuits are also analyzed for PVT(process voltage and temperature) variability.This whole analysis makes us to conclude that proposed INDIDO approach reduces power dissipation and delay penalty, have high noise tolerance capacity, more immunity against PVT variations and high energy efficiency in comparison to the already existing techniques.

Global RC Interconnects with ADL Buffers for Low-Power Applications

Introduction: Interconnects are an essential requirement for any circuit completion. They are utilised to connect two or more blocks, yet when creating a circuit, certain problems have been observed. Scaling back technology is one such problem. Methods: With technology scaled down their aspects change which can straightforwardly affect the circuit boundaries. Because of this, the time constant and power consumption in the interconnect circuits has increased. Certain wire (RC) models and techniques have previously been characterized to control these performance parameters however in this paper, authors have proposed a new interconnect structure with a buffer insertion technique using adiabatic dynamic logic (ADL). Results: To optimise power, a Schmitt trigger is inserted as a buffer between lengthy interconnect circuits utilising an energy-recovery mechanism. The TSPICE tool is used to model and simulate the entire circuit. Conclusion: The suggested model's performance is compared to that of other cutting-edge methods.

Medication Recommendation System Using AI

Abstract: In an era marked by the convergence of personal and technical facets of life, the need for a versatile and intelligent companion becomes increasingly apparent. This project introduces the development of a novel Chatbot, aptly named the "Personal and Technical Companion," designed to seamlessly integrate into users' lives by offering personalized support and technical assistance. The Chatbot employs state-of-the-art Natural Language Processing (NLP) techniques to engage users in natural language conversations, providing a unique blend of personal and technical guidance. The system architecture encompasses a user-friendly interface, a robust NLP engine for intent recognition, a dynamic backend server handling logic and personalization, and integration with external services to enrich the user experience. Simultaneously, it serves as a technical companion by offering code assistance, troubleshooting guidance, and recommending relevant learning resources. Implementation leverages cutting-edge technologies, including Python for backend logic, Flask for the server, TensorFlow for NLP processing, and HTML/CSS for the user interface.

Modal algebra of multirelations

Abstract We formalize the modal operators from the concurrent dynamic logics of Peleg, Nerode and Wijesekera in a multirelational algebraic language based on relation algebras and power allegories, using relational approximation operators on multirelations developed in a companion article. We relate Nerode and Wijesekera’s box operator with a relational approximation operator for multirelations and two related operators that approximate multirelations by different kinds of deterministic multirelations. We provide an algebraic soundness proof of Goldblatt’s axioms for concurrent dynamic logic and one for a multirelational Hoare logic based on Nerode and Wijesekera’s box as applications.

Singly exponential translation of alternating weak Büchi automata to unambiguous Büchi automata

We introduce a method for translating an alternating weak Büchi automaton (AWA), which corresponds to a Linear Dynamic Logic (LDL) formula, to an unambiguous Büchi automaton (UBA). Our translations generalize constructions for Linear Temporal Logic (LTL), a less expressive specification language than LDL. In classical constructions, LTL formulas are first translated to alternating very weak Büchi automata (AVAs)—automata that have only singleton strongly connected components (SCCs); these AVAs are then handled by efficient disambiguation procedures. However, general AWAs can have larger SCCs, which complicates disambiguation. Currently, the only available disambiguation procedure has to go through an intermediate construction of nondeterministic Büchi automata (NBAs), which would incur an exponential blow-up of its own. We introduce a translation from general AWAs to UBAs with a singly exponential blow-up, which also immediately provides a singly exponential translation from LDL to UBAs. Interestingly, the complexity of our translation is smaller than the best known disambiguation algorithm for NBAs (broadly (0.53n)n vs. (0.76n)n), while the input of our construction can be exponentially more succinct.

Towards determinism in PDL: relations and proof theory

Abstract Guarded Kleene Algebra with Tests (GKAT) was presented as a fragment of KAT to abstract imperative programming languages, where only if-then-else and while-do statements are allowed in the language. The loss of expressiveness is, nevertheless, compensated by a clear advantage over KAT: it allows almost linear decidability of program equivalence. In this work, we give the first step to optimizing the complexity of dynamic logic equivalence, which is EXPTIME-complete in propositional dynamic logic (PDL). First, and based on strict deterministic PDL, we present guarded propositional dynamic logic (GPDL), a fragment of PDL where programs correspond to GKAT terms. It comes embedded, as expected, with a semantics over relational models and a sound axiomatisation. Then, we present a Natural Deduction system for GPDL, proving its soundness and completeness, concerning the axiomatisation. Based on Smolka et al. (2019, Proceedings of the ACM Programming Language 4), we obtain the main result of this work—the equivalence of GPDL programs can be established in almost linear time.

Exploring Non-Regular Extensions of Propositional Dynamic Logic with Description-Logics Features

We investigate the impact of non-regular path expressions on the decidability of satisfiability checking and querying in description logics extending ALC. Our primary objects of interest are ALCreg and ALCvpl, the extensions of with path expressions employing, respectively, regular and visibly-pushdown languages. The first one, ALCreg, is a notational variant of the well-known Propositional Dynamic Logic of Fischer and Ladner. The second one, ALCvpl, was introduced and investigated by Loding and Serre in 2007. The logic ALCvpl generalises many known decidable non-regular extensions of ALCreg. We provide a series of undecidability results. First, we show that decidability of the concept satisfiability problem for ALCvpl is lost upon adding the seemingly innocent Self operator. Second, we establish undecidability for the concept satisfiability problem for ALCvpl extended with nominals. Interestingly, our undecidability proof relies only on one single non-regular (visibly-pushdown) language, namely on r#s# := { r^n s^n | n in N } for fixed role names r and s. Finally, in contrast to the classical database setting, we establish undecidability of query entailment for queries involving non-regular atoms from r#s#, already in the case of ALC-TBoxes.

Determinism of multirelations

Binary multirelations allow modelling alternating nondeterminism, for instance, in games or nondeterministically evolving systems interacting with an environment. Such systems can show partial or total functional behaviour at both levels of alternation, so that nondeterministic behaviour may occur only at one level or both levels, or not at all. We study classes of inner and outer partial and total functional multirelations in a multirelational language based on relation algebra and power allegories. While it is known that general multirelations do not form a category, we show in the multirelational language that the classes of deterministic multirelations mentioned form categories with respect to Peleg composition from concurrent dynamic logic, and sometimes quantaloids. Some of these categories are isomorphic to the category of binary relations. We also introduce determinisation maps that approximate multirelations either by binary relations or by deterministic multirelations. Such maps are useful for defining modal operators on multirelations.

An energy-efficient reconfigurable 18/12-bit 1 MS/s pipelined-SAR ADC

This paper presents a low-power 18/12-bit 1 MS/s reconfigurable pipelined-successive approximation register analog-to-digital converter (pipelined-SAR ADC). The proposed pipelined-SAR ADC employs a two-stage SAR structure coupled through a residual amplifier, with the forestage performing 12-bit coarse quantization and the poststage handling 7-bit fine quantization, one bit of redundancy is contained in the rear stage. The input signal is sampled by the first stage, SAR ADC, using the upper plate of a capacitor array and is quantized by charge redistribution. This eliminates the need of additional DACs and subtractors, allowing direct acquisition of the residuals. In addition, the residuals can be amplified by reusing the capacitor array as a switching capacitor. To minimize static power consumption, dynamic logic is adopted in each module of the pipelined-SAR ADC. The calibration of comparator offset and capacitor mismatch is carried out using analog front-end calibration techniques. Implemented in a 180 nm CMOS process, the simulation results show an extra-low power consumption of 1.76 mW with 3.3 V supply voltage and 1 MS/s sampling rate, indicating an exceptional Schreier figure of merit (FOMs) at 181.64 dB. By shutting down the modules of the second-stage SAR ADC, the 18-bit ADC can be dynamically configured as a 12-bit ADC with a sampling rate of up to 5 MS/s.

Real-Time Traffic Light Optimization Using AI and IOT

Abstract: Urban traffic congestion is a significant challenge globally, impacting transportation efficiency, environmental sustainability, and urban liveability. Traditional traffic control systems often struggle to adapt to changing traffic dynamics, leading to increased congestion and delays. This paper presents a novel traffic management system developed by our team, leveraging cutting-edge technologies such as computer vision, artificial intelligence (AI), and the Internet of Things (IoT). Deployed at intersections, our system utilizes real-time CCTV feeds for traffic analysis, employing advanced image processing and machine learning algorithms, including YOLO V7, to dynamically assess traffic density. These insights inform adaptive adjustments to traffic light timings, with the aim of reducing congestion, enhancing transit efficiency, and mitigating pollution. Our signal switching algorithm, incorporating dynamic logic developed by our team, iteratively adjusts signal timings based on real-time traffic conditions, ensuring responsive and efficient traffic flow management. Through the integration of our innovative technologies, our system seeks to revolutionize urban transportation networks, promoting smarter, more adaptive, and sustainable urban environments.