Logic Networks Research Articles

All-optical temporal logic gates in localized exciton polaritons

Exciton polaritons—quasi-particle excitations consisting of strongly coupled photons and excitons—present fascinating possibilities for photonic circuits, owing to their strong nonlinearity, ultrafast reaction times and their ability to form macroscopic quantum states at room temperature via non-equilibrium condensation. Past implementations of transistors and logic gates with exciton polaritons have been mostly realized using the spatial propagation of polariton fluids, which place high demands on the fabrication of the microcavities and typically require complex manipulations. In this work we have implemented the full set of logical gate functionalities (that is, temporal AND, OR and NOT gates) in localized exciton polaritons at room temperature, on the basis of precisely controlling the interplay between polariton condensate and exciton reservoir dynamics, using a two-pulse excitation scheme. The dynamics intrinsically covers the cascadability required by the logical operations, enabling efficient information processing without the need for spatial flow. The temporal polariton logic gates demonstrate advantages in ultrafast switching, universality and simplified compatibility with other dimensional controls, showing great potential for building polariton logic networks in strongly coupled light–matter systems.

Study on Talent Cultivation Management Model of Universities Based on Fuzzy Neural Network Algorithm

The Talent Cultivation Management Model for Universities represents a strategic framework designed to optimize the development and oversight of academic programs. This model focuses on identifying, nurturing, and assessing talents within the university ecosystem. It incorporates innovative methodologies to align educational offerings with individual needs and career goals. This study presents an innovative approach to developing a talent cultivation management model for universities, leveraging the integration of the Fuzzy Neural Network Algorithm with the proposed Optimized Spider Monkey Fuzzy Neural Network (OSMF-NN). Recognizing the critical importance of talent development in higher education, this research seeks to enhance the efficacy and adaptability of existing management models. The OSMF-NN algorithm, inspired by the optimization capabilities of spider monkey behavior, enhances the traditional fuzzy neural network algorithm, enabling more precise and efficient talent management. By harnessing the synergies between fuzzy logic and neural networks, the proposed model offers a robust framework for identifying, nurturing, and evaluating talents within the university ecosystem. Through comprehensive experimentation and validation, this study demonstrates the effectiveness of the OSMF-NN algorithm in optimizing talent cultivation strategies, promoting personalized learning experiences, and fostering student success in higher education institutions.

LORE++: Logical location regression network for table structure recognition with pre-training

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Current approaches address this issue by either predicting the adjacency of detected cells or direct generation of structural sequences. Nonetheless, these approaches either count on additional heuristic rules for post-processing, or involve the generation of extremely long-range sequences that lead to computational intricacy. In this paper, We redefine TSR as a LOgical location REgression paradigm, which effectively captures inherent logical dependencies and constraints among table cells. Correspondingly, we propose LORE, a novel approach for TSR. LORE simultaneously predicts accurate geometric coordinates of table cells and the logical structures of the entire table. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other TSR paradigms. Moreover, to enhance the model’s spatial and logical representation capabilities, we propose two pre-training tasks, resulting in an upgraded version named LORE++. The incorporation of pre-training is proven to enjoy significant advantages, leading to a substantial enhancement in terms of accuracy, generalization, and few-shot capability compared to its predecessor. Experiments on standard benchmarks demonstrate the superiority of LORE++, which highlights the potential and promising prospect of the logical location regression paradigm for TSR.

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN)

ABSTRACT Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development.

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Improving the Detectionand Warning Fire System on the Smart Campus Area using ANFIS

The adoption of IoT technology in universities has significantly improved campus security, emergency response, and control systems, particularly through IoT-based surveillance and fire detection systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary resource allocation. This paperproposes an intelligent fire detection system leveraging IoT and adaptive fuzzy systems to enhance accuracy and reduce false alarms.The system uses sensors to collect real-time data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions.After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies, the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the importance of safety andtechnology in serving humanity.

WS2 Nanotube Transistor for Photodetection and Optoelectronic Memory Applications.

Nanotube and nanowire transistors hold great promises for future electronic and optoelectronic devices owing to their downscaling possibilities. In this work, a single multi-walled tungsten disulfide (WS2) nanotube is utilized as the channel of a back-gated field-effect transistor. The device exhibits a p-type behavior in ambient conditions, with a hole mobility µp ≈ 1.4 cm2V-1s-1 and a subthreshold swing SS ≈ 10 V dec-1. Current-voltage characterization at different temperatures reveals that the device presents two slightly different asymmetric Schottky barriers at drain and source contacts. Self-powered photoconduction driven by the photovoltaic effect is demonstrated, and a photoresponsivity R ≈ 10 mAW-1 at 2V drain bias and room temperature. Moreover, the transistor is tested for data storage applications. A two-state memory is reported, where positive and negative gate pulses drive the switching between two different current states, separated by a window of 130%. Finally, gate and light pulses are combined to demonstrate an optoelectronic memory with four well-separated states. The results herein presented are promising for data storage, Boolean logic, and neural network applications.

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Predicting the Influence of Ammonium Toxicity Levels in Water Using Fuzzy Logic and ANN Models

The study aimed to address the complex and critical issue of surface water quality monitoring by proposing a diversified approach that incorporates a range of chemical indicators. (1) Background: the purpose of the study was to address the problem of surface water quality monitoring in relation to the toxic effects of ammonium on aquatic ecosystems by developing predictive models using fuzzy logic and artificial neural networks. (2) Water samples from the Styr River, influenced by the Rivne Nuclear Power Plant, were analyzed using certified standard methods and measured parameters, while fuzzy logic and artificial neural network models, including Mamdani’s algorithm and various configurations of activation functions and optimization algorithms, were employed to assess water quality and predict ammonium toxicity. (3) A fuzzy logic system was developed to classify water quality based on ammonia content and other parameters, and six Artificial Neural Network (ANN) models were tested, with the ANN#2 model (using ReLU activation and ADAM optimizer) showing the best performance. (4) This study emphasizes the critical need for precise monitoring and modeling of total ammonium in surface water, considering its variable toxicity and interactions with environmental factors, to effectively protect aquatic ecosystems, namely ichthyofauna.

Physical layer encryption-based secure slicing in 5G RAN with hybrid-trusted links

In a 5G radio access network (RAN), network slicing enables dividing a single RAN infrastructure into multiple logical networks, efficiently accommodating services with diverse requirements. Although RAN slicing can help improve resource efficiency and reduce network costs, it is accompanied by various security risks. One of the security threats in RAN slicing is potential eavesdropping, resulting in the leakage of sensitive data within slices. Encryption technologies have been developed to address the eavesdropping problem at different layers in optical networks. We focus on physical layer encryption since it has been demonstrated beneficial in line-speed processing, low latency, and small encryption overhead. The problem of utilizing physical layer encryption technologies to achieve secure RAN slices remains unexplored since physical layer encryption introduces additional hardware costs. In this paper, we study how to realize secure RAN slicing based on physical layer encryption in a metro aggregation network that consists of hybrid-trusted links (i.e., links with different risks for eavesdropping). We propose an integer linear programming (ILP) model and an auxiliary graph-based heuristic for small-scale and large-scale networks, respectively. The objective is to maximize the number of deployed slices and minimize the total cost of secure slice deployment, which includes the costs of servers, line cards (LCs), encryption cards (ECs), and bandwidth resources. To evaluate the benefit of encryption, we compare it with a detour solution, which protects slices by routing through trusted links (i.e., where no additional hardware for encryption is deployed). Simulation results show that the encryption-based solution exhibits a lower cost than the benchmark when the same number of slices are deployed, and it can reduce the blocking ratio by up to 8.5% as slice requests increase. In addition, the average latency of slices is also reduced by up to 14.6%.

Fuzzy logic and biometric-based lightweight cryptographic authentication for metaverse security

As Metaverse advances, robust authentication systems become more critical. Traditional security measures are not efficient and adaptable for metaverse scenarios. In this context, this study proposed a two-phase security framework that combines fuzzy logic and convolutional neural network (CNN) techniques for biometric authentication and a lightweight Cryptographic protocol for securing the Metaverse. In the first stage, the CNN model is used to extract accurate biometric signatures from hand tremor data. This innovative method takes into account the inherent unpredictability of biometrics. During the second phase, a cryptographic scheme is used to provide safe mutual authentication between the user and the Metaverse infrastructure. Our findings, which have been verified by BAN Logic analysis, show that the proposed framework achieves a high level of accuracy in user authentication and has robust defensive capabilities against typical cyber-attacks.

A Ratiometric Fluorescent Probe Dye-Functionalized MOFs Integrated with Logic Gate Operation for Efficient Detection of Acetaldehyde.

Volatile organic compounds (VOCs) are a class of hazardous gases that are widely present in the atmosphere and cause great harm to human health. In this paper, a ratiometric fluorescent probe (Dye@Eu-MOFs) based on a dye-functionalized metal-organic framework was designed to detect VOCs, which showed high sensitivity and specificity for acetaldehyde solution and vapor. A linear correlation between the integrated fluorescence intensity (I510/I616) and the concentration of acetaldehyde was investigated, enabling a quantitative analysis of acetaldehyde in the ranges of 1 × 10-4~10-5 μL/mL, with a low detection limit of 8.12 × 10-4 mg/L. The selective recognition of acetaldehyde could be clearly distinguished by the naked eye under the excitation of UV light. The potential sensing mechanism was also discussed. Significantly, a molecular logic gate was constructed based on the whole system, and finally, a molecular logic network system for acetaldehyde detection connecting basic and integrated logic operations was realized. This strategy provided an effective guiding method for constructing a molecular-level logic gate for acetaldehyde detection on a simple platform.

Markov conditions and factorization in logical credal networks

We examine the recently proposed language of Logical Credal Networks, a powerful representation formalism that combines probabilities and logic. In particular we investigate the consequences of distinct Markov conditions upon their underlying semantics. We introduce the notion of structure for a Logical Credal Network and show that a structure without directed cycles leads to a well-known factorization result. For networks with directed cycles, we discuss the differences between Markov conditions, factorization results, and specification requirements. We consider several scenarios in causal reasoning that can be tackled by the formalism, in particular looking at partial identifiability and cycles.

SLE: a cognitive step forward-a synthesis of rethinking theories, causality, and ignored DNA structures.

Systemic lupus erythematosus (SLE) is classified by instinctual classification criteria. A valid proclamation is that these formally accepted SLE classification criteria legitimate the syndrome as being difficult to explain and therefore enigmatic. SLE involves scientific problems linked to etiological factors and criteria. Our insufficient understanding of the clinical condition uniformly denoted SLE depends on the still open question of whether SLE is, according to classification criteria, a well-defined one disease entity or represents a variety of overlapping indistinct syndromes. Without rational hypotheses, these problems harm clear definition(s) of the syndrome. Why SLE is not anchored in logic, consequent, downstream interdependent and interactive inflammatory networks may rely on ignored predictive causality principles. Authoritative classification criteria do not reflect consequent causality criteria and do not unify characterization principles such as diagnostic criteria. We need now to reconcile legendary scientific achievements to concretize the delimitation of what SLE really is. Not all classified SLE syndromes are "genuine SLE"; many are theoretically "SLE-like non-SLE" syndromes. In this study, progressive theories imply imperative challenges to reconsider the fundamental impact of "the causality principle". This may offer us logic classification and diagnostic criteria aimed at identifying concise SLE syndromes as research objects. Can a systems science approach solve this problem?

Necessary and Sufficient Conditions for Event-Triggered Set Stabilizability of Markovian Jump Logical Networks.

This technical paper utilizes the Lyapunov theory to characterize the event-triggered set stabilizability of Markovian jump logical control networks (MJLCNs). Whereas the existing result for checking the set stabilizability of MJLCNs is only sufficient, this technical paper further establishes its necessary and sufficient condition. First, the Lyapunov function is established to describe the set stabilizability of MJLCNs necessarily and sufficiently by combining recurrent switching modes and desired state set. Then, the triggering condition and the input updating mechanism are designed regarding the value change of the Lyapunov function. Finally, the effectiveness of theoretical results is demonstrated by a biological example concerning the lac operon in Escherichia coli.

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

Predictive modeling of oleuropein release from double nanoemulsions: An analytical study comparing intelligent models and Monte Carlo simulation

The objective of this study was to evaluate the release of oleuropein (OLP) from double nanoemulsions stabilized with polymeric complexes. Initially, W1/O nano-emulsions loaded with OLP were prepared and re-emulsified into an aqueous phase (W2), which included a complex of whey protein concentrate (WPC)/pectin, to form W1/O/W2 emulsions. The microstructure of the final double emulsions was analyzed by scanning electron microscopy (SEM), and particles with smooth, comparatively spherical, and somewhat asymmetrical surfaces with a size range of 100–200 nm were observed, which were compatible with dynamic light scattering (DLS) data. The release trend of OLP was determined by fitting it to several empirical models including zero order, first order, Higuchi, Hixson-Crowell, Korsemeyer-Peppas, Baker-Lonsdale, and utilizing intelligent modeling techniques such as Fuzzy Logic (FL) and Artificial Neural Networks (ANNs). Among the mathematical models, the zero order equation had the highest coefficient of determination (R2 = 0.988), while the first order equation had the lowest root-mean-square error (RMSE = 0.0176) and sum of squared errors (SSE = 0.0009) for the goodness of fit of the model, when considering the release trend of OLP. FL and ANNs proved effective in modeling controlled release of OLP-loaded nanocarriers, achieving high R2 values. Additionally, Monte Carlo (MC) simulation showed potential for evaluating the release process when compared to other methods.

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

Operational Matrix Framework for Energy Balance Analysis for Early Stage Design of Complex Vessels

Considering vital ship systems or distributed ship service systems at the early stage of complex vessels is a challenging task. The recent UCL Network Block Approach aimed to enable ship designer to address ship systems design synthesis simultaneously as a logical network using MATLAB with a CPLEX Toolbox in MATLAB and representative piping, cabling, and trunking routings on the physical description of the ship using a proven CASD tool PARAMARINESURFCON. This was possible due to adopting a set of frameworks, as part of this comprehensive approach. The paper presents one of the frameworks: the Operational Matrix, to formulate distributed ship service systems network in the early stage design of complex vessels. The application of the framework could take on many forms and can be manipulated to suit a specific distributed ship service system’s design. In this paper, a tutorial is given, leading from the simplest application of the Operational Matrix Framework to an example of a complex Operational Matrix application for the 3D multiplex submarine systems problem. The use of the proposed Operational Matrix Framework is shown to reveal the relationship between objective functions, constraints, bounds, and solutions of that linear programming formulation. The Operational Matrix Framework can enable the solvers (CPLEX Toolbox in MATLAB) to be very efficient in advancing early stage ship design applications. The Framework could be developed further for investigating the analysis of energy balances for new systems to achieve net zero energy demands for future naval vessels.

Overhead and Performance of Dynamic Network Slice Allocation for Mobile Users

Based on virtualisation technologies like SDN and NFV, modern networks like 5G create customised logical networks known as Network Slicing to serve distinct user groups. The mobility of a significant part of these users brings great challenges for that scenario, e.g. demand variations and continuous connectivity. In such a scenario, the dynamic resource allocation of these slices is shown as a possible solution. However, that approach may be sensitive to computing overhead and trade-off scenarios, which are not fully evaluated in the literature. This work focuses on that gap by researching the impact of network slice reconfiguration overhead on the overall network performance. An extensive evaluation of Dynamic Network Slicing in providing user mobility support in Fog computing-based architectures is presented. This paper presents a resource allocation model that calculates the required time to allocate a slice instance. Furthermore, based on those values, this work presents an evaluation of the impact of resource allocation delays in terms of bandwidth allocation, latency, and service outage in different dynamic slicing scenarios that include service migration. Simulations based on realistic values point out that reducing the allocation delay from 60 to 30 s can lead the network to 25% more stable latency to its users in 60 min slice lifetime.