Rule-based Research Articles

Single-zinc vacancy unlocks high-rate H2O2 electrosynthesis from mixed dioxygen beyond Le Chatelier principle.

Le Chatelier's principle is a basic rule in textbook defining the correlations of reaction activities and specific system parameters (like concentrations), serving as the guideline for regulating chemical/catalytic systems. Here we report a model system breaking this constraint in O2 electroreduction in mixed dioxygen. We unravel the central role of creating single-zinc vacancies in a crystal structure that leads to enzyme-like binding of the catalyst with enhanced selectivity to O2, shifting the reaction pathway from Langmuir-Hinshelwood to an upgraded triple-phase Eley-Rideal mechanism. The model system shows minute activity alteration of H2O2 yields (25.89~24.99 mol gcat-1 h-1) and Faradaic efficiencies (92.5%~89.3%) in the O2 levels of 100%~21% at the current density of 50~300 mA cm-2, which apparently violate macroscopic Le Chatelier's reaction kinetics. A standalone prototype device is built for high-rate H2O2 production from atmospheric air, achieving the highest Faradaic efficiencies of 87.8% at 320 mA cm-2, overtaking the state-of-the-art catalysts and approaching the theoretical limit for direct air electrolysis (~345.8 mA cm-2). Further techno-economics analyses display the use of atmospheric air feedstock affording 21.7% better economics as comparison to high-purity O2, achieving the lowest H2O2 capital cost of 0.3 $ Kg-1. Given the recent surge of demonstrations on tailoring chemical/catalytic systems based on the Le Chatelier's principle, the present finding would have general implications, allowing for leveraging systems "beyond" this classical rule.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

A grid-connected eleven-level power conversion interface

ABSTRACT This paper proposes a grid-connected eleven-level power conversion interface (GCELPCI) for renewable power systems. The proposed GCELPCI is composed of a four-port DC-DC power converter (FPDDPC) and an eleven-level inverter (ELI). The FPDDPC combines a boost power converter and a transformer with a turn ratio of 2:1 to generate multiple combinations of the output voltages for the DC bus voltage of the ELI. The ELI integrates an asymmetric voltage technique on a T-type inverter to further convert the DC output voltages of FPDDPC into eleven-level AC voltage. A sinusoidal current is generated by the proposed GCELPCI through the filter inductor and injected into the power grid. Only nine power electronic switches are used in the ELI to generate an eleven-level AC voltage, the number of power electronic switches is reduced to simplify the power circuit. In addition, the capacity of the filter inductor is reduced. A digital signal processor (DSP) and a complex programmable logic device (CPLD) chip are used as the digital controller to complete a hardware prototype to verify the performance of the proposed GCELI.

An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries

An Automatized Rebalancing System to Address Faradaic Imbalance and Prolong Cycle Life in Alkaline Ferrocyanide – Anthraquinone Redox Flow Batteries

XAI Human-Machine collaboration applied to network security

Cyber attacking is easier than cyber defending—attackers only need to find one breach, while the defenders must successfully repel all attacks. This research demonstrates how cyber defenders can increase their capabilities by joining forces with eXplainable-AI (XAI) utilizing interactive human-machine collaboration. With a global shortfall of cyber defenders there is a need to amplify their skills using AI. Cyber asymmetries make propositional machine learning techniques impractical. Human reasoning and skill is a key ingredient in defense and must be embedded in the AI framework. For Human-Machine collaboration to work requires that the AI is an ultra-strong machine learner and can explain its models. Unlike Deep Learning, Inductive Logic Programming can communicate what it learns to a human. An empirical study was undertaken using six months of eavesdropped network traffic from an organization generating up-to 562K network events daily. Easier-to-defend devices were identified using a form of the Good-Turing Frequency estimator which is a promising form of volatility measure. A behavioral cloning grammar in explicit symbolic form was then produced from a single device's network activity using the compression algorithm SEQUITUR. A novel visualization was generated to allow defenders to identify network sequences they wish to explain. Interactive Inductive Logic Programming (the XAI) is supplied the network traffic meta data, sophisticated pre-existing cyber security background knowledge, and one recurring sequence of events from a single device to explain. A co-inductive process between the human cyber defender and the XAI where the human is able to understand, then refute and shape the XAI's developing model, to produce a model that conforms with the data as well as the original device designers programming. The acceptable model is in a form that can be deployed as an ongoing active cyber defense.

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.

Cybersecurity Measures Safeguarding Digital Assets and Mitigating Risks in an Increasingly Interconnected World

As interconnectivity and reliance on digital technologies continue to rise exponentially, so too do cybersecurity threats and risks. Malicious cyber actors are employing increasingly sophisticated techniques to target valuable data and disrupt critical systems and infrastructure. The impacts of successful cyber-attacks can be financially devastating and undermine an organization's reputation and customer trust. This paper examines how firms may utilize strong cybersecurity to protect digital assets and reduce risks in a connected society. As technology and communications improve, cyber threats do too. Problems keep popping up. Malware, phishing scams, and data breaches still harm businesses. Hackers can also get in simpler with more linked devices and the Internet of Things. This makes protecting digital assets and systems, networks, and critical infrastructure from sophisticated cyberattacks even more crucial. Cybercriminals can steal data, intellectual property, money, and customer information in many ways. Advanced persistent threats are used by state-backed parties for eavesdropping and geopolitics. Therefore, organizations in all professions and industries must prioritize safety and risk reduction. Businesses may increase their safety with technical, process-based, and strategic cybersecurity solutions, according to the report. This requires strong access restrictions, networks, encryption, software security, and audits. Human aspects like cyber awareness training and a comprehensive plan that matches an organization's risks and resources are considered. A thorough security plan discusses international conventions, following the rules, and using modern technologies. Two case studies demonstrate real-world operations. The first describes how a large retailer stopped a complex APT with numerous defenses. Learn how crucial segmentation, detection tools, incident reaction, and public-private partnerships are. The second one examines a major hotel chain data breach and offers simple measures like strong passwords and vendor tracking to reduce similar dangers. Even if digital adversaries are getting smarter, basic cybersecurity "hygiene." can block many attempts, the report concludes. No organization is totally safe, but following basic rules, laws, and best practices can help protect people, organizations, and governments' assets in today's linked globe. Researchers may investigate how blockchain and AI might lessen hacking risks.

A logic device based on memristor-diode crossbar and CMOS periphery as spike router for hardware neural network

A programmable logic device based on a memristor-diode crossbar and CMOS logic has been developed. The crossbar implements NAND logic gates using memristor ratioed logic and CMOS inverters. The digitally controlled peripheral circuit provides digital signals transmission and allows modification and evaluation memristor states in the crossbar. The proposed logic device circuit requires fewer transistors than known analogues and less area on the chip.The maximum size of the crossbar in a logic device is estimated by numerical simulation at the level of electrical circuits. The limited size is caused by the degradation of the logic levels voltages in the memristor-diode crossbar. The operability of peripheral circuits as part of a complete electrical circuit of a logic device is demonstrated during the simulation of the execution of logical operations, the processes of modification and evaluation states of individual memristors.

Analisis Penerapan Sanksi Pidana Pelaku Residivis Tindak Pidana Narkotika

Abstract In positive law, narcotics crimes are qualified as extraordinary crimes, because the impact of these crimes has a dangerous situation that is widespread. Law No. 35 of 2009 is the basic rule for law enforcement officers to carry out law enforcement for perpetrators. In Article 54 of the Law it is explained that judges are obliged to consider conducting medical rehabilitation for drug users. In Decision No. 745/Pid. Sus/2018/PN Srg, the defendant is a drug case recidivist who had previously been rehabilitated. By using a normative juridical research method that uses the approach of legislation and legal principles, this study aims to examine and determine the application of punishment to recidives in Decision No. 745/Pid. Sus/2018/PN Srg by using the theory of judges' considerations and the purpose of sentencing. The results of this study are the application of sanctions against narcotics crime recidivists must use the purpose of punishment which results in a deterrent effect for the perpetrator, so that the perpetrator does not repeat the action.

The generalised distribution semantics and projective families of distributions

We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic programming as such to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks. To demonstrate the usefulness of such a general approach, we completely characterise the projective families of distributions representable in the generalised distribution semantics and we demonstrate both that large classes of interesting projective families cannot be represented in a generalised distribution semantics and that already a very limited fragment of logic programming (acyclic determinate logic programs) in the deterministic part suffices to represent all those projective families that are representable in the generalised distribution semantics at all.

Research on the optimization of belief rule bases using the Naive Bayes theory

The belief rule base is crucial in expert systems for intelligent diagnosis of equipment. However, in the belief rule base for fault diagnosis, multiple antecedent attributes are often initially determined by domain experts. Multiple fault symptoms related to multiple antecedent attributes are different when an actual fault occurs. This leads to multiple antecedent attributes matching with multiple fault symptoms non-simultaneously, thereby resulting in a fault diagnosis lacking timeliness and accuracy. To address this issue, this paper proposes a method for belief rule-based optimization based on Naive Bayes theory. First, a fault sample is taken in a long enough window and divided into several interval samples, making the analysis samples approximate the overall samples. Second, using Gaussian mixture clustering and Naive Bayes optimization, iteration is performed over the threshold and limit values of fault symptoms in the belief rule base based on the requirements of the timeliness and accuracy of fault diagnosis results. Finally, the belief rule base is optimized. Using fault samples from high-pressure heaters and condensers, the validation results show that there is a there is a significant improvement in the timeliness and accuracy of fault diagnosis with the optimal belief rule base.

A semantic reasoning-based emergency rescue assistant decision method for maritime accidents involving chemicals

Maritime accidents involving chemicals (MAICs) constitute a unique category of marine emergency characterized by low frequency, high risk, and complex disposal challenges. These accidents often entail fires, explosions, and pollutant leakage, demanding effective emergency responses. The success of MAICs rescue operations relies on multiple factors, encompassing the structural features of the involved vessel, personnel, cargo, marine environment, and the availability of resources and response procedures. Proficiency in various professional fields, including ship transportation, chemistry, firefighting, and environmental science, is essential for informed decision-making. This paper addresses the need for a standardized description of knowledge in the MAICs emergency rescue decision-making process, aiming to enhance practical application frequency and decision-making efficiency. Employing ontology theory, the approach involves structurally modeling the knowledge of MAICs emergency rescue, constructing an ontology model, and establishing a decision rule base. Validating the proposed method, the emergency response to the “Longqing 1″ accident is examined as a case study, illustrating its potential for supporting MAICs emergency rescue decision-making. The application of ontology theory in this context holds both theoretical and practical significance. It facilitates the sharing, reuse, and expansion of knowledge in the emergency rescue field, providing crucial technical support for intelligent decision-making in MAICs emergencies.

A novel approach detection for IIoT attacks via artificial intelligence

AbstractThe Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices.

Direct and indirect switch from semiconductor to metallic behavior defined by the competition between Cation–Anion–Cation and Cation–Cation interactions leading to an important technological aspect of perovskite systems

We discuss in this work the key interactions that explain the possible electrical behaviors in different perovskite systems. Multi-behavior observation characterizes each frequency and temperature dependence of various perovskite systems. We classify such observations according to the temperature regimes. Indeed, a direct switch from semiconductor to metallic feature describes the conductivity evolution indicating the direct change from the localized (cation–anion–cation interactions) to delocalized (cation–cation interactions) state. The metallic character is reached due to the reinforcement of charge delocalization effect at high temperatures and frequencies. An indirect transformation separated by a frequency-independent regime is equally observed and discussed. Subsequently, a mixed regime is described by the conductivity temperature-independence which is explored at a specific frequency. Such investigations lead to demonstrate basic and common rules relevant for oxide-perovskites multi-behavior. Equally, important technological aspects are confirmed for systems that displays such multi-behavior character according to the temperature range.

The Narrations of Female Companions in Surat Al-Ma’idah According to Imam Al-Suyuti in his Book Al-Dur Al-Manthur fi Al-Tafsir Bilma’athoor (Asma’ bint Yazid as an Example)

This research aims to briefly introduce Imam Al-Suyuti and study the narrations of one of the female companions, other than the Mothers of the Believers, in Surah Al-Ma’idah in his book “Al-Dur Al-Manthur fi Al-Tafsir bi Al-Ma’thur”. This woman is Asma’ bint Yazid. This study will be through basic rules in the science of Hadith and the science of interpretation and combining them. It is through these narrations it becomes clear to us the explanation of its narrators, the judging on it, the types of chain of transmission, the general meaning of the narration, and what can be learned from it, as well as an explanation of the exegetical aspects of the verse related to the narration in terms of explaining the interpretation, the reasons for the revelation, the aspects of the readings, and the rhetorical aspects of the verse, if any.

Fruit Sorting by Pizzo-electric Sensor and PLC Controlling

Abstract: The fruit sorting process traditionally relies on visual inspection, primarily considering size as a key quality parameter. However, industries engaged in large-scale fruit trading have turned to image processing technology for sorting, despite its high cost and impracticality for small traders. This paper proposes an alternative sorting system that offers an economical solution suitable for automated fruit sorting at various scales. Aim: The aim of this study is to develop an economical automated fruit sorting system capable of classifying fruits based on their weight, thereby eliminating the need for manual labor and improving sorting accuracy and efficiency. Place and Duration of Study: Department of Electrical Engineering Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, between July 2023 to April 2024. Study Design: This study employs a practical approach to design an automated fruit sorting system that utilizes Programmable Logic Controller (PLC) technology in conjunction with load cells. The system is designed to classify fruits based on their weight as they move along a conveyor belt, thereby automating the sorting process. Methodology: The proposed sorting system integrates PLC technology and load cells to accurately measure and classify fruits based on their weight. By eliminating the need for manual intervention, the system enhances sorting efficiency and accuracy. The system is designed to be cost-effective and suitable for implementation by small traders and farmers. Results: The developed sorting system demonstrates precise, reliable, and consistent sorting outcomes based on fruit weight. By automating the sorting process, the system achieves improved efficiency and productivity compared to traditional manual sorting methods. Moreover, the cost-effectiveness of the system makes it accessible to small traders and farmers seeking to enhance their sorting practices. Conclusion: The proposed sorting system offers an economical and efficient solution for automated fruit sorting, particularly for small traders and farmers. By leveraging PLC technology and load cells, the system provides reliable alternative to costly image processing-based methods. The automation of the sorting process eliminates the risk of errors and inconsistencies associated with manual sorting, while ensuring accurate and quantitative classification based on fruit weight. This sorting system presents a practical approach to improving fruit trading operations, enhancing efficiency, and productivity.

On Verified Automated Reasoning in Propositional Logic: Teaching Sequent Calculus to Computer Science Students

As the complexity of software systems is ever increasing, so is the need for practical tools for formal verification. Among these are automatic theorem provers, capable of solving various reasoning problems automatically, and proof assistants, capable of deriving more complex results when guided by a mathematician/programmer. In this paper we consider using the latter to build the former. In the proof assistant Isabelle/HOL we combine functional programming and logical program verification to build a theorem prover for propositional logic. We also consider how such a prover can be used to solve a reasoning task without much mental labor. The development is extended with a formalized proof system for writing machine-checked sequent calculus proofs. We consider how this can be used to teach computer science students about logic, automated reasoning and proof assistants.

Computer network big data detection based on Internet of things technology

In order to solve the problems of traditional network security intrusion detection system in detection accuracy and missed detection rate, this paper proposes a computer network data detection based on Internet of things technology. This paper designs an intrusion detection system based on data clustering analysis algorithm. The hardware part of the intrusion detection system is composed of six parts: detection module, adaptive module, control management module, risk early warning module, control access module and data acquisition module. Each module is closely connected and updated with the rule base and database in real time; In the software flow design of the system, based on the data clustering analysis algorithm, the collected original data are classified and processed, and the Mings distance is determined, revealing the difference between normal data and abnormal data, and finally realizing the accurate detection of cloud computing network intrusion behavior. The experimental results show that the proposed intrusion detection system has a higher detection accuracy for malicious data, and the average missed detection rate can be controlled below 0.3%. Conclusionthe detection system designed in this paper has more advantages in stability performance.

Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV’s equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations.

A deep belief rule base-based fault diagnosis method for complex systems

Complex systems are prone to faults due to their intricate structures, potentially impacting system stability. Therefore, fault diagnosis has become crucial for maintaining stable operation. In the field of complex systems, the combinatorial explosion problem in belief rule base (BRB) has attracted significant attention. The interdependence among system components leads to numerous variables and the need for rules, heightening model complexity. Regarding the combinatorial explosion problem, an improved belief rule network structure called deep BRB (DBRB) is proposed. First, the extreme gradient boosting (XGBoost) feature selection method is employed to choose the relatively important feature subset. Next, driven by the importance of features, different levels of features are input into the model, forming a complete and progressive network structure. Finally, the model undergoes the reasoning and optimization process. The effectiveness of the model is confirmed with a bearing fault dataset. After a comprehensive evaluation of multiple indicators, this method demonstrates a consistent improvement in classification performance as the depth increased. Moreover, compared to the traditional BRB model, this method notably reduces the number of parameters, improving its efficiency of processing complex data. In short, this method effectively tackles combinatorial explosion while ensuring model performance. The selection and assignment of feature subsets enhance the logic and readability of the model. Through the network structure, various fault features are captured well. This fault diagnosis method, rooted in the DBRB, offers a novel perspective on diagnosing complex system faults.