Rule Execution Research Articles

On the Implementation of Fuzzy Inference Engines on Quantum Computers

Quantum computers can be a revolutionary tool to implement inference engines for fuzzy rule-based systems. In fact, the use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This paper introduces the very first quantum-based fuzzy inference engine that is capable of providing exponential acceleration in fuzzy rule execution compared to its classical counterpart, and allows a quantum computer to be programmed by fuzzy linguistic rules. The proposed inference engine was implemented using a quantum algorithm design scheme based on the oracle notion. This scheme allows the modeling of a fuzzy rule-based system as a Boolean function, the oracle, which is able to reconstruct the relationships between the antecedent and consequent parts of fuzzy rules, and can be efficiently computed on a quantum computer. The suitability of the proposed quantum algorithm for use as a fuzzy inference engine was tested in a typical benchmark scenario, such as that provided by inverted pendulum control.

Clientelism and Subservience in Organizational Behaviour and Professional Practice

The purpose of this qualitative study was to discuss the opinions of diverse employees in a developing country where social inequalities, stratification and behavioural attitudes encourage the type of subservient behaviour and clientelistic attitudes that impact organizational behaviour and a tendency towards corruption. A range of qualitative techniques, such as observation, third-person listening, formal and informal dialogue and an in-depth interview approach was adopted to collect data from selected participants through a non-probability sampling strategy. An extreme lack of professional behaviour was observed. The qualitative study of contributions and motivations to these practices reveals a misunderstanding of professional etiquette, leading to a culture of senior officials and subordinate relationships (corruption), lack of responsibility and accountability and negative impacts on organizational policy implementation. The research revealed that networks may have a layered system that informs the activities of actors involved in organizational management. This study is the first evidence that subservient attitudes and senior-subordinate relationships may cause barriers to the execution of professional attitudes and other official rules and norms.

Design and Simulation of an Autonomous Molecular Mechanism Using Spatially Localized DNA Computation.

As a well-established technique, DNA synthesis offers interesting possibilities for designing multifunctional nanodevices. The micro-processing system of modern semiconductor circuits is dependent on strategies organized on silicon chips to achieve the speedy transmission of substances or information. Similarly, spatially localized structures allow for fixed DNA molecules in close proximity to each other during the synthesis of molecular circuits, thus providing a different strategy that of opening up a remarkable new area of inquiry for researchers. Herein, the Visual DSD (DNA strand displacement) modeling language was used to design and analyze the spatially organized DNA reaction network. The execution rules depend on the hybridization reaction caused by directional complementary nucleotide sequences. A series of DNA strand displacement calculations were organized on the locally coded travel track, and autonomous movement and addressing operations are gradually realized. The DNA nanodevice operates in this manner follows the embedded "molecular program", which improves the reusability and scalability of the same sequence domain in different contexts. Through the communication between various building blocks, the DNA device-carrying the target molecule moves in a controlled manner along the programmed track. In this way, a variety of molecular functional group transport and specific partition storage can be realized. The simulation results of the visual DSD tool provide qualitative and quantitative proof for the operation of the system.

The Impact of Ethical Climate on Work-Family Conflict and Job Stress: An Empirical Research from Türkiye

One of the most important factors in the execution of ethical values, ethical rules and ethical climate and their acceptance among employees is to determine what is right and wrong. In this sense, to explain moral rules to employees or managers and to make moral practices available stays critical. It has been proven that ethical climate in tourism and hotel businesses gives positive results in the working environment. In this context, the current study aimed to examine the impact of ethical climate on work-family conflict and job stress in a Turkish hotel setting. A sample of full-time frontline personnel was chosen for this study, and 131 usable questionnaires were individually collected from them on the survey grounds. A quantitative method was used in this study and SPSS 20 version was applied for the study analyses such as descriptive statistics, factor analysis, correlation and regression. The findings depicts that ethical climate was found to be negatively related to work-family conflict but this relationship was not significant. On the other hand, a significant negative relationship was confirmed among ethical climate and job stress. There is paucity of research regarding the outcomes of ethical climate in hotel settings, so the current work sheds light on the recent literature and provides implications for the industry role players. Keywords: Ethical climate; Work-family conflict; Job stress; Hotels; Türkiye

Lifecycle Support for IoT-Driven Business Rules

The Internet of Things (IoT) is prevalent in various areas and enables a variety of smart applications such as smart factory, smart home, and smart health. The IoT corresponds to a network of physical objects, fostering the collection and exchange of data over the Internet. Each physical object is equipped with sensors, actuators, and other technologies, and is uniquely identifiable through its embedded computing system. Business Process Management (BPM), in turn, is concerned with the modeling, execution, discovery, analysis, and monitoring of business processes. By enriching business processes with real-world IoT data and IoT-driven business rules, process automation as well as real-time process monitoring and decision making can be enhanced. Despite the increasing interest in IoT-enhanced business process support, IoT-driven business rules have not been well integrated with business process execution so far. In this context, providing integrated lifecycle support for IoT-driven business rules in BPM systems constitutes a challenge. This paper discusses how to integrate lifecycle support of IoT-driven business rules with BPM systems covering the modeling, execution, monitoring, analysis, and evolution of IoT-driven business rules. Furthermore, all lifecycle phases need to be covered by holistic approaches to IoT-driven business rules. For each lifecycle phase, we elaborate on the requirements emerging in this context and discuss existing approaches.

Mandatory Individual Amendments: a Change in the Pattern of Executive Dominance in the Brazilian Budgetary and Financial Cycle

Until 2013, the Brazilian executive branch had control over the execution of all discretionary public spending. In that context, all Brazilian legislators could do was amend the budget proposal prepared by the government. This article analyzes whether EC 86/2015, known as the mandatory budget amendment EC, has reduced executive dominance over the financial cycle of individual budget amendments. To this end, we examine descriptively and inferentially the main changes proposed by EC 86/2015, as well as data about the individual amendments executed before and after the constitutional change. Our results indicate that executive dominance was reduced after the change in the budget execution rules: the amount paid and the number of executed individual amendments have increased, while the profile of legislators with executed amendments also changed: Being part of the government coalition became less important. This article contributes to legislative studies by exploring the empirical implications of this important constitutional change. From a theoretical point of view, our findings challenge the expectation that the executive uses amendment execution as a bargaining tool to secure legislative support in roll-call votes in the National Congress, especially in the post-EC 86/2015 period.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.

An SQWRL-Based Method for Assessing Regulatory Compliance in the Pharmaceutical Industry

Nowadays, data integrity has become a critical issue in the pharmaceutical regulatory landscape, one that requires data to be compliant to ALCOA principles (i.e., data must be Attributable, Legible, Contemporaneous, Original, and Accurate). In this paper, we propose a method which exploits semantic web technologies to represent pharma manufacturing data in a unified manner and evaluate in a systematic manner their ALCOA compliance. To this purpose, in the context of a pharma manufacturing environment, a data integrity ontology (DIOnt) is proposed to be utilized as the basis for the semantic representation of pharma production data and the associated regulatory compliance management processes. We further show that semantic annotations can be used to represent the required ALCOA compliance information, and that semantic reasoning combined with SQWRL queries can be used to evaluate ALCOA compliance. The proposed approach has been implemented in a proof-of-concept prototype and validated with real world pharma manufacturing data, supporting the combined execution of SWRL rules and SQWRL queries with the aim to support the ALCOA compliance assessment and calculate non-compliance percentages per each ALCOA principle.

Spiking Neural P Systems With Enzymes.

The neurotransmitter is a chemical substance that transmits information between neurons. Its metabolic process includes four links: synthesis, storage, release and inactivation. As one of the important chemical components of neurotransmitters, acetylcholine is synthesized under the catalysis of acetylcholine coenzyme A and choline acetylase. Inspired by the biological fact that enzymes exist in neurons and that enzymes are involved in neurotransmitter synthesis, we propose spiking neural P systems with enzymes (SNPE). Different from the previous spiking neural P systems and their variants, each neuron of SNPE contains two classes of objects, and each spiking rule has the participation of enzymes. In addition, the number of spikes and enzymes in a neuron can also serve as a consumption condition for controlling whether a reaction (rule execution) occurs. When the number of enzymes meets the requirements of a specific biochemical reaction, the number of occurrences of the reaction can also be controlled. As number generation and acceptance devices, the proposed SNPE systems are proved to be Turing universal. In addition, 61 neurons are used to construct an SNPE system that realizes function computation, which proves the Turing universality in this mode. Finally, we also explore using a uniform SNPE model to solve the subset sum problem and compare it with the standard SN P and its several variants.

Event-Triggered ADP for Tracking Control of Partially Unknown Constrained Uncertain Systems.

An event-triggered adaptive dynamic programming (ADP) algorithm is developed in this article to solve the tracking control problem for partially unknown constrained uncertain systems. First, an augmented system is constructed, and the solution of the optimal tracking control problem of the uncertain system is transformed into an optimal regulation of the nominal augmented system with a discounted value function. The integral reinforcement learning is employed to avoid the requirement of augmented drift dynamics. Second, the event-triggered ADP is adopted for its implementation, where the learning of neural network weights not only relaxes the initial admissible control but also executes only when the predefined execution rule is violated. Third, the tracking error and the weight estimation error prove to be uniformly ultimately bounded, and the existence of a lower bound for the interexecution times is analyzed. Finally, simulation results demonstrate the effectiveness of the present event-triggered ADP method.

Distributed rule execution mechanism in smart home system

<p>Smart home systems become an interesting topics in the last few years. Many researchers have been studied some features. Most of smart home system use a centralized architecture know as centralized smart home system (CSHS). The centralizedmechanism is easy to manage and to configure. However, in fault-tolerant systemparadigm it produces a problem. The entire system will fail, if the master station fails.Another problem of CSHS is centralized mechanism gives more data-flow. This condition makes the system has a greater delay time. To solve the problem, we proposea distributed rule execution mechanism (DREM). Compared to the centralized mechanism, the DREM allows a device to provide its service without any commands fromthe master station. In this mechanism, since the information does not need to go tothe master station, the data-flow and the delay-time can be decreased. The experimentresults show that the DREM is able to mask the failure in the master station by directlytransmit the data from trigger device to service device. This mechanism makes the services provision without master station possible. The mathematical analysis also shows that the delay time of the service provision of the DREM is less than the delay time ofCSHS.</p>

Dynamic analysis of fire induced domino effects to optimize emergency response policies in the chemical and process industry

A fire accident is one the most dangerous accidents that may lead to knock-on effects, especially in the plants dealing with large quantities of hazardous substances. Different from other accidents, e.g., explosions, fires can last certain period of time and the thermal radiations emitted by them have a synergistic effect. At the same time, the heat-up of target installations under the effect of thermal radiation is also a process that takes a certain amount of time. These characteristics make fire induced domino effects a dynamic process. During an emergency response due to a fire accident, emergency personnel may arrive at the fire scene at different times, so they may face different accident situations. In this work, an adaptive timed Petri-net (ATPN) based approach is proposed to model the propagation of fires and perform a dynamic analysis of potential domino effects. The definition of ATPN as well as the enabling and execution rules is provided. Through simulations, not only the probabilities of fires in different installations, but also the probabilities of the fire extension propagated over time can be obtained. An example of a tank farm fire illustrates the approach. Our developed approach for carrying out a dynamic analysis of domino effects is helpful for emergency preparation.

On the design of an advanced business rule engine

AbstractBusiness rules govern how well‐managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi‐language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what‐if analyses natively. In this article, we present Tier‐Rules, which is a system that overcomes the previous problems. We also report on an industrial case study that helps illustrate it in practice.

A simulation of synchronized curriculum for IT-specialist training based on Petri nets

In this study, the issues of contemporary IT specialists training in terms of constant innovation and rapid expansion of computing are examined in the context of synchronized curriculum shaping. The authors' curriculum simulation technique, which uses Petri nets, is shown. Some didactical foundations of curriculum design and academic discipline structure, as well as the theoretical basics of Petri nets in terms of their considerable facilities for multiple processes synchronization, formed the theoretical framework that allowed us to develop the technique. The technique of academic disciplines structuring based on the frame model of knowledge representation is described as a series of processes that culminate in a net of disciplines modules (frames) that include the appropriate learning materials while preserving and propagating links between them. The practical-driven approach to curriculum modeling based on Petri nets simulation is offered to tackle the problem of synchronization of learning elements mastery across academic disciplines with regard to the established links in the frames. The many types of precedence relations for the learning elements (frame inputs and outputs) were identified. Using these precedence relations, we were able to model a learning element and the process of mastering it using Petri nets. We were able to create a model of a curriculum discipline module and, eventually, a curriculum discipline model using Petri nets modifications and learning element connections of various types. All of the evolution of the learning elements mastering by students in the curriculum courses is represented by built Petri nets models. The rules of Petri net execution and alterations ensure that their mastering is synchronized. The advantages and disadvantages of the proposed technique are discussed. The advantages of using such a simulation to create a synchronized curriculum for IT-specialist training are outlined. The research's potential are presented in terms of applying the findings to the development of specialized software.

Game Never Over: Replaying History in Interactive Digital Simulation

This article looks at a novel digital format for historical-literary research: executable rules for a computer-based interactive social simulation. This kind of historical writing imposes a different set of evaluative criteria on its intended outcomes, which share similar ambitions with the classical humanities essay but whose mode of consumption is closer to that of video games. In this context, play emerges as a humanities method rather than an object of study. Inasmuch as user performance determines the progression of the narrative, iterability becomes a crucial feature of 'reading' interactive simulations whose full expressivity can only be captured through replay.

Thailand's COVID‐19: How public financial management facilitated effective and accountable health sector responses

AbstractEffective Public Financial Management (PFM) systems are crucial during COVID‐19 pandemic to ensure timely mobilisation of sufficient resources and distribution to frontline service providers. All aspects of PFM, from budget acquisition to execution and expenditure reporting and auditing are important aspects in pursuing effective pandemic responses with transparency and accountability. This commentary analyzes how PFM in Thailand adapted to support purchasing of COVID‐19 health services, including laboratories and treatment, vaccines and vaccination servicing, and no‐fault compensation from adverse effects following immunisation. It also discusses the limitations which delay implementation. Financing COVID‐19 services was decided by the Cabinet under a State of Emergency Decree, resulting in expedited budget approval process. Though delays in budget execution were caused by bureaucratic budget spending rules, regulations and approvals, PFM adaptation allowed for services to be provided through the use of hospital revenue with rapid budget execution rules and regulations while maintaining accountability, reporting and auditing. Lastly, while reporting is mandatory with internal audit by related government agencies, and external audit by Office of the Auditor General in place, as of September 2021, report of the COVID‐19 expenditure in 2020 has yet to be made publicly available for transparency and check and balance by the public. It is unclear the degree to which audit systems are fully enforced. Overall, Thailand's PFM systems have provided rapid fund mobilisation sourcing from central budget and loans and clarity in authorisation of spending; the use of hospital revenue provides more flexibility and rapid budget execution.

BIOMEDICAL WASTE MANAGEMENT - AN UPDATE ON COVID 19

The concept of environmental hygiene and sanitation has been there in India since the time of Rig Veda. Biomedical waste is the most dangerous waste in the world . It pose a significant impact on health and the environment. Management of the bio medical waste which is becoming a challenging issue in India. Since the implementation of biomedical waste management rule 1998, every concerned health care professional should have knowledge about proper management and disposal of the biomedical waste. Protected and effective execution of waste management rules is not only a legal necessity but also a social liability. There is urgent need of extensive study on this medical waste and its management aspects. Proper waste management strategy is needed to ensure health and environmental safety.

CL-BC: A Secure Data Storage Model for Social Networks

While bringing convenience to people, social networks cause various security problems, such as information leakage, which belongs to the security category of data storage. The related research focuses more on the confidentiality design and lacks the discussion of data integrity protection technology. Thus, the electronic data editability invites the risk of data tampering and destruction, making other operations meaningless once the data are inaccurate. A social network-oriented data security storage model, CL-BC, included conceptual design, preliminary framework design, and detailed flow design, to improve the security threats such as overauthorization and illegal execution in data storage. The blockchain technology is combined with the improved Clark–Wilson model to achieve data integrity access control protection through execution rules and authentication rules. The system uses the subalgorithm of DR-BFT for reference when deploying the algorithm program and adopts the intelligent contract to complete the design rules. The experimental analysis shows the CL-BC model adapts to the actual security environment, ensures the data storage integrity in the social network stored process, and has application value.

An Artificial - Intelligent - Based System to Automate the Design of Complex Mechanical Products

Over the last decades, there has been growing pressure on industrial companies to offer to their costumers products with high quality, in the minimum deadlines and with reasonable prices. Since the design phase plays a key role to achieve these difficult goals, many traditional, DFX (Design For X) and integrated approaches have been proposed. However, many limits are still present. Thus, the main objectives of this work were first to identify these limits and then to overcome them by proposing and developing an automated framework for integrated product design. In this work, we automated the integrated DFMMA (Design For Materials, Manufacturing and Assembly) approach by developing an architecture composed of four levels, namely: the Common Information Modeling Level, the Selection Systems Level, the Inference and Computation Level and finally the Application Level. The proposed automated system is based on ontologies, on the CBR (Cases Based Reasoning) and the RBR (Rules Based Reasoning). The first main result obtained throughout the contributions consists on the integration of Manufacturing process selection, Assembly solution selection and materials selection in one integrated design approach. The second main result obtained consists on the exploitation of all the previous design studies developed by the design team and the ability to reuse the designers experience throughout the case based reasoning used in the proposed architecture. Another important result consists on the formalization and the automation of the execution of the design rules and the ability to infer new results and to check inconsistencies in the developed product using the data and information modeled in the ontological model and throughout the Cases Based Reasoning that we have incorporated in the developed approach. In this way, the redundancy in work and the difficulties faced in case of having a high number of design alternatives are avoided. Consequently, the product quality increases and wastes of time and money decrease. Finally, to validate the functioning and the efficacy of the proposed DFMMA system, an application on the design of a complex mechanical product is developed in the end of the work.

PFedSim: An Efficient Federated Control Method for Clustered Training

To further promote the implementation and application of artificial intelligence technology under data silos, federated ecology has defined a set of standardized and systematic solutions from upstream data to downstream applications from a systematic perspective. As the core of the federated ecology, federated control can transform the abstract tasks of federated management into concrete execution rules. Clustered training is a common cooperation rule for the personalized federated training task of multi-party cooperation. However, it is difficult to generalize in large-scale cooperation scenarios because of increased data transmission pressure and server storage burden. Therefore, this paper designs a personalized federated control method based on similarity measures(pFedSim) so that the server can accurately distribute the cluster model to clients, thereby reducing the transmission cost. Compared with the existing clustered training method IFCA, pFedSim transfers the selection rights of cluster models from the client to the server. Model selection preference, accurate distribution of cluster models. Experiments show that in the personalized training task with s clusters, the method can obtain the same model level with 1/s data transmission. It is better than the current optimal clustering training method Macro-Acc increased by 2.25%.