Update Of Rules Research Articles

Reflections on governing Japan’s discharge of Fukushima nuclear-contaminated water: from the perspective of building a Maritime Community with a shared future

Since August 2023, Japan has discharged some eight rounds of Fukushima nuclear-contaminated water, totaling some 62,800 tons. Despite global criticism and domestic opposition, the Japanese government continues with this discharge plan, anticipating its continuance for decades. As distinct from conventional nuclear wastewater discharges from nuclear power plants, Japan’s recent discharges of Fukushima nuclear-contaminated water will release a significant number of radioactive nuclides into the ocean. It is foreseeable that the discharge of nuclear-contaminated water will adversely affect various sectors, including the marine environment, human health, the seafood trade, the maritime economy, and the international marine legal order. Currently, the governance of the Fukushima nuclear-contaminated water discharge faces three major dilemmas: insufficient participation by stakeholders, outdated regulations, and a crisis of trust. The concept of a titular Maritime Community with a Shared Future (MCSF) could address the discharge of Fukushima nuclear-contaminated water, incorporating diverse interests while also considering both current and future development. This paper argues that it is imperative to jointly engage in negotiation within the perspective of MCSF, to jointly build a governance mechanism and advance the update of rules, and to share data and information sustainably to manage the discharge of the Fukushima nuclear-contaminated water.

Concrete with Recycled Aggregates ‐ Update of the Application Rules in Germany and Outlook

AbstractIn Germany the use of recycled aggregates is possible within certain limits since the introduction of the DAfStb guideline in 1998. Due to increasing demands of concretes fulfilling requirements regarding sustainability, conservation of resources and avoidance of waste the topic of recycled aggregates has again become the focus of public, economic, and political interest in recent years. Although the results of the BMBF research project “R‐Beton” have led to positive changes with respect to the regulation gap in environmentally relevant characteristics several certain barriers still prevent the broad use of recycled aggregates in the German concrete market. In the forthcoming DIN 1045, regulations will be created which, particularly in concrete class BK‐N in the application range of up to 25 % volume fraction, will permit simple and thus practical use of recycled aggregates. In addition, fine recycled aggregates of type 1 may be used, which was previously not permitted. Here, changes in the regulatory framework of DIN 1045 will be discussed in the sense of a broader use of recycled aggregates in Germany as well as the context of other national regulations. An outlook regarding further need of research is also given.

Deception attack detection based on bandwidth allocation for switched systems with quantization

This article discusses a more practical deception attack on the continuous-time switched systems, i.e. a malicious attacker tampers with the binary 01 code transmitted through the network. Assume that the switching is slow enough and the attacker’s energy is limited, we propose a coding transmission strategy based on bandwidth allocation, which selects a part of the network bandwidth for reverse data transmission. Under such strategy, partial detection of attacks and synchronous update of quantization rules are realized. Combined the increasing/decreasing rate of Lyapunov function with the success rate of attack detection, the sufficient conditions ensuring the mean square exponential stability of the system are given. Taking the maximum convergence rate as a performance indicator, the optimal bandwidth allocation scheme is provided for the specific values, which realizes a trade-off between attack detection rate and quantization error. Two-tank system is adopted to illustrate the effectiveness of the theoretical analysis.

Minimizing Update Makespan in SDNs Without TCAM Overhead

Efficient and consistent update of the network routing rules is a challenging task that significantly affects the performance, correctness, and security of Software-Defined Networks (SDN). In this work, we consider the problem of minimizing the makespan of updating the routing rules in SDNs, while guaranteeing three crucial consistency requirements: (1) WayPoint Enforcement, (2) Loop Freedom, and (3) Conflict Freedom. This problem is known to be NP-hard, and thus we focus on designing approximate algorithms that run in polynomial time without incurring TCAM storage overhead. To compute consistent rule-update schedules, we propose two algorithms, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TimeX</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMS</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TimeX</i> employs the solution of a linear program (LP) to address the makespan minimization goal systematically. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RMS</i> is an LP-independent heuristic that provides higher scalability. We demonstrate and utilize a property of rule-updates, called reversibility, to reduce the makespan in RMS. Extensive simulations show that our algorithms reduce the makespan by 2% to 18% and attain a 4.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> speedup compared to previous studies. Moreover, Mininet experiments reveal that the proposed algorithms can mitigate the transient congestion caused by conflicting flows.

An example of pricing in competitive supply chain using association rule mining algorithm based on interval concept lattice

The substitutability between products or the intensity of market competition is the key parameter affecting the supplier’s pricing decision. However, the parameter cannot be accurately measured in real life. This paper provides a method based on prior information to solve this issue. First, compared to classical concept lattice theory, the interval concept lattice theory can deal with uncertain information more accurately. It is used to extract the objects within the interval parameters [α, β], and then interval concepts and lattice structure are built. Second, based on the interval concepts and lattice structure, the association rule mining algorithm is designed to further extract the association rules under different interval parameters. Third, to obtain the effective association degree between two objects, the rule optimization algorithm is put forward by comparing the update of rules. Finally, the association degree can indirectly reflect the substitutability between products. Then the price of a new product can be determined. Our paper provides some implication on pricing for suppliers in competitive supply chain.

The Effect of Corporate Governance Mechanisms on Earnings Management in Malaysian Manufacturing Companies

Earnings management (EM) firms produce less predictable accounting earnings that do not reflect the company's genuine financial success. However, EM is regarded as one of the most significant issues regarding financial reporting, especially in light of the COVID-19 pandemic. Nevertheless, this paper aims to investigate the effect of corporate governance mechanisms on earnings management in Malaysian manufacturing companies (MMCs). Data was collected from 2010 to 2019 for MMCs. By highlighting the updating of corporate governance rules, the market value and competitiveness of institutions will improve. Moreover, this paper provides evidence of the effect of corporate governance on earnings management. Therefore, the results may help policymakers to take a new approach to the development of corporate governance instructions, and stakeholders, which would enhance earnings quality and improve the quality of financial reports. This could help them build a more comprehensive picture of the firm and improve the decision-making process.

100 Gbps Dynamic Extensible Protocol Parser Based on an FPGA

In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.

A survey of Blockchain technologies applied to software‐defined networking: Research challenges and solutions

AbstractSoftware‐Defined Networking (SDN) brought a groundbreaking idea to facilitate network system management by decoupling and abstracting the Control plane and Data plane of traditional networks. The centralised control offers network administrators many benefits such as a global view of the network, programmability, dynamic updating of forwarding rules, and software‐based traffic analysis. The SDN architecture has been applied a lot in practice, and especially in Internet of Things (IoT) platforms. With the superiority of SDN, IoT devices can be managed and configured much more easily when combined. However, SDN also raises many challenges in terms of scalability, reliability, and security. Blockchain is another promising solution for secure information storage and transmission technology that operates without a centralised authority. Applying Blockchain technology into SDN can address some of the current issues of SDN by providing decentralised methods to authenticate exchanged network information. This study provides a comprehensive survey on Blockchain technologies applied to SDN in both security and non‐security fields. First, related studies and an overview of SDN and the background of Blockchain technology are presented. Then, the authors review how Blockchain technologies are applied in SDN from two perspectives: non‐security and security‐aware approaches. Finally, challenges and broader perspectives are discussed.

Numerical strategies for variational updates in large strain inelasticity with incompressibility constraint

AbstractIn finite deformation inelasticity, one often has to deal with the incompressibility constraint. In the past, this was dealt with using, for example, an exponential mapping approach, which yields exact volume preservation in plastic deformations. In this article however, the special‐linear update approach by Hurtado et al. The special‐linear update: An application of differential manifold theory to the update of isochoric plasticity flow rules, Int J Numer Methods Eng. 2014;97(4):298‐312, which utilizes a projection method to fulfill the incompressibility constraint is used. The model is applied to isotropic plasticity by a novel approximation of the logarithm and treats kinematic hardening without losing the symmetry of the internal variables. The model results are compared to models utilizing an exponential mapping approach in numerical experiments.

INTERNATIONAL ORGANIZATIONS AS SUBJECTS OF INTERNATIONAL LAW RULES INTERPRETATION

This paper aims to analyze an interpretive activity of international organizations as a means to solve the problem of paramount importance for ensuring updating of international law rules and the whole mechanism of international law action in the process of rules implementation. The methodology is based on a comprehensive approach to the analysis of the object and subject of research, which covers philosophical and legal methods – dialectical method of scientific knowledge, system-structural method, empirical method, hermeneutic method, synergetic and formal-legal methods of scientific research. As a result, the growing role of international organizations as subjects of interpretation, which do not only play an essential role in international law-making and the implementation of international law but also take an active part in their interpretation, was proved. In interpreting their charters, international bodies and organizations certainly influence the content of other international norms. At the current stage, it is necessary to define international organizations’ interpretive activity as a particular type of international law-making, which consists in the official interpretation of international legal rules contained in the relevant acts. Nevertheless, they do not acquire the status of law-making bodies, and their decisions and conclusions are of a recommendatory nature.

Linking: Essential functionality on the internet and never-ending story?

It might seem surprising that one of the most essential functions on the internet, linking –or “what webs the Web” – is still topical twenty years after the adoption of the InfoSoc Directive. Yet, on 9 March 2021 the Grand Chamber of the Court of Justice of the European Union (CJEU), comprising 15 judges, handed its 56-paragraph long judgment in the latest piece of the linking puzzle: This time in relation to thumbnails, framing and effective technological protection measures in Case C-392/19, VG Bild-Kunst. Hyperlinks, as Advocate General (AG) Szupnar notes in his Opinion to the case, allow internet users to “‘travel’ through ‘cyberspace’” in a similar fashion as “[t]he heroes of George Lucas’ Star Wars film saga were able to travel through ‘hyperspace’ faster than the speed of light using a ‘hyperdrive’”. He continues in his science fiction analogy that “[a]lthough those links do not defy the laws of physics, as did the hyperdrives of the spacecraft in Star Wars, they nonetheless present a number of challenges from the point of view of the law, in particular copyright law.” With this science fiction analogy in mind, it is less surprising that the question of whether hyperlinking to copyright-protected material constitutes a communication to the public in the sense of Article 3(1) InfoSoc Directive has kept the CJEU and legal scholarship busy for the last decade. With the recently performed update of EU copyright rules in the CDSM Directive, no legislative clarification is in sight. And just like George Lucas’ successful Star Wars imperium, the linking saga keeps on going.

Intelligent Software-Defined Network for Cognitive Routing Optimization using Deep Extreme Learning Machine Approach

In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor analysis, and dynamic updating of forwarding rules) make it simpler to apply machine learning strategies. Various perspectives of fiber-optic communications including fiber nonlinearity coverage, optical performance checking, cognitive shortcoming detection/anticipation, and arranging and improvement of software-defined networks are examined in Machine Learning (ML) applications. This research paper has presented an imaginative framework concept called Intelligent Software Defined Network (ISDN) for Cognitive Routing Optimization (CRO) using Deep Extreme Learning Machine (DELM) approach (ISDN-CRO-DELM) in light of the new challenges in the development and operation of communication systems, and capturing motivation from how living creatures deal with difficulty and usability. The proposed methodology develops around the planned applications of progressive DELM methods and, specifically, probabilistic generative models for framework wide learning, demonstrating, improvement, and information description. Furthermore, ISDN-CRO-DELM, suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level. MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.

Developing reservoir evaporation predictive model for successful dam management

Evaporation is a primary component of the hydrological cycle, water resources management and forward planning. The succeed management for the dam system is based on the accurate prediction of the reservoir evaporation magnitude. Physical models applied in the prediction of evaporation can encounter obstacles in respect to accurate estimations of evaporation due to the inherent challenges in respect to the mathematical procedure that could fail to address the natural processes and initial conditions that drive the evaporation patterns. To address these limitations, the present study aims to design a new model using the modified Coactive Neuro-Fuzzy Inference System (CANFIS) algorithm to improve feature extraction process in a purely data-driven model. The new approach comprised of the adjustments made to the back-propagation algorithm, allowing the automatic updating of the membership rules and hence, providing the center-weighted set rather than the global weight sets for input-target feature mapping. The predictive ability of the modified CANIFIS model is benchmarked in respect to the conventional ANFIS, SVR and RBF-NN model by statistical performance metrics. To explore its efficiency, the modified CANFIS method is applied for evaporation prediction in two diverse climatic environments. The results revealed the superiority of the modified CANFIS model for evaporation prediction in both Aswan High Dam (AHD) and Timah Tasoh Dam (TTD). The statistical indicators supported the better performance of the modified CANFIS model, which significantly outperforms other proposed models to attain relative error value less than (23% for AHD, 20% for TTD), MAE (12.72 mm month−1 for AHD, 7.63 mm month−1 for TTD), RMSE (15.42 mm month−1 for AHD, 8.53 mm month−1 for TTD) and a relative large coefficient of determination (0.96 for AHD, 0.91 for TTD).

Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Individualised prediction of psychosis in individuals meeting at-risk mental state (ARMS) criteria: protocol for a systematic review of clinical prediction models

BackgroundPsychotic disorders affect about 3% of the population worldwide and are associated with high personal, social and economic costs. They tend to have their first onset in adolescence. Increasing emphasis has been placed on early intervention to detect illness and minimise disability. In the late 1990s, criteria were developed to identify individuals at high risk for psychotic disorder. These are known as the at-risk mental state (ARMS) criteria. While ARMS individuals have a risk of psychosis much greater than the general population, most individuals meeting the ARMS criteria will not develop psychosis. Despite this, the National Institute for Health and Care Excellence recommends cognitive behavioural therapy (CBT) for all ARMS people.Clinical prediction models that combine multiple patient characteristics to predict individual outcome risk may facilitate identification of patients who would benefit from CBT and conversely those that would benefit from less costly and less intensive regular mental state monitoring. The study will systematically review the evidence on clinical prediction models aimed at making individualised predictions for the transition to psychosis.MethodsDatabase searches will be conducted on PsycINFO, Medline, EMBASE and CINAHL. Reference lists and subject experts will be utilised. No language restrictions will be placed on publications, but searches will be restricted to 1994 onwards, the initial year of the first prospective study using ARMS criteria. Studies of any design will be included if they examined, in ARMS patients, whether more than one factor in combination is associated with the risk of transition to psychosis. Study quality will be assessed using the prediction model risk of bias assessment tool (PROBAST). Clinical prediction models will be summarised qualitatively, and if tested in multiple validation studies, their predictive performance will be summarised using a random-effects meta-analysis model.DiscussionThe results of the review will identify prediction models for the risk of transition to psychosis. These will be informative for clinicians currently treating ARMS patients and considering potential preventive interventions. The conclusions of the review will also inform the possible update and external validation of prediction models and clinical prediction rules to identify those at high or low risk of transition to psychosis.Trial registrationThe review has been registered with PROSPERO (CRD42018108488).

Brexit and the Future of UK Capitalism

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Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Context awareness in smart mobile applications is a growing area of study because of its intelligence in the applications. To build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules, a number of issues, such as the quality of smartphone data, understanding the relevancy of contexts, discretization of continuous contextual data, discovery of useful behavioral rules of individuals and their ordering, knowledge-based interactive post-mining for semantic understanding, and dynamic updating and management of rules according to their present behavior, are investigated. In this paper, we briefly discuss these issues and their potential solution directions for mining individuals’ behavioral rules, for the purpose of building various context-aware intelligent mobile applications. We also summarize a number of real-life rule-based applications that intelligently assist individual smartphone users according to their behavioral rules in their daily activities.

The State of the Art of Software Defined Networking (SDN)

The complex design of the current network architecture, which has inevitably resulted in poor network resources management, has triggered researchers to propose a Software Defined Networking (SDN)-based network model to simplify the management of the limited bandwidth of a network. The key idea of the SDN-based model is to simplify network management by introducing a centralized control through which the dynamic update of forwarding rules, the simplification of network devices tasks, and flow abstractions can be realized. This proposed model utilizes the limited network bandwidth systematically by giving real-time traffic higher priority than non-real-time traffic to access limited resources. The experimental results showed that the proposed model helped ensure real-time traffic would be given greater priority to access the limited bandwidth, where the major portion of the limited bandwidth was allocated to the real-time traffic.