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Purpose – The paper aims to analyse typology of dynamic management studies and their specificity. Design/methodology/approach – The study analyses more than 200 studies published in English, French and Romanian management literature in the last 15 years. The data analysis follows a qualitative methodology. Findings – The study provides: four classifications of the dynamic managerial approach and their specificity; and the main advantages and limits of dynamic management analysis. Research limitations/implications – Further research should use the classifications, advantages and limits identified to investigate certain dynamic management analyses and formulate conclusions and recommendations for better dynamic managerial analysis. Practical implications – The paper highlights the specificity of different types of dynamic management analyses, that are often underestimated by researchers, professors, managers, students etc. The findings could be used by management practitioners to better understand the management evolution and performance of different organizations, to elaborate company strategy and policy, to change organizational culture a.s.o. Social implications – The increasement of social evaluation and prognosis quality. Originality/value – In the international management literature, the paper provides the first classification of dynamic management studies and their specificity and the synthesis of the main advantages and limits of dynamic management studies for both management theoreticians and practitioners. These elements are useful to increase the value of dynamic management studies and to improve management practices in organizations.

A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.

Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%–15% because excessive on-chip temperature shortens the chip’s life cycle. In this paper, we address the scheduling & energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling (EA-EDF) based technique for multiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor system-on-chip while lowering energy and power consumption. The selection of core and migration of tasks prevents the system from reaching its maximum energy utilization while effectively using the dynamic power management (DPM) policy. Increase in the execution of tasks the temperature and utilization factor on-chip increases that dissipate more power. The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs. The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments, where excellent results were reported when compared to other current techniques, the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7% on a utilization of 6%, 36% & 46% at 520 & 624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs. Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of the chip. In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first (EA-EDF) scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip (SOC). Dynamic power management (DPM) enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration. Task migration avoids peak temperature values in the multi-core system. High utilization factor ( on central processing unit (CPU) core consumes more energy and increases the temperature on-chip. Our technique switches the core by migrating such task to a core that has less temperature and is in a low power state. The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core. The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works. The simulation results show the improvement in performance by optimizing 4.8% on 9%, 16%, 23% and 25% at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Purpose This study focuses on establishing relations with some important but underestimated elements of knowledge dynamics and firm orientations to characterize organizational circular economy activities through eco-innovation (EIN). The advent of the circular economy (CE) in this post-pandemic era has brought unpredictable sustainable challenges for the manufacturing industries. This research paper aims to bring more clarity to the extant literature on the relationship between environmental innovation (EI) and CE. Design/methodology/approach In this study, a systematic literature review methodology was used to research the determinants of EI in the knowledge environment that drives the implementation of a CE. Findings This paper proposes a framework that articulates organizational learning and orientation dynamics and offers a new set of internal knowledge resources for a corporate CE. It is found that change toward CE requires connection with EI. However, successful CE growth largely depends on leveraging knowledge resources and orientation dynamics (stakeholder orientation, sustainability orientation, organization learning orientation and entrepreneurial orientation). CE techniques are still in their early phases of adoption and their implementation is still in its development. Circular knowledge economy (CKE) has the potential to be a useful alternative to achieving thriving CE to achieve sustainability in local and global businesses operations. Practical implications This study helps companies to understand the organizational learning and different orientation dynamics for achieving CE principles. The research findings imply that EI is critical in establishing a sustainable transition toward CE through organizational learning and orientation dynamics and has garnered significant attention from academics, public policymakers and practitioners. The proposed framework can guide managers to develop sustainable policies related to the CE. This research recognizes that firm-level CKE is important in shaping how knowledge resources relate to CE within transition management literature. Originality/value This paper abridges the knowledge gap in identifying key drivers and presents the current eminence, challenges and prognostications of sustainable EI parameters in the changing climate of CE. This study builds a framework that combines insights from different viewpoints and disciplines and extends one’s understanding of the relationship between EI and CE. From a theoretical perspective, this study explains the knowledge management complexity links between EI and CE. It builds a theoretical bridge between EI and CE to illustrate how firms transition toward CE following the recommendations. Thus, researchers should continue to support their research with appropriate theories that have the potential to explain EI and CE relationship phenomena, with a particular emphasis on some promising but underutilized theories such as organizational learning, dynamic capabilities and stakeholder theories.

The operation simulation system of the power grid is a complex and huge dynamic model, and its establishment and management are of great significance in power enterprises. The intelligence of the power grid is to manage the power system through computer technology, so that it can provide users with high-quality, convenient, safe, reliable and high-efficiency services. To this end, this article is based on operating simulation experiments to construct a power grid planning and dynamic intelligent management system. The purpose is to provide people with a fast and accurate power management system to facilitate life. This article mainly constructs the system through investigation method, experimental analysis method, data analysis method and so on. Experimental research shows that the accuracy of data obtained by using information technology in power grid planning and intelligent management system construction is relatively high, reaching 93.2%.

Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one‐time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self‐contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly). Here we present a start‐to‐finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study. Our DM tool operates in near real‐time and was designed to maximize target catch and minimize bycatch of non‐target and protected species in a US‐based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool. Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives.

In this paper, based on industrial robotics, a production line system based on KUKA robots is built. Aiming at the dynamic management operation scheduling problem of the robot production line, a digital twin-based production scheduling system is proposed. A two-layer progressive production scheduling strategy consisting of job sequencing and job control layers is adopted, which is combined with the production line scheduling optimization model to achieve the optimal scheduling plan for the production line. The performance tests and simulation experiments show that the workpiece conveying link in the robot production line is shortened by 15.18 s. Compared to the traditional GA algorithm, the algorithm in this paper reduces the time required to obtain the optimal scheduling solution to 35 time units, which results in faster convergence speed and better convergence results. In practice, the method in this paper generates an optimal scheduling strategy for a company’s beer packaging line.

[Purpose/Significance] In order to meet the needs of intensive agricultural data research, the security protection of agricultural data needs to be developed in a dynamic and adaptive direction. Trust management is one of the core means to ensure data security, the dynamic research and application exploration of its management model is of great significance. [Method/Process] This paper reviews the development process and theoretical framework of dynamic trust management model, analyzes the characteristics of the whole life cycle of agricultural data, summarizes the practical needs of trust management of agricultural data, and expounds the rationality and feasibility of practicing the dynamic trust management model in the field of agricultural data security. On the basis of the above research, combined with the business characteristics of agricultural research itself, this paper explores the application methods and key points of dynamic trust management model in the field of agricultural data security from the aspects of user trust, equipment trust and application trust. [Results/Conclusions] At the end of this paper, the application prospect and future development direction of dynamic trust management model are prospected. The possibility and necessity of expansion and development of a dynamic trust management model to dynamic traffic management and dynamic authorization management, as well as combining with SDP, are discussed.

Purpose This study aims to investigate the effects of leaders’ stewardship behavior (LSB) on followers’ radical innovation (RI). Followers’ knowledge management dynamic capability (KMDC) has been a mediating role, while environmental uncertainty (EU) acted as a moderating factor in the context of the textile and apparel industry in the developing country. Design/methodology/approach A cross-sectional quantitative study has been designed to evaluate the conceptual framework. Data were collected from the relevant stakeholders with a structured survey questionnaire – a total of 304 responses considered from industry–university collaborative leaders and followers. A partial least square-based structural equation modeling technique was applied to test the hypothesis using Smart-PLS 3.8 package program. Findings The result reveals that the KMDC has a significant mediating impact between LSB and RI. Similarly, the EU significantly moderates the relationship between KMDC and RI, especially as the intensity of environmental instability increases–decreases, LSB and adherents of KMDC is likely to enhance RI performances. Research limitations/implications This study contributes to the current literature extending the scope of steward leadership behavior and the theory of knowledge-based view incorporating EU factors. Practical implications While industries have invested a lot of money and resources to improve the followers’ radical creative thinking, skills and abilities, this study provides specific implications for the textile industry managers, leaders, policymakers and practitioners to comprehend and implement the strategy of RI. Originality/value Overall, the current research contributes to the LSB literature by highlighting significant complementarities between KMDC and RI under the EU.