Update Scheme Research Articles

Impact of fit between knowledge potential and organizational routine updating on subsidiaries’ new product development performance

Purpose This study aims to examine new product development (NPD) performance to identify effective knowledge update strategies and assess the impact of environmental uncertainty on these dynamics. It aims to understand how different knowledge potentials and organizational routines interact to enhance product outcomes. The specific subsidiary context enriches understanding by identifying challenges and opportunities that are not typically visible in broader organizational studies. Design/methodology/approach A survey of 310 business managers was conducted to measure their knowledge potential and organizational routine updating. The hypotheses were tested using hierarchical regression analysis to determine the optimal combinations of knowledge and practice updates for NPD performance. Findings Results indicate that both knowledge potential and organizational routine updating significantly enhance subsidiary performance. Specifically, knowledge accumulation paired with routine creation and knowledge difference paired with routine revision optimally boosts product development. Moreover, this study reveals an inverted U-shaped relationship between environmental uncertainty and the effectiveness of these combinations, suggesting a complex interplay that affects NPD performance. Originality/value This study enhances understanding of NPD performance by integrating resource concordance theory with empirical analysis of knowledge and organizational strategy adaptations. It underscores the moderating role of environmental uncertainty, offering new theoretical insights into enhancing product development performance. Although the focus on subsidiaries limits broader applicability, it provides valuable insights into the nuanced NPD dynamics in these specific entities, suggesting avenues for future research to expand this study’s findings across different organizational types.

Prediction of SO2 concentration at desulfurization outlet of thermal power units based on reliefF-SC and ISAO-ARELM

Abstract The flue gas desulfurization (FGD) system of thermal power units operates under complex conditions and exhibits significant nonlinearity. Establishing an accurate prediction model for outlet SO2 concentration is crucial for optimizing the control of the FGD system. The study constructs an autoregressive limit learning machine (ARELM) prediction model for SO2 concentration at the desulfurization outlet of thermal power units, leveraging the improved feature selection algorithm ReliefF-SC and the improved snow ablation optimizer (ISAO). Initially, the time delays of the input variables are compensated and the ReliefF-SC algorithm, which incorporates the Spearman correlation coefficient and cosine similarity, is designed to obtain the optimal feature set for predicting SO2 concentration at the outlet. To enhance the ELM's capacity to process time-series data, the autoregressive (AR) concept is integrated into the ELM framework. Furthermore, to mitigate the impact of random initialization of ARELM network parameters on model stability, the ISAO algorithm is proposed by introducing the sine-cosine position update strategy and adaptive adjustment of subpopulation size. Finally, experimental validation is conducted using actual plant operation data. The results indicate that the established SO2 concentration prediction model for desulfurization outlets of thermal power units is highly accurate and offers valuable theoretical guidance and technical support for optimizing the control of the FGD systems.

Conceptualization of state policy regarding economic security and sustainable development of the economy

The article clarified that the state policy in the field of economic security can be considered as a process of adopting legislative acts or management decisions regarding stable and significant economic growth; effective satisfaction of economic needs; state control over the movement and use of national resources; protection of the country’s economic interests at both the national and international levels and further control over the implementation of such strategic directions, which is determined by the state leadership and implemented by the authorized subjects of the public administration, as well as organizing the purposeful influence of the authorized subjects of the public administration on public relations in the field of economic security with the help of normatively determined forms and methods. The goal of state regulation is to optimize the level of economic security, which involves achieving a balance between savings and investments, as well as ensuring macroeconomic stability. This includes supporting sustainable economic growth and creating favorable economic and legal conditions for business entities. In accordance with the tasks of state policy in the field of ensuring the economic security of the state, its subspecies are regulatory and institutional policy, policy of ensuring the economic rights of citizens, policy of the development of the national economy and economic relations, policy of digitization of economic relations, ensuring economic and financial literacy of the population, as well as law enforcement policy in the field of economic security. It is emphasized that the principles of the state policy of sustainable development of the economy are divided into general principles that can be applied to most spheres of socio-political and socio-economic life of the state, as well as organizational principles that characterize the subject and spatial components of the formation and implementation of state policy in the sphere of ensuring economic state security. It was established that the existence of the Ukrainian economy in a state of crisis requires the implementation at the state level of a system-complex program of actions, an institutional concept of economic security in the system of the updated economic strategy of Ukraine, aimed at the sustainable development of the state economy, revival of economic potential, increasing the level of national security and its economic component, determination of destabilizing factors, localization of their negative impact, implementation of preventive measures, timely compensation of deviations from the normal state, which necessitates further scientific developments in this direction.

An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation

Abstract. Aerosols constitute important substance components of the Earth's atmosphere and have a profound influence on climate dynamics, radiative properties, and biogeochemical processes. Here we introduce updated emission schemes for dust, sea-salt, and marine primary organic aerosols (MPOA), as well as augmented secondary organic aerosol (SOA) formation pathways within the Community Earth System Model (CESM; version 2.1.3). The modified dust emission scheme shifts the original hotspot-like dust emission to a more continuous distribution, improving the dust aerosol optical depth (DAOD) simulations at stations in north Africa and central Asia. This update also reduces dust residence time from 4.1 to 1.6 d, enhancing concentration simulations downwind of dust source regions. For sea-salt emissions, we incorporate an updated sea surface temperature (SST) modulation and introduce a relative-humidity-dependent correction factor for sea-salt particle size, with SST having a significantly larger impact on sea-salt emissions (16.1 %) compared to the minor effect of humidity (−0.3 %). We then extend our modelling to incorporate emissions of marine primary organic aerosols (MPOA) as mixed externally with sea-salt aerosols, coupled offline with the ocean component Parallel Ocean Program (POP2). The results underscore the substantial influence of phytoplankton diversity on MPOA emissions, with 148 % variability simulated among different phytoplankton types, highlighting the role of biological variability in aerosol modelling. Furthermore, we refine the model's chemical mechanisms by including the irreversible aqueous uptake of dicarbonyl compounds as a new pathway for SOA formation, contributing an additional 37 % to surface SOA concentrations. These improvements enrich the ability of the CESM to use intricate linkages between different components of the Earth system, thereby enabling a more comprehensive description of natural aerosol emissions, chemical processes, and their impacts.

Lost in translation: The need for updated messaging strategies in invasion biology communication

Societal Impact StatementAs with any complex environmental challenge, information about the “invasive species problem” must be communicated effectively to generate widespread public trust, understanding, and engagement in solutions. However, there is growing recognition that conventional messaging strategies can be ineffective or even detrimental at activating the broader public. Flawed communication practices can have harmful societal impacts by perpetuating misinformation, propagating feelings of panic and fatalism, and disaffecting audience members within historically marginalized communities. In this article, we draw attention to existing communication barriers in invasion biology and attempt to address the flawed messaging strategies which perpetuate negative societal outcomes.SummaryEffective communication about introduced, invasive species is necessary for the public to engage in productive dialogue and participate in solutions. Best practices in communication are more essential than ever before, as the harmful environmental and societal impacts of invasive species are exacerbated by globalization and anthropogenic disturbance. In this article, we review how public misunderstanding and mistrust present persistent barriers to invasion biology communication. We then describe three flawed messaging strategies that underlie these barriers. These strategies include fear appeals, simplified invasion metaphors, and militaristic and xenophobic language. Though intended to inspire a public sense of urgency and action, these strategies more often stoke sentiments of confusion and mistrust when employed in practice. Efforts to develop new best practices in communication are already underway. However, there is a great need for efficacy testing and wider adoption among science and education professionals alike. We draw from the interdisciplinary literature to recommend specific forms of value appeals, metaphors, and language that are a promising foundation for improvement.

Nociplastic pain and associated functional disorders: Update, diagnostic tools, and communication strategies for the general practitioner

Nociplastic pain and associated non-pain symptoms are frequent health issues in primary care. Professionals may feel apprehensive about their management and face challenges due to the complexity and chronicity of the somatopsychic conditions. A thorough understanding of nociplastic mechanisms, associated clinical elements, and the biopsychosocial model are necessary for individualized evaluation, identifying affected patients, selecting dedicated treatments to desensitize the central nervous system, and building a solid partnership between the patient and the clinician.

Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

Abstract While the use of expected goals (xG) as a metric for assessing soccer performance is increasingly prevalent, the uncertainty associated with their estimates is often overlooked. This work bridges this gap by providing easy-to-implement methods for uncertainty quantification in xG estimates derived from Bayesian models. Based on a convenient posterior approximation, we devise an online prior-to-posterior update scheme, aligning with the typical in-season model training in soccer. Additionally, we present a novel framework to assess and compare the performance dynamics of two teams during a match, while accounting for evolving match scores. Our approach is well-suited for graphical representation and improves interpretability. We validate the accuracy of our methods through simulations, and provide a real-world illustration using data from the Italian Serie A league.

Optimization of Rotary Drilling Rig Mast Structure Based on Multi-Dimensional Improved Salp Swarm Algorithm

The mast is a critical component of rotary drilling rigs, which has a cross-section consisting of a rectangular shape formed by two web plates and two flange plates. Structural optimization of the mast is necessary to address the issue of excessive weight. The shortcomings of the traditional structural optimization algorithms are summarized as follows: the optimized steel plate thickness is a non-integer, where rounding upwards may increase the cost to a certain extent, but it can ensure the safety of the structure; rounding downwards its load carrying capacity may not satisfy the requirements, and thus a novel Salp Swarm Algorithm is proposed to solve the optimization problem. First, this study improves the initialization and update strategy in the traditional Salp Swarm Algorithm. In order to obtain a solution for engineering, an innovative multi-dimensional running comparison is carried out. Secondly, the optimization model of rotary drilling rigs is established based on the division of the working conditions. The objective function of the optimization model is to minimize the weight of the mast while considering the constraints of strength, stiffness, stability, and welding process. Finally, the proposed optimization algorithm and the established optimization model are applied to optimize the design of the mast for a rotary drilling rig. The empirical results demonstrate that the weight of the mast has been reduced by 20%. In addition, the Improved Salp Swarm Algorithm exhibits higher solution quality, faster iteration capability, and extreme stability in optimizing welded box sections compared to the conventional algorithm. The example shows that the Improved Salp Swarm Algorithm is applicable to the optimization problem of box sections.

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

SOHUPDS+: An Efficient One-phase Algorithm for Mining High Utility Patterns over a Data Stream

Existing algorithms for mining high utility patterns over a data stream are two-phase algorithms that are not scalable due to the large number of candidates generation in the first phase, particularly when the minimum utility threshold is low. Moreover, in the second phase, the algorithm needs to scan the database again to find out actual utility for candidates. In this paper, we propose one-phase algorithm SOHUPDS+ to mine high utility itemsets in the current sliding window of the data stream with respect to absolute or relative minimum utility threshold. To facilitate SOHUPDS+, we propose a data structure IUDataListSW+ , which stores and maintains utility and upper-bound values of the items in the current sliding window when sliding window advances. In addition, we propose a transaction merging strategy, called BitmapTransactionMerging , which saves execution time for utility and upper-bound values computations in denser datasets. Moreover, we propose update strategies to utilize mined high utility patterns from the previous sliding window to update high utility patterns in the current sliding window. The results of experiments illustrate that SOHUPDS+ is more efficient than the state-of-the-art algorithms in terms of execution time as well as memory usage in most of the experiments on various datasets.

Evolutionary dynamics of stochastic games in set-structured populations.

In structured populations, the ecology of games may vary over neighborhoods. The effect of the ecological variations on population dynamics remains largely unknown. We here incorporate the ecological variations into the set-structured populations to explore the coevolutionary dynamics of the ecology and cooperation. Individuals of a population are distributed over sets. Interactions occur in the form of evolutionary games. When two individuals share more common sets, they play the weak prisoner's dilemma. Otherwise, they play the strong prisoner's dilemma. Both the set memberships and the strategy update in the evolutionary process. Changes in set memberships hold sway over the games to be played, which, in turn, influences the performance of strategies. Combining evolutionary set theory and random walks on graphs, we derived the conditions for cooperation to be selected under the weak selection limit. We find that a denser set-structured population increases the probability of individuals participating in a weak prisoner's dilemma, and thereby promoting the spread of cooperation. Properly modulating the population structure and the payoff feedback can further lower the critical benefit-cost ratio required for cooperation to be selected. Our results may help better understand the effects of ecological variations in enhancing cooperative behavior in set-structured populations.

Edge Intelligence-Driven Joint Offloading and Resource Allocation for Future 6G Industrial Internet of Things

The 6G will undergo an unprecedented transformation to revolutionize the wireless system evolution from connected things to connected intelligence. Additionally, data scattered around the industrial environments can be collected for the sake of enabling intelligent operations. In this paper, the promising multi-access edge computing (MEC) service is introduced into the IIoT system to assist the computation offloading and resource allocation for different compelling applications. Moreover, relying on defining a total cost function as a weighted sum of task delay and energy consumption, a novel deep reinforcement learning (DRL) based framework is proposed to jointly optimize task offloading and resource allocation. More specifically, the task offloading is decomposed with the aid of the new isotone action generation technique (IAGT) and adaptive action aggregation update strategy (3AUS) based on the proposed DRL framework, and the initial problem can be transformed into a convex optimization problem to solve the resource allocation for each IIoT device. Additionally, we periodically renovate the offloading policy in the DRL framework so that our proposed DRL-based decision-making algorithm can beneficially adapt to different network environments. Finally, extensive simulation demonstrate that our proposed algorithm for each IIoT device can obtain quasi-optimal system performance compared with some conventional baseline algorithms

A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab

In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.

A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab

In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning.

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model's capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.

Cooperative behavior in multi-agent systems with intrinsic learners and extrinsic imitators

Under the framework of evolutionary games, how to update strategies when individuals interact with multiple neighbors at the same time is a key problem to understand the evolution of cooperation. At present, a widely known method is imitation updating. However, it is undeniable that when resources or capabilities are limited, it is difficult for individuals to obtain more comprehensive information about their counterparts, resulting in imitation method no longer applicable. Reinforcement learning algorithms attract attention because players do not need to know information such as the opponent's payoff, and only adjust strategies based on their own experience and expectations. In view of this, we consider both imitation and reinforcement learning as two behavioral adjustment models, and explore the evolution of cooperative behavior in mixed populations of extrinsic imitators and intrinsic learners. Numerous simulations have shown that when the value of temptation to defect is small, there exists an optimal proportion of intrinsic learners bring group cooperation to its peak. However, in interactive environments with larger social dilemmas, intrinsic learners are effective in resisting betrayal and maintaining group behavior compared to traditional extrinsic imitators. The proposed model reflects the characteristics of seeking advantages, avoiding disadvantages, and imitation in biological groups, and provides new ideas for the design of new game models.

Cardiomyopathies in 100,000 genomes project: interval evaluation improves diagnostic yield and informs strategies for ongoing gene discovery

BackgroundCardiomyopathies are clinically important conditions, with a strong genetic component. National genomic initiatives such as 100,000 Genome Project (100KGP) provide opportunity to study these rare conditions at scale beyond conventional research studies.MethodsWe present the clinical and molecular characteristics of the 100KGP cohort, comparing paediatric and adult probands with diverse cardiomyopathies. We assessed the diagnostic yield and spectrum of genetic aetiologies across clinical presentations. We re-analysed existing genomic data using an updated analytical strategy (revised gene panels; unbiased analyses of de novo variants; and improved variant prioritisation strategies) to identify new causative variants in genetically unsolved children.ResultsWe identified 1918 individuals (1563 probands, 355 relatives) with cardiomyopathy (CM) in 100KGP. Probands, comprising 273 children and 1290 adults, were enrolled under > 55 different recruitment categories. Paediatric probands had higher rates of co-existing congenital heart disease (12%) compared to adults (0.9%). Diagnostic yield following 100KGP’s initial analysis was significantly higher for children (19%) than for adults (11%) with 11% of diagnoses overall made in genes not on the existing UK paediatric or syndromic CM panel. Our re-analysis of paediatric probands yields a potential diagnosis in 40%, identifying new probable or possible diagnoses in 49 previously unsolved paediatric cases. Structural and intronic variants accounted for 11% of all potential diagnoses in children while de novo variants were identified in 17%.Conclusions100KGP demonstrates the benefit of genome sequencing over a standalone panel in CM. Re-analysis of paediatric CM probands allowed a significant uplift in diagnostic yield, emphasising the importance of iterative re-evaluation in genomic studies. Despite these efforts, many children with CM remain without a genetic diagnosis, highlighting the need for better gene-disease relationship curation and ongoing data sharing. The 100KGP CM cohort is likely to be useful for further gene discovery, but heterogeneous ascertainment and key technical limitations must be understood and addressed.

Research progress on coral reef growth factors and their ecosystem restoration

Healthy coral reef ecosystems have various functions such as reef-building, reef protection, reef stabilization, wave protection, shore protection, and the prevention of land erosion. Additionally, coral reef ecosystems are characterized by high biodiversity, often dubbed the “rainforest” of the ocean, as one of the most crucial ecosystems on Earth. In recent decades, global coral reefs have been significantly affected or damaged to varying degrees by climate change and human activities, posing threats to marine ecology and reef safety. This paper outlines the factors influencing coral reef growth, including global warming, ocean acidification, eutrophication, suspended solids, and tourism activities. It also discusses the status of coral reefs and viable restoration methods. Finally, this paper proposes an updated strategy for coral reef conservation, offering fresh insights into coral reef restoration efforts.

Asymmetric iterated prisoner’s dilemma on weighted complex networks and evolutionary strategies analysis

Abstract In real-world scenarios, individuals often cooperate for mutual benefit. However, differences in wealth, reputation, and rationality can lead to varying outcomes for similar actions. Besides, in complex social networks, an individual’s choices are frequently influenced by their neighbors. To explore the evolution of strategies in realistic settings, we conduct repeated asymmetric iterated prisoner’s dilemma experiments on weighted networks using a Memory-one strategy framework and different strategy update rules. During the strategy evolution on the network, two key strategies emerge, and we name them as ‘self-bad, partner-worse’ and the ‘altruists’. Then, we perform separate evolutionary experiments on several strong strategies on corresponding networks and find that strategy ‘self-bad, partner-worse’ can still stand out from the dominant strategies. Finally, by introducing optimization mechanisms, we increase the cooperation levels among individuals within the group. The models utilize in these studies diverge from conventional approaches, scrutinizing the evolutionary process at a macroscopic scale. These findings broaden the scope of evolutionary games and furnish a foundation for addressing real-world challenges.

Carotid versus axillary artery cannulation for descending aorta remodeling in type A acute aortic dissection.

Arterial cannulation sites for the surgical repair of type A aortic dissection (AAD) have evolved from right axillary artery (AA) cannulation to bilateral carotid artery (CA) based of femoral artery (FA) cannulation. Postoperative descending aorta remodeling is closely linked to the false lumen area ratio (FLAR), defined as false lumen area/aortic area, as well as to the incidence of renal replacement therapy (RRT). To investigate the effect of the updated arterial cannulation strategy on descending aortic remodeling. A total of 443 AAD patients who received FA combined cannulation between March 2015 and March 2023 were included in the study. Of these, 209 received right AA cannulation and 234 received bilateral CA cannulation. The primary outcome was the change in FLAR, as calculated from computed tomography angiography in three segments of the descending aorta: Thoracic (S1), upper abdominal (S2), and lower abdominal (S3). Secondary outcomes were the incidence of RRT and the serum inflammation response, as observed by the levels of high sensitivity C reaction protein (hs-CRP) and Interleukin-6 (IL-6). The postoperative/preoperative ratio of FLAR in S2 and S3 was higher in the AA group compared to the CA group (S2: 0.80 ± 0.08 vs 0.75 ± 0.07, P < 0.001; S3: 0.57 ± 0.12 vs 0.50 ± 0.12, P < 0.001, respectively). The AA group also had a significantly higher incidence of RRT (19.1% vs 8.5%, P = 0.001; odds ratio: 2.533, 95%CI: 1.427-4.493) and higher levels of inflammation cytokines 24 h after the procedure [hr-CRP: 117 ± 17 vs 104 ± 15 mg/L; IL-6: 129 (103, 166) vs 83 (69, 101) pg/mL; both P < 0.001] compared to the CA group. The CA cannulation strategy was associated with better abdominal aorta remodeling after AAD repair compared to AA cannulation, as observed by a greater change in FLAR and lower incidence of RRT.