Update Scheme Research Articles

Self-Organization of Square Nitrogen Islands on Copper (100) Surfaces: Island Pair Distributions Based on an Ab-Initio DFT and Mesoscopic Interaction Model.

Earlier scanning tunneling microscopy (STM) studies have shown that nitrogen forms square c(2×2) islands on Cu(100) surfaces, the assembly of which depends on coverage. Recent calculations have revealed that adatom-adatom interactions are compatible with the formation of square c(2×2) islands. The pair distribution of these islands is a topic of the current investigation. In addition, the domain structure of islands found recently in STM experiments is discussed and explained using two types of island interactions. The potential used for the interaction between N square islands on copper is a combination of previous ab initio calculations and an updated mesoscopic interaction scheme. The 2D-BGY integral equation is extended to handle binary objects like two different types of islands. It is solved for up to a quarter island coverage using a numerical recursion algorithm. Coverage beyond half is accessible with island vacancy symmetry. The calculation predicts the formation of ordered island assemblies with increasing coverage. These results reasonably explain the previous experimental observations. The handling of binary objects also seems to be applicable to assemblies of two adatom types as found in catalysis. The assumptions and limitations of the model are discussed and open questions are formulated.

Task offloading for Industrial Internet of Things based on evolutionary multiobjective multitasking optimization

In mobile edge computing(MEC) scenarios, offloading computation-intensive and delay-critical tasks to nearby mobile edge servers with abundant computing resources can reduce the time delay(TD) of tasks and energy consumption(EC) of terminal devices, thus improving application performance and user experience. In order to reduce the TD of tasks and the EC of factory terminal devices(FTDs), MEC is introduced into the Industrial Internet of Things(IIoT). Because TD and EC are conflicting objectives, most existing research models the task offloading problem(TOP) as a multiobjective optimization problem to optimize both TD and EC. However, as the number of FTDs and tasks in the MEC network increases, the scale and computational cost of the TOP increases, and it is very challenging to obtain the optimal task offloading scheme through multiobjective optimization. Multitasking optimization can make use of knowledge transfer between related tasks to promote the solving efficiency of each task. Based on this, we model the TOP in IIoT as a multiobjective TOP (MTOP). A cheap task that is highly similar to MTOP is constructed, and a novel evolutionary multiobjective multitasking framework is developed, which uses the positive knowledge transfer between tasks to optimize TD and EC in the network. Then, an effective evolutionary multiobjective multitasking algorithm is proposed, which includes multi-functional knowledge transfer strategy(MFKT) and adaptive cheap task update strategy(ACTU). MFKT uses decision variables with different functions in the cheap tasks for positive knowledge transfer, so as to improve the performance of MTOP. ACTU dynamically updates the cheap task to maintain knowledge transfer between tasks. In different test instances, the proposed algorithm is compared with the existing multiobjective and multitasking algorithms. The experimental results show that the proposed algorithm is more competitive in terms of TD and EC, it can guarantee the service quality of the IIoT system and has strong robustness and scalability.

Adaptive model-agnostic meta-learning network for cross-machine fault diagnosis with limited samples

Deep learning-based methods have been extensively studied in rotating machinery defect diagnosis. However, training an accurate and robust diagnostic model is still a challenge under severe domain bias and limited samples. For this reason, a new adaptive model-agnostic meta-learning (AMAML) is proposed for cross-machine fault diagnosis with limited samples. First, a novel adaptive feature encode network is built, incorporating lightweight spatial-bilateral channel attention. This enables the network to extract critical fault information in multiple dimensions adaptively within limited samples, which improves the learning efficiency of generalized diagnostic knowledge. Then, an adaptive loss computation (ALC) method is devised, which inventively realizes the interaction between loss computation and model performance. The underfitting and overfitting dilemmas under few-shot conditions are tackled by ALC. Finally, an adaptive meta-optimization strategy is proposed for dynamically adapting the update strategy of the base learner, so that the model is always optimized in the direction of strong generalizability while obtaining high performance. Six cross-machine diagnosis tasks are conducted to verify the effectiveness of AMAML. The average diagnostic accuracy of the AMAML under the 5-shot setting reached 97.42%. Experiments confirm that AMAML is superior to other prevailing methods and is potentially promising for engineering applications.

Past-performance-driven strategy updating promote cooperation in the spatial prisoner's dilemma game

Strategy update rules play an important role in repeated Prisoner's Dilemma games. This work proposes a modified strategy update rule based on the traditional Fermi function, in which individual past performance is taken into account in strategy update. Then, the consistency aspiration α serves as a benchmark to measure an individual's past performance, and the past performance score is dynamically adjusted during the evolution process according to the BM reinforcement learning rules. The computational results indicate that the proposed modified strategy update rules can significantly improve system cooperation t than the traditional version, and the network reciprocity effect is enhanced as the result of past performance is coupled into the strategy update rule. Moreover, different temptation to defection b exist a corresponding aspiration α result in maximizing system cooperation. Furthermore, an optimal sensitivity level β can also result in a maximizing system cooperation. As a whole, for α − β phase diagram, different a will correspond to an optimal value β that allows the system to achieve the maximum cooperation. Finally, the proposed mechanism is robust. Hopefully this can help to inspire further research on how to deal with social dilemmas.

A Fast flatness deviation evaluation algorithm for point cloud data

This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for high-speed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed methods can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.

Complex product quality prediction based on ResNeSt model combining class-aware attention mechanism and dynamically updating class weights

Abstract The processing state data of complex products often exhibits high dimensionality, intricate feature relationships, and imbalanced distributions, hindering the accuracy of data-driven quality prediction models. To address these challenges, this paper proposes a complex product quality prediction model that integrates a class-aware attention mechanism and dynamic class weight update strategy. To handle the high dimensionality and complex correlations of the data, Pearson Correlation Coefficient and Gradient Boosting Decision Tree-based Recursive Feature Elimination (GBDT-RFE) algorithms are employed for feature selection. In response to the imbalanced distribution of data categories, a class-aware attention mechanism module is built upon the ResNeSt network, utilizing prior knowledge of data distribution to adjust the model’s focus. Furthermore, a loss function is designed to dynamically update class weights based on classification error rates, enabling the model to adaptively adjust the weight allocation for different classes, thereby enhancing its generalization capability. Experimental results on a semiconductor industry dataset demonstrate that the proposed model outperforms the original ResNeSt model in multiple metrics, with accuracy improved by 3.5%, AUC increased by 12.6%, F1 score raised by 18.9%, and recall enhanced by 24%, ultimately achieving an overall prediction accuracy of 98.7%. These multidimensional improvements make the model suitable for various complex product data scenarios such as electronics manufacturing, automotive industry, and biopharmaceuticals, demonstrating broad applicability.

Trends and determinants of time spent in the emergency department among Australian hospital presenting self-harm cases

AimsWaiting time (WT) and length of stay (LOS) are important metrics used to assess service delivery in emergency departments (EDs), and have been associated with subsequent re-presentations in EDs. This study investigated socio-demographic and health service determinants of WT and LOS among hospital treated self-harm (SH) cases presenting to EDs in Australian based hospitals. MethodsThis study employed a retrospective cohort study of all SH cases (N=6203) presenting to public hospital EDs in the Western Sydney catchment area from January 2016 to December 2022. Cox proportional hazard models were used to analyse demographic and service factors related to WT and LOS in Australian hospitals along with a description of secular trends in WT and LOS. ResultsLonger WT was evident for patients aged 15-64 than 0-14 years, non-admitted patients and patients arriving at the ED via transport other than ambulance whereas longer LOS was associated with older patients (65+ years). The inflow of non-SH cases to the ED did not have an impact on WT or LOS among SH cases. WT and LOS increased over time, particularly post the onset of the COVID-19 pandemic. The number of current self-harm cases receiving treatment in ED at the time of patient’s arrival also increased WT and LOS. ConclusionsWT and LOS have increased, especially post the onset of the COVID-19 pandemic. These findings necessitate the urgent need for updated strategies to manage ED resources, and suggest initiatives relating to ED diversion where appropriate for patients presenting to hospital following SH.

Radioembolization for hepatocellular carcinoma: updated strategies and evolving clinical applications

Radioembolization for hepatocellular carcinoma: updated strategies and evolving clinical applications

Transformer-based probabilistic demand forecasting with adaptive online learning

Demand forecasting is crucial for the operation and planning of the energy and power industry. Accurate demand forecasting can assist decision-makers in reducing operational or planning costs while optimizing supply chains and resource utilization. Due to various uncertainties and dynamically changing environments, traditional offline deterministic forecasting methods may face difficulty in forecasting demands accurately and be unable to effectively quantify the uncertainties in forecasts. To adapt to dynamic environmental changes, several online learning methods have been proposed, which require large computational resources. To overcome this limitation, this paper proposes a Transformer-based probabilistic demand forecasting with an adaptive online learning algorithm. More specifically, we design a probabilistic decoder for the Transformer to quantify forecast uncertainty. Furthermore, an adaptive online learning algorithm is employed to selectively update parameters based on an adaptive update strategy, reducing computational burden while allowing for timely adaptation to dynamic environmental changes. The performance of the proposed method is evaluated on multiple benchmark datasets and real electricity demand data from fused magnesium furnace (FMF). The results show that the proposed method exhibits superior accuracy in both deterministic and probabilistic forecasting scenarios. Finally, through ablation experiments, the effectiveness of the proposed adaptive online probabilistic forecasting algorithm is validated.

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.

Ontology and production rules-based dynamic knowledge base construction methodology for machining process

With advancements in manufacturing, knowledge engineering has become important in supporting intelligent decision-making within manufacturing systems. However, existing process knowledge bases, integral to knowledge engineering, and essential for machining efficiency, product cost, and production cycles by integrating multi-source knowledge, are limited to generality, scalability, and adaptability to real production environments. These constraints undermine the application and reliability of process knowledge bases in decision-making. To overcome these challenges, an approach to constructing a dynamic machining process knowledge base (DMPKB) utilizing ontology and production rules is proposed. Firstly, a machining process knowledge model is developed by reorganizing concepts and relations to restructure process cases and experiences, thereby building a comprehensive knowledge base. Secondly, different update strategies are devised to fulfill the requirements of various components within the knowledge base. Finally, the effectiveness is validated by constructing a DMPKB for CNC boring machine bearing seats. Meanwhile, application verification is performed by generating process plans for a CNC boring machine bearing seat, showcasing the feasibility and utility of the developed knowledge base.

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.

Waves in Earth's core and geomagnetic field forecast

We use advanced numerical geodynamo series to derive a reduced stochastic model of the dynamics at the surface of Earth's core. Considering order 3 autoregressive (AR-3) processes allows to replicate the simulated spatiotemporal spectrum over a broad range of time-scales, spanning millennia to a fraction of year, including the cut-off found for periods shorter than approximately 2 years and associated with magnetic dissipation. We show how to derive such a forward model from a variety of input simulation series, and present its implementation into the pygeodyn data assimilation algorithm, based on a sequential ensemble method. The updated scheme is applied to perform magnetic field hindcasts and core flow reanalyses. For all observable length-scales, the rate of change of the observed magnetic field is most of the time accounted for within the spread of the forward model trajectories. AR-3 predictions on average supersede by about 35 % linear extrapolations on short (2 yr) time-scales, reducing high-frequency spurious variations in reanalysed flow motions. This improvement is reduced to ≈10% for 5 yr increments, with a large variability from one epoch to the other depending on the overall curvature of the magnetic field evolution. We perform a reanalysis over the period 1880–2023 covered by observatory and satellite records. We find enhanced kinetic energy in three period ranges around 12.5, 6.5 and 3.5 years. At all three periods, fluid motions share geometrical properties compatible with quasi-geostrophic magneto-Coriolis waves: equatorial symmetry, larger amplitude near the equator, flow dominated by low azimuthal wave number and modulated in longitude, phase speed much faster than the fluid velocity and decreasing with the period. At 6.5 yr period we trace back to the mid-1990's the patterns previously detected from satellite data. We also find in the 1960–70's a similar wave-train, possibly in link with the 1969 geomagnetic jerk. The AR-3 model, in conjunction with early satellite records, likely helps isolate such coherent features on interannual time-scales. Similar wave-like motions also show up at 3.5 yr period around 1970 and during the past decades. At periods around 12.5 yr we detect recurrent patterns starting as far back as 1920, and modulated over decadal time-scales. Our results show growing evidence for core dynamics governed by the presence of hydro-magnetic waves over a wide range of periods. This may allow deterministic and/or empirical descriptions of the signal that may help sound deep Earth's properties, and improve predictions of the magnetic field evolution.

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.