Real-world Processes Research Articles

Impact of Aerodynamic Design on Vehicle Performance and Vortex Wake Flow

This thesis examines vortex and wake formation mechanisms in automotive aerodynamics and their impact on vehicle performance, emphasizing strategies to mitigate adverse airflow effects through optimal design and computational techniques. It demonstrates that vortices and wake turbulence elevate drag, diminish fuel efficiency, and compromise high-speed vehicle stability. Streamlined body design, optimized rear-end shape, and underbody airflow management can effectively reduce wake turbulence and vortex intensity, enhancing aerodynamic performance. This paper also introduces a variety of computational techniques, including theoretical analysis, wind tunnel experiments and Computational Fluid Dynamics (CFD), and explores the application of these methods in the real-world design process. CFD simulation demonstrates its advantages in evaluating airflow characteristics and optimising design solutions, enabling engineers to carry out efficient iterative design. Future research directions include the application of active aerodynamic systems and the integration of intelligent design algorithms for more efficient airflow control and vehicle performance enhancement.

Compartment model of strategy-dependent time delays in replicator dynamics.

Real-world processes often exhibit temporal separation between actions and reactions - a characteristic frequently ignored in many modelling frameworks. Adding temporal aspects, like time delays, introduces a higher complexity of problems and leads to models that are challenging to analyse and computationally expensive to solve. In this work, we propose an intermediate solution to resolve the issue in the framework of evolutionary game theory. Our compartment-based model includes time delays while remaining relatively simple and straightforward to analyse. We show that this model yields qualitatively comparable results with models incorporating explicit delays. Particularly, we focus on the case of delays between parents' interaction and an offspring joining the population, with the magnitude of the delay depending on the parents' strategy. We analyse Stag-Hunt, Snowdrift, and the Prisoner's Dilemma game and show that strategy-dependent delays are detrimental to affected strategies. Additionally, we present how including delays may change the effective games played in the population, subsequently emphasising the importance of considering the studied systems' temporal aspects to model them accurately.

Reduced Forgetfulness in Continual Learning for Named Entity Recognition Through Confident Soft-Label Imitation

Continual Learning for Named Entity Recognition (CL-NER) is a crucial task in recognizing emerging concepts when constructing real-world natural language processing applications. It involves sequentially updating an existing NER model with new entity types while retaining previously learned information. However, current CL methods are struggling with a major challenge called catastrophic forgetting. Owing to the semantic shift of the non-entity type, the issue is further intensified in NER. Most existing CL-NER methods rely on knowledge distillation through the output probabilities of previously learned entities, resulting in excessive stability (recognition of old entities) at the expense of plasticity (recognition of new entities). Some recent works further extend these methods by improving the distinction between old entities and non-entity types. Although these methods result in overall performance improvements, the preserved knowledge does not necessarily ensure the retention of task-related information for the oldest entities, which can lead to significant performance drops. To address this issue while maintaining overall performance, we propose a method called Confident Soft-Label Imitation (ConSOLI) for continual learning in NER. Inspired by methods that balance stability and plasticity, ConSOLI incorporates a soft-label distillation process and confident soft-label imitation learning. The former helps to gather the task-related knowledge in the old model and the latter further preserves the knowledge from diluting in the step-wise continual learning process. Moreover, ConSOLI demonstrates significant improvements in recognizing the oldest entity types, achieving Micro-F1 and Macro-F1 scores of up to 8.72 and 9.72, respectively, thus addressing the challenge of catastrophic forgetting in CL-NER.

Real-World Social Reward Processes are Linked to Momentary Positive Affect in Adolescent Girls.

Positive peer interactions are critical for adolescent development and well-being. Showing little interest in interacting socially with peers and/or extracting little reward from positive peer interactions can be markers of social anhedonia, which impacts many youths, especially girls, with social anxiety and depressive disorders. Reduced interest or reward in peer interactions may contribute to social anxiety and depression in girls through effects on positive affect (PA), though associations between social anhedonia and momentary PA have yet to be tested. The present study used ecological momentary assessment to test such associations between real-world anticipatory social reward (i.e., interest in upcoming peer events), consummatory social reward (i.e., reward extracted from positive peer interactions), and momentary PA in a sample of 129 girls (aged 11-13 years) who were oversampled for high shy/fearful temperament, a risk factor for future social anxiety and depression. Girls reported higher PA following a more socially rewarding peer interaction, and higher PA on days they reported higher anticipatory social reward. Exploratory analyses showed that these associations were specific to PA; neither anticipatory nor consummatory social reward was associated with changes in negative affect. Findings may inform the development of clinical interventions that target social anhedonia to modify PA in youth with affective disorders.

Integrating BERT, GPT, Prophet Algorithm, and Finance Investment Strategies for Enhanced Predictive Modeling and Trend Analysis in Blockchain Technology

This paper explores the integration of advanced machine learning models, including BERT, GPT, and the Prophet algorithm, with finance investment strategies to enhance predictive modeling and trend analysis in blockchain technology. The rapid evolution of blockchain has transformed financial ecosystems, offering decentralized platforms for secure and transparent transactions. However, predicting market trends and investment opportunities within this domain remains a complex challenge due to high volatility and the multifaceted nature of financial data. By leveraging the natural language processing capabilities of BERT and GPT for sentiment analysis and market behavior prediction, combined with the time-series forecasting strength of the Prophet algorithm, this study aims to provide a robust framework for analyzing blockchain-driven financial markets. Furthermore, the integration of finance investment strategies ensures practical applicability by aligning machine learning insights with real-world investment decision-making processes. The proposed approach demonstrates potential for optimizing portfolio management, enhancing risk mitigation, and improving strategic investment in blockchain ecosystems. This work bridges the gap between cutting-edge machine learning technologies and financial innovation, offering valuable insights for researchers and practitioners in both domains.

Visual information processing of 2D, virtual 3D and real-world objects marked by theta band responses: Visuospatial processing and cognitive load as a function of modality.

While pictures share global similarities with the real-world objects they depict, the latter have unique characteristics going beyond 2D representations. Due to its three-dimensional presentation mode, Virtual Reality (VR) is increasingly used to further approach real-world visual processing, yet it remains unresolved to what extent VR yields process comparable to real-world processes. Consequently, our study examined visuospatial processing by a triangular comparison of 2D objects, virtual 3D objects and real 3D objects. The theta band response (TBR) was analysed as an electrophysiological correlate of visual processing, allowing for the differentiation of predominantly stimulus-driven processes mirrored in the evoked response and internal, complex processing reflected in the induced response. Our results indicate that the differences between conditions driven by sensory features go beyond a binary division into 2D and 3D materials but are based on further sensory features: The evoked posterior TBR differentiated between all conditions but revealed fewer differences between processing of real-world and VR objects. Moreover, the induced midfrontal TBR indicated higher cognitive load for 2D objects compared to VR and real-world objects, while no difference between both latter conditions was revealed. In conclusion, our results demonstrate that the transferability of 2D- and VR-based findings to real-world processes depends to some degree on whether predominantly sensory stimulus features or higher cognitive processes are examined. Yet although VR and real-world processes are not to be equated based on our results, their comparison yielded fewer significant differences relative to the PC condition, advising the use of VR to examine visuospatial processing.

Reinforcement Learning for Intra- & Inter-Node Recommender Data Pipeline Optimization

Deep learning-based recommender models (DLRMs) have become an essential component of many modern recommender systems. Several companies are now building large compute clusters reserved for DLRM training, driving new interest in cost- & time- saving optimizations. The systems challenges faced in this setting are unique; while typical deep learning (DL) training jobs are dominated by model execution times, the most important factor in DLRM training performance is often online data ingestion. In this paper, we study real-world DLRM data processing pipelines taken from our compute cluster at Netflix to observe the performance impacts of online ingestion and identify shortfalls in existing data pipeline optimizers. Our studies lead us to design a new solution for data pipeline optimization, InTuneX . InTuneX is designed for production-scale multi-node recommender data pipelines. It unifies & tackles the challenges of both intra- and inter- node pipeline optimization. We achieve this with a multi-agent reinforcement learning (RL) design, simultaneously optimizing node assignments at the cluster level & CPU assignments within nodes. Our experiments show that InTuneX can build optimized data pipeline configurations within minutes. We apply InTuneX to our cluster, and find that it increases single-node data ingestion throughput by as much as 2.29X versus state-of-the-art optimizers, while improving the cost-efficiency of multi-node pipelines by 15-25%.

When abstract becomes concrete, naturalistic encoding of concepts in the brain.

Language is acquired and processed in complex and dynamic naturalistic contexts, involving the simultaneous processing of connected speech, faces, bodies, objects, etc. How words and their associated concepts are encoded in the brain during real-world processing is still unknown. Here, the representational structure of concrete and abstract concepts was investigated during movie watching to address the extent to which brain responses dynamically change depending on visual context. First, across contexts, concrete and abstract concepts are shown to encode different experience-based information in separable sets of brain regions. However, these differences are reduced when multimodal context is considered. Specifically, the response profile of abstract words becomes more concrete-like when these are processed in visual scenes highly related to their meaning. Conversely, when the visual context is unrelated to a given concrete word, the activation pattern resembles more that of abstract conceptual processing. These results suggest that while concepts generally encode habitual experiences, the underlying neurobiological organisation is not fixed but depends dynamically on available contextual information.

A deep reinforcement learning-based optimization approach for containerized microservice scheduling in Hybrid Fog/Cloud environments

The deployment of microservices in Hybrid Fog/Cloud (HFC) environments for Internet of Things (IoT) applications presents a significant challenge in efficiently scheduling containerized services across distributed resources. While existing studies have explored microservice scheduling, a comprehensive approach that considers resource constraints, workflow dependencies, and dynamic hybrid environments remains elusive. This paper introduces a novel Deep Reinforcement Learning-based Algorithm (DRLA) for containerized microservice scheduling in HFC environments. DRLA utilizes a multi-constrained Binary Quadratic Program (BQP) model to optimize execution time, resource consumption, and occupancy rates while considering microservice dependencies and resource capabilities. The algorithm leverages two Deep Reinforcement Learning (DRL) agents, DQN and REINFORCE, to learn and adapt to the dynamic nature of the HFC federation. Experimental evaluations using five real-world Business Process (BP) use cases demonstrate that DRLA outperforms existing scheduling approaches such as default Kubernetes, Reward Sharing Deep Q-Learning (RSDQL), and Deep Reinforcement Learning (DRL) schedulers. Compared to existing schedulers, ours delivers optimal or near-optimal solutions, demonstrating significant improvements in key performance values. Indeed, DRLA achieves average optimality gaps of just 0.16% and 0.21%, significantly outperforming the Kubernetes scheduler, which exhibits a gap of 2.88%. It is worth noting that other similar algorithms see a far higher increase in optimality gaps. This highlights DRLA’s excellent performance and ability to schedule containerized microservices in hybrid fog/cloud environments, resulting in near-optimal solutions for a variety of use cases.

A STAR-based Cointegration Analysis of Globalization and Air Transport in Türkiye

This study employs the smooth transition autoregressive (STAR) model-based cointegration tests developed by Kapetanios et al. (2006) and Maki (2010) to investigate the long-term relationship between air transport and globalization, including its economic, social, and political dimensions in Türkiye. STAR models reflect the smooth nature of real-world processes and can capture the complex and nonlinear relationships between variables that linear models struggle to represent effectively. We offer a novel methodological approach to examine the relationship between air transport and globalization in Türkiye, providing valuable insights for policymakers and researchers. Our empirical results reveal a long-term equilibrium relationship between air transport and overall globalization and the economic and political dimensions of the globalization index. These results imply that both variables move together in the long run. However, no cointegration was found between air transport and the social dimension of globalization.

Learning in practice: reinforcement learning-based traffic signal control augmented with actuated control

ABSTRACT Most Reinforcement Learning (RL) based Traffic Signal Control (TSC) models are trained in simulation platforms, which inevitably suffer from the performance degradation after deployment due to the mismatch between the traffic simulator and the real-world traffic system. Toward the real-world training of TSC agents, this study integrates the actuated control into the RL-based TSC framework as a defense mechanism, enabling the traffic system to remain resilient to suboptimal actions of agent during the real-world training process. The introduction of actuated control can significantly reduce the green time wastes due to unreasonable timing plans. The numerical results demonstrate that the proposed approach can reduce interferences to the traffic system operation during the real-world training of TSC agents, particularly at the early stage of agent learning, thereby promoting the practical deployment of RL-based TSC models. Meanwhile, agents can converge to better signal policies with the help of defense mechanism.

Social learning with complex contagion

Traditional models of social learning by imitation are based on simple contagion-where an individual may imitate a more successful neighbor following a single interaction. But real-world contagion processes are often complex, meaning that multiple exposures may be required before an individual considers changing their type. We introduce a framework that combines the concepts of simple payoff-biased imitation with complex contagion, to describe how social behaviors spread through a population. We formulate this model as a discrete time and state stochastic process in a finite population, and we derive its continuum limit as an ordinary differential equation that generalizes the replicator equation, a widely used dynamical model in evolutionary game theory. When applied to linear frequency-dependent games, social learning with complex contagion produces qualitatively different outcomes than traditional imitation dynamics: it can shift the Prisoner's Dilemma from a unique all-defector equilibrium to either a stable mixture of cooperators and defectors in the population, or a bistable system; it changes the Snowdrift game from a single to a bistable equilibrium; and it can alter the Coordination game from bistability at the boundaries to two internal equilibria. The long-term outcome depends on the balance between the complexity of the contagion process and the strength of selection that biases imitation toward more successful types. Our analysis intercalates the fields of evolutionary game theory with complex contagions, and it provides a synthetic framework to describe more realistic forms of behavioral change in social systems.

Kanban method in digital data processing

AbstractCurrent digital education operates as a push-system, despite the content and subject characteristics suggesting a need for a pull-system. One of the features of the push-system is a tool-centered approach, where the focus on tools, including both hardware and software, is the belief that it is possible to build knowledge inventory. The consequences of the push digital education approaches are that (1) the fundamental concepts of Computer Science are not being transferred, (2) the lack of the development of supporting methods, (3) and education does not seem to be interested in revealing the root causes and to be open for fundamental changes. This paper proposes the extension of the theory of the industrial pull systems to present as a potential solution to increase the effectiveness and efficiency of digital education and reduce or eliminate data processing inefficiencies generated by undereducated but misled end-users. In addition to theoretical discussions, it also delves into the detailed analysis, design, implementation, and testing of a real-world data processing problem. The presented problem, its analysis, solution, and accompanying discussion reveal how one of the tools (kanban) of lean production can be adapted to support the Just-in-Time philosophy in digital education, focusing on end-user programming.

Leveraging more information for blind stereo super-resolution via large-window cross-attention

Stereo image super-resolution aims to reconstruct high-resolution images by effectively utilizing cross-view complementary information from stereo image pairs. A prevalent method in Stereo image super-resolution is the stereo cross-attention module, which allows the model to focus on and integrate relevant features from both the left and right views. Despite its advantages, our analysis using a diagnostic tool called local attribution map (LAM) reveals that current methods exhibit limitations in effectively leveraging this complementary information. To address this issue, we propose the Double Stereo Cross-Attention Module (DSCAM), which utilizes an Overlapping Stereo Cross-Attention (OSCA) mechanism that enhances the integration of cross-view complementary information by using overlapping windows, followed by an additional multiplication step to refine and emphasize the combined features. Additionally, we develop a stereo image degradation model that ensures the consistency of degradation between stereo pairs, accurately simulating the real-world degradation process of stereo images. Extensive experiments have demonstrated that our method achieves visually pleasing results, making it the first to address the problem of stereo image super-resolution in real-world scenarios. The source code is available at https://github.com/nathan66666/LCASSR.git.

Changes in microbial communities across the whole A2/O wastewater treatment process and their drivers—Reduced community diversity but increased proportion of certain pathogens

Microorganisms play a crucial role in pollutant removal and water quality stabilizing. However, limited research exists on the microbial variability and the factors driving it at different stages of wastewater treatment. In this study, the physicochemical properties of water and the composition of bacterial communities were thoroughly investigated across the entire A2/O wastewater treatment process, encompassing 3 stages (12 steps). The results revealed a significant reduction in alpha diversity, whereas the beta diversity remained largely unchanged across stages. Alpha diversity was primarily influenced by dissolved oxygen (DO) and pH, with DO having the most notable influence, while beta diversity was mainly constrained by nutrient conditions such as COD, BOD5, NH4-N, TN, and TP. Additionally, analyses of relative abundance, LEfSe, variance, and functional prediction indicated a significant increase in the relative abundance of certain pathogenic bacteria (e.g., Legionella, Leptospira), exhibiting different removal characteristics compared to Escherichia coli across various treatment steps. Even after UV disinfection, these pathogens persist, highlighting a potential pathogenic risk, which deserves more attention. In addition, this study helps explore the relatively under-researched area of microbial variability at different stages (steps) of wastewater treatment, especially in terms of how microbial communities respond to operational processes and environmental conditions. This will offer valuable guidance for addressing water treatment safety challenges encountered in real-world processes.

Dynamics and numerical analysis of a fractional-order toxoplasmosis model incorporating human and cat populations

Toxoplasmosis is a significant zoonotic disease that poses risks to public health and animal health, making the understanding of its transmission dynamics crucial. In this study, we present a novel fractional-order model that captures complex interactions among human, cat, and mouse populations, providing deeper insights into the disease spread and control. We utilize mathematical techniques to analyze the model properties, including the existence, uniqueness, positivity, and boundedness of solutions, along with stability analysis of the equilibrium states. The basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$R_{0}$\\end{document} is derived, revealing the threshold for potential outbreaks. Our findings indicate that key parameters significantly influence the dynamics of toxoplasmosis, with implications for targeted intervention strategies. We propose the QLM-FONP numerical scheme for efficient resolution of the model and provide a comprehensive convergence analysis, demonstrating the reliability of the numerical solutions. The results confirm the effectiveness of our approach, illustrating that the proposed model not only offers accurate predictions but also extends beyond previous efforts in the literature by incorporating fractional-order dynamics, which better reflect real-world transmission processes. Overall, this study enhances the understanding of toxoplasmosis transmission and informs future research and control efforts.

Operational vehicle state of health estimation framework based on local-global attention mechanism

The state of health (SOH) of electric vehicles directly correlates with operational safety and driving range. Accurate assessment of SOH is crucial for improving the safety and reliability of electric vehicles. This article proposes a novel SOH estimation framework based on the local-global attention mechanism (L-G-A) and real-world incomplete charging processes. Firstly, an analysis of the charging modes is conducted. Specifically, the average value of capacity is calculated for multiple constant current segments as the reference capacity, and the effect of ambient temperature on charging efficiency and battery capacity is considered. Subsequently, aging characteristics of interval capacities are extracted through incremental capacity analysis. To investigate the impact of different usage patterns on battery degradation during daily use by vehicle owners, the historical cycle charging segments are categorized based on segment similarity. Then, the L-G-A model is established by utilizing convolutional layers to extract local-scale temporal information and computing global attention scores based on degradation information between each segment and across classes. The experimental results demonstrate that the proposed framework has the highest estimation accuracy with mean absolute error and root mean square error of 0.069 % and 0.084 %, respectively, which outperforms the traditional LSTM, CNN, and SVR models in terms of accuracy of SOH evaluation.

"All the Stars Will Be Wells with a Rusty Pulley": Neural Processing of the Social and Pragmatic Content in a Narrative.

Making sense of natural language and narratives requires building and manipulating a situation model by adding incoming information to the model and using the context stored in the model to comprehend subsequent details and events. Situation model maintenance is supported by the default mode network (DMN), but comprehension of the individual moments in the narrative relies on access to the conceptual store within the semantic system. The present study examined how these systems are engaged by different narrative content to investigate whether highly informative, or semantic, content is a particularly strong driver of semantic system activation compared with contextually driven content that requires using the situation model, which might instead engage DMN regions. The study further investigated which subregions of the graded semantic hub in the left anterior temporal lobe (ATL) were engaged by the type of narrative content. To do this, we quantified the semantic, pragmatic, social, ambiguous, and emotional content for each sentence in a complete narrative, the English translation of The Little Prince. Increased activation in the transmodal hub in the ventral ATL was only observed for high semantic (i.e., informative) relative to low semantic sentences. Activation in the dorsolateral and ventrolateral ATL subregions was observed for both high relative to low semantic and social content sentences, but the ventrolateral ATL effects were more extensive in the social condition. There was high correspondence between the social and pragmatic content results, particularly in the ventrolateral ATL. We argue that the ventrolateral ATL may be particularly engaged by internal, or endogenous, processing demands, aided by functional connections between the anterior middle temporal gyrus and the DMN. Pragmatic and social content may have driven endogenous processing given the pervasive and plot-progressing nature of this content in the narrative. We put forward a revised account of how the semantic system is engaged in naturalistic contexts, a critical step toward better understanding real-world semantic and social processing.

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

Blockchain and Digital Twins in Smart Industry 4.0: The Use Case of Supply Chain-A Review of Integration Techniques and Applications

The Fourth Industrial Revolution has transformed industries and supply chains by integrating advanced operations, tools, and logistics services. Despite these advancements, challenges persist, particularly in ensuring data dependability, security, and operational efficiency. Digital twins (DTs), which replicate real-world components and processes, have emerged as essential tools for enhancing predictive analytics, simulation, and product lifecycle management in Industry 4.0. However, traditional DT development relies on centralized systems, which are vulnerable to data tampering and security breaches, especially in the management of transaction logs and historical data. To address these challenges, this review provides a comprehensive analysis of the current state of integrating blockchain with DTs. Using a qualitative research methodology, including desk research, case studies, and interviews with industry experts, we analyze various blockchain-based DT applications across industries and specifically in supply chain management. The findings reveal that blockchain-enhanced DTs can significantly improve data integrity, traceability, and security, thus boosting operational efficiency and quality control in supply chains. Additionally, this study identifies key integration techniques and the role of blockchain in automating processes through smart contracts. This review provides insights into the practical implications of blockchain-based DTs, highlighting their potential to enhance the reliability and scalability of Industry 4.0 operations.