Parent Node Research Articles

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Establishing disease models for the Belgian national burden of disease study: challenges and perspectives

Abstract The Belgian national burden of disease study (BeBOD) was launched in 2022, and currently provides disease burden estimates for 38 key diseases. The calculation of the non-fatal component of the disease burden, the Years Lived with Disability (YLD), follows a stepwise approach that aims to establish priority outcomes; quantify prevalence “best estimates”; establish disease models; and perform expert evaluation of methods and results. The disease models visualize the relationship between the different health states associated with a given outcome. Health states include the different acute and chronic stages of the outcome (including complications), which may be stratified in different severity levels (e.g., mild, moderate, severe). Disease models used in burden of disease studies primarily aim to document the considered health states, and do not aim at a representation of the complete clinical picture of the condition. The disease models instead help in understanding how the number of cases for each health state is calculated. Models typically start with one “parent node”, which contains all cases. This parent node then gives rise to multiple “child nodes”, with the terminal child nodes representing the individual health states. In BeBOD, disease models and severity distributions are adapted from the Global Burden of Disease (GBD) study, and complemented with local data where possible. This process ensures consistency with available disability weights, while still allowing for local data to feed into the calculations. In this presentation, we will demonstrate the BeBOD process for establishing disease models based on a number of archetypical examples, which represent varying challenges linked to disease model complexity, clarity and completeness of the GBD disease model documentation, and availability of local data. Future perspectives will be discussed, including the improved leveraging of local clinical data, and the implementation of tailored active data collection.

Intelligent ultrasonic aspirator: Advancing tissue differentiation through hierarchical classification during hand-held resection

Modern neurosurgery strives to maximize tumor removal while preserving healthy tissue integrity. Accurate intraoperative differentiation between tumor and healthy tissue is crucial yet challenging. Often neurosurgeons rely on their experience and haptic feedback during palpation to distinguish between tumor and healthy tissue. A commonly used hand-held tool for tissue removal during neurosurgery is the ultrasonic aspirator, which changes its electrical properties as it interacts with tissue. The goal is to equip the ultrasonic aspirator with the ability to differentiate between different types of tissue while at the same time not interfering with the surgical workflow and providing comprehensible outcomes. To this end, a hierarchical classification approach is employed as a proof of concept, enabling precise identification of tissue stiffness during resection.The hierarchical approach is compared with the standard flat classification, commonly used in machine learning. Within the hierarchical approach, two strategies are employed: mandatory leaf-node predictions (MLNP) and non-mandatory leaf-node predictions (NMLNP). The NMLNP allows prediction to revert to a parent node when certainty is low. Data are acquired on three artificial tissue models – differing in stiffness – with an ultrasonic aspirator in a hand-held manner. The dataset comprises 1,821 data points for training and 186 for testing after balancing.The results indicate a slight performance advantage for the hierarchical classification MLNP approach over the flat classification approach in the absence of confidence thresholds, with weighted F2-scores of 0.781 and 0.762, respectively. However, the application of confidence thresholds results in both approaches exhibiting comparable performance, with the hierarchical NMLNP approach achieving a weighted F1-score of 0.920, thereby demonstrating superior overall performance. The effects of enforcing these thresholds and excluding data with low certainty are thoroughly investigated. This work emphasizes the feasibility of tissue differentiation using a hand-held ultrasound aspirator while resecting tissue. Moreover, it highlights the capability of hierarchical classification in advancing tissue differentiation accuracy during neurosurgical procedures, which could ultimately aid surgeons and enhance the safety of intraoperative workflows.

Chronotype Differences and Symptom Network Dynamics of Post-Pandemic Sleep in Adolescents and Young Adults.

Background: Social restrictions during the COVID-19 pandemic resulted in altered sleep patterns and mental health challenges, particularly among adolescents and young adults. Our objective was to examine the potential difference in insomnia prevalence and sleep patterns in this population between the first COVID-19 lockdown and the post-lockdown period, with a focus on chronotype. Additionally, we explored the network of sleep-related differences between these two periods. Methods: A total of 946 respondents participated in our online questionnaire. We performed mixed ANOVA, Ising network and Directed Acyclic Graph (DAG) analyses. Results: Respondents reported going to bed earlier, waking up earlier, sleeping less, and feeling less mentally tired than during the lockdown. The severity of insomnia symptoms did not change. The lethargic chronotype reported more insomnia symptoms, depressive feelings, and agitation than others. Mental fatigue was the central symptom in the Ising network and served as the parent node in the DAG. Conclusions: Post-lockdown, adolescents and young adults have shifted to earlier sleep and wake times with reduced overall sleep, and they experience fewer depressive feelings and less agitation, though insomnia symptoms remain unchanged. Participants who reported increased irritability or poorer sleep quality during confinement also reported similar or diminished attentional capacities compared to their usual levels.

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study

BackgroundFrailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.MethodsWe conducted a cross-sectional and longitudinal study using data from the “Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),” which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).ResultsAt baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model’s AUC was 0.943 (95%CI 0.913–0.974), indicating good discrimination, with Hosmer–Lemeshow (H–L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model’s AUC was 0.722 (95%CI 0.656–0.788), indicating moderate discrimination, with H–L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.ConclusionFrailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

The controllability and structural controllability of Laplacian dynamics

In this paper, classic controllability and structural controllability under two protocols are investigated. For classic controllability, the multiplicity of eigenvalue zero of general Laplacian matrix L ∗ is shown to be determined by the sum of the numbers of zero circles, identical nodes and opposite pairs; however, it is always simple for the Laplacian L with diagonal entries in absolute form. For a fixed structurally balanced topology, the controllable subspace is proved to be invariant even if the antagonistic weights are selected differently under the corresponding protocol with L. For a graph expanded from a star graph rooted from a single leader, the dimension of controllable subspace is two under the protocol associated with L ∗ . In addition, the system is structurally controllable under both protocols if and only if the topology without unaccessible nodes is connected. As a reinforcing case of structural controllability, strong structural controllability requires the system to be controllable for any choice of weights. The connection between parent nodes and child nodes affects strong structural controllability because it determines the linear relationship of the control information from parent nodes. This discovery is a major factor in establishing the sufficient conditions on strong structural controllability for multi-agent systems under both protocols, rather than for complex networks, about latter results are already abundant. Finally, we present a sufficient and necessary condition to identify the strong structural controllability. Moreover, a method to construct a strongly structurally controllable graph is proposed.

RPL*: An Explainable AI-based routing protocol for Internet of Mobile Things

The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.

ITRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its ϵ-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

Bayesian varying‐effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury

AbstractIn this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi‐subject Bayesian vector autoregressive model that estimates group‐specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group‐specific networks and selection of relevant covariate effects. We show improved performance over competing two‐stage approaches on simulated data. We apply our method on resting‐state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group‐level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.

Mining Delay Propagation Causality within an Airport Network from Historical Data

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport’s delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks.

Bayesian network prediction study on the impact of occupational health psychological factors on insomnia among thermal power generation workers

Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

A multi path routing protocol with efficient energy consumption in IoT applications real time traffic

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility.

Continuous-Time Quantum Walk in Glued Trees: Localized State-Mediated Almost Perfect Quantum-State Transfer.

In this work, the dynamics of a quantum walker on glued trees is revisited to understand the influence of the architecture of the graph on the efficiency of the transfer between the two roots. Instead of considering regular binary trees, we focus our attention on leafier structures where each parent node could give rise to a larger number of children. Through extensive numerical simulations, we uncover a significant dependence of the transfer on the underlying graph architecture, particularly influenced by the branching rate (M) relative to the root degree (N). Our study reveals that the behavior of the walker is isomorphic to that of a particle moving on a finite-size chain. This chain exhibits defects that originate in the specific nature of both the roots and the leaves. Therefore, the energy spectrum of the chain showcases rich features, which lead to diverse regimes for the quantum-state transfer. Notably, the formation of quasi-degenerate localized states due to significant disparities between M and N triggers a localization process on the roots. Through analytical development, we demonstrate that these states play a crucial role in facilitating almost perfect quantum beats between the roots, thereby enhancing the transfer efficiency. Our findings offer valuable insights into the mechanisms governing quantum-state transfer on trees, with potential applications for the transfer of quantum information.

Arsitektur Jaringan Menggunakan Topologi Tree

Architecture using tree topology has become a popular approach in managing computer networks. Tree topology combines elements of star and bus topologies, resulting in an efficient and scalable network for a variety of purposes. This paper discusses the basic concepts of architecture using tree topology, including hierarchical structures and design advantages. First, the tree topology consists of a parent node (root node) which leads to a number of branch nodes (branch nodes). Each branch node can then connect to other nodes, forming a multilevel hierarchy. In computer networks, this topology is often used to connect a number of LANs (Local Area Networks) into a larger network. The main advantages of tree topology are scalability and management efficiency. The network can be expanded by adding new branch nodes without affecting the rest of the network. Additionally, network monitoring and maintenance becomes more organized due to a clear hierarchical structure. However, tree topology also has disadvantages. If the parent node fails, then all parts of the network connected to that node can be affected. Additionally, complex implementations may require more sophisticated hardware and software. In the modern computing era, tree topologies are used in a variety of contexts, ranging from corporate network infrastructure to online content distribution. Its ability to provide an organized and extensible structure makes it an attractive choice in designing efficient and reliable network architectures.

Li-MSD: A lightweight mitigation solution for DAO insider attack in RPL-based IoT

Many IoT applications run on a wireless infrastructure supported by resource-constrained nodes which is popularly known as Low-Power and Lossy Networks (LLNs). Currently, LLNs play a vital role in digital transformation of industries. The resource limitations of LLNs restrict the usage of traditional routing protocols and therefore require an energy-efficient routing solution. IETF’s Routing Protocol for Low-power Lossy Networks (RPL, pronounced “ripple”) is one of the most popular energy-efficient protocols for LLNs, specified in RFC 6550. In RPL, Destination Advertisement Object (DAO) control message is transmitted by a child node to pass on its reachability information to its immediate parent or root node. An attacker may exploit the insecure DAO sending mechanism of RPL to perform “DAO insider attack” by transmitting DAO multiple times. This paper shows that an aggressive DAO insider attacker can drastically degrade network performance. We propose a Lightweight Mitigation Solution for DAO insider attack, which is termed as “Li-MSD”. Li-MSD uses a blacklisting strategy to mitigate the attack and restore RPL performance, significantly. By using simulations, it is shown that Li-MSD outperforms the existing solution in the literature.

Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.

HCEL: Hybrid Clustering Approach for Extending WBAN Lifetime

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

A Bayesian network method using transfer learning for solving small data problems in abnormal condition diagnosis of fused magnesia smelting process

Intelligent abnormal condition diagnosis is key to the safe and stable operation of the fused magnesia smelting process (FMSP). Existing studies have applied the Bayesian network (BN) to the FMSP with sufficient data. However, learning an accurate BN model for a new FMSP using small data isn't easy. Furthermore, the unreasonable diagnosis may seriously affect product quality and security threats. To this end, we present a novel BN transfer learning (BNTL) method to resolve this abnormal condition diagnosis problem using small data. The abnormal condition diagnosis model is established offline based on the proposed BNTL method, and the occurrence or extent of abnormal conditions is identified online based on the diagnosis model and real-time process information. The BNTL involves structure and parameter transfer learning. Among them, structure transfer learning is based on score and search. A new scoring function is proposed to evaluate the local structure of the candidate target domain by weighted integration of source and target domain data based on similarity. The search process is based on the genetic algorithm with other constraints that remove loops and control the number of parent nodes. After learning the structure, parameter transfer learning is done according to a new similarity evaluation and fusion algorithm. The public network is first used to evaluate the BNTL method. Then, it is utilized to build the abnormal condition diagnosis model of the FMSP using small data. Experimental results show that the recognition accuracy is significantly improved compared with other advanced methods in a limited data environment.

Time Series Prediction Based on Multi-Scale Feature Extraction

Time series data are prevalent in the real world, particularly playing a crucial role in key domains such as meteorology, electricity, and finance. Comprising observations at historical time points, these data, when subjected to in-depth analysis and modeling, enable researchers to predict future trends and patterns, providing support for decision making. In current research, especially in the analysis of long time series, effectively extracting and integrating long-term dependencies with short-term features remains a significant challenge. Long-term dependencies refer to the correlation between data points spaced far apart in a time series, while short-term features focus on more recent changes. Understanding and combining these two features correctly are crucial for constructing accurate and reliable predictive models. To efficiently extract and integrate long-term dependencies and short-term features in long time series, this paper proposes a pyramid attention structure model based on multi-scale feature extraction, referred to as the MSFformer model. Initially, a coarser-scale construction module is designed to obtain coarse-grained information. A pyramid data structure is constructed through feature convolution, with the bottom layer representing the original data and each subsequent layer containing feature information extracted across different time step lengths. As a result, nodes higher up in the pyramid integrate information from more time points, such as every Monday or the beginning of each month, while nodes lower down retain their individual information. Additionally, a Skip-PAM is introduced, where a node only calculates attention with its neighboring nodes, parent node, and child nodes, effectively reducing the model’s time complexity to some extent. Notably, the child nodes refer to nodes selected from the next layer by skipping specific time steps. In this study, we not only propose an innovative time series prediction model but also validate the effectiveness of these methods through a series of comprehensive experiments. To comprehensively evaluate the performance of the designed model, we conducted comparative experiments with baseline models, ablation experiments, and hyperparameter studies. The experimental results demonstrate that the MSFformer model improves by 35.87% and 42.6% on the MAE and MSE indicators, respectively, compared to traditional Transformer models. These results highlight the outstanding performance of our proposed deep learning model in handling complex time series data, particularly in capturing long-term dependencies and integrating short-term features.