Configuration Information Research Articles

A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise.

Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed. Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments. The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM). This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

Cooperative control for automatic towing operation by multiple DP tugboats with fully unknown model parameters

This paper presents a cooperative consensus control scheme for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating object through cables, aiming to promote the automation and intelligence of marine towing operation. The scheme implements a leader-follower formation strategy among distributed tugboats, maneuvering the object by adjusting the position of the virtual leader in a reasonable manner. To ensure the formation configuration, a cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system. In addition, a customized model parameter-free DP system is proposed to enhance the tracking performance of a single tugboat, addressing the challenges of full unknown model parameters and large cable tension disturbances. The proposed automatic towing control scheme is verified through full-scale nonlinear time domain simulations under variable environmental conditions and formation configurations, demonstrating its effectiveness.

Game Analysis and Optimization for Evolutionary Dynamic Heterogeneous Redundancy

Dynamic Heterogeneous Redundancy (DHR) has been considered as a proactive defense architecture against unknown vulnerabilities and backdoors. It is a heterogeneous, fault-tolerant, and redundant system, which ensures the security by constantly switching heterogeneous executors. However, a limited number of heterogeneous executors can be merely switched and combined statically. It is difficult to cope with persistent threats. In this paper, we propose an evolutionary DHR system, add evolutionary sub-strategies of executors to solve this problem, optimizing defense mechanisms of DHR. To demonstrate theoretical validity, a game-theoretic analysis of the DHR security mechanism is performed under dynamic incomplete information, and the dilemma of defense is discussed. We construct a DHR game model based on evolutionary, and the results are extended to the general case to analyze the Bayesian equilibrium when each service strategy has a different number of evolved sub-strategies. In addition, the correlation factor is added to the game to investigate the impact of the correlation between different heterogeneous executors on defense results. Finally, Gambit and NS2 simulation experiments for the proposed method are shown. To verify the applicability of the physical system, a prototype DHR system based on dynamic evolution was constructed. We collect key configuration information and modified them randomly. It ensures that the information scanned by the attacker is different each time. Finally, the effectiveness of the physical system is verified by nmap and metasploit tools.

Distributed Adaptive Fault-Tolerant Formation-Containment Control With Prescribed Performance for Heterogeneous Multiagent Systems.

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses

BackgroundAmyotrophic lateral sclerosis (ALS) is a serious neurodegenerative disorder affecting nerve cells in the brain and spinal cord that is caused by mutations in the superoxide dismutase 1 (SOD1) enzyme. ALS-related mutations cause misfolding, dimerisation instability, and increased formation of aggregates. The underlying allosteric mechanisms, however, remain obscure as far as details of their fundamental atomistic structure are concerned. Hence, this gap in knowledge limits the development of novel SOD1 inhibitors and the understanding of how disease-associated mutations in distal sites affect enzyme activity.Methods: We combined microsecond-scale based unbiased molecular dynamics (MD) simulation with network analysis to elucidate the local and global conformational changes and allosteric communications in SOD1 Apo (unmetallated form), Holo, Apo_CallA (mutant and unmetallated form), and Holo_CallA (mutant form) systems. To identify hotspot residues involved in SOD1 signalling and allosteric communications, we performed network centrality, community network, and path analyses.Results: Structural analyses showed that unmetallated SOD1 systems and cysteine mutations displayed large structural variations in the catalytic sites, affecting structural stability. Inter- and intra H-bond analyses identified several important residues crucial for maintaining interfacial stability, structural stability, and enzyme catalysis. Dynamic motion analysis demonstrated more balanced atomic displacement and highly correlated motions in the Holo system. The rationale for structural disparity observed in the disulfide bond formation and R143 configuration in Apo and Holo systems were elucidated using distance and dihedral probability distribution analyses.Conclusion: Our study highlights the efficiency of combining extensive MD simulations with network analyses to unravel the features of protein allostery.

Model Transformasi Informasi Artikel Minangkabau sebagai Preservasi Pengetahuan tentang Budaya Lokal

Newspaper articles are intellectual works of individuals that need to be respected as a form of information which can provide valuable knowledge to the reading community about various aspects of life. This source of information is frequently used as a primary reference to support various community activities such as developing knowledge and general insight. So far, there is no system of information available for continuous documentation for articles containing Minangkabau in printed newspapers. The writing of this paper aimed to explain the transformation model of Minangkabau articles in printed newspapers become information that can be accessed digitally. The research method used was research and design. Determination of the sample was using purposive sampling technique with specific criteria. The conditions is sample contains information that can be used all the time and comprehensively discuss one aspect of the lives of Minangkabau community. The study was conducted on 127 articles containing Minangkabau found in four newspapers which is spread in West Sumatra particularly Singgalang, Padang Ekspres, Posmetro, and Haluan through the use of the concept of information transformation and preservation of knowledge. The results of the study showed that the information transformation model of Minangkabau articles begins with article selection, article documentation in the form of clipping, indexation, context diagram formation and User Interface configuration as a design for digital documentation.

Numerical Investigation on Interactive Hydrodynamic Performance of Two Adjacent Unmanned Underwater Vehicles (UUVs)

This study investigates the effectiveness of UUV formations during navigation to designated target areas. The research focuses on propeller-equipped UUVs and employs a computational fluid dynamics (CFD) methodology to analyze the hydrodynamic interactions among multiple UUV formations while en route to their targeted exploration areas. Utilizing the relative drag coefficients (rl and rf) and static thrust (Rfleets) as analytical parameters, this paper defines the relative distances (a and b) between UUVs within a formation and conducts a comparative analysis of the hydrodynamic performance between individual UUVs and formation configurations. The study establishes correlations between relative distances and the hydrodynamic performance of formations. The findings reveal the following: 1. For both the lead UUV and the following UUV within the formation, the rl and rf heatmaps exhibit two distinct regions: a thrust region and a drag region. Notably, these regions significantly overlap. The maximum rl is 31.23%, while the minimum rf is −20.9%, corresponding to relative distances of a = 0.12 and b = 1.5. Conversely, the minimum rl is −12.2%, while the maximum rf is 22.03%, with relative distances of a = 1.1 and b = 0.2; 2. An analysis of formation static thrust Rfleets reveals that it can be up to 7% greater than the drag experienced by self-propelled UUVs when relative distances a and b are set to 1.1 and 1, respectively. This highlights the enhanced performance achievable through formation navigation. The results presented in this paper offer valuable theoretical insights into the optimal design of relative distances within UUV formations, contributing to the advancement of UUV formation navigation strategies.

Time-Varying Topology Formation Reconfiguration Control of the Multi-Agent System Based on the Improved Hungarian Algorithm

Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system’s stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment.

Distributed edge-based event-triggered optimal formation control for air-sea heterogeneous multiagent systems

This paper addresses the optimal formation control problem for a class of air-sea heterogeneous multiagent systems with external disturbances and model uncertainties. Multiple unmanned aerial vehicles (UAVs) and multiple unmanned surface vessels (USVs) are considered in this system. First, a distributed adaptive state compensator is designed for each agent to estimate the state information of the virtual leader. This compensator is equipped with an edge-based event-triggered scheme to reduce the amount of communication for each edge. Second, a reinforcement learning-based optimal formation controller with the appointed-time prescribed performance is designed to obtain the formation configuration for an air-sea system. This method not only optimizes the formation controller but also ensures the steady-state accuracy and stabilization time. Additionally, an event-triggered mechanism is proposed to reduce the number of optimal formation controller communications. A self-structuring actor-critic neural network and a self-structuring radial basis function neural network are also proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation and approximate the uncertainty, respectively. It can constantly adjust the number of neurons, eventually obtaining an optimal number. Finally, it is proven by Lyapunov theory that all signals are semiglobally uniform and eventually bounded, and the simulation results are provided to illustrate the effectiveness of the scheme.

Configurational information measure of mesonic states in 4-flavor AdS/QCD

Strange axial-vector kaons, K1, and f1 meson resonances are investigated in the 4-flavor AdS/QCD model. Their underlying differential configurational entropy is computed and the mass spectra of higher-excited resonances, in both these mesonic families, are achieved and discussed. This technique merges the 4-flavor AdS/QCD and experimental data regarding the mass spectrum of K1 and f1 meson resonances that have been already detected and reported in the Particle Data Group, also bringing forth a route to explore physical features of the next generation of resonances in the K1 and f1 meson families.

Estimating 5G Network Service Resilience Against Short Timescale Traffic Variation

5G networks are designed to create a new ecosystem for vertical industries such as health care, energy, and public transport. These novel applications, on the other hand, bring new challenges to network resilience. Among them, traffic variation is one of the most vital threats to the 5G network. With tens of thousands of devices connected to the network, network service resilience is threatened by the heavy traffic change induced by the end users or malicious attacks. While long timescale traffic variation can be easily predicted based on historical data, short timescale abnormal traffic is hard to forecast yet can significantly violate the service requirements. The impact of short timescale traffic variation can be mitigated by 5G management and control systems. However, the complexity and dynamics of the virtualized 5G system make it hard to estimate its resilience. This paper provides a 5G network model that captures the data traffic changes and network dynamic management mechanism. The model is able to evaluate the performance of different network services with different requirements under traffic variation events. We analyze the effectiveness of auto-scaling and compare different isolation strategies for traffic congestion. The simulation results on service resilience estimation can become strong supporting information for 5G network deployment and configuration.

A Low Voltage Prediction Based on LSTM-BP Combined Model for Distribution Station Areas

The timely management of low voltage issues in the distribution network relies heavily on accurate predictions of voltage in the station areas. Many current methods for voltage prediction require complex data collection involving power grid topology parameters and electricity information. However, these methods suffer from drawbacks such as the need for extensive data, limited real-time performance, and significant prediction errors. Hence, this study proposes an approach to voltage prediction utilizing a combined LSTM-BP model. Initially, an analysis of the mechanism of low voltage formation in the station areas reveals that the primary factor influencing node points voltage is the power consumption of users. Subsequently, the LSTM neural network is employed to forecast short-term load curves in the distribution station areas, while the self-learning capability of the BP neural network is utilized to establish the correlation between users’ power and their corresponding voltage levels. By effectively combining the above two neural network models, the historical load’s data can be used to accurately and quickly predict the future low voltage situation in the station areas. Finally, taking station areas as the research object and comparing the actual voltage data with the voltage data predicted in this paper, the results show that within the 1V error range, the prediction accuracy is 99.8%. In contrast to the conventional voltage prediction approach, the method put forward in this research paper enables instantaneous and precise anticipation of low voltage levels without relying on details such as station configuration, line characteristics, or user voltage information.

Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks

The extraction of time series features is essential across various fields, yet it remains a challenging endeavor. Therefore, it's crucial to identify appropriate methods capable of extracting pertinent information that can significantly enhance classification performance. Among these methods are those that translate time series into different domains. This study investigates three distinct time series transformation approaches for addressing time series classification challenges within biomedical data. The first method involves a response vector transformation, while the other two employ image transformation techniques: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields, and Markov Transition Fields. These transformation methods were applied to five biomedical datasets, exploring various format configurations to ascertain the optimal representation technique and configuration for input, which in turn improves classification performance. Evaluations were conducted on the effectiveness of these methods in conjunction with two classification algorithms. The outcomes underscore the significance of these time series transformation techniques as facilitators for enhanced classification algorithms documented in current literature.

Exploring Relationships between Boltzmann Entropy of Images and Building Classification Accuracy in Land Cover Mapping.

Remote sensing images are important data sources for land cover mapping. As one of the most important artificial features in remote sensing images, buildings play a critical role in many applications, such as population estimation and urban planning. Classifying buildings quickly and accurately ensures the reliability of the above applications. It is known that the classification accuracy of buildings (usually indicated by a comprehensive index called F1) is greatly affected by image quality. However, how image quality affects building classification accuracy is still unclear. In this study, Boltzmann entropy (an index considering both compositional and configurational information, simply called BE) is employed to describe image quality, and the potential relationships between BE and F1 are explored based on images from two open-source building datasets (i.e., the WHU and Inria datasets) in three cities (i.e., Christchurch, Chicago and Austin). Experimental results show that (1) F1 fluctuates greatly in images where building proportions are small (especially in images with building proportions smaller than 1%) and (2) BE has a negative relationship with F1 (i.e., when BE becomes larger, F1 tends to become smaller). The negative relationships are confirmed using Spearman correlation coefficients (SCCs) and various confidence intervals via bootstrapping (i.e., a nonparametric statistical method). Such discoveries are helpful in deepening our understanding of how image quality affects building classification accuracy.

Upgrade of EAST data acquisition system

After the 2021 spring campaign, EAST (Experimental Advanced Superconducting Tokamak) facility carried out maintenance. EAST data acquisition system, which is responsible for the unified diagnostic data acquisition and long-term data storage, also has been upgraded. This article will present an overview of the upgrade data acquisition system. The EAST data acquisition system consists of a DAQ console, DAQ units, and data storage database. The DAQ console is used to manage the data acquisition configuration information and control the data acquisition workflow. The old DAQ console has been developed for many years, however, it has become inadequate with increasing of data acquisition units. After upgrade, it is replaced by a data acquisition workflow control program and a configuration management module. The configuration management module provides a website based on Spring Boot framework for administrator and diagnostic users to manage the DAQ units, data storage servers, and signal conditioning equipment, while the data acquisition workflow control program runs as a Linux service. Up to 65 DAQ units with about 4000 channels are distributed in different physical positions for different diagnostic systems. The raw diagnostic data acquired by DAQ units is quasi-real-time transferred to the data storage database. The data acquisition driver plugin has been updated for various kinds of data acquisition cards. The data server cluster keeps these data with MDSPlus for long-term storage. The upgraded data acquisition system has been used in the EAST campaign.

Spatial Modulation Aided Physical Layer Security for NOMA-VLC Systems

We consider the physical layer security (PLS) problem in multi-user non-orthogonal multiple access (NOMA) enabled multiple-input multiple-output (MIMO) visible light communication systems intercepted by a passive eavesdropper (Eve). We propose a novel transmit precoding scheme based on receive spatial modulation (RSM) to degrade the signal-to-interference-noise ratio (SINR) of Eve by exploiting only the slow-fading characteristics of the visible light channel of the legitimate users (Bobs). The proposed PLS precoder is reinforced with secret parameter exchange with Bobs and a CSI acquisition model is proposed to reduce the PLS algorithm's computational load substantially at the transmitter. The closed-form expressions for the achievable secrecy rates and their upper and lower bounds are derived. Via Monte Carlo simulations, we confirm that Bobs can successfully decode their information in various user configurations while Eve's received SINR is significantly worsened by the jamming signal induced by the proposed precoder with secret key exchange. It is also shown that Eve's bit error rate (BER) is increased to the 0.5-level for almost any position in the considered indoor environment. Finally, we corroborate the derived secrecy expressions by computer simulations and show that the proposed scheme provides PLS for Bobs.

Infants' fast neural categorization of artificial objects: The impact of stimulus and task characteristics.

This study explores how 7-month-old infants categorize graphical images varying in basic perceptual features by using a fast periodic visual stimulation (FPVS) task. Most participants were Caucasian and their parents had a higher education, but the family's socioeconomic background was mixed. Experiment 1 (N = 23) tested brain responses to configurational versus scrambled image sequences and found an oddball effect for both conditions, with configurational information leading to stronger neural responses. Experiment 2 explored the role of category learning for later FPVS performance: Group A (control; n = 22) participated in the FPVS categorization task only. Group B (fam; n = 21) was first familiarized with standard exemplars. Group C (fam + contrast; n = 29) was presented an additional exemplar of the contrasting category before the FPVS task. Electroencephalogram analyses revealed a decrease in Nc amplitude (measuring attention) throughout the familiarization phase. Long looking at the out-of-category exemplar in Group C indicated infants' visual interest. Brain responses in the subsequently presented FPVS were strong but did not differ between groups. This highlights the ability of the infant brain to organize perceptual input very rapidly without requiring familiarization but also points to the need for further studies exploring brain processes associated with infants' category learning and categorization at the brain level. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Adaptive observer-based event-triggered finite time formation tracking for multi-agent systems with a non-cooperative leader

The event-triggered time-varying formation tracking for a class of second-order multi-agent systems (MASs) subject to a non-cooperative leader is investigated in this paper. First, in the presence of the unknown input of the leader and external disturbances, a distributed observer with adaptive parameters is put forward for followers to estimate the velocity tracking error. Then, based on the estimated tracking error and an auxiliary variable, a finite time formation controller is further constructed, which is updated depending on a pre-designed event-triggered mechanism. As a result, the desired time-varying formation configuration can be realized in finite time with less communication resource consumption. It’s noted that the constructed formation strategy doesn’t rely on any global information and thus is fully distributed. The stability of the controlled multi-agent system is proved rigorously and it’s verified that event-triggered intervals are with a positive lower bound. At last, simulations are carried out to illustrate the effectiveness of the presented algorithm.

Flexible Microcomb Printed PbS Quantum Dot Film Enables Scalable Fabrication of Near Infrared Photodetector

AbstractColloidal PbS quantum dots (QDs) have attracted tremendous attention in near‐infrared photodetection for their superior optoelectronic properties. However, appropriate fabrication methods remain a challenge for scalable and high performance PbS QD photodetectors. Herein, a flexible microcomb printing (FMCP) technique is first introduced to fabricate PbS QD photoconductors without a layer‐by‐layer ligand exchange technique, and the detectors demonstrate a responsivity of 2.1 A W−1. A computational fluid dynamics model is built to simulate the flow field distribution and analyze the relationship between the key parameters of FMCP and QD film morphology. The QD ink flow field at the tip of the microcomb exhibits high shear rates that lead to a better crystallization configuration for solid QD films formation, which is verified by Grazing‐incidence small and wide‐angle X‐ray scattering. Moreover, the relationship between the intrinsic nature of the QD ink (viscosity and surface tension), substrate temperature, and printing speed are systematically analyzed to experimentally achieve high performance QD films. Benefiting from the good crystallization configuration, FMCP‐printed photodetectors using the direct PbS QD ink without ligand exchange show high responsivities. The results indicate that FMCP is a promising method for scalable and high‐performance PbS QD photodetectors.

Antecedent configurations and performance of business models of intelligent manufacturing enterprises

Tackling climate change crises needs intelligent manufacturing and effective business models. This paper uses the adaptive structuration theory (AST) and configuration perspective to investigate the effects of digital infrastructure, digital orientation, top management team heterogeneity, servitization, government support, and customer demand uncertainty on the business model of intelligent manufacturing enterprises in China. The fuzzy set qualitative comparative analysis (fsQCA) was employed to analyze the data. The study found that there were five configurations of business model formation, which classified business models into five types: executive-led enhanced, digital leadership-enhanced, adaptive, extended, and complex business models. There was an intrinsic relationship between the five models with respect to the dimensions of digitalization and servitization. Further analysis revealed that executive-led, digital leadership-enhanced, and adaptive business models were positively associated with enterprise performance. The paper discusses the potential implications of these findings.