Channel Information Research Articles

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers’ mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method’s stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.

Image channel and spatial information integrated method for fall detection

Image channel and spatial information integrated method for fall detection

Estimation of spectral spacing and linear ICI effects using IQ constellation images and CNN in gridless WDM systems

The estimation of spectral spacing (guard band) among optical channels in gridless WDM systems would be decisive for making swift decisions in reconfigurable optical add-drop multiplexers (ROADMs) to avoid linear interchannel interference (ICI) effects during the channel aggregation process in transit nodes. In this work, we propose a method based on the construction of heat scatter images from constellation diagrams along with convolutional neuronal networks (CNN) to identify when optical channels are spectrally overlapped as well as the value in GHz of the channel separation in a specific optical channel without adjacent channels information. We validate our method in a gridless 16-QAM Nyquist-WDM system with different channel spacing and optical signal-to-noise ratio (OSNR). Experimental results demonstrated that overlap detection can be achieved with 3×16GBd accuracy. Additionally, the estimation of spectral spacing achieved an error (RMSE) of less than 0.6 GHz. The penalty in accuracy and RMSE is only ∼98% and , respectively, when there is no knowledge of the OSNR value. Thus, this method has the potential to be integrated into monitoring tools designed for future dynamic gridless optical transmission systems.

A membrane-targeted photoswitch restores physiological ON/OFF responses to light in the degenerate retina

The lack of effective therapies for visual restoration in Retinitis pigmentosa and macular degeneration has led to the development of new strategies, such as optogenetics and retinal prostheses. However, visual restoration is poor due to the massive light-evoked activation of retinal neurons, regardless of the segregation of visual information in ON and OFF channels, which is essential for contrast sensitivity and spatial resolution. Here, we show that Ziapin2, a membrane photoswitch that modulates neuronal capacitance and excitability in a light-dependent manner, is capable of reinstating, in mouse and rat genetic models of photoreceptor degeneration, brisk and sluggish ON, OFF, and ON-OFF responses in retinal ganglion cells evoked by full-field stimuli, with reactivation of their excitatory and inhibitory conductances. Intravitreally injected Ziapin2 in fully blind rd10 mice restores light-driven behavior and optomotor reflexes. The results indicate that Ziapin2 is a promising molecule for reinstating physiological visual responses in the late stages of retinal degeneration.

Networks of Opportunity’: Unpacking the Role of Family, Peers, and Community in Shaping Economic Mobility in Low-Income Urban Settings

This paper examines the role of social networks, specifically family, peers, and community, in influencing economic mobility in low-income urban areas, where access to opportunities is often constrained by education, employment, and social connections. Drawing on qualitative interviews and survey data, the study investigates how social capital structure and dynamics enable or hinder individuals' access to key resources for education, employment, and mentorship. The findings highlight the dual nature of family structures, where intergenerational support can serve as a pathway to economic mobility or function as a mechanism of exclusion. Additionally, peer networks and community ties are explored to understand how informal channels, such as employment referrals and social support, contribute to financial success and mobility. The paper also critically examines the intersections of race, gender, and social class, revealing how these factors can exacerbate or limit individuals' ability to leverage social capital for improved economic standing. By analysing these dynamics, this study underscores the significant role of social capital in shaping economic outcomes and offers policy recommendations for fostering community-based approaches that open avenues for economic opportunities. Ultimately, the research illustrates how social networks, whether through support or exclusion, can either perpetuate or mitigate poverty, depending on the unequal distribution of resources within urban environments.

Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.

Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.

Analisis Isi Konten Media Sosial Instagram Pemerintah Indonesia di Tingkat Kementerian dan Lembaga

Government institutions need social media channels as a public communication tool to disseminate policies, programs and public information. The government needs to use social media channels to reach a wider community amidst the low level of public trust in government communications and information channels. The aim of the research is to analyze the substance of the content, namely the type and nature of the content, what the audience needs and what the content message needs to communicate to the audience. This research also seeks the content structure related to how content is prioritized, arranged, formatted and displayed in the media. The objects analyzed are Instagram social media in 100 ministries and agencies. This research uses a descriptive quantitative approach that focuses on analyzing the use of Instagram social media by ministries and government agencies in Indonesia. This method aims to provide a comprehensive picture of government digital activities and to analyze the effectiveness of the substance and structure of the content implemented. Instagram was chosen as the main focus because it is the social media platform most widely used by ministries/agencies. The research results show that each agency has various needs with different strategies for using Instagram social media. The ministry has optimized Instagram social media more than the government agencies. Interestingly, the agency produces more content than the ministry, but it is still not optimal. Meanwhile, on average, ministerial agencies still prioritize personal figures and institutional agencies prioritize public information, although they also still predominantly prioritize leadership elements. The results of this research suggest that the government, especially the Ministry of Communication and Informatics, can create standards in managing social media, especially in producing the structure and substance of content so that it can more optimally convey information and interact with the audience.

Social Media Data Analysis for Enhancing Student Evaluation of Teaching Styles

In the realm of education, understanding the impact of different teaching styles on student engagement and satisfaction is essential. Recent advancements in sentiment analysis provide new avenues for evaluating student feedback, particularly through informal channels such as social media. While formal student evaluations offer structured feedback on teaching styles, they may not fully capture the nuanced opinions and sentiments expressed by students in informal settings, such as social media. This research aims to address the gap by integrating sentiment analysis of social media data to evaluate teaching effectiveness across various styles and comparing it with formal evaluation results. This study employs sentiment analysis using the VADER (Valence Aware Dictionary and sEntiment Reasoner) tool to analyze student posts on social media platforms. The analysis includes the extraction of sentiment distributions, identification of common keywords, and tracking of sentiment trends over time. Additionally, formal student evaluations (Likert scale) are collected to offer a direct comparison. The teaching styles analyzed include lecture-based teaching, project-based learning, flipped classrooms, online learning, hybrid learning, and traditional exam-based learning. The findings demonstrate that student sentiment varies significantly across teaching styles. Flipped classrooms and project-based learning received the highest positive sentiment scores, while traditional exam-based teaching showed the most negative sentiment. Social media feedback tended to align with formal evaluations for certain teaching styles, such as the flipped classroom and hybrid learning but showed divergence in others, like online learning, which received higher sentiment in social media feedback. Trends over time reveal evolving sentiments, with fluctuating satisfaction as the academic semester progressed. The integration of social media sentiment analysis provides a more dynamic and real-time understanding of student experiences, offering deeper insights into teaching style effectiveness.

Fine‐grained multi‐path channel estimation and matched reception by single metasurface antenna

AbstractThis paper investigates methods to enhance antenna's channel information extraction capabilities in wireless communications, particularly in challenging multi‐path environments. Focusing on the issues of channel information loss due to multi‐path superposition at the receiver and multi‐path fading during wireless channel propagation, this paper utilizes metasurface antennas to rapidly reconfigure antenna patterns and construct high‐dimensional projection spaces, thus improving information sensing resolution. Based on this, a channel‐matched receiving pattern design method is proposed to mitigate the communication performance loss caused by multi‐path fading. Furthermore, this paper analyses the comprehensive impact of various metasurface parameters on the performance of the proposed scheme, providing theoretical guidance for engineering implementation. Simulation results demonstrate that the proposed scheme can achieve the performance of multi‐path channel estimation and reception close to those of multi‐antenna arrays using a single radio frequency link.

Transformation of the hexagon fraud model (S.C.C.O.R.E.L.L Model): moderated by social media as a whistleblowing system information channe

This research investigates the impact of stimulus, capability, collusion, opportunity, rationalization, ego, lack of empathy, and spirituality on financial reporting fraud. This chapter also explores how social media can moderate these impacts as a channel for whistleblowing information. Based on balanced panel data with a sample of 441 research data, the research focuses on commercial banks registered with the Financial Services Authority, Indonesia, from 2016 to 2022. The research results reveal that opportunity has a negative influence on fraudulent financial reporting; lack of empathy also has a negative influence on fraudulent financial reporting; lack of spirituality has a positive influence on fraudulent financial reporting; social media as an information channel for the whistleblowing system weakens the influence of lack of spirituality on fraudulent financial reporting, and social media strengthens the influence of opportunity and lack of empathy on fraudulent financial reporting. This research not only aims to bridge the research gap regarding financial reporting that contains fraud (fraud theory) but also provides practical insights and recommendations for regulators, especially in monitoring financial reporting that contains fraud in the banking sector. Hence, these findings are very relevant and can be applied.

Differences in Overseas Employment Status and Job Satisfaction of Hairdressers Depending on the Country and the Employment Channel: Focused on Australia and Singapore

The purpose of this study is to examine differences in overseas employment status and job satisfaction of hairdressers depending on the country and the employment channel. For this study, 131 hairdressers with working experiences in Korean hair salons in Australia and Singapore were surveyed. The results of this study were as follows: First, there were significant differences in employment status depending on the country: type of employment, period of employment, hair salon CEO, number of employees, work experience at the time of the employment, and level of English at the time of the employment. Second, employment status depending on the employment channel showed significant differences in type of employment, period of employment, work experience at the time of the employment, and level of English at the time of employment. Third, one-way ANOVA analysis showed that there were significant differences in job satisfaction depending on the country: personal experience, salon welfare, and working conditions. Fourth, job satisfaction depending on the employment channel showed significant differences in four factors of job satisfaction except for working conditions. Those who were employed through the official channel were more satisfied than those who were hired through an informal channel.

The Role of Short Selling in the Korean Stock Market : Review and Implications of Empirical Research

Issues related to the short-selling system in the Korean stock market continue to be debated among academics, individual investors, and financial regulatory authorities. This study comprehensively reviews the major findings of empirical research on short-selling in the Korean stock market and offers policy implications for future research directions on short-selling. Although short-selling studies in Korea exhibit mixed empirical findings, depending on the characteristics of the data used, sample periods, measurement methods of short-selling, selection of related variables, and research design, a large body of previous studies reported that short selling increases the market efficiency and price discovery of overvalued stocks in the Korean stock market. Considering the areas lacking research on short-selling in the Korean stock market, the following research topics are proposed for future research: first, the behavior of arbitrage and hedge trading by short-sellers; second, the information channels of short-sellers; and third, the influence of short-sellers on managerial decision-making in corporate finance.

THE IMPACT OF SOURCES OF INFORMATION ON INDIVIDUAL INVESTORS’ EQUITY DIVERSIFICATION DECISIONS: A STUDY OF INDIAN STOCK MARKET

This study aims at finding the influence of the source of information preferred by individual investors on their equity diversification behavior. The data of 200 individual investors investing in Indian stock markets residing particularly in Punjab has been collected through survey questionnaires. The analysis has been done by carrying out EFA andstructural equation modelling. Three factors have been extracted namely self-research, Formal and paid channels and informal channels. The study confirms that information sources used by investors to gather information while making equity diversification decisions play a significant role.

Optimized Bayesian Matching Pursuit for Joint Channel Estimation and Impulse Noise Mitigation

Orthogonal frequency division multiplexing (OFDM) is adopted in most wireless communication systems, and the performance of OFDM is affected by impulse noise (IN). Better channel estimation (CE) performance is required to detect the channel information on the receiving side. The OFDM system is considered with the number of subcarriers carrying the data and pilot symbols. In the OFDM modulator, the symbols of the frequency domain are transformed into the signals of the time domain using inverse discrete Fourier transformation (IDFT). Effective impulsive noise mitigation is crucial for improving the effectiveness of OFDM communication systems, and it improves the signal-to-noise ratio (SNR) at the receiver. A cyclic prefix is then appended before transmission through the channel. In the receiver noise, the effect of IN is mitigated jointly with CE using the Bayesian matching pursuit (BMP) and Moth-flame algorithm (MFA). In this approach, the IN and channel information is considered as a sparse vector. Here, the data symbols used are considered an unknown parameter. The approach incorporating all subcarriers has a lower mean square error (MSE) of IN estimate for impulsive noise reduction. The MFA algorithm is used to optimize the sensor matrix in BMP. The sparsity of the channel and the impulse response are observed in the time domain to represent the channel and IN together. It exactly recovers the sparse signal for finding the convex objectives of the sparse minimizer, and the sparse solution is obtained with fixed point updates. The proposed BMP algorithm improves the CE by explicitly considering the existence of IN. The BMP is a greedy algorithm that selects the most correlated residuals at each column. Under mutual incoherence, it recovers the signal with a higher probability. The simulated outcomes proved that the proposed CE and noise mitigation model achieved better performance based on the bit error rate (BER), throughput and MSE.

Advancing skeleton-based human behavior recognition: multi-stream fusion spatiotemporal graph convolutional networks

In the realm of daily human interactions, a rich tapestry of behaviors and actions is observed, encompassing a wealth of informative cues. In the era of burgeoning big data, extensive repositories of images and videos have risen to prominence as the primary conduits for disseminating information. Grasping the intricacies of human behaviors depicted within these multimedia contexts has evolved into a pivotal quandary within the domain of computer vision. The technology of behavior recognition finds its practical application across domains such as human-computer interaction, intelligent surveillance, and anomaly detection, exhibiting a robust blend of pragmatic utility and scholarly significance. The present study introduces an innovative human body behavior recognition framework anchored in skeleton sequences and multi-stream fused spatiotemporal graph convolutional networks. Developed upon the foundation of graph convolutional networks, this method encompasses three pivotal refinements tailored to ameliorate extant challenges. First and foremost, in response to the complex task of capturing distant interdependencies among nodes within graph convolutional networks, we incorporate a spatial attention module. This module adeptly encapsulates long-term node interdependencies via precision-laden positional information, thus engendering interconnections that span diverse temporal and spatial contexts. Subsequently, to elevate the discernment of channel information within the network and to optimize the allocation of attention across distinct channels, we introduce a channel attention mechanism. This augmentation fortifies the discernment of motion-related features. Lastly, confronting the lacuna of information gaps prevalent within single-stream data, we deploy a multi-stream fusion methodology to fortify model outputs, ultimately fostering more precise prognostications concerning action classifications. Empirical results bear testament to the efficacy of the proposed multi-stream fused spatiotemporal graph convolutional network paradigm for skeleton-centric behavior recognition, evincing a pinnacle recognition accuracy of 96.0% on the expansive NTU-RGB+D skeleton dataset, alongside a zenithal accuracy of 37.3% on the Kinetics-Skeleton dataset—emanating from RGB data and furthered through pose estimation.

Workshop Design on Fast Fashion Impacting Sustainability

Fast fashion has raised sustainable issues in the clothing cycle from the production stage to the stage after the clothes are worn and end up generating waste. Therefore, it is necessary to adopt a sustainable approach to overcome practices that damage the ecology. This research uses qualitative methods by means of observation, literature studies, questionnaires, interviews, and the design process using Gustami theory (observation, design, manufacturing process). This research will involve several communities and their contributions to fast fashion and sustainable fashion in Indonesia to find out how much they understand about fast fashion as well as the concept of sustainable that can minimize the negative impact of fast fashion. The results of the survey above discussed public awareness and behavior towards fast fashion, as many as 62.7% of respondents knew the impact of the dangers of the fashion, while there were 29.4% of respondents who did not know the issue of fashion. However, the circumstances and conditions that result in the lack of sensitivity of the community to the environment resulted in a lack of knowledge and doubt about the issue. The data of the respondents who do not know and doubt, the need for an equitable education about this issue and the impact on the environment through media event workshops as information channels.

Population synthesis of hot-subdwarf B stars with COMPAS: parameter variations and a prescription for hydrogen-rich shells

Abstract Subdwarf B stars are a well-known class of hot, low-mass stars thought to be formed through interactions in stellar binary systems. While different formation channels for subdwarf B stars have been studied through a binary population synthesis approach, it has also become evident that the characteristics of the found populations depend on the initial set of assumptions that describe the sometimes poorly constrained physical processes, such as common envelope episodes or angular momentum loss during mass transfer events. In this work we present a parameter study of subdwarf B populations, including a novel analytic prescription that approximates the evolution of subdwarf B stars with hydrogen-rich outer shells, an element previously overlooked in rapid binary population synthesis. We find that all studied parameters strongly impact the properties of the population, with the possibility of igniting helium below the expected core-mass value near the tip of the red giant branch strongly affecting the total number of subdwarf B candidates. Critically, our newly proposed prescription for the evolution of subdwarf B stars with hydrogen-shells helps to reconcile theoretical predictions of surface gravity and effective temperature with observational results. Our prescription is useful in the context of rapid binary population synthesis studies and can be applied to other rapid binary population synthesis codes’ output.

Seroprevalence of Brucellosis in Livestock and Farmers’ Choice of Milk Marketing Channels in Rwamagana District, Rwanda

Brucellosis remains an endemic disease in livestock populations in Rwanda, but the prevalence of the disease varies by geographic region. The common use of informal milk marketing channels represents a health hazard to humans when milk from Brucella-infected cows from one or more households is mixed with milk from other households for human consumption. In Rwanda, knowledge about the burden of brucellosis in livestock and factors associated with farmers’ choice of milk marketing channels is very limited. The objectives of this study were 1) to estimate the seroprevalence of brucellosis in livestock in Rwamagana District in Rwanda and 2) to estimate the frequency of and identify determinants associated with farmers’ choice of milk marketing channels. The seroprevalence of brucellosis in livestock at the household level was 1/160 (0.6%; 95% CI = 0.1–3.4%). Among 100 households that sold milk, 72 used informal milk trade channels. By the use of logistic regression, milk price and distance to closest milk collection center were positively associated with the use of informal milk marketing channels. Although the seroprevalence of brucellosis in livestock in Rwamagana District was low, the risk of disease transmission to humans through consumption of unpasteurized milk or milk products is not negligible. In this report, we present several policy options available to animal health authorities that can mitigate the risk of brucellosis disease transmission in populations.

A mutual inclusion mechanism for precise boundary segmentation in medical images.

Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process. We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation. The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images

Shadows degrade image quality and complicate interpretation, underscoring the importance of accurate shadow detection for many image analysis tasks. However, due to the complex backgrounds and variable shadow characteristics of remote sensing images (RSIs), existing methods often struggle with accurately detecting shadows of various scales and misclassifying dark, non-shaded areas as shadows. To address these issues, we proposed a comprehensive shadow detection network called MAMNet. Firstly, we proposed a multi-scale spatial channel attention fusion module, which extracted multi-scale features incorporating both spatial and channel information, allowing the model to flexibly adapt to shadows of different scales. Secondly, to address the issue of false detection in non-shadow areas, we introduced a criss-cross attention module, enabling non-shadow pixels to be compared with other shadow and non-shadow pixels in the same row and column, learning similar features of pixels in the same category, which improved the classification accuracy of non-shadow pixels. Finally, to address the issue of important information from the other two modules being lost due to continuous upsampling during the decoding phase, we proposed an auxiliary branch module to assist the main branch in decision-making, ensuring that the final output retained the key information from all stages. The experimental results demonstrated that the model outperformed the current state-of-the-art RSI shadow detection method on the aerial imagery dataset for shadow detection (AISD). The model achieved an overall accuracy (OA) of 97.50%, an F1 score of 94.07%, an intersection over union (IOU) of 88.87%, a precision of 95.06%, and a BER of 4.05%, respectively. Additionally, visualization results indicated that our model could effectively detect shadows of various scales while avoiding false detection in non-shadow areas. Therefore, this model offers an efficient solution for shadow detection in aerial imagery.