Channel Information Research Articles

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer’s disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

ATP-gated P2x7 receptor is a major channel type at type II auditory nerves and required for hearing sensitivity efferent controlling and noise protection.

Hearing sensitivity and noise protection are mediated and determined by negative feedback of the cochlear efferent system. Type II auditory nerves (ANs) innervate outer hair cells (OHCs) in the cochlea and provide an input to this efferent control. However, little is known about underlying channel information. Here, we report that ATP-gated P2x7 receptor had a predominant expression at type II ANs and the synaptic areas under inner hair cells and OHCs with lateral and medial olivocochlear efferent nerves. Knockout (KO) of P2x7 increased hearing sensitivity with enhanced acoustic startle response (ASR), auditory brainstem response (ABR), and cochlear microphonics (CM) by increasing OHC electromotility, an active cochlear amplifier in mammals. P2x7 KO also increased susceptibility to noise. Middle level noise exposure could impair active cochlear mechanics resulting in permanent hearing loss in P2x7 KO mice. These data demonstrate that P2x7 receptors have a critical role in type II AN function and the cochlear efferent system to control hearing sensitivity; deficiency of P2x7 receptors can impair the cochlear efferent suppression leading to hearing oversensitivity and susceptibility to noise.

Weak Gravitational Lensing around Low Surface Brightness Galaxies in the DES Year 3 Data

We present galaxy-galaxy lensing measurements using a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data as lenses. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. These dark-matter-dominated objects are intriguing due to potentially unusual formation channels that lead to their diffuse stellar component. Given the faintness of LSBGs, using standard observational techniques to characterize their total masses proves challenging. Weak gravitational lensing, which is less sensitive to the stellar component of galaxies, could be a promising avenue to estimate the masses of LSBGs. Our LSBG sample consists of 23,790 galaxies separated into red and blue color types at g−i≥0.60 and g−i<0.60, respectively. Combined with the DES Y3 shear catalog, we measure the tangential shear around these LSBGs and find signal-to-noise ratios of 6.67 for the red sample, 2.17 for the blue sample, and 5.30 for the full sample. We use the clustering redshifts method to obtain redshift distributions for the red and blue LSBG samples. Assuming all red LSBGs are satellites, we fit a simple model to the measurements and estimate the host halo mass of these LSBGs to be . We place a 95% upper bound on the subhalo mass at . By contrast, we assume the blue LSBGs are centrals, and place a 95% upper bound on the halo mass at log(Mhost/M⊙)<11.84. We find that the stellar-to-halo mass ratio of the LSBG samples is consistent with that of the general galaxy population. This work illustrates the viability of using weak gravitational lensing to constrain the halo masses of LSBGs.

The Power of Words in Agile vs. Waterfall Development: Written Communication in Hybrid Software Teams

Software development is constantly evolving, adapting to emerging technologies and development paradigms while leveraging advancements in communication technologies and work modes. We conduct an exploratory case study in a large software organization to investigate how the development paradigm and the formality of communication channels affect written communication within hybrid teams. We perform statistical and content analysis of written conversations from 20 projects involving two software products that use industry adaptations of the Waterfall model and of Scrum, respectively. We found that in agile-developed projects, communication related to the execution-monitoring-control phase of the Project Management Life Cycle is more prevalent, and communication related to the initiation phase occurs more frequently in informal channels. For both project types, communication primarily pertains to the software construction phase of the Software Development Life Cycle. After annotating communication contents using speech acts, representatives are found to be prevalent in informal channels for agile-developed projects, directives are more prevalent in informal channels for waterfall-developed projects, and expressives are more frequent in informal channels for both project types. We provide empirical evidence that development paradigms and communication channel formality impact written communication, with agile-developed projects showing more collaborative interactions in informal channels compared to waterfall-developed projects.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Are we preparing healthy & responsible adolescents? Exploratory qualitative study to understand the health and social issues of adolescent living in Karachi, Pakistan.

This study aimed to understand the lives of adolescents living in squatter settlements of Karachi, Pakistan regarding their assigned roles and responsibilities, health and social issues, their decision-making process, and mechanism and channels of information. An exploratory descriptive qualitative study design was employed. Purposive sampling techniques were used to carry out Focus Group Discussions (FGDs) with adolescents (n = 10, Participants = 190), adolescents' parents (n = 10, Participants = 180), and In-Depth Interviews of with key adolescent stakeholders (n = 20). Adolescent stakeholder mapping was conducted for enrolling participants. The data were analysed thematically using deductive and inductive approaches. Based on gender norms parents assign specific roles and responsibilities to adolescent girls and boys. Due to societal norms, communication gap exists between adolescents and their parents. The most popular information channels are social media platforms and friends. Adolescents reported being subjected to a variety of physical, sexual, mental, social, and environmental pressures. Lack of guidance from parents, inadequate knowledge and skills to deal with physical, sexual, mental, social and environmental hazards, and misuse of social media lead to risky decisions, injuries, and social instability. This study underlines the urgent need for targeted interventions for addressing gender issues and improving adolescents' decision-making and life skills. We recommend Behavior Change Communication interventions to dismantle gender stereotypes and support a balanced domestic environment for children's education and well-being, awareness raising among parents, teachers, and healthcare providers about adolescent risks emphasizing their role in youth guidance, and advocacy for youth-led forums to co-create educational content engaging parents, educators, and health professionals focusing on life skills. These strategies would turn demographic transitions into productive dividends.

Unsupervised bearing raceway surface defect detection based on improved f-AnoGAN

Abstract For addressing bearing raceway surface defects, supervised deep learning methods face challenges such as insufficient defect samples and imbalance between defect and non-defect samples. To overcome these issues, we propose an enhanced fast anomaly detection with generative adversarial networks (f-AnoGAN) unsupervised detection algorithm. Firstly, SE-Block modules based on SE attention mechanism and residual structure are integrated into the encoder, aiding the network in focusing on channel information while alleviating gradient vanishing problems. Secondly, transfer learning is introduced to effectively enhance the algorithm’s detection performance and generalization ability. Finally, utilizing bearing images collected from industrial sites, a self-built bearing raceway surface defect dataset is constructed, and extensive experiments are conducted. Experimental results demonstrate that the improved algorithm achieves an area under curve score of 99.96% on the self-built bearing raceway surface defect dataset, representing a 7.07% improvement over the f-AnoGAN algorithm, meeting the requirements for online detection in bearing industry applications.

Infrared target detection algorithm based on multipath coordinate attention mechanism

Abstract The current generation of infrared target detection algorithms frequently exhibits a high degree of dependency on parameter configurations within complex operational environments. This often results in a reduction in detection accuracy, an increase in the number of model parameters, and a slowing of the detection process. To address these limitations, a new algorithm, CGhostNet-Attention-YOLO (CAY), is proposed in this paper. Firstly, we designed a lightweight backbone network, CGhostNet, with the objective of improving feature extraction efficiency, thereby enabling accurate and real-time feature extraction. Furthermore, we proposed a Multipath Coordinate Attention (MPCA) mechanism, which incorporates both channel and positional information, thereby facilitating enhanced context awareness and the comprehension of relationships between different positions. This effectively enhances the model's ability to comprehend the overall meaning and addresses the issue of missed detections in infrared targets, significantly improving detection accuracy. Moreover, we employed the Inner-SIoU loss function to accelerate model convergence, reduce loss, and enhance the robustness of the model. Finally, comparative experiments were conducted on our dataset (IFD) as well as publicly available datasets, including FLIR, Pascal VOC, and NEU-DET. The results demonstrate that the CAY algorithm achieved a mean Average Precision (mAP@0.5) of 81.3% on the IFD dataset, 86.1% on the FLIR dataset, 79.2% on the Pascal VOC dataset, and 79.9% on the NEU-DET dataset, with a 27% reduction in the number of parameters. These findings validate the feasibility of the proposed algorithm.

PERAN MEDIA SOSIAL INSTAGRAM DALAM PEMBENTUKAN IDENTITAS KEWARGANEGARAAN

In the midst of the rapid flow of information and digital connectivity, social media has become a public space that is inseparable from modern human life. Platforms like Instagram allow us to connect with people from all over the world instantly. This makes it easier for us to maintain relationships with friends and family, even if they are physically far from us. More than just an information channel, Instagram also has the potential to be an effective tool for instilling the values of civic identity. The method that the author uses in this research is a qualitative method using library research, namely conducting research by collecting information from books and journals. The aim of this research is to see how social media can influence citizenship identity. The results of this research show that the role of Instagram in forming civic identity is very complex and influenced by various factors such as consumer content, social interactions, and government regulations. Keywords: Social Media, Instagram, Citizenship Identity

Channel-facilitated transport under resetting dynamics.

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological membranes is facilitated by specialized proteins that create water-filled channels. Valuable insights can be obtained by studying the transition paths of particles through a channel and gathering information on their lifetimes inside the channel as well as their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls of the channel. We study the statistics of conditional and unconditional escape times in the presence of resetting-an intermittent dynamics that brings the particle back to its initial coordinate stochastically. We determine analytically the physical conditions under which such a resetting mechanism becomes beneficial for the faster escape of the particles from the channel, thus enhancing transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extents. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.

A Cross-Regional Study on the Public's Channel Preferences for Accessing Information

In this study, an online questionnaire was administered simultaneously in Beijing and Seoul to investigate the public's channel preferences for air quality index (AQI) information and risk perception of air pollution in order to comparatively analyze the public's information acquisition behaviors in different media environments. As people become more aware of the health risks of air pollution, the AQI has become routine information released when there is a need to warn of health hazards, and many countries are releasing it to the public through a variety of information channels to ensure that the public can perceive the risks and take appropriate measures in a timely manner. This study, through a comparative cross-regional analysis, found that public preferences for accessing AQI information varied across regions with differing media environments. In addition, the correlation between air pollution risk perceptions and information access behaviors varied across regions.

Accretion mediated spin-eccentricity correlations in LISA massive black hole binaries

Abstract We examine expected effective spin (χeff, 1yr) and orbital eccentricity ($e_{1\rm yr}$) correlations for a population of observable equal-mass massive black hole binaries (MBHBs) with total redshifted mass Mz ∼ [104.5, 107.5] M⊙ embedded in a circumbinary disc (CBD) at redshifts z = 1 and z = 2, one-year before merging in the LISA band. We find a strong correlation between measurable eccentricity and negative effective spin for MBHBs that are carried to merger by retrograde accretion. This is due to the well-established eccentricity pumping of retrograde accretion and less-well-established formation of retrograde minidiscs coupled with a stable retrograde CBD throughout the binary evolution from the self-gravitating radius. Conversely, prograde accretion channels result in positive $\chi _{{\rm eff},1\rm yr}$ and non-measurable $e_{1\rm yr}$ except for almost unity Eddington ratio and Mz ≲ 105 M⊙ MBHBs at z = 1. This clear contrast between the two CBD orientations – and particularly the unique signature of retrograde configurations – provides a promising way to unlock the mysteries of MBHB formation channels in the LISA era.

Sales Channel Strategies of FMCG: A Comprehensive Analysis in Rural Maharashtra

The document focuses on Colgate-Palmolive's sales strategy, specifically in rural Maharashtra, as part of a Sales and Channel Management project. It examines the brand's market position, competitive landscape, and sales initiatives. Colgate-Palmolive maintains a dominant role in oral care and focuses on tailored approaches to different markets, such as affordability strategies for rural areas and digital engagement for urban consumers. The analysis includes a competitor review, highlighting how the company positions itself against Hindustan Unilever, Procter & Gamble, Dabur, and Patanjali. Key objectives are outlined, focusing on both quantitative goals, such as expanding market share and distribution, and qualitative objectives like building brand loyalty and launching new products. Sales initiatives include eco-friendly packaging, community outreach, customer loyalty programs, and the use of digital platforms to engage consumers. Additionally, the document suggests trade initiatives and retailer network improvements, recommending digital tools and gamified approaches to enhance performance. The integration of a Channel Information System (CIS) and various models of retail distribution, including the role of C&F agents, distributors, and general trade, are also discussed. The report concludes with insights on expanding Colgate's market reach and optimizing its distribution strategy. Key Words: Sales Strategy, Rural Marketing, Brand Loyalty, Digital Engagement

Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction

A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3DCNN), this algorithm enables the classification model to have good tolerance for various types of noise by using a Minimum Noise Fraction (MNF) as dimensionality reduction module for hyperspectral image input data. In addition, by introducing lightweight hybrid attention modules with the spatial and the channel information, the deep features extracted by the Convolutional Neural Network are further refined, ensuring that the model has high classification accuracy. Public dataset experiments have shown that compared to traditional methods, the MNF in this algorithm reduces the dimensionality of input spectral data, preserves information with higher signal-to-noise ratio(SNR) in the spectral bands, and aggregates spectral features into class feature vectors, greatly improving the noise robustness of the model. At the same time, based on a lightweight spectral–spatial hybrid attention mechanism, combined with fewer spectral dimensions, the model effectively avoids overfitting. With less loss in model training speed, it achieved better classification accuracy in small-scale training sample experiments, fully demonstrating the good generalization ability of this algorithm.

Inflationary Butterfly Effect: Nonperturbative Dynamics from Small-Scale Features.

For the first time, we investigate the nonperturbative dynamics of single field inflation with a departure from slow roll. Using simulations, we find that oscillatory features in the potential can drastically alter the course of inflation, with major phenomenological implications. In certain cases, the entire Universe gets trapped in a forever inflating de Sitter state. In others, only some regions get stuck in a false vacuum, offering an alternative channel for primordial black hole formation. Analogous to the flap of a butterfly, these results show that small-scale phenomena can have profound consequences on the evolution of the entire Universe. More generally, our work shows the power of simulations in the exploration of the small-scale physics of inflation, particularly in the regime relevant for gravitational-wave astronomy.

Color restoration of mural images based on a reversible neural network: leveraging reversible residual networks for structure and texture preservation

AbstractThe Mogao Grottoes in Dunhuang, a treasure of China's and the world's cultural heritage, contains rich historical and cultural deposits and has left precious relics of the history of human art. Over centuries, the Mogao Caves have been affected by natural and human factors, resulting in irreversible fading and discoloration of many murals. In recent years, deep learning technology has shown great potential in the field of virtual mural color restoration. Therefore, this paper proposes a mural image color restoration method based on a reversible neural network. The method first employs an automatic reference selection module based on structural and texture similarity to choose suitable reference mural images for the faded murals. Then, it utilizes a reversible residual network to extract deep features of the mural images without information loss. Next, a channel refinement module is used to eliminate redundant information in the network channels. Finally, an unbiased color transfer module restores the color of the faded mural images. Compared to other image color restoration methods, the proposed method achieves superior color restoration effects while effectively preserving the original structure and texture details of the mural images. Compared to baseline methods, the Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Perception-based Image Quality Evaluator (PIQE) values are improved by 7.97%, 3.46%, and 13.98%, respectively. The color restoration of the Dunhuang Mural holds significant historical, artistic, cultural, and economic values, and plays a positive role in the preservation and inheritance of Chinese culture, as well as in the promotion of cultural exchange and mutual understanding.

Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

Origin of LAMOST J1010+2358 Revisited

Signature from Pop III massive stars of 140– that end their lives as pair-instability supernovae (PISNe) are expected to be seen in very metal-poor (VMP) stars of . Although thousands of VMP stars have been discovered, the identification of a VMP star with a PISN signature has been elusive. Recently, the VMP star LAMOST J1010+2358 was claimed to be the first star with a clear PISN signature. A subsequent study showed that ejecta from low-mass core-collapse supernovae (CCSNe) can also fit the abundance pattern equally well and additional elements such as C and Al are required to differentiate the two sources. Follow-up observations of LAMOST J1010+2358 by two independent groups were able to detect both C and Al. Additionally, key odd elements such as Na and Sc were also detected whose abundances were found to be higher than the upper limits found in the original detection. We perform a detailed analysis of the newly observed abundance patterns by exploring various possible formation channels for VMP stars. We find that purely low-mass CCSN ejecta as well as the combination of CCSN and Type 1a SN ejecta can provide an excellent fit to the newly observed abundance pattern. Our results confirm earlier analysis that the newly observed abundance pattern is peculiar but has no signatures of PISN.

No-reference image quality assessment based on global awareness.

In the field of computer vision, the application of hand-crafted as well as computer-learning-based methods in the field of image quality assessment has yielded remarkable results. However, in the field of no-reference image distortion, it is still challenging to accurately perceive and determine the quality of an image. To address the difficulties of Image Quality Assessment (IQA) in the field of authentic distorted images, we consider the use of the Swin Transformer (ST) to extract features. To enable the model to focus on both spatial and channel information of features, we design a plug-and-play Global Self-Attention Block (GSAB). At the same time, we introduce a Transformer block in the model to enhance the model's ability to capture long-range dependencies. Finally, we derive the prediction of image quality scores through a Dual-Branching structure. Our method is experimented on four synthetic datasets as well as two authentic datasets, and the results of the experiments are weighted according to the size of the datasets, and the results show that our method outperforms all the current methods and works well in the Generalization ability test, which proves that our method has a good generalization ability. The code will be posted subsequently at https://github.com/yanggege-new/NR-IQA-based-on-global-awareness.

A trifecta of modelling tools: a Bayesian binary black hole model selection combining population synthesis and galaxy formation models

ABSTRACT Gravitational waves (GWs) have revealed surprising properties of binary black hole (BBH) populations, but there is still mystery surrounding how these compact objects evolve. We apply Bayesian inference and an efficient method to calculate the BBH merger rates in the Shark host galaxies, to determine the combination of COMPAS parameters that outputs a population most like the GW sources from the LIGO, Virgo, and KAGRA (LVK) transient catalogue. For our COMPAS models, we calculate the likelihood with and without the dependence on the predicted number of BBH merger events. We find strong correlations between hyper-parameters governing the specific angular momentum (AM) of mass lost during mass transfer, the mass-loss rates of Wolf–Rayet stars via winds and the chemically homogeneous evolution (CHE) formation channel. We conclude that analysing the marginalized and unmarginalized likelihood is a good indicator of whether the population parameters distribution and number of observed events reflect the LVK data. In doing so, we see that the majority of the models preferred in terms of the population-level parameters of the BBHs greatly overpredict the number of events we should have observed to date. Looking at the smaller number of models that perform well with both likelihoods, we find that those with no CHE, AM loss occurring closer to the donor during the first mass-transfer event, and/or higher rates of mass-loss from Wolf–Rayet winds are generally preferred by current data. We find these conclusions to be robust to our choice of selection criteria.