Search Results (142)

The zero-error capacity of discrete memoryless channels (DMCs), introduced by Shannon, is a fundamental concept in information theory with significant operational relevance, particularly in settings where even a single transmission error is unacceptable. Despite its importance, no general closed-form expression or algorithm is known for computing this capacity. In this work, we investigate the computability-theoretic boundaries of the zero-error capacity and establish several fundamental limitations. Our main result shows that the zero-error capacity of noisy channels is not Banach–Mazur-computable and therefore is also not Borel–Turing-computable. This provides a strong form of non-computability that goes beyond classical undecidability, capturing the inherent discontinuity of the capacity function. As a further contribution, we analyze the deep connections between (i) the zero-error capacity of DMCs, (ii) the Shannon capacity of graphs, and (iii) Ahlswede’s operational characterization via the maximum-error capacity of 0–1 arbitrarily varying channels (AVCs). We prove that key semi-decidability questions are equivalent for all three capacities, thus unifying these problems into a common algorithmic framework. While the computability status of the Shannon capacity of graphs remains unresolved, our equivalence result clarifies what makes this problem so challenging and identifies the logical barriers that must be overcome to resolve it. Together, these results chart the computational landscape of zero-error information theory and provide a foundation for further investigations into the algorithmic intractability of exact capacity computations. Full article

Background/Objectives: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. Methods: Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available (n = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. Results: The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. Conclusions: This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance. Full article

In the processing of industry front-end waste, the reactor plays a critical role as a key piece of equipment, making its operational status monitoring essential. However, in practical applications, issues such as equipment aging, data transmission failures, and storage faults often lead to data loss, which affects monitoring accuracy. Traditional methods for handling missing data, such as ignoring, deleting, or interpolation, have various shortcomings and struggle to meet the demand for accurate data under complex operating conditions. In recent years, although artificial intelligence-based machine learning techniques have made progress in data imputation, existing methods still face limitations in capturing the coupling relationships between the sequential and channel dimensions of time series data. To address this issue, this paper proposes a time series decoupling-based data imputation model, referred to as the Decomposite-based Transformer Model (DTM). This model utilizes a time series decoupling method to decompose time series data for separate sequential modeling and employs the proposed MixTransformer module to capture channel-wise information and sequence-wise information, enabling deep modeling. To validate the performance of the proposed model, we designed data imputation experiments under two fault scenarios: random data loss and single-channel data loss. Experimental results demonstrate that the DTM model consistently performs well across multiple data imputation tasks, achieving leading performance in several tasks. Full article

At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision. Full article

Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that VideoMamba enhances with attention and learnable Fourier transform (CFVM) for SD identification. With a lower computational complexity and feature extraction capabilities comparable to transformers, VideoMamba offers the CFVM model a stronger feature extraction basis. The channel attention mechanism (CAM) block can achieve information exchange between channels. Through matrix eigenvalue manipulation, the learnable nonlinear Fourier transform (LNFT) block may guarantee stable convergence of the model. Furthermore, the LNFT block can efficiently use mixed frequency domain channels to capture global dependency information. The model is trained using the aluminum electrolysis fire hole dataset. Compared with recent fire hole identification models that primarily rely on neural networks, the method proposed in this paper is based on the concept of state space modeling, offering lower model complexity and enhanced feature extraction capability. Experimental results demonstrate that the proposed model achieves competitive performance in fire hole video identification tasks, reaching an identification accuracy of 85.7% on the test set. Full article

The accurate identification of combustion status can effectively improve the efficiency of municipal solid waste incineration and reduce the risk of secondary pollution, which plays a key role in promoting the sustainable development of the waste treatment industry. Due to the low accuracy of the incinerator flame combustion state recognition in the current municipal solid waste incineration process, this paper proposes a Res-Transformer flame combustion state recognition model based on three feature enhancement strategies. In this paper, Res-Transformer is used as the backbone network of the model to effectively integrate local flame combustion features and global features. Firstly, an efficient multi-scale attention module is introduced into Resnet, which uses a multi-scale parallel sub-network to establish long and short dependencies. Then, a deformable multi-head attention module is designed in the Transformer layer, and the deformable self-attention is used to extract long-term feature dependencies. Finally, we design a context feature fusion module to efficiently aggregate the spatial information of the shallow network and the channel information of the deep network, and enhance the cross-layer features extracted by the network. In order to verify the effectiveness of the model proposed in this paper, comparative experiments and ablation experiments were conducted on the municipal solid waste incineration image dataset. The results showed that the Acc, Pre, Rec and F1 score indices of the model proposed in this paper were 96.16%, 96.15%, 96.07% and 96.11%, respectively. Experiments demonstrate the effectiveness and robustness of this method. Full article

Secure and privacy-preserving health status representation learning has become a critical challenge in clinical prediction systems. While deep learning models require substantial high-quality data for training, electronic health records are often restricted by strict privacy regulations and institutional policies, particularly during emerging health crises. Traditional approaches to data integration across medical institutions face significant privacy and security challenges, as healthcare providers cannot directly share patient data. This work presents MultiProg, a secure federated learning framework for clinical representation learning. Our approach enables multiple medical institutions to collaborate without exchanging raw patient data, maintaining data locality while improving model performance. The framework employs a multi-channel architecture where institutions share only the low-level feature extraction layers, protecting sensitive patient information. We introduce a feature calibration mechanism that ensures robust performance even with heterogeneous feature sets across different institutions. Through extensive experiments, we demonstrate that the framework successfully enables secure knowledge sharing across institutions without compromising sensitive patient data, achieving enhanced predictive capabilities compared to isolated institutional models. Compared to state-of-the-art methods, our approach achieves the best performance across multiple datasets with statistically significant improvements. Full article

Hemodynamic homeostasis is essential for adapting the heart rate (HR) to postural and physiological changes during daily activities. Traditional HR monitoring, such as 24 hour (h) Holter monitoring, provides important information on homeostasis during daily living. However, this approach lacks concurrent activity recording, limiting insights into hemodynamic adaptation and our ability to interpret changes in HR. To address this, we utilized a novel wearable sensor system (ANNE^@Sibel) to capture time-locked HR and daily activity (i.e., lying, sitting, standing, walking) data in 105 community-dwelling older adults. We developed custom tools to extract 24 h time-locked measurements and introduced a “heart rate response score” (HRRS), based on root Jensen–Shannon divergence, to quantify HR changes relative to activity. As expected, we found a progressive HR increase with more vigorous activities, though individual responses varied widely, highlighting heterogeneous HR adaptations. The HRRS (mean: 0.38 ± 0.14; min: −0.11; max: 0.74) summarized person-specific HR changes and was correlated with several clinical measures, including systolic blood pressure changes during postural transitions (r = 0.325, p = 0.003), orthostatic hypotension status, and calcium channel blocker medication use. These findings demonstrate the potential of unobtrusive sensors in remote phenotyping as a means of providing valuable physiological and behavioral data to enhance the quantitative description of aging phenotypes. This approach could enhance personalized medicine by informing targeted interventions based on hemodynamic adaptations during everyday activities. Full article

Background/Objectives: Understanding health information-seeking behavior is critical in providing effective interventions and improving quality of life for patients, especially those facing complex diagnoses like cancer. The purpose of this study is to understand rural–urban differences in trust levels for various information sources and how trust may differ by cancer status (no cancer, newly diagnosed, survived for six and more years). Methods: We examined 5775 responses from the 2022 Health Information National Trends Survey^®. Using the component analysis, eight sources of information were classified into three domains: structured (doctor, government, scientist, and charity), less structured (family and religion), and semi-structured (health system and social media). Respondents answered questions on a scale of 1–4. Weighted linear regression models were constructed to examine trust level in three domains by rural residency and cancer status, while adjusting for demographic and socioeconomic status. Results: Urban patients reported higher trust in more structured sources of information (2.999 > 2.873, p = 0.005) whereas rural counterparts reported higher trust in less structured sources of information (2.241 > 2.153, p = 0.012). After adjusting for covariates, urban respondents with cancer are more likely to trust doctors (Coeff. = 0.163, p < 0.001) than those without cancer. Rural respondents with cancer are less likely to trust charities (Coeff. = −0.357, p < 0.01) and scientists (Coeff. = −0.374, p < 0.05) than rural respondents without cancer. Conclusions: Newly diagnosed cancer patients in rural areas are less likely to trust structured sources of information even after adjusting for all covariates. Additional studies about misinformation and disinformation being channeled through less structured sources of information are needed to prevent any delay in care among cancer patients, especially rural patients who are more likely to access these sources of information. Full article

Visible Light Communication (VLC) has the potential to advance Intelligent Transportation Systems (ITS). This study explores the current advancements of VLC in ITS applications that may enhance traffic flow, road safety, and vehicular communication performance. The potential, benefits, and current research trends of VLC in ITS applications are discussed first. Then, the state-of-the-art VLC technologies including overall concept, IEEE communication protocols, hybrid VLC systems, and software-defined adaptive MIMO VLC systems, are discussed. We investigated different potential applications of VLC in ITS, such as signalized intersection and ramp metering control, collision warning and avoidance, vehicle localization and detection, and vehicle platooning using vehicle–vehicle (V2V), infrastructure–vehicle (I2V), and vehicle–everything (V2X) communications. Besides, VLC faces several challenges in ITS applications, and these concerns, e.g., environmental issues, communication range issues, standards and infrastructure integration issues, light conditions and integration issues are discussed. Finally, this paper discusses various advanced techniques to enhance VLC performance in ITS applications, such as machine learning-based channel estimation, adaptive beamforming, robust modulation schemes, and hybrid VLC integration. With this review, the authors aim to inform academics, engineers, and policymakers about the status and challenges of VLC in ITS. It is expected that, by applying VLC in ITS, mobility will be safer, more efficient, and sustainable. Full article

The aim of this study was to assess the association between the use of and trust in sources of information, knowledge about human papillomavirus (HPV) and vaccines against it, perceived lack of information, and the decision to receive the HPV vaccine in undergraduate students in Belgrade. The sample of this cross-sectional study included students aged 18 to 27 who received the second dose of the HPV vaccine or used other services of the general medicine department at the Institute for Students’ Health of Belgrade during the period June–July 2024. The research instrument was a questionnaire consisting of socio-demographic data, information environment (sources of information, trust in sources of information, as well as questions related to perceived lack of information), knowledge about HPV and HPV vaccines, and vaccination status. Participants filled out an online questionnaire created on the RedCap platform of the Faculty of Medicine, University of Belgrade, which they accessed via a QR code. Hierarchical logistic regression was used to assess the association between vaccine status and socio-demographic characteristics, use and trust in information sources, knowledge, and perceived lack of information. Of the 603 participants who filled out the questionnaire completely, 78.6% were vaccinated against HPV. Key factors associated with vaccine uptake were female gender (OR = 2.33, p < 0.05), use of scientific literature (OR = 1.40, p < 0.05) and family as a source of information (OR = 1.37, p < 0.01), less frequent use of regional TV channels (OR = 0.76, p < 0.05), higher level of knowledge (OR = 1.43, p < 0.01), and lower perceived lack of information (OR = 0.50, p < 0.01). These variables explained 41% of variability in vaccine uptake in the multivariate hierarchical logistic regression model. Exposure to and trust in sources of information were significantly associated with knowledge about HPV and HPV vaccination, as well as with the perceived lack of information regarding HPV vaccination, and were the most significant determinants of the decision to accept HPV vaccine in the student population. Full article

Calorimetry at the High Luminosity LHC (HL-LHC) faces many challenges, particularly in the forward direction, such as radiation tolerance and large in-time event pileup. To meet these challenges, the CMS Collaboration is preparing to replace its current endcap calorimeters from the HL-LHC era with a high-granularity calorimeter (HGCAL), featuring an unprecedented transverse and longitudinal segmentation, for both the electromagnetic and hadronic compartments, with 5D information (space–time–energy) read out. The proposed design uses silicon sensors for the electromagnetic section (with fluences above ${10}^{16} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) and high-irradiation regions (with fluences above ${10}^{14} {\mathrm{n}}_{eq}{\mathrm{/cm}}^2$) of the hadronic section, while in the low-irradiation regions of the hadronic section, plastic scintillator tiles equipped with on-tile silicon photomultipliers (SiPMs) are used. Full HGCAL will have approximately 6 million silicon sensor channels and about 280 thousand channels of scintillator tiles. This will allow for particle-flow-type calorimetry, where the fine structure of showers can be measured and used to enhance particle identification, energy resolution and pileup rejection. In this overview we present the ideas behind HGCAL, the current status of the project, results of the beam tests and the challenges that lie ahead. Full article

Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling ‘On’ and ‘Off’ states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications. Full article

In forest ecosystems, the application of hyperspectral (HS) imagery offers unprecedented opportunities for refined identification and classification. The diversity and complexity of forest cover make it challenging for traditional remote-sensing techniques to capture subtle spectral differences. Hyperspectral imagery, however, can reveal the nuanced changes in different tree species, vegetation health status, and soil composition through its nearly continuous spectral information. This detailed spectral information is crucial for the monitoring, management, and conservation of forest resources. While Convolutional Neural Networks (CNNs) have demonstrated excellent local context modeling capabilities in HS image classification, their inherent network architecture limits the exploration and representation of spectral feature sequence properties. To address this issue, we have rethought HS image classification from a sequential perspective and proposed a hybrid model, the Spectral Weaver, which combines CNNs and Transformers. The Spectral Weaver replaces the traditional Multi-Head Attention mechanism with a Channel Attention mechanism (MCA) and introduces Centre-Differential Convolutional Layers (Conv2d-cd) to enhance spatial feature extraction capabilities. Additionally, we designed a cross-layer skip connection that adaptively learns to fuse “soft” residuals, transferring memory-like components from shallow to deep layers. Notably, the proposed model is a highly flexible backbone network, adaptable to both hyperspectral and multispectral image inputs. In comparison to traditional Visual Transformers (ViT), the Spectral Weaver innovates in several ways: (1) It introduces the MCA mechanism to enhance the mining of spectral feature sequence properties; (2) It employs Centre-Differential Convolutional Layers to strengthen spatial feature extraction; (3) It designs cross-layer skip connections to reduce information loss; (4) It supports both multispectral and hyperspectral inputs, increasing the model’s flexibility and applicability. By integrating global and local features, our model significantly improves the performance of HS image classification. We have conducted extensive experiments on the Gaofen dataset, multispectral data, and multiple hyperspectral datasets, validating the superiority of the Spectral Weaver model in forest hyperspectral image classification. The experimental results show that our model achieves 98.59% accuracy on multispectral data, surpassing ViT’s 96.30%. On the Jilin-1 dataset, our proposed algorithm achieved an accuracy of 98.95%, which is 2.17% higher than ViT. The model significantly outperforms classic ViT and other state-of-the-art backbone networks in classification performance. Not only does it effectively capture the spectral features of forest vegetation, but it also significantly improves the accuracy and robustness of classification, providing strong technical support for the refined management and conservation of forest resources. Full article

In response to the poor performance of long-distance small target recognition tasks and real-time intelligent monitoring, this paper proposes a deep learning-based recognition method aimed at improving the ability to recognize and monitor various behaviors of captive ewes. Additionally, we have developed a system platform based on ELFN-YOLO to monitor the behaviors of ewes. ELFN-YOLO enhances the overall performance of the model by combining ELFN with the attention mechanism CBAM. ELFN strengthens multiple layers with fewer parameters, while the attention mechanism further emphasizes the channel information interaction based on ELFN. It also improves the ability of ELFN to extract spatial information in small target occlusion scenarios, leading to better recognition results. The proposed ELFN-YOLO achieved an accuracy of 92.5%, an F1 score of 92.5%, and a mAP@0.5 of 94.7% on the ewe behavior dataset built in commercial farms, which outperformed YOLOv7-Tiny by 1.5%, 0.8%, and 0.7% in terms of accuracy, F1 score, and mAP@0.5, respectively. It also outperformed other baseline models such as Faster R-CNN, YOLOv4-Tiny, and YOLOv5s. The obtained results indicate that the proposed approach outperforms existing methods in scenarios involving multi-scale detection of small objects. The proposed method is of significant importance for strengthening animal welfare and ewe management, and it provides valuable data support for subsequent tracking algorithms to monitor the activity status of ewes. Full article