Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progression. However, the high dimensionality of multi-metric data with diverse structural forms, the heterogeneity of neuroimaging and phenotypic data, and class imbalance collectively pose significant challenges to early ND diagnosis. To address these challenges, we propose a dynamically weighted dual graph attention network (DW-DGAT) that integrates: (1) a general-purpose data fusion strategy to merge three structural forms of multi-metric data; (2) a dual graph attention architecture based on brain regions and inter-sample relationships to extract both micro- and macro-level features; and (3) a class weight generation mechanism combined with two stable and effective loss functions to mitigate class imbalance. Rigorous experiments, based on the Parkinson Progression Marker Initiative (PPMI) and Alzhermer's Disease Neuroimaging Initiative (ADNI) studies, demonstrate the state-of-the-art performance of our approach.

As an essential component of the Industrial Internet of Things (IIoT), lights-out manufacturing (LoM) relies heavily on the rapid detection of anomalies. However, LoM anomalies often arise from complex, cross-modal correlations, and traditional detection models struggle with the dual challenges of limited local data and stringent data privacy requirements, leading to poor generalization. To address these challenges, this paper introduces a novel Federated Graph Process Distillation framework (Fed-GPD) for multi-modal anomaly detection. Our approach first represents heterogeneous industrial data as unified graph structures to effectively model the underlying device relationships. We then propose a new graph knowledge distillation paradigm designed for Graph Neural Networks (GNNs) in a federated setting. Instead of merely distilling final predictions, we introduce two novel distillation mechanisms: 1) Neighborhood Aggregation Process Distillation (NAPD), which transfers the knowledge of how a model processes local neighborhood information at each GNN layer, and 2) Relational Knowledge Matrix Distillation (RKMD), which aligns the global understanding of node-to-node relationships learned by the models. These mechanisms are integrated into an asynchronous mentor-mentee architecture, enabling efficient and deep knowledge transfer from powerful, private mentor models to a lightweight, global mentee model. Simulation results on multiple real-world datasets demonstrate that Fed-GPD significantly outperforms existing federated and graph-based anomaly detection methods. Notably, our in-depth ablation studies validate the effectiveness of the proposed process distillation mechanisms, showing substantial improvements in model accuracy and communication efficiency.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-view data learning has become an essential research area due to the capacity of multiple views to provide complementary insights from various perspectives. However, the discontinuous distribution, data missing complexity, small sample size, and redundant features in multi-view datasets pose a substantial obstacle, and most existing multi-view learning methods are unable to handle these challenges effectively. In this study, we propose a novel incomplete multi-view data learning framework (IMVDL) via dynamic embedding and partiall2,1norm constraints for PD diagnosis. Specifically, multi-view dynamic embedding can adapt to any view missing scene, thereby linearly/nonlinearly mapping incomplete multi-view data to low-dimensional manifold spaces and generating complete multi-view data representations. The partiall2,1norm constraint can ignore larger feature weight values and performl2,1norm sparse on the remaining weights, thereby avoiding the sparse bias problem caused by larger weight values. An efficient iterative algorithm is derived to find the optimal solution of the IMVDL method. We conduct extensive experiments using multi-modal neuroimage data from the Parkinson's Progression Markers Initiative (PPMI) database. The results demonstrate that the IMVDL method is superior to other comparative methods. The source code for IMVDL is available at https://github.com/a610lab/IMVDL/.

Deep reinforcement learning (DRL) has shown significant potential in adaptive traffic signal control (ATSC) by adapting to real-time traffic conditions. However, controlling multiple intersections faces challenges, mainly due to the isolated actions of agents and non-stationary caused by other intersections. To address these issues, this paper proposes a novel knowledge collaboration-based actor-critic policy gradient (KCACPG) method to achieve cooperative traffic scheduling across multiple intersections. KCACPG includes a knowledge collaboration learning mechanism that allows heterogeneous agents to exchange knowledge across experience tuples, achieving globally optimal decision-making and coordination. KCACPG also integrates an off-policy prioritized experience replay mechanism to improve knowledge reuse efficiency and reduce the negative impact of knowledge transfer. Simulation results show that KCACPG converges quickly, generalizes to fluctuant traffic and load well, improves the network throughput by up to 17.8%, and reduces the pressure imbalance by up to 11.6% compared with the existing collaborative methods. The proposed method has significant implications for intelligent transportation systems and smart cities.

Parkinson's disease (PD) is a progressive and currently incurable neurological disorder, where early diagnosis plays a critical role in slowing disease progression. Multi-metric data from multimodal neuroimages provide complementary perspectives that can enhance early PD diagnosis. In this paper, we propose a Self-Supervised Learning Dual Attention Network (SSL-DAN) to address the uncertainty of key metrics and brain regions of interest (ROIs), the global dependencies among ROIs within each metric, and the information conflicts arising from the multi-branch architecture. Extensive experiments conducted on the Parkinson's Progression Markers Initiative study demonstrate the effectiveness of the proposed method.

Alzheimer's Disease (AD) progression involves complex pathological cascades through brain networks. Current neuroimaging approaches analyze connectivity modalities in isolation, lack frameworks for integrating clinical knowledge, and ignore directional causal relationships. We propose PKFA-GCN, a multimodal graph convolutional network through: (1) Feature Space Embedding Alignment (FSEA) for cross-modal fusion, (2) Prior knowledge-guided region selection, and (3) Effective connectivity integration. Experiments on ADNI dataset (396 subjects) achieve: 94.56% (AD vs NC),$85.67\%$(AD vs MCI), 91.34% (MCI vs NC), and 86.12% (three-way classification), outperforming 12 methods. Visualization analyses confirm consistency with known AD pathophysiology.

Design for disassembly (DFD) is a basic design technology serving the scrap and recycling stages, which can relieve the environmental pressure and improve the economic benefits. Considering the wide variety of mechanical products and the shortening of product lifecycle, the product design integrating customer requirements can effectively improve the market competitiveness of products. Thus, this paper proposes a QFD-based human-centric decision-making approach for disassembly scheme selection. The theory of the interval 2-tuple q-rung orthopair fuzzy sets (I2q-ROFSs) is proposed to better describe the ambiguity of the environment and avoid information loss/distortion in the information aggregation stage. A scheme evaluation system based on the disassembly technical features is established. A hybrid multi-attribute decision-making (MADM) method combing BWM-FQFD (best worst method and fuzzy quality function deployment) and RT-EDAS (the regret theory based on distance from average solution) is presented to obtain the optimal alternative. A case study, i.e., four refrigerator DFD schemes, is applied to verify the effectiveness of the proposed method. The result demonstrates that this work provides an effective tool to select the optimal scheme of disassembly considering customer requirements and some references for designersNote to Practitioners—The selection of DFD schemes is the last task of the disassembly design. The decision information of the disassembly design scheme has insufficient evaluation complexity ability and the information distortion of the evaluation information in the aggregation stage. To end this, this paper proposes I2q-ROFSs which is a fuzzy set with the higher level of fuzzy description and avoiding information distortion. At present, the disassembly design research lacks the correlation between customer demand and disassembly technology features. Therefore, a fuzzy QFD method can well assist manufacturing enterprises to improve the market competitiveness of their products. Then, RT-EDAS can describe consumer psychology, which can choose a more market-appropriate DFD scheme.

Alzheimer’s disease is a neurodegenerative disorder that gradually impairs cognitive abilities. Early detection, diagnosis, and treatment are crucial for slowing the progression of the disease. In the diagnosis of Alzheimer’s disease, Graph Convolutional Networks (GCN) provide a powerful tool to enhance accuracy. However, the training of GCN faces challenges due to the tedious annotation process and limited data.To address this issue, we employ contrastive learning for pre-training GCN to improve classification performance under limited data conditions. Firstly, we augment graph data through singular value decomposition, preserving the brain’s primary topological structure and avoiding the loss of intrinsic semantic structure during augmentation. Secondly, we design a Soft-Clustering GCN to obtain more robust representations of brain data. Lastly, our framework clusters graphs with similar feature semantics into the same group and encourages clustering consistency between different augmentations of the same graph. In negative sampling, we select graphs from different groups as negative samples to ensure semantic differences between positive and negative samples. Experimental results demonstrate that our approach outperforms state-of-the-art methods on the Alzheimer’s disease dataset.

Parkinson’s disease (PD) is a common neurodegenerative disorder in the elderly population. The progressive symptoms of PD can have significant physical and economic implications for patients. Therefore, the development of a method to aid in the diagnosis and prediction of PD is crucial. However, medical neuroimaging data often have redundant features and high data dimensions, which can negatively impact algorithm accuracy. To solve this challenge, a supervised algorithm for feature selection is proposed for the early diagnosis and prediction of PD. Specifically, the proposed method incorporates adaptive learning during iterations, which allows adaptive updating of the similarity matrix and selection of informative features. Meanwhile, we introduce flexible mapping to address the limitation that linear mapping is too strict. To measure the effectiveness of the algorithm, we test it on the Parkinson’s Progression Markers Initiative (PPMI) public dataset. According to the outcomes of the experiment, the proposed method outperforms the competing feature selection methods and graph neural network approaches.

In future 6G-enabled Internet of Things (IoT), users and devices will be divided into numerous distributed domains with smaller base station coverage due to the utilization of terahertz high-frequency band communication. Deep reinforcement learning (DRL) agents will be increasingly deployed in the domain to achieve intelligent service provisioning and resource allocation. However, the existing DRL-based method faces the problem of repeated model training and poor generalization ability when service demand fluctuates and environmental changes occur. In addition, limited training samples in each domain also lead to insufficient model training. Inspired by the collaborative learning of human knowledge, we propose a knowledge collaboration-based resource allocation mechanism for future 6G-enabled IoT and address two basic issues: 1) which agent should collaborate with and 2) how to collaborate. Specifically, we first model the distributed network as a graph and use graph attention (GAT) to capture the fluctuant service demands and time-varying resource capacities in temporal and spatial domains, and then calculate the similarity between the agents. We further propose a collective reinforcement learning (CRL) algorithm that facilitates knowledge collaboration between the agents through the policy distribution. Simulation results verify that the proposed GAT-CRL achieves fast convergence as deep deterministic policy gradient (DDPG) in 4K steps, computing the similarity score more accurately with the increasing attention heads, and achieves higher successful flow than the soft actor-critic (about 3.6%–5.4%) and DDPG (about 14.6%–21%) when adapting to unseen traffic patterns/loads and increasing topology scales.

Network function virtualization (NFV) has attracted attention because of its flexible configuration and management of network functions. Based on NFV, the service function chain (SFC) defines a group of virtual network functions (VNFs) connected sequentially, enabling flexible customization and provisioning of network services. In the large-scale and heterogeneous Internet of Things (IoT) environment, e.g., industrial IoT, servers provided by a single infrastructure provider (InP) cannot support the deployment of all VNFs, and SFCs must be deployed across multiple domains. However, SFCs deployed across multiple domains will inevitably bring privacy leakage and resource coordination difficulties, thereby reducing the efficiency of network services. To address these issues, this paper proposes a privacy-preserving deployment mechanism (PPDM) for SFCs that achieves near-optimal SFC deployment across multiple domains while protecting resource and topology privacy. PPDM first performs virtual resource prediction and forms the service intention response matrix (SIRM) based on SFC requests (SFCRs). Second, the multi-domain controller (MDC) discovers a near-optimal SFCs deployment strategy by deep Q-network (DQN) using SIRM as input to protect domains’ privacy. Finally, the learned strategies are distributed to intra-domain controllers (IDCs) to implement specific services. Simulation results demonstrate that the proposed method outperforms privacy-preserving and non-privacy-preserving methods.

The emerging computing paradigms offer effective resolutions for the escalating conflict arising from the heightened computational demands of portable terminals and their constrained capacity. Concurrently, the architecture has transitioned from a single-tier structure to a multi-tier collaborative framework, enhancing flexibility and enabling fine-grained computation offloading. Nevertheless, existing research on multi-tier computation offloading faces challenges, including inefficient resource perception and task decomposition; there is a notable absence of an effective hierarchical task scheduling strategy within the multi-tier collaborative architecture. To bridge these gaps, our paper investigates the multi-granularity task decomposition and hierarchical task scheduling in a cloud-edge-end collaborative computing network. We first introduce a large-small resource tree (LST) model to facilitate efficient resource perception across three-tier network nodes. Then we propose a multi-granularity task decomposition algorithm (MTDA) based on long short-term memory (LSTM) network resource prediction to fully utilize the distributed node resources. Finally, we propose a parallelized LST-DDQN task offloading algorithm to maximize the delay and energy consumption weighted utility function. Simulation results demonstrate the efficacy of our proposed task decomposition and parallel scheduling methods, showcasing a reduction in utility by approximately 6.31% to 13.01% compared to baseline algorithms.

Parkinson’s disease (PD) is a neurodegenerative geriatric disease commonly occurring in middle-aged and elderly adults. Since PD is irreversible and its treatment only slows down its rate of development, the early diagnosis by accurate prediction is of great significance to retard its deterioration. However, the existing computer-assisted diagnosis methods for PD have limitations in exploring the implicit and spatial information in the brain. In view of this limitation, a 3D network based on self-supervised learning strategy and attention mechanism is proposed for PD classification in this paper. The proposed method put the input of successive frames from the preprocessed magnetic resonance imaging (MRI) data into 3D ResNet18 with the classifier module for PD classification. Specifically, the attention mechanism is used to explore the discriminative features. Meanwhile, a self-supervised learning pretext task and a regression task are designed to assist in training and improve the robustness of the proposed model. We use a 5-fold cross-validation strategy to corroborate our method’s effectiveness on the Parkinson's Progression Markers Initiative (PPMI) dataset. The experimental results indicate that our proposed method has achieved an accuracy of 87.50% for PD classification, which outperforms the most state-of-the-art deep learning methods.

Parkinson’s disease (PD), a common and irreversible neurodegenerative progressive disease, brings huge pain and economic burden to the patients and their families in the late stage. As the disease is incurable, its early diagnosis and treatment are of paramount importance to ameliorate its deterioration. In this paper, we adopt an attention-based graph neural network (AGNN) for early diagnosis of PD via diffusion tensor imaging (DTI) data and phenotypic information. Firstly, we construct a structural brain connectivity network for every subject of DTI data. Secondly, we construct a graph based on phenotypic information and feature similarity from the brain connectivity network. Thirdly, we input the graph into AGNN to get the final classification and prediction results. Our method is validated on the public available Parkinson’s Progression Markers Initiative (PPMI) datasets. The results demonstrate that our adopted AGNN method is practical to classify and predict early PD deterioration and better for selected algorithms, with a mean accuracy of 96.48% in our early PD classification tasks.

Parkinson’s disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient’s motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the${l}_{\mathrm {2,p}}$norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson’s Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

By replacing traditional hardware-based middleboxes with software-based Virtual Network Functions (VNFs) running on general-purpose servers, network function virtualization represents a promising technique to reduce the cost of service creation and increase the agility of network operations. Typically, Service Function Chains (SFCs) are adopted to orchestrate dynamical network services and facilitate management of network applications. Recently, SFC parallelism that implements parallel processing of VNFs has been investigated to further improve SFC service quality. However, the unreasonable service graph of parallel processing in existing parallelized SFCs (PSFCs) might cause excessive resource consumption; incoordination between PSFC deployment and scheduling also increases the queuing delay of VNFs and degrades PSFC performance. In this article, an adaptive parallel processing optimization mechanism (APPM) is proposed to self-adaptively adjust the service graph of PSFCs and intelligently solve the joint problem of PSFC deployment and scheduling. Specifically, APPM uses a parallelism optimization algorithm (POA) based on the bin packing problem with soft bin capacity to optimize the structure of the PSFC service graph. Afterward, APPM employs a joint optimization algorithm based on reinforcement learning (JORL) to jointly deploy and schedule the PSFCs optimized by POA via the online perception of environment status. Simulation results showed that POA reduces the SFC parallelism degree and resource consumption by about 35%; JORL lowers SFC delay by reducing the queuing delay and has better overall performance than the state of the art algorithms even with limited resources.

Parkinson's disease (PD) is an irreversible neurodegenerative disease that seriously affects patients' lives. To provide patients with accurate treatment in time and to reduce deterioration of the disease, it is critical to have an early diagnosis of PD and accurate clinical score predictions. Different from previous studies on PD, most of which only focus on feature selection methods, we propose a network combining joint learning from multiple modalities and relations (JLMMR) with sparse nonnegative autoencoder (SNAE) to further enhance the ability of feature expression. We first preprocess and extract features of the modal neuroimaging data with multiple time points. To extract discriminative and informative features from longitudinal data, we apply JLMMR method for feature selection to avoid over-fitting issues. We further exploit SNAE to learn longitudinal discriminative features for joint disease diagnosis and obtain clinical score predictions. Extensive experiments on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset show the proposed method produces promising classification and prediction performance, which outperforms state-of-the-art methods as well.

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

Neuroimaging techniques have been widely applied to various neurodegenerative disease analysis to reveal the intricate brain structure. The high dimensional neuroimaging features and limited sample size are the main challenges for the diagnosis task due to the unbalanced input data. To handle it, a sparse low-rank learning framework is proposed, which unveils the underlying relationships between input data and output targets by building a matrix-regularized feature network. Then we obtain the feature weight from the network based on local clustering coefficients. By discarding the irrelevant features and preserving the discriminative structured features, our proposed method can select the most relevant features and identify different stages of Parkinson's disease (PD) from normal controls. Extensive experimental results evaluated on the Parkinson's progression markers initiative (PPMI) dataset demonstrate that the proposed method achieves promising classification performance and outperforms the conventional algorithms. Furthermore, it can detect potential brain regions related to PD for future medical analysis.

Sparsity-based approaches have been considered useful for target detection in hyperspectral imagery. Based on the sparse reconstruction theory, the vectors representing the spectral signature of hyperspectral pixels can be a linear combination of linearly dependent training vectors. The training vectors constitute an overcomplete dictionary, which allow for sparse representations for test pixel vectors as only a few of training vectors are used. Such sparsity can be applied in hyperspectral target detection. However, since the sparse decomposition has the potential instability, similar data often have different estimates. In this letter, we propose a nonlocal similarity regularized sparsity model to deal with the problem. Nonlocal similarity enhances classical sparsity model as it preserves the manifold structure of original data and makes more stable estimations for similar data. In addition, the nonlocal sparsity model is effectively solved with a developed greedy algorithm. Experimental results suggest an advantage of the nonlocal sparsity model over conventional sparsity models and a better performance of the proposed algorithm compared with conventional sparsity-based algorithms.