Neurodegenerative diseases involve progressive neuronal dysfunction, requiring the identification of specific pathological features for accurate diagnosis. While cerebrospinal fluid analysis and neuroimaging are commonly used, their invasive nature and high costs limit clinical applicability. Recently advances in plasma proteomics offer a less invasive and cost-effective alternative, further enhanced by machine learning (ML). However, most ML-based studies overlook synergetic effects from protein-protein interactions (PPIs), which play a key role in disease mechanisms. Although graph convolutional network and its extensions can utilize PPIs, they rely on locality-based feature aggregation, overlooking essential components and emphasizing noisy interactions. Moreover, expanding those methods to cover broader PPIs results in complex model architectures that reduce explainability, which is crucial in medical ML models for clinical decision-making. To address these challenges, we propose Protein-Protein Interaction-based eXplainable Graph Propagational Network (PPIxGPN), a novel ML model designed for plasma proteomic profiling of neurodegenerative biomarkers. PPIxGPN captures synergetic effects between proteins by integrating PPIs with independent effects of proteins, leveraging globality-based feature aggregation to represent comprehensive PPI properties. This process is implemented using a single graph propagational layer, enabling PPIxGPN to be configured by shallow architecture, thereby PPIxGPN ensures high model explainability, enhancing clinical applicability by providing interpretable outputs. Experimental validation on the UK Biobank dataset demonstrated the superior performance of PPIxGPN in neurodegenerative risk prediction, outperforming comparison methods. Furthermore, the explainability of PPIxGPN facilitated detailed analyses of the discriminative significance of synergistic effects, the predictive importance of proteins, and the longitudinal changes in biomarker profiles, highlighting its clinical relevance.

Digitalization has shaped the relationships among companies, consumers, and social participants with the advancements of digital technology. As a result, companies are increasingly adopting and integrating the YouTube system for content dissemination and engagement with their customers and stakeholders. This study proposes a prediction model that analyzes 16 278 image datasets collected from 137 brand channels’ videos with over 100 000 views, generated before September 26, 2022. Using the dataset, we analyze the factors affecting the number of content views of brand channels and construct a view prediction model. This study finds that the characteristics of the thumbnail image, offline top brand characteristics, and channel size (number of subscribers and number of channel videos) significantly influence YouTube’s online channel views. The results of this study provide a strategy for brands to communicate more actively with stakeholders through the YouTube system.

Summary Learning and analyzing graph data is one of the most fundamental research areas in machine learning and data mining. Among numerous graph‐based data structures, this paper focuses on a graph bag (simply, bag), which corresponds to a training object containing one or more graphs, and a label is available only for a bag. This type of a bag can represent various real‐world objects such as drugs, web pages, XML documents, and images, among many others, and there have been many researches on models for learning this type of bag data. Within this research context, we define a novel problem of dynamic graph bag classification, and propose an algorithm to solve this problem. Dynamic bag classification aims to build a classification model for bags, which are presented in a streaming fashion, ie, frequent emerging of new bags or graphs over time. Given such changes made to the bag dataset, our proposed algorithm aims to update incrementally the top‐m most discriminative features instead of searching for them from scratch. Incremental gSpan and incremental gScore are proposed as core parts of our algorithm to deal with a stream of bags efficiently. We evaluate our algorithm on two real‐world datasets in terms of both feature selection time and classification accuracy. The experimental results demonstrate that our algorithm derives an informative feature set much faster than the existing one originally designed for targeting static bag data, with little accuracy loss.

We propose the multicarrier on-off keying (MC-OOK) as a bandwidth-efficient modulation method for frequency-hopped multiple-access (FHMA) communications. The motivation for using MC-OOK is that a more bandwidth-efficient modulation scheme allows a larger number of frequency slots, and thus provides a higher immunity against multiple-access interference in FHMA systems. We analyze the average bit-error probability in slow frequency-nonselective Rayleigh fading channels with background noise. We find that the capacity gain that MC-OOK/FHMA system provides over the MFSK/FHMA system in an interference-limited region is more than 2.5 when the modulation alphabet size M is 8, and even a higher capacity gain can be obtained with a larger M.

Presents our finding of a critical point that has not been reported thus far in the inverse kinematics of redundant manipulators under inequality constraints. The critical point, named algorithmic barrier was first encountered in the form of peculiar phenomena in our experiment with an 8-DOF robot, which we believe to have significance in the order of so-called algorithmic singularity. In addition, the paper deals with the characteristics of optimal solutions (COS) in resolving manipulator redundancy under inequality constraints. In order to analyze the COS, analytic functions of sufficient conditions and critical point conditions are derived. As a result, we find that COS is drastically affected by the introduction of inequality constraints. That is, COS under no inequality constraints is known to change only at algorithmic singularity while COS under inequality constraints turns out to change at semi-singularity and algorithmic barrier as well as algorithmic singularity. We prove the existence the critical points and present their analytical properties by using a planar 3-DOF manipulator.

For resolving the manipulator redundancy under inequality constraints, the compact QP method (including the improved Compact QP method) is very effective and efficient. From a viewpoint of practical applications, however, it turns out, we found, to have some performance limitations such as unrealistically high torque due to joint angle limit constraints and tracking errors due to joint torque limit constraints under parameter variation and disturbance. Remedying the limitations, the enhanced compact QP method is developed by using the practical inequality constraints with p-step-ahead predictor and time delay estimation. Through dynamic simulation results, it is verified that the enhanced compact QP method significantly improves the compact QP method in terms of efficiency and effectiveness for the real-time control of redundant manipulators under physical limits.

We propose a motion planning method for a mobile manipulator to execute a multiple task which consists of a sequence of point-to-point tasks. In the case of multiple tasks, because the motion trajectories between tasks and the commutation configurations are closely related, we formulate the problem as a global non-linear optimization one which takes into account both the motion trajectories and the commutation configurations. In order to simplify the problem, we divide the problem into two subproblems. At the first step, we obtain the motion trajectories assuming that the commutation configurations are given and at the next step, we obtain the optimal commutation configurations by using genetic algorithms. A series of computer simulation results are demonstrated to verify the effectiveness of the proposed method.

A two-dimensional array code, referred to as product code, has been extensively used to combat the errors in digital image recording systems such as digital VCR. An error-control coding scheme for multispeed play of digital video cassette recorder (DVCR) is proposed and analyzed. The proposed coding scheme is shown to yield a significant performance improvement over the conventional scheme with only a minor or no increase, depending on the play mode, in decoding time. A computer simulation is done on DCT-based quantized images to verify our analysis.>