Search Results (8,659)

Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience. Full article

Background: Augmented Reality (AR) technology provides a new way for product design and display due to its unique interaction, enabling customers to experience products more comprehensively and immersively. Objective: To study the interactive form and application of AR technology in the interior design and display of RV, to provide new ideas and solutions for the interior design of RV, to optimize the user experience of customers and improve the work efficiency in the process of customized interior design of RV. Methods: Qualitative and quantitative research methods were adopted, the target customer groups were investigated based on the KANO model, the data were analysed, and the design suggestions for the function and interactive interface of the RV interior design display platform were summarized. Results: Based on the investigation and research, the specific functions of the RV indoor experience design platform and the needs of the target user groups were identified, including 12 essential needs, 5 charm needs, 1 expectation needs and 2 undifferentiated needs, which correspond to different functions, respectively. Finally, the main features of the platform are outlined, and the functional framework of the RV interior design and display platform is drawn. Conclusions: The interactive form of AR RV interior design includes user interface design and interactive mode design, which can greatly enhance user experience, optimize space utilization and enhance customized services. The application of AR technology in RV design has a high potential, providing theoretical and practical support for the design and development of the RV AR RV design display platform. Full article

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE₅ of 1.26 and minFDE₅ of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics. Full article

The global VTuber market is experiencing rapid growth, with VTubers extending beyond mere content creators to be utilized in various fields such as social interaction, public relations, and health. VTubers have the potential to expand the existing content market and contribute to increasing economic and public value. This study aims to investigate the perception of VTubers in South Korea and to provide insights that can contribute to the global activation of the VTuber entertainment industry. For this purpose, unstructured data on VTubers from the past three years, during which interest in VTubers has significantly grown in South Korea, was collected. A total of 57,891 samples were gathered from Naver, Daum, and Google, of which 50 highly relevant data points between VTubers and users were selected for analysis. First, key terms such as ‘Broadcast’, ‘YouTube’, ‘Live’, ‘Game’, ‘Youtuber’, ‘Japan’, ‘Character’, ‘Video’, ‘Sing’, ‘Virtual’, ‘Woowakgood’, ‘Fan’, ‘Idol’, ‘Korea’, ‘Twitch’, ‘IsegyeIdol’, ‘Communication’, ‘Worldview’, ‘VTuberIndustry’, ‘Contents’, ‘AfricaTV’, ‘Nijisanji’, and ‘Streamer’ were extracted. Second, CONCOR analysis revealed four clusters: ‘Famous VTubers’, ‘Features of VTubers’, ‘VTuber Industry’, and ‘VTuber Platforms’. Based on these findings, the study offers various academic and practical implications regarding VTubers in South Korea and explores the potential for global growth in the VTuber industry. Full article

R-loops can act as replication fork barriers, creating transcription–replication collisions and inducing replication stress by arresting DNA synthesis, thereby possibly causing aberrant processing and the formation of DNA strand breaks. RNase H1 (RH1) is one of the enzymes that participates in R-loop degradation by cleaving the RNA strand within a hybrid RNA–DNA duplex. In this study, the kinetic features of the interaction of RH1 from Escherichia coli with R-loops of various structures were investigated. It was found that the values of the dissociation constants K_d were minimal for complexes of RH1 with model R-loops containing a 10–11-nt RNA–DNA hybrid part, indicating effective binding. Analysis of the kinetics of RNA degradation in the R-loops by RH1 revealed that the rate-limiting step of the process was catalytic-complex formation. In the presence of RNA polymerase, the R-loops containing a ≤16-nt RNA–DNA hybrid part were efficiently protected from cleavage by RH1. In contrast, R-loops containing longer RNA–DNA hybrid parts, as a model of an abnormal transcription process, were not protected by RNA polymerase and were effectively digested by RH1. Full article

In recent years, mobility as a service (MaaS) has been thought as one of the opportunities for shifting towards shared travel solutions with respect to private transport modes, particularly owned cars. Although many MaaS aspects have been explored in the literature, there are still issues, such as platform implementations, travel solution generation, and the user’s role for making an effective system, that require more research. This paper extends and improves a previous study carried out by the authors by providing more details and experiments. The paper proposes a diachronic network model for representing travel services available in a given MaaS platform by using an agent-based approach to simulate the interactions between travel operators and travelers. Particularly, the diachronic network model allows the consideration of both the spatial and temporal features of the available transport services, while the agent-based framework allows the representation of how shared services might be used and which effects, in terms of modal split, could be expected. The final aim is to provide insights for setting the architecture of an agent-based MaaS platform where transport operators would share their data for providing seamless travel opportunities to travelers. The results obtained for a simulated test case are promising. Particularly, there are interesting findings concerning the traffic congestion boundary values that would move users towards shared travel solutions. Full article

Ship image classification identifies the type of ships in an input image, which plays a significant role in the marine field. To enhance the ship classification performance, various research focuses on studying multi-modal ship classification, which aims at combining the advantages of visible images and infrared images to capture complementary information. However, the current methods simply concatenate features of different modalities to learn complementary information, which neglects the multi-level correlation between different modalities. Moreover, the existing methods require a large amount of labeled ship images to train the model. How to capture the multi-level cross-modal correlation between unlabeled and labeled data is still a challenge. In this paper, a novel semi-supervised multi-modal ship classification approach is proposed to solve these issues, which consists of two components, i.e., multi-level cross-modal interactive network and semi-supervised contrastive learning strategy. To learn comprehensive complementary information for classification, the multi-level cross-modal interactive network is designed to build local-level and global-level cross-modal feature correlation. Then, the semi-supervised contrastive learning strategy is employed to drive the optimization of the network with the intra-class consistency constraint based on supervision signals of unlabeled samples and prior label information. Extensive experiments on the public datasets demonstrate that our approach achieves state-of-the-art semi-supervised classification effectiveness. Full article

As an accessible and low-risk mode of transportation and recreational activity, walking both produces and is produced by socio-spatial urban features. The health benefits of walking transcend physical fitness, remaining integral to mental health and to fostering social connectedness in urban communities. Understanding what drives walking behaviour, therefore, warrants attention from a public health perspective. This qualitative case study focuses on the social interactions of inhabitants during neighbourhood walks and how built environment features influence walking patterns and experience. Using diaries, maps, and semi-structured interviews with 45 inhabitants of a mid-sized Canadian city, this research investigates the influence of permanent and temporary physical features on the perceived quality of inhabitants’ walks. The findings show the public visibility of urban modifications influences walking behaviour and improves social interactions, leading to a heightened sense of belonging and community. Inhabitant-led modifications in the urban space were mostly neighbourhood-bound and voyeuristic, whereas administrative interventions were more successful for collectivization. Both types of interventions are argued to foster social connectedness through different mechanisms, with positive impacts on inhabitants’ health and wellbeing. The findings underscore the relevance of community-led and administratively planned interventions in built environments in positioning public health policies associated with social cohesion and connectedness. Full article

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme. Full article

Tissue engineering is an interdisciplinary field that combines materials, methods, and biological molecules to engineer newly formed tissues to replace or restore functional organs. Biomaterials-based scaffolds play a crucial role in developing new tissue by interacting with human cells. Tissue engineering scaffolds with ideal characteristics, namely, nontoxicity, biodegradability, and appropriate mechanical and surface properties, are vital for tissue regeneration applications. However, current biocomposite scaffolds face significant limitations, particularly in achieving structural durability, controlled degradation rates, and effective cellular integration. These qualities are essential for maintaining long-term functionality in vivo. Although commonly utilized biomaterials can provide physical and chemical properties needed for tissue regeneration, inadequate biomimetic properties, as well as insufficient interactions of cells-scaffolds interaction, still need to be improved for the application of tissue engineering in vivo. It is impossible to achieve some essential features using a single material, so combining two or more materials may accomplish the requirements. In order to achieve a proper scaffold design, a suitable fabrication technique and combination of biomaterials with controlled micro or nanostructures are needed to achieve the proper biological responses. This review emphasizes advancements in scaffold durability, biocompatibility, and cellular responsiveness. It focuses on natural and synthetic polymer combinations and innovative fabrication techniques. Developing stimulus-responsive 3D scaffolds is critical, as these scaffolds enhance cell adhesion and promote functional tissue formation while maintaining structural integrity over time. This review also highlights the natural polymers, smart materials, and recent advanced techniques currently used to create emerging scaffolds for tissue regeneration applications. Full article

Nanothermodynamics provides the theoretical foundation for understanding stable distributions of statistically independent subsystems inside larger systems. In this review, it is emphasized that extending ideas from nanothermodynamics to simplistic models improves agreement with the measured properties of many materials. Examples include non-classical critical scaling near ferromagnetic transitions, thermal and dynamic behavior near liquid–glass transitions, and the 1/f-like noise in metal films and qubits. A key feature in several models is to allow separate time steps for distinct conservation laws: one type of step conserves energy and the other conserves momentum (e.g., dipole alignment). This “orthogonal dynamics” explains how the relaxation of a single parameter can exhibit multiple responses such as primary, secondary, and microscopic peaks in the dielectric loss of supercooled liquids, and the crossover in thermal fluctuations from Johnson–Nyquist (white) noise at high frequencies to 1/f-like noise at low frequencies. Nanothermodynamics also provides new insight into three basic questions. First, it gives a novel solution to Gibbs’ paradox for the entropy of the semi-classical ideal gas. Second, it yields the stable equilibrium of Ising’s original model for finite-sized chains of interacting binary degrees of freedom (“spins”). Third, it confronts Loschmidt’s paradox for the arrow of time, showing that an intrinsically irreversible step is required for maximum entropy and the second law of thermodynamics, not only in the thermodynamic limit but also in systems as small as $N=2$ particles. Full article

Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. In this study, the classification of emotional states using electroencephalography (EEG) data were investigated. Specifically, the efficacy of the combination of various feature selection methods and hyperparameter tuning of machine learning algorithms for accurate and robust emotion recognition was studied. The following feature selection methods were explored: filter (SelectKBest with analysis of variance (ANOVA) F-test), embedded (least absolute shrinkage and selection operator (LASSO) tuned using Bayesian optimization (BO)), and wrapper (genetic algorithm (GA)) methods. We also executed hyperparameter tuning of machine learning algorithms using BO. The performance of each method was assessed. Two different EEG datasets, EEG Emotion and DEAP Dataset, containing 2548 and 160 features, respectively, were evaluated using random forest (RF), logistic regression, XGBoost, and support vector machine (SVM). For both datasets, the experimented three feature selection methods consistently improved the accuracy of the models. For EEG Emotion dataset, RF with LASSO achieved the best result among all the experimented methods increasing the accuracy from 98.78% to 99.39%. In the DEAP dataset experiment, XGBoost with GA showed the best result, increasing the accuracy by 1.59% and 2.84% for valence and arousal. We also show that these results are superior to those by the previous other methods in the literature. Full article

To address the problem of low accuracy in joint point estimation in hand pose estimation methods due to the self-similarity of fingers and easy self-obscuration of hand joints, a hand pose estimation method based on coordinate correction and graph convolution is proposed. First, the standard coordinate encoding is improved by generating an unbiased heat map, and the distribution-aware method is used for decoding coordinates to reduce the error in decoding the coordinate encoding of joints. Then, the complex dependency relationship between the joints and the relationship between pixels and joints of the hand are modeled by using graph convolution, and the feature information of the hand joints is enhanced by determining the relationship between the hand joints. Finally, the skeletal constraint loss function is used to impose constraints on the joints, and a natural and undistorted hand skeleton structure is generated. Training tests are conducted on the public gesture interaction dataset STB, and the experimental results show that the method in this paper can reduce errors in hand joint point detection and improve the estimation accuracy. Full article

The interaction of viral proteins with host factors represents a crucial aspect of the infection process in plants. In this work, we developed a strategy to identify host factors in Nicotiana tabacum that interact with movement proteins (MPs) of the 30K family, a group of viral proteins around 30 kDa related to the MP of tobacco mosaic virus, which enables virus movement between plant cells. Using the alfalfa mosaic virus (AMV) MP as a model, we incorporated tags into its coding sequence, without affecting its functionality, enabling the identification of 121 potential interactors through in vivo immunoprecipitation of the tagged MP. Further analysis of five selected candidates (histone 2B (H2B), actin, 14-3-3A protein, eukaryotic initiation factor 4A (elF4A), and a peroxidase-POX-) were conducted using bimolecular fluorescence complementation (BiFC). The interactions between these factors were also studied, revealing that some form part of protein complexes associated with AMV MP. Moreover, H2B, actin, 14-3-3, and eIF4A interacted with other MPs of the 30K family. This observation suggests that, beyond functional and structural features, 30K family MPs may share common interactors. Our results demonstrate that tagging 30K family MPs is an effective strategy to identify host factors associated with these proteins during viral infection. Full article

In this study, a new species of Alanomyces was isolated as an endophyte from the bark of Azadirachta indica from Mulshi, Maharashtra. The identity of this isolate was confirmed based on the asexual morphological characteristics as well as multi-gene phylogeny based on the internal transcribed spacer (ITS) and large subunit (LSU) nuclear ribosomal RNA (rRNA) regions. As this was the second species to be reported in this genus, we sequenced the genome of this species to increase our knowledge about the possible applicability of this genus to various industries. Its genome length was found to be 35.01 Mb, harboring 7870 protein-coding genes as per Augustus and 8101 genes using GeMoMa. Many genes were annotated using the Clusters of Orthologous Groups (COGs) database, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Swiss-Prot, NCBI non-redundant nucleotide sequences (NTs), and NCBI non-redundant protein sequences (NRs). The number of repeating sequences was predicted using Proteinmask and RepeatMasker; tRNA were detected using tRNAscan and snRNA were predicted using rfam_scan. The genome was also annotated using the Pathogen–Host Interactions Database (PHI-base) and AntiSMASH. To confirm the evolutionary history, average nucleotide identity (ANIb), phylogeny based on orthologous proteins, and single nucleotide polymorphisms (SNPs) were carried out. Metabolic profiling of the methanolic extract of dried biomass and ethyl acetate extract of the filtrate revealed a variety of compounds of great importance in the pharmaceutical and cosmetic industry. The characterization and genomic analysis of the newly discovered species Alanomyces manoharacharyi highlights its potential applicability across multiple industries, particularly in pharmaceuticals and cosmetics due to its diverse secondary metabolites and unique genetic features it possesses. Full article