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Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

Transferring MoS2 films from growth substrates onto target substrates is a critical issue for their practical applications. Moreover, it remains a great challenge to avoid sample degradation and substrate destruction, because the current transfer method inevitably employs a wet chemical etching process. We developed an etching-free transfer method for transferring MoS2 films onto arbitrary substrates by using ultrasonication. Briefly, the collapse of ultrasonication-generated microbubbles at the interface between polymer-coated MoS2 film and substrates induce sufficient force to delaminate the MoS2 films. Using this method, the MoS2 films can be transferred from all substrates （silica, mica, strontium titanate, and sapphire） and retains the original sample morphology and quality. This method guarantees a simple transfer process and allows the reuse of growth substrates, without involving any hazardous etchants. The etching-free transfer method is likely to promote broad applications of MoS2 in photodetectors.

This study investigates the relationship of neutrophil–lymphocyte ratio (NLR) with the risk of all-cause and cardiovascular mortality in patients with cardiovascular disease. The data for this analysis came from 2239 participants with cardiovascular disease of the National Health and Nutrition Examination Survey conducted between 1999–2018. The optimal cutoff point for NLR was determined using maximally selected rank statistics. Survival analysis was performed using Cox regression models to assess the impact of NLR on the risk of all-cause mortality and cardiovascular mortality. Restricted cubic spline was used to visualize the association of NLR with mortality risk. Subgroup analysis was performed to examine the relationship between NLR and mortality within subgroups based on age, sex, diabetes and hypertension. During a median follow-up period of 6.7 (IQR, 3.3–10.9) years, 992 all-cause deaths occurred, including 381 cardiovascular deaths. Our study revealed that NLR is a risk factor for all-cause mortality (HR: 1.15 95%Cl: 1.11 ~ 1.19) and cardiovascular mortality (HR: 1.14 95%Cl: 1.08 ~ 1.2) among patients with cardiovascular disease. The restricted cubic spline regression analysis showed a non-linear association between NLR and all-cause mortality (p < 0.05 for nonlinearity) in cardiovascular patients. This association remained robust in subgroup analyses stratified by age, sex, diabetes, and hypertension. Conclusion NLR stands as a significant risk factor for both all-cause and cardiovascular mortality among patients with cardiovascular disease.

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.

Embedding non-hexagonal rings into sp 2 -hybridized carbon networks is considered a promising strategy to enrich the family of low-dimensional graphenic structures. However, non-hexagonal rings are energetically unstable compared to the hexagonal counterparts, making it challenging to embed non-hexagonal rings into carbon-based nanostructures in a controllable manner. Here, we report an on-surface synthesis of graphene-like nanoribbons with periodically embedded four- and eight-membered rings. The scanning tunnelling microscopy and atomic force microscopy study revealed that four- and eight-membered rings are formed between adjacent perylene backbones with a planar configuration. The non-hexagonal rings as a topological modification markedly change the electronic properties of the nanoribbons. The highest occupied and lowest unoccupied ribbon states are mainly distributed around the eight- and four-membered rings, respectively. The realization of graphene-like nanoribbons comprising non-hexagonal rings demonstrates a controllable route to fabricate non-hexagonal rings in nanoribbons and makes it possible to unveil their unique properties induced by non-hexagonal rings. Graphene nanoribbons consist of carbon atoms arranged in a hexagonal lattice. Despite non-hexagonal rings generally being more unstable, the authors demonstrate the successful synthesis of graphene-like nanoribbons with periodically embedded four- and eight-membered carbon rings, with tailored electronic properties.

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931 .

The chiral charge density wave is a many-body collective phenomenon in condensed matter that may play a role in unconventional superconductivity and topological physics. Two-dimensional chiral charge density waves provide the building blocks for the fabrication of various stacking structures and chiral homostructures, in which physical properties such as chiral currents and the anomalous Hall effect may emerge. Here, we demonstrate the phase manipulation of two-dimensional chiral charge density waves and the design of in-plane chiral homostructures in 1T-TaS 2 . We use chiral Raman spectroscopy to directly monitor the chirality switching of the charge density wave—revealing a temperature-mediated reversible chirality switching. We find that interlayer stacking favours homochirality configurations, which is confirmed by first-principles calculations. By exploiting the interlayer chirality-locking effect, we realise in-plane chiral homostructures in 1T-TaS 2 . Our results provide a versatile way to manipulate chiral collective phases by interlayer coupling in layered van der Waals semiconductors. Two-dimensional charge density waves in layered semiconductors may exhibit chirality. Here, the authors utilize thermal annealing to reversibly switch the in-plane chirality of charge density waves in 1T-TaS 2 and demonstrate a vertical chirality-locking effect between the van der Waals-stacked layers.

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people’s dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Focus on the weak feature expression ability of traditional Convolution Neural Network (CNN) in image recognition and classification, the CNN has been improved and optimized by introduction of Mlpconv layer to form a Network in Network (NIN). The NIN enhances feature extraction and abstract expression of the local patch; get better performance of the image recognition. The experiments of weld image recognition and classification show that the optimized CNN can improve the feature expression ability of the whole network by reducing the number of parameters, obtain higher recognition and classification precision, and avoid the fitting of the network model effectively. The CNN achieve improvement in both performance and efficiency.