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This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a “hex” reconstruction critical for CO 2 electroreduction, explored through 50,000 sampled configurations; and the oxidation dynamics of Pt clusters, where oxygen incorporation renders the clusters less active during oxygen reduction reactions, investigated through 100,000 sampled configurations. In both cases, the predicted active phases and their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that the proposed strategy can model complex catalytic systems and discovery active phases under specific environmental conditions. Discovering active phases in heterocatalysis entails efficient configuration sampling and optimization. Here, the authors developed a framework based on topology and machine learning to effectively explore the active structures, applied in the CO2 electroreduction and Oxygen Reduction Reaction

This article is a Commentary on Zhang et al., 224: 761–774.

As green energy technology develops, so too grows research interest in topics such as solar power forecasting. The output of solar power generation is uncontrollable, which makes accurate prediction of output an important task in the management of power grids. Despite a plethora of theoretical models, most frameworks encounter problems in practice because they assume that received data is error-free, which is unlikely, as this type of data is gathered by outdoor sensors. We thus designed a robust solar power forecasting model and methodology based on the concept of ensembling, with three key design elements. First, as models established using the ensembling concept typically have high computational costs, we pruned the deep learning model architecture to reduce the size of the model. Second, the mediation model often used for pruning is not suitable for solar power forecasting problems, so we designed a numerical–categorical radial basis function deep neural network (NC-RBF-DNN) to replace the mediation model. Third, existing pruning methods can only establish one model at a time, but the ensembling concept involves the establishment of multiple sub-models simultaneously. We therefore designed a factor combination search algorithm, which can identify the most suitable factor combinations for the sub-models of ensemble models using very few experiments, thereby ensuring that we can establish the target ensemble model with the smallest architecture and minimal error. Experiments using a dataset from real-world solar power plants verified that the proposed method could be used to build an ensemble model of the target within ten attempts. Furthermore, despite considerable error in the model inputs (two inputs contained 10% error), the predicted NRMSE of our model is still over 10 times better than the recent model.

Urban green spaces offer vital ecosystem services such as regulating elevated temperatures in cities. Less information exists, however, on how urban green spaces influence outdoor thermal comfort (OTC), which is dependent on people’s perceptions of the complex interactions amongst ambient humidity, wind and both air and radiant temperatures. In this study, we analysed an existing OTC dataset compiled within a large Singapore urban park and calibrated OTC thresholds for physiological equivalent temperatures (PET) by analysing PET against thermal perception survey responses from the park visitors (n = 1508). We examined OTC according to (i) neutral, (ii) acceptable and (iii) preferred temperatures, where respondents felt ‘comfortable’ outdoors in the park. We estimated that neutral temperature, when all respondents experience neither heat nor cold stress, is 26.2 °C; acceptable temperatures, when only slight heat or cold stress is experienced, range between 21.6 and 31.6 °C; and preferred (‘ideal’) temperature for all respondents is 24.2 °C. Respondents residing for more than 6 months in Singapore achieved thermal neutrality, suggesting that a greater degree of thermal adaptation likely developed during acclimatisation to local climate through a combination of physiological, behavioural and psychological circumstances. Comparisons with other OTC studies showed differences in synoptic climates are linked to variations in the magnitude and ranges of perceived PET. Lastly, respondents in this study perceived lower neutral and preferred temperatures compared to respondents surveyed over a variety of urban land use categories in another local study. The differences in neutral and preferred temperatures between studies suggest that lower park temperatures and different environmental attitudes influence perceived OTC.

High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

Efficient charge-carrier injection and transport in organic light-emitting devices (OLEDs) are essential to simultaneously achieving their high efficiency and long-term stability. However, the charge-transporting layers (CTLs) deposited by various vapor or solution processes are usually in amorphous forms, and their low charge-carrier mobilities, defect-induced high trap densities and inhomogeneous thickness with rough surface morphologies have been obstacles towards high-performance devices. Here, organic single-crystalline (SC) films were employed as the hole-transporting layers (HTLs) instead of the conventional amorphous films to fabricate highly efficient and stable OLEDs. The high-mobility and ultrasmooth morphology of the SC-HTLs facilitate superior interfacial characteristics of both HTL/electrode and HTL/emissive layer interfaces, resulting in a high Haacke’s figure of merit (FoM) of the ultrathin top electrode and low series-resistance joule-heat loss ratio of the SC-OLEDs. Moreover, the thick and compact SC-HTL can function as a barrier layer against moisture and oxygen permeation. As a result, the SC-OLEDs show much improved efficiency and stability compared to the OLEDs based on amorphous or polycrystalline HTLs, suggesting a new strategy to developing advanced OLEDs with high efficiency and high stability.Organic single-crystalline films are employed as the hole-transporting layers for developing advanced OLEDs with high efficiency and high stability.

Objectives: Absorbable biomaterials as adjuvant therapy after thoracoscopy are sometimes used in clinical scenarios. With the prevalence of enhanced rapid recovery in thoracic surgery, drainless video-assisted thoracoscopy surgery (VATS) is often adopted by thoracic surgeons. Here, we discuss utilizing an absorbable biomaterial, NeoveilTM (Polyglycolic Acid sheet), for drainless VATS to treat early lung cancer. Methods: This single-center retrospective study was conducted from January 2018 to December 2022 at the National Taiwan University Hospital. We included patients who underwent drainless VATS for early-stage non-small cell lung cancer (NSCLC) in our institute. Propensity analysis was used to minimize selection bias. Outcome measurements were in-hospital stay, operation time, rate of thoracocentesis or chest drain re-insertion, complication rate, and perioperative course. Results: During the study period, 158 lung cancer patients were performed with drainless VATS wedge resection. Among them, Neoveil for stapling line coverage was done in 72 patients, while 86 patients did not receive Neoveil. After propensity analysis, we had 58 patients using Neoveil after drainless thoracoscopic lung resection, compared fairly with 58 patients without Neoveil after the same procedure. The basic characteristics are comparable regarding age, gender, BMI, operation methods, and lung cancer stage after propensity matching. The in-hospital stay (3.2 days in the Neoveil group and 5.6 days in the non-Neoveil group) and operation time (95.7 min in the Neoveil group and 59.3 min in the non-Neoveil group) are significantly different (p = 0.0001). One versus four patients was noted for postoperative conversion chest drainage insertion in each group (p = 0.17). Neither late complications nor recurrence/metastasis occurred in both groups during the following. Conclusions: Based on our 5-year retrospective study, which is balanced with propensity analysis, drainless thoracoscopic surgery treating early lung cancer can be enhanced by Neoveil with faster recovery by reducing the hospital stay, though with longer operation time.

In recent years, the two-dimensional (2D) semiconductor α-In 2 Se 3 has great potential for applications in the fields of electronics and optoelectronics due to its spontaneous iron electrolysis properties. Through ab initio electronic structure calculations and quantum transport simulations, the interface properties and transport properties of α-In 2 Se 3 /Au contacts with different polarization directions are studied, and a two-dimensional α-In 2 Se 3 asymmetric metal contact design is proposed. When α-In 2 Se 3 is polarized upward, it forms an n-type Schottky contact with Au. While when α-In 2 Se 3 is polarized downward, it forms a p-type Schottky contact with Au. More importantly, significant rectification effect is found in the asymmetric Au/α-In 2 Se 3 /Au field-effect transistor. The carrier transports under positive and negative bias voltages are found to be dominated by thermionic excitation and tunneling, respectively. These findings provide guidance for the further design of 2D α-In 2 Se 3 -based transistors.