The highly selective detection of trace gases using transparent sensors at room temperature remains challenging. Herein, transparent nanopatterned chemiresistors composed of aligned 1D Au–SnO2 ...nanofibers, which can detect toxic NO2 gas at room temperature under visible light illumination is reported. Ten straight Au–SnO2 nanofibers are patterned on a glass substrate with transparent electrodes assisted by direct‐write, near‐field electrospinning, whose extremely low coverage of sensing materials (≈0.3%) lead to the high transparency (≈93%) of the sensor. The sensor exhibits a highly selective, sensitive, and reproducible response to sub‐ppm levels of NO2, and its detection limit is as low as 6 ppb. The unique room‐temperature NO2 sensing under visible light emanates from the localized surface plasmonic resonance effect of Au nanoparticles, thereby enabling the design of new transparent oxide‐based gas sensors without external heaters or light sources. The patterning of nanofibers with extremely low coverage provides a general strategy to design diverse compositions of gas sensors, which can facilitate the development of a wide range of new applications in transparent electronics and smart windows wirelessly connected to the Internet of Things.
Transparent and visible light‐activated NO2 sensor that can operate at room temperature is presented. The pattern of Au–SnO2 nanofibers with extremely low coverage fabricated by direct‐write near‐field electrospinning exhibits high transparency (≈93%), ultrahigh response to NO2, and reversible sensing behaviors under visible light or natural sunlight, enabling the ppb‐level monitoring of indoor or outdoor NO2.
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This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the ...most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.
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The transition of fleshy fruit maturation to ripening is regulated by exogenous and endogenous signals that coordinate the transition of the fruit to a final state of attractiveness to seed ...dispersing organisms. Tomato is a model for biology and genetics regulating specific ripening pathways including ethylene, carotenoids and cell wall metabolism in addition to upstream signaling and transcriptional regulators. Ripening‐associated transcription factors described to date including the RIN‐MADS, CLEAR NON‐RIPENING, TAGL1 and LeHB‐1 genes all encode positive regulators of ripening phenomena. Here we describe an APETALA2 transcription factor (SlAP2a) identified through transcriptional profiling of fruit maturation that is induced during, and which negatively regulates, tomato fruit ripening. RNAi repression of SlAP2a results in fruits that over‐produce ethylene, ripen early and modify carotenoid accumulation profiles by altering carotenoid pathway flux. These results suggest that SlAP2a functions during normal tomato fruit ripening as a modulator of ripening activity and acts to balance the activities of positive ripening regulators.
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The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning ...(SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities.
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This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion ...classification is important in diverse fields, including tailored corporate services, AI advancement, and more. Additionally, most sentiment classification techniques in speaking situations are based on a single modality: voice, conversational text, vital signs, etc. However, analyzing these data presents challenges because of the variations in vocal intonation, text structures, and the impact of external stimuli on physiological signals. Korean poses challenges in natural language processing, including subject omission and spacing issues. To overcome these challenges and enhance emotion classification performance, this paper presents a case study using Korean multimodal data. The case study model involves retraining two pretrained models, LSTM and CNN, until their predictions on the entire dataset reach an agreement rate exceeding 0.75. Predictions are used to generate emotional sentences appended to script data, which are further processed using BERT for final emotion prediction. The research result is evaluated by using categorical cross-entropy (CCE) to measure the difference between the model's predictions and actual labels, F1 score, and accuracy. According to the evaluation, the case model outperforms the existing KLUE/roBERTa model with improvements of 0.5 in CCE, 0.09 in accuracy, and 0.11 in F1 score. As a result, the HDECS is expected to perform well not only on Korean multimodal datasets but also on sentiment classification considering the speech characteristics of various languages and regions.
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A robust H ∞ sliding mode descriptor observer for simultaneous state and disturbance estimation of uncertain system is developed. Inspired by a singular system theory, a descriptor observer design is ...presented to estimate some class of output disturbances. A sliding mode scheme is used for the observer to reconstruct the input fault based on the transformed coordinate system. Simulation study illustrates the effectiveness of the proposed method.
Surface‐enhanced Raman scattering (SERS) is one of the most promising methods to detect small molecules for point‐of‐care analysis as it is rapid, nondestructive, label‐free, and applicable for ...aqueous samples. Here, microgels containing highly concentrated yet evenly dispersed gold nanoparticles are designed to provide SERS substrates that simultaneously achieve contamination‐free metal surfaces and high signal enhancement and reproducibility. With capillary microfluidic devices, water‐in‐oil‐in‐water (W/O/W) double‐emulsion drops are prepared to contain gold nanoparticles and hydrogel precursors in innermost drop. Under hypertonic condition, water is selectively pumped out from the innermost drops. Therefore, gold nanoparticles are gently concentrated without forming aggregates, which are then captured by hydrogel matrix. The resulting microgels have a concentration of gold nanoparticles ≈30 times higher and show Raman intensity two orders of magnitude higher than those with no enrichment. In addition, even distribution of gold nanoparticles results in uniform Raman intensity, providing high signal reproducibility. Moreover, as the matrix of the microgel serves as a molecular filter, large adhesive proteins are rejected, which enables the direct detection of small molecules dissolved in the protein solution. It is believed that this advanced SERS platform is useful for in situ detection of toxic molecules in complex mixtures such as biological fluids, foods, and cosmetics.
Surface‐enhanced Raman scattering (SERS)‐active microgels are designed by uniformly loading highly concentrated gold nanoparticles in microgel using microfluidic technology. High density and uniform distribution of gold nanoparticles enhance SERS activity and secure signal reproducibility. Moreover, the hydrogel matrix enables the direct detection of small target molecules without interruption from large adhesives. This class of SERS substrates is promising for point‐of‐care analysis of complex samples.
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The Korea Meteorological Administration uses soil moisture (SM) observed by the Advanced Microwave Scanning Radiometer-2 (AMSR2) to monitor drought. However, it may not be appropriate for monitoring ...drought in South Korea due to significant underestimation of SM. In this study, we used a deep learning method that performs better than traditional statistical and physical models for reliable estimation of SM based on remotely sensed satellite data. For estimating SM, we carefully selected input variables that exhibit a feedback loop with SM. To build an effective deep learning model, we examined the influences of sampling criteria and input parameters as well as the accuracy of several deep neural networks. The selected model was cross-validated to determine its stability. The estimated SM using deep learning had a high correlation coefficient (R) of 0.89 and a low root mean square error (RMSE; 3.825%) and bias (−0.039%) compared to in-situ measurements. A time series analysis using dynamic time warping was conducted which showed that the estimated SM was almost similar to the in-situ SM. In order to investigate the improvement in SM estimation using our method, it was compared with the Global Land Data Assimilation System and AMSR2. Significant improvements in R and a reduction in error values by more than half were achieved using our method. The estimated SM has finer spatial resolution at 4 km, and it can be rapidly produced, which will be useful for drought monitoring over the Korean Peninsula in near-real-time.
The concept of open innovation has recently gained wide academic attention, as it seems to have significant impact for company performance. Most empirical investigations about this emerging concept ...have been case studies of successful early adopters of open innovation, and their analyses have largely been at the company level. Although case studies at that level provide meaningful implications, the new phenomena merit a more in‐depth examination: that is, we need to collect and analyze data on multiple companies to explore more systematic findings about open innovations across companies. Moreover, analyses may need to go down to the individual project rather than the whole company level because innovation activities are often conducted as part of research and development (R&D) projects.
To meet these needs, this study examines companies' open innovation efforts at the level of the individual R&D project. Specifically, the present study focuses on project‐level openness to better understand the mechanisms of open innovation. It explores systematic relationships between various antecedent factors and the degree of openness. Project‐level openness could be affected by team and task characteristics, such as team size, learning distance, strategic importance, technology and market uncertainty, and relevance to the main business. Relevant data collected from 303 companies in Korea were used to identify the antecedents that affect inbound and outbound openness. The research findings are expected to help provide a concrete theoretical framework suited for more generalized application and further practical development of open innovation strategy.
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