Recently, as the paradigm of medical services has shifted from treatment to prevention, there is a growing interest in smart healthcare that can provide users with healthcare services anywhere, at ...any time, using information and communications technologies. With the development of the smart healthcare industry, there is a growing need for collecting large-scale personal health data to exploit the knowledge obtained through analyzing them for improving the smart healthcare services. Although such a considerable amount of health data can be a valuable asset to the smart healthcare fields, they may cause serious privacy problems if sensitive information of an individual user is leaked to outside users. Therefore, most individuals are reluctant to provide their health data to smart healthcare service providers for data analysis and utilization purpose, which is the biggest challenge in smart healthcare fields. Thus, in this paper, we develop a novel mechanism for privacy-preserving collection of personal health data streams that is characterized as temporal data collected at fixed intervals by leveraging local differential privacy (LDP). In particular, with the proposed approach, a data contributor uses a given privacy budget of LDP to report a small amount of salient data, which are extracted from an entire health data stream, to a data collector. Then, a data collector can effectively reconstruct a health data stream based on the noisy salient data received from a data contributor. Experimental results demonstrate that the proposed approach provides significant accuracy gains over straightforward solutions to this problem.
Many studies have explored emotional and mental services that robots can provide for older adults, such as offering them daily conversation, news, music, or health information. However, the ethical ...issues raised by using sensors for frail older adults to monitor their daily movements or their medication intake, for instance, are still being discussed. In this study, we develop an older adult-guided, caregiver-monitored robot, Dori, which can detect and recognize movement by sensing human poses in accordance with two factors from the human-centered artificial intelligence (HCAI) framework. To design the care robot’s services based on sensing movement during daily activities, we conducted focus group interviews with two groups—caregivers and medical staff—on the topic of care robot services not for patients but for prefrail and frail elderly individuals living at home. Based on their responses, we derived the focal service areas of cognitive support, emotional support, physical activity support, medication management, and caregiver management. We also found the two groups differed in their ethical judgments in the areas of dignity, autonomy, controllability, and privacy for services utilizing sensing by care robots. Therefore, the pose recognition technology adopted in the present work uses only joint coordinate information extracted from camera images and thus is advantageous for protecting human dignity and personal information.
Acute myelogenous leukemia (AML) is a heterogeneous disorder characterized by clonal proliferation of stem cell-like blasts in bone marrow (BM); however, their unique cellular interaction within the ...BM microenvironment and its functional significance remain unclear. Here, we assessed the BM microenvironment of AML patients and demonstrate that the leukemia stem cells induce a change in the transcriptional programming of the normal mesenchymal stromal cells (MSC). The modified leukemic niche alters the expressions of cross-talk molecules (i.e., CXCL12 and JAG1) in MSCs to provide a distinct cross-talk between normal and leukemia cells, selectively suppressing normal primitive hematopoietic cells while supporting leukemogenesis and chemoresistance. Of note, AML patients exhibited distinct heterogeneity in the alteration of mesenchymal stroma in BM. The distinct pattern of stromal changes in leukemic BM at initial diagnosis was associated with a heterogeneous posttreatment clinical course with respect to the maintenance of complete remission for 5 to 8 years and early or late relapse. Thus, remodeling of mesenchymal niche by leukemia cells is an intrinsic self-reinforcing process of leukemogenesis that can be a parameter for the heterogeneity in the clinical course of leukemia and hence serve as a potential prognostic factor.
Pesticides are indispensable tools in modern agriculture for enhancing crop productivity. However, the inherent toxicity of pesticides raises significant concerns regarding human exposure, ...particularly among agricultural workers. This study investigated the exposure and associated risks of two commonly used pesticides in open-field pepper cultivation, namely, chlorothalonil and flubendiamide, in the Republic of Korea. We used a comprehensive approach, encompassing dermal and inhalation exposure measurements in agricultural workers during two critical scenarios: mixing/loading and application. Results revealed that during mixing/loading, dermal exposure to chlorothalonil was 3.33 mg (0.0002% of the total active ingredient a.i.), while flubendiamide exposure amounted to 0.173 mg (0.0001% of the a.i.). Conversely, dermal exposure increased significantly during application to 648 mg (chlorothalonil) and 93.1 mg (flubendiamide), representing 0.037% and 0.065% of the total a.i., respectively. Inhalation exposure was also evident, with chlorothalonil and flubendiamide exposure levels varying across scenarios. Notably, the risk assessment using the Risk Index (RI) indicated acceptable risk of exposure during mixing/loading but raised concerns during application, where all RIs exceeded 1, signifying potential risk. We suggest implementing additional personal protective equipment (PPE) during pesticide application, such as gowns and lower-body PPE, to mitigate these risks.
In this paper, we propose a novel memetic algorithm, which is explorative particle swarm optimization (ePSO), combined with mesh adaptive direct search and apply it to the design of a permanent ...magnet synchronous machine (PMSM). The ePSO, which is modified from the PSO, drastically improves search time and iteration number at an exploration search stage. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. By applying the proposed algorithm to PMSM, we clarify the effectiveness of the proposed algorithm.
Hollow carbon–silica nanospheres that exhibit angle‐independent structural color with high saturation and minimal absorption are made. Through scattering calculations, it is shown that the structural ...color arises from Mie resonances that are tuned precisely by varying the thickness of the shells. Since the color does not depend on the spatial arrangement of the particles, the coloration is angle independent and vibrant in powders and liquid suspensions. These properties make hollow carbon–silica nanospheres ideal for applications, and their potential in making flexible, angle‐independent films and 3D printed films is explored.
Monodisperse hollow carbon–silica nanospheres show saturated, angle‐independent Mie‐resonant structural colors by suppressing multiple scattering due to carbon. The color of the structure can be precisely adjusted by varying the thickness of the shell. These new pigments of hollow carbon–silica particles can be useful in functional coating or displays.
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various ...sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.
In the mix: Au–Pd alloy, Au@Pd core–shell, Pd, and Au nanocrystals (NCs) with an identical octahedral shape and with similar NC size were prepared to examine exclusively the effect of atomic ...distribution on the catalytic performance of NCs (see picture). The catalytic activities and stabilities toward formic acid oxidation highly depend on the atomic distribution in the NCs: Au–Pd alloy > Au@Pd core–shell > Pd ≫ Au NCs.
This study proposes a computational design method for determining a hybrid power system’s sizing and ratio values that combines the national electric, solar cell, and fuel cell power sources. The ...inequality constraints associated with the ranges of power storage exchange and the stored energy are reflected as penalty functions in the overall cost function to be minimized. Using the energy hub model and the actual data for the solar cell power and the load of the residential sector in one Korean city for one hundred days, we optimize the ratio of fuel cell energy and solar cell energy to 0.46:0.54 through our proposed approach. We achieve an average cost-reduction effect of 19.35% compared to the cases in which the fuel-cell energy ratio is set from 0.1 to 0.9 in 0.1 steps. To optimize the sizing and the ratio of fuel-cell energy in the hybrid power system, we propose the modified version of the univariate dynamic encoding algorithm for searches (uDEAS) as a novel optimization method. The proposed novel approaches can be applied directly to any place to optimize an energy hub system model comprising three power sources, i.e., solar power, fuel cell, and power utility.
Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ...ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict and recommend next user locations based on the current position. Thus, in this paper, we explore the computation of the transition patterns, especially with a trajectory dataset collected using differential privacy, which is a de facto standard for privacy-preserving data collection and processing. Specifically, the proposed scheme relies on geo-indistinguishability, which is a variant of the well-known differential privacy, to collect trajectory data from users in a privacy-preserving manner, and exploits the functionality of the expectation-maximization algorithm to precisely estimate hidden transition patterns based on perturbed trajectory datasets collected under geo-indistinguishability. Experimental results using real trajectory datasets confirm that a good estimation of transition pattern can be achieved with the proposed method.
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