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The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using laboratory test results. 86 attributes (laboratory tests) were selected from datasets based on value counts, clinical importance-related features, and missing values. We collected sample datasets on 5145 cases, including 326,686 laboratory test results. We investigated a total of 39 specific diseases based on the International Classification of Diseases, 10th revision (ICD-10) codes. These datasets were used to construct light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost) ML models and a DNN model using TensorFlow. The optimized ensemble model achieved an F1-score of 81% and prediction accuracy of 92% for the five most common diseases. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Our new ML model achieved high efficiency of disease prediction through classification of diseases. This study will be useful in the prediction and diagnosis of diseases.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 min with 60-min demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

Quantum dots (QDs) are used progressively in sensing areas because of their special electrical properties due to their extremely small size. This paper discusses the gas sensing features of QD-based resistive sensors. Different types of pristine, doped, composite, and noble metal decorated QDs are discussed. In particular, the review focus primarily on the sensing mechanisms suggested for these gas sensors. QDs show a high sensing performance at generally low temperatures owing to their extremely small sizes, making them promising materials for the realization of reliable and high-output gas-sensing devices.

PurposeIn 2018, an artificial intelligence (AI) interview platform was introduced and adopted by companies in Korea. This study aims to explore the perspectives of applicants who have experienced an AI-based interview through this platform and examines the opinions of companies, a platform developer and academia.Design/methodology/approachThis study uses a phenomenological approach. The participants, who had recent experience of AI video interviews, were recruited offline and online. Eighteen job applicants in their 20s, two companies that have adopted this interview platform, a software developer who created the platform and three professors participated in the study. To collect data, focus group interviews and in-depth interviews were conducted.FindingsAs a result, all of them believed that an AI-based interview was more efficient than a traditional one in terms of cost and time savings and is likely to be adopted by more companies in the future. They pointed to the possibility of data bias requiring an improvement in AI accountability. Applicants perceived an AI-based interview to be better than traditional evaluation procedures in procedural fairness, objectivity and consistency of algorithms. However, some applicants were dissatisfied about being assessed by AI. Digital divide and automated inequality were recurring themes in this study.Originality/valueThe study is important, as it addresses the real application of AI in detail, and a case study of smart hiring tools would be valuable in finding the practical and theoretical implications of such hiring in the fields of employment and AI.

Quercetin (3,3′,4′,5,6‐pentahydroxyflavone) is a well‐known antioxidant and a flavonol found in many fruits, leaves, and vegetables. Quercetin also has known anti‐inflammatory effects on lipopolysaccharide‐induced macrophages. However, the effects of quercetin on virus‐induced macrophages have not been fully reported. In this study, the anti‐inflammatory effect of quercetin on double‐stranded RNA (dsRNA)‐induced macrophages was examined. Quercetin at concentrations up to 50 μM significantly inhibited the production of NO, IL‐6, MCP‐1, IP‐10, RANTES, GM‐CSF, G‐CSF, TNF‐α, LIF, LIX, and VEGF as well as calcium release in dsRNA (50 μg/mL of polyinosinic‐polycytidylic acid)‐induced RAW 264.7 mouse macrophages (p < 0.05). Quercetin at concentrations up to 50 μM also significantly inhibited mRNA expression of signal transducer and activated transcription 1 (STAT1) and STAT3 in dsRNA‐induced RAW 264.7 cells (p < 0.05). In conclusion, quercetin had alleviating effects on viral inflammation based on inhibition of NO, cytokines, chemokines, and growth factors in dsRNA‐induced macrophages via the calcium‐STAT pathway.

Despite highly promising characteristics of three-dimensionally (3D) nanostructured catalysts for the oxygen evolution reaction (OER) in polymer electrolyte membrane water electrolyzers (PEMWEs), universal design rules for maximizing their performance have not been explored. Here we show that woodpile (WP)-structured Ir, consisting of 3D-printed, highly-ordered Ir nanowire building blocks, improve OER mass activity markedly. The WP structure secures the electrochemically active surface area (ECSA) through enhanced utilization efficiency of the extended surface area of 3D WP catalysts. Moreover, systematic control of the 3D geometry combined with theoretical calculations and various electrochemical analyses reveals that facile transport of evolved O 2 gas bubbles is an important contributor to the improved ECSA-specific activity. The 3D nanostructuring-based improvement of ECSA and ECSA-specific activity enables our well-controlled geometry to afford a 30-fold higher mass activity of the OER catalyst when used in a single-cell PEMWE than conventional nanoparticle-based catalysts. Improved design of three-dimensionally nanostructured catalysts for oxygen evolution reaction (OER) can play a key role in maximizing the catalytic performance. Here, the authors show that woodpile-structured iridium consisting of 3D-printed, highly-ordered nanowire building blocks significantly improve OER mass activity.

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors.

Phase-coherent transfer of optical frequencies over a long distance is required for diverse photonic applications, including optical clock dissemination and physical constants measurement. Several demonstrations were made successfully over fiber networks, but not much work has been done yet through the open air where atmospheric turbulence prevails. Here, we use an 18 km outdoor link to transmit multiple optical carriers extracted directly from a frequency comb of a 4.2 THz spectral width. In stabilization to a high-finesse cavity with a 1.5 Hz linewidth, the comb-rooted optical carriers are simultaneously transferred with collective suppression of atmospheric phase noise to −80 dBc Hz −1 . Microwaves are also delivered by pairing two separate optical carriers bound with inter-comb-mode coherence, for example a 10 GHz signal with phase noise of −105 dBc Hz −1 at 1 Hz offset. Lastly, an add-on demonstration is given for multi-channel coherent optical communications with the potential of multi-Tbps data transmission in free space. Phase-coherent transfer of optical frequencies over long open-air paths is necessary in photonic applications. Here the authors demonstrate the parallel transmission of multiple optical carriers in air up to 18 km using a stable near-infrared frequency comb.