•We propose a social media mining approach for product opportunity exploration.•The approach is built on topic modeling and sentiment analysis of social media data.•The product opportunity consists ...of the importance and satisfaction of product topics.•Opportunity levels and improvement directions of product topics are identified.•The approach contributes to systematic product opportunity discovery from social media.
Social media data have recently attracted considerable attention as an emerging voice of the customer as it has rapidly become a channel for exchanging and storing customer-generated, large-scale, and unregulated voices about products. Although product planning studies using social media data have used systematic methods for product planning, their methods have limitations, such as the difficulty of identifying latent product features due to the use of only term-level analysis and insufficient consideration of opportunity potential analysis of the identified features. Therefore, an opportunity mining approach is proposed in this study to identify product opportunities based on topic modeling and sentiment analysis of social media data. For a multifunctional product, this approach can identify latent product topics discussed by product customers in social media using topic modeling, thereby quantifying the importance of each product topic. Next, the satisfaction level of each product topic is evaluated using sentiment analysis. Finally, the opportunity value and improvement direction of each product topic from a customer-centered view are identified by an opportunity algorithm based on product topics’ importance and satisfaction. We expect that our approach for product planning will contribute to the systematic identification of product opportunities from large-scale customer-generated social media data and will be used as a real-time monitoring tool for changing customer needs analysis in rapidly evolving product environments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless ...signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.
This study aims to examine the mediating role of open innovation and the moderating effect of digitalization capabilities in the relationship between coopetition strategy and sustainable performance ...in the Belt and Road Initiative (BRI), which offers a coopetitive climate and is the most widely recognized business ecosystem. We conducted an empirical analysis using the partial least squares (PLS) structural equation model (SEM) based on 520 firm datasets from multiple hubs of BRI. The results show that open innovation partially mediates the relationship between coopetition strategy and sustainable performance. The results also indicate that digitalization capability significantly moderates the relationship between coopetition strategy and open innovation. However, there was insignificant moderating effect between coopetition strategy and sustainable performance of digitization capability. We believe that our research, which is based on the dynamic capability perspective, provides a structured perspective and understanding of how and why coopetition strategy, open innovation, and digitalization capabilities can be leveraged to achieve a firm's sustainable performance in the BRI.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Printing technology can be used for manufacturing stretchable electrodes, which represent essential parts of wearable devices requiring relatively high degrees of stretchability and conductivity. In ...this work, a strategy for fabricating printable and highly stretchable conductors are proposed by transferring printed Ag ink onto stretchable substrates comprising Ecoflex elastomer and tough hydrogel layers using a water‐soluble tape. The elastic modulus of the produced hybrid film is close to that of the hydrogel layer, since the thickness of Ecoflex elastomer film coated on hydrogel is very thin (30 µm). Moreover, the fabricated conductor on hybrid film is stretched up to 1780% strain. The described transfer method is simpler than other techniques utilizing elastomer stamps or sacrificial layers and enables application of printable electronics to the substrates with low elastic moduli (such as hydrogels). The integration of printed electronics with skin‐like low‐modulus substrates can be applied to make wearable devices more comfortable for human skin.
Printable and highly stretchable conductors are realized by transferring printed Ag ink onto stretchable substrates comprising Ecoflex and tough hydrogel layers. The elastic modulus of the produced hybrid film is close to that of the hydrogel layer, since the thickness of Ecoflex coated on hydrogel is very small. The fabricated conductor on hybrid film is stretched up to 1780% strain.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Evidence for the associations between mental illness and the likelihood of a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result and the clinical outcomes of COVID-19 is ...scarce. We aimed to investigate these associations with data from a national register in South Korea.
A nationwide cohort study with propensity score matching was done in South Korea using data collected from the Health Insurance Review and Assessment Service of Korea. We defined mental illness as present if one of the relevant ICD-10 codes was recorded at least twice within 1 year for an outpatient or inpatient. Severe mental illness was considered as non-affective or affective disorders with psychotic features. We included all patients aged older than 20 years who were tested for SARS-CoV-2 through services facilitated by the Korea Centers for Disease Control and Prevention, the Health Insurance Review and Assessment Service of Korea, and the Ministry of Health and Welfare, South Korea. We investigated the primary outcome (SARS-CoV-2 test positivity) in the entire cohort and the secondary outcomes (severe clinical outcomes of COVID-19: death, admission to the intensive care unit, or invasive ventilation) among those who tested positive.
Between Jan 1 and May 15, 2020, 216 418 people were tested for SARS-CoV-2, of whom 7160 (3·3%) tested positive. In the entire cohort with propensity score matching, 1391 (3·0%) of 47 058 patients without a mental illness tested positive for SARS-CoV-2, compared with 1383 (2·9%) of 48 058 with a mental illness (adjusted odds ratio OR 1·00, 95% CI 0·93-1·08). Among the patients who tested positive for SARS-CoV-2, after propensity score matching, 109 (8·3%) of 1320 patients without a mental illness had severe clinical outcomes of COVID-19 compared with 128 (9·7%) of 1320 with a mental illness (adjusted OR 1·27, 95% CI 1·01-1·66).
Diagnosis of a mental illness was not associated with increased likelihood of testing positive for SARS-CoV-2. Patients with a severe mental illness had a slightly higher risk for severe clinical outcomes of COVID-19 than patients without a history of mental illness. Clinicians treating patients with COVID-19 should be aware of the risk associated with pre-existing mental illness.
National Research Foundation of Korea.
Owing to their large surface‐area‐to‐volume ratios, 2D titanium carbides and nitrides (MXenes) have emerged as promising materials for energy storage devices. However, poor interlayer and ...interparticle conductivity of MXenes (due to its anisotropic nature) is a bottleneck for widening their applications. Additionally, the stacked structure of MXene sheets limits the exposed surface area and renders a complex electrolyte diffusion. To address these issues, a unique composite comprising of homogeneously grown multiwall carbon nanotubes (MWCNTs) on carbon cloth (CC)‐supported MXene sheets (denoted as MWCNTs‐MXene@CC) is proposed. The MWCNTs‐MXene@CC reveal the synergistic combination of exfoliated large surface area and excellent conductivity. Consequently, the fabricated electrode exhibits a specific capacitance of 114.58 mF cm−2 at a discharge current of 1 mA cm−2, while maintaining high retention after 1.6 × 104 cycles at 10 mA cm−2. Such high performance of the composite structure is attributed to the superb interlayer and interparticle conductivity imparted by the grown MWCNTs. Furthermore, the grown MWCNTs also serve as the interlayer pillar in MXene sheets, thus preventing the spontaneous collapse of the latter. The approach can be extended to other electrocatalyst systems in which ion transport and electrolyte diffusion need to be addressed simultaneously.
An MWCNT‐MXene nanocomposite prepared by electrodeposition and low‐pressure chemical vapor deposition is applied as an active material in flexible supercapacitors. The grown MWCNT effectively maintains the hierarchical structure of the MXene and accelerates electron transport in the electrode. Based on the excellent synergetic effect between MXene and MWCNTs, the work can provide a new route toward improving the electrochemical performance of MXene.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines ...two types of sparse convolutional layers-depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.
•The optimal THP conditions of 180 °C and 76 min were obtained from RSM.•The SMPs and EPSs in sewage sludge converted to low molecular substances through THP.•Both acetoclastic and hydrogenotrophic ...methanogens were dominant in the AD with THP.
This study was performed to optimize thermal hydrolysis pretreatment (THP) of sewage sludge for enhanced anaerobic digestion (AD). Using the response surface methodology (RSM), the optimal conditions were found 180 °C of reaction temperature and 76 min of reaction time. Through THP under optimal conditions, high molecular substances in sewage sludge such as soluble microbial by-products (SMPs) and extracellular polymeric substances (EPSs) were hydrolyzed into low molecular ones without the generation of refractory compounds. The microbial community analysis revealed that relative abundances of Methanomicrobia such as Methanosarcina, Methanosaeta (acetoclastic methanogens), and Methanoculleus (hydrogenotrophic methanogens) in AD with THP were higher than those in conventional AD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
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