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Soft electronic devices that are bendable and stretchable require stretchable electric or electronic components. Nanostructured conducting materials or soft conducting polymers are one of the most promising fillers to achieve high performance and durability. Here, we report silver nanoparticles (AgNPs) embedded with single-walled carbon nanotubes (SWCNTs) synthesized in aqueous solutions at room temperature, using NaBH 4 as a reducing agent in the presence of highly oxidized SWCNTs as efficient nucleation agents. Elastic composite films composed of the AgNPs-embedded SWCNTs, Ag flake, and polydimethylsiloxane are irradiated with radiation from a Xenon flash lamp within a time interval of one second for efficient sintering of conductive fillers. Under high irradiation energy, the stretchable electrodes are created with a maximum conductivity of 4,907 S cm −1 and a highly stretchable stability of over 10,000 cycles under a 20% strain. Moreover, under a low irradiation energy, strain sensors with a gauge factor of 76 under a 20% strain and 5.4 under a 5% strain are fabricated. For practical demonstration, the fabricated stretchable electrode and strain sensor are attached to a human finger for detecting the motions of the finger.

Frequency-modulated continuous-wave light detection and ranging (FMCW LiDAR) is a sensor that uses light interference to measure distance. FMCW LiDARs have recently gained attention because the wave nature of laser makes it resistant to harsh environmental conditions. FMCW LiDAR transmits and receives a linearly modulated beam to obtain an interference frequency which is proportional to distance. Fast Fourier transform (FFT) is mainly used to measure interference frequency. FFT has the problem that it takes a long time to acquire the interference frequency. In this study, the interference frequency is obtained using a frequency-to-voltage converter (FVC) which is capable of high-speed frequency measurement. FVC converts the interference frequency into a voltage signal using the one-shot conversion. However, FVC has the disadvantage that ripple noise generated in the process of averaging one-shot signals included in the output signal. To solve this problem, we propose a high-resolution frequency measurement method that uses frequency control to shift the frequency to a high frequency band where ripple noise can be minimized and indirectly measures the frequency through the controller output. In this paper, the principle of frequency control is mathematically analyzed and the performance of frequency control is confirmed through experiments.

Liquid-crystalline (LC)-spinning of graphene oxide (GO) is a promising method for producing conducting fibres. However, achieving continuous wet-spinning via a multi-hole spinneret with organic solvent-based spinning dopes remains challenging, primarily because of the limitations in coagulation without ion crosslinking agents, such as Ca 2+ and Fe 3+ . In this study, we report the colloidal engineering of an LC GO-based spinning dope with highly oxidised single-walled carbon nanotubes (ox-SWCNTs) for continuous multi-hole wet-spinning. With 10 wt% ox-SWCNTs, GO retains its LC phase using a controlled solvent exchange strategy in N -methyl-2-pyrrolidone, which is a prerequisite for wet-spinning. The heterogeneous mixing of the ox-SWCNTs in the LC GO phase allows coagulation in ethyl acetate within a few seconds, which is facilitated by the rapid exchange of the dope solvent and coagulant through the ox-SWCNT networks. Moreover, ox-SWCNTs are utilised to modify fibre surfaces for applications in textile supercapacitors. The GO/ox-SWCNT@ox-SWCNT fibres exhibit an enhanced specific capacity of 138 mF/cm 2 . This study presents a promising approach for the continuous wet-spinning of nanocarbon materials through a multi-hole spinneret for textile electronics, addressing the challenges associated with dispersion in colloidal nanocarbon systems.

Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim , a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra . We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim , including its ability to help identify stably clustered items across multiple clustering results.

Graphite tattoos are rarely reported because they are mainly caused by an accidental injury or habits during childhood that cause a pencil to penetrate the oral mucosa. Unlike other pigmentations, it stains layers that are deeper than the subepithelial and mucosal layers, and in most cases, it takes the form of a grayish black macule. This case report describes depigmentation with the denudation technique that was followed by a novel approach of using platelet‐rich fibrin to cover exposed bone. A 41‐year‐old male patient presented with an aesthetic complaint from a grayish black staining on the labial gingiva near the maxillary central and lateral incisors. The lesion was diagnosed as a graphite tattoo due to the patient’s history of sticking his gum with pencils when he was young. The entire pigmented gingiva was surgically removed and covered with two layers of PRF membrane to protect the exposed bone surface and provide an extracellular matrix for migration of gingival fibroblasts. Healing patterns were observed at 1, 2, 4, and 8 weeks, and satisfactory clinical and aesthetic results were obtained. Creeping attachment was observed at 8 years postop, and there was no recurrence for a long‐term period of 13 years.

Peri-implantitis (PI) presents a growing challenge in implant dentistry, with regenerative surgical approaches often incorporating barrier membranes despite the uncertainty of their clinical value. This systematic review and meta-analysis of in vivo studies aimed to evaluate the efficacy of barrier membranes in the reconstructive surgical treatment of PI. A comprehensive electronic search was performed in PubMed, Scopus, Google Scholar, and the Cochrane Library, covering studies published from 1990 to 2024. The protocol followed PRISMA guidelines and was registered in PROSPERO (CRD42025625417). Eligible studies included in vivo investigations comparing regenerative procedures with and without membrane use, with a minimum follow-up of 6 months and at least 10 implants per study. Risk of bias (RoB) was assessed using the Cochrane RoB tool. The meta-analysis was conducted using a random-effects model and included 15 studies comprising 560 patients. Although not consistently statistically significant, the findings suggested that membrane use may offer enhanced outcomes in terms of probing pocket depth (PPD) reduction and marginal bone level (MLB) gain. The evidence was limited by high clinical heterogeneity, variability in outcome definitions, and short follow-up durations. While membranes are commonly utilized, current evidence does not justify their routine use. Further well-designed, long-term clinical trials are needed to establish specific indications and optimize treatment strategies.

Charge transporting materials (CTMs) in perovskite solar cells (PSCs) have played an important role in improving the stability by replacing the liquid electrolyte with solid state electron or hole conductors and enhancing the photovoltaic efficiency by the efficient electron collection. Many organic and inorganic materials for charge transporting in PSCs have been studied and applied to increase the charge extraction, transport and collection, such as Spiro-OMeTAD for hole transporting material (HTM), TiO2 for electron transporting material (ETM) and MoOX for HTM etc. However, recently inorganic CTMs are used to replace the disadvantages of organic materials in PSCs such as, the long-term operational instability, low charge mobility. Especially, atomic layer deposition (ALD) has many advantages in obtaining the conformal, dense and virtually pinhole-free layers. Here, we review ALD inorganic CTMs and their function in PSCs in view of the stability and contribution to enhancing the efficiency of photovoltaics.

Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application. This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems. We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models. Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07). Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Most synthetic processes of metallic nanostructures were assisted by organic/inorganic or polymeric materials to control their shapes to one-dimension or two-dimension. However, these additives have to be removed after synthesis of metal nanostructures for applications. Here we report a straightforward method for the low-temperature and additive-free synthesis of nanobelt-like silver nanostructures templated by nanocarbon (NC) materials via bio-inspired shape control by introducing supramolecular 2-ureido-4[1H]pyrimidinone (UPy) groups into the NC surface. The growth of the Ag nanobelt structure was found to be induced by these UPy groups through observation of the selective formation of Ag nanobelts on UPy-modified carbon nanotubes and graphene surfaces. The synthesized NC/Ag nanobelt hybrid materials were subsequently used to fabricate the highly conductive fibres (>1000S/cm) that can function as a conformable electrode and highly tolerant strain sensor, as well as a highly conductive and robust paper (>10000S/cm after thermal treatment).