Fabrication of multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors by using CF4 plasma treatment

Multianalyte CeO2 biosensors have been demonstrated to detect pH, glucose, and urine concentrations. To enhance the multianalyte sensing capability of these biosensors, CF4 plasma treatment was applied to create nanograin structures on the CeO2 membrane surface and thereby increase the contact surface area. Multiple material analyses indicated that crystallization or grainization caused by the incorporation of flourine atoms during plasma treatment might be related to the formation of the nanograins. Because of the changes in surface morphology and crystalline structures, the multianalyte sensing performance was considerably enhanced. Multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors exhibit potential for use in future biomedical sensing device applications

On the Economic Sustainability of Cloud Sharing Systems: Are Dynamic Single Resource Sharing Markets Stable?

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature? In this short paper, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.Peer reviewe

Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation

With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultaneously. The task of inspired headline generation can be viewed as a specific form of Headline Generation (HG) task, with the emphasis on creating an attractive headline from a given news article. To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG). PORL-HG exploits the extractive-abstractive architecture with 1) Popular Topic Attention (PTA) for guiding the extractor to select the attractive sentence from the article and 2) a popularity predictor for guiding the abstractor to rewrite the attractive sentence. Moreover, since the sentence selection of the extractor is not differentiable, techniques of reinforcement learning (RL) are utilized to bridge the gap with rewards obtained from a popularity score predictor. Through quantitative and qualitative experiments, we show that the proposed PORL-HG significantly outperforms the state-of-the-art headline generation models in terms of attractiveness evaluated by both human (71.03%) and the predictor (at least 27.60%), while the faithfulness of PORL-HG is also comparable to the state-of-the-art generation model.Comment: AAAI 202

Recurrent Massive Subcutaneous Hemorrhage in Neurofibromatosis Type 1: A Case Report

Neurofibromatosis type 1 (NF-1) is an autosomal dominant disorder that has three major features: multiple neural tumors, café-au-lait spots, and pigmented iris hamartomas (Lisch nodules). The purpose of this case report is to advise physicians of the danger associated with the progression of fast-onset massive hemorrhage to hemodynamic instability, which mandates rapid treatment to prevent the development of a life-threatening condition. A 64-yr-old woman with NF-1 was admitted to the Emergency Department (ED) because of a rapidly growing, 10×5×3 cm-sized mass on the left back area. She had previously undergone surgery for a large subcutaneous hematoma, which had developed on her right back area 30 yr before. She became hemodynamically unstable with hypotension during the next 3 hr after admission to ED. Resuscitation and blood transfusion were done, and the hematoma was surgically removed. The mass presented as a subcutaneous, massive hematoma with pathologic findings of neurofibroma. We report a case of NF-1 that presented as recurrent, massive, subcutaneous hemorrhage on the back region combined with hypovolemic shock

Age-related hearing loss: Unraveling the pieces.

Age-related hearing loss (ARHL) is the most common cause of hearing loss in the world. The development of ARHL in each individual is multifactorial, involving both intrinsic and extrinsic factors. This review highlights several of the key findings in the ARHL literature and discusses future directions. Level of Evidence:NA

Spontaneous left external iliac vein rupture

We report a 72-year-old female patient with spontaneous rupture of the left external iliac vein. She visited our hospital for abdominal and back pain. She had the abnormal finding of hemoperitoneum. We performed an emergency operation with diagnosis of left ovarian cyst rupture though she suffered from spontaneous rupture of the left external iliac vein. This case provides insight to the experience of spontaneous rupture of the left external iliac vein

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0