Certificateless public auditing with data privacy preserving for cloud-based smart grid data

As the promising next generation power system, smart grid can collect and analyze the grid information in real time, which greatly improves the reliability and efficiency of the grid. However, as smart grid coverage expands, more and more data is being collected. To store and manage the massive amount of smart grid data, the data owners choose to upload the grid data to the cloud for storage and regularly check the integrity of their data. However, traditional public auditing schemes are mostly based on Public Key Infrastructure (PKI) or Identity Based Cryptography (IBC) system, which will lead to complicated certificate management and inherent key escrow problems. We propose a certificateless public auditing scheme for cloud-based smart grid data, which can avoid the above two problems. In order to prevent the disclosure of the private data collected by the smart grid during the phase of auditing, we use the random masking technology to protect data privacy. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient

Study on Barriers and Countermeasures of Technological Innovation of Ecological Service Function Assessment of Urban Greenbelt

AbstractAs the main body of the natural productivity, the urban greenbelt, an irreplaceable comprehensive ecological service system, plays a leading role in beautifying the urban appearance, improving the quality of the urban ecological environment, adjusting the urban ecological balance. The research of ecological service function assessment is a hot research of many domains in recent times, such as ecology, environmental science, sociology, science of culture, economics, etc. Technological innovation is not only the urgent need and significant support system of technological development, but also the urgent need of solving the existing problems of the ecological service function assessment of urban greenbelt. This paper analyzes the main barriers and corresponding countermeasures of the technological innovation, such as unsound innovation mechanism, dated technological means, fault information transform, unsound assessment system, etc., which provides enlightenment and reference for the assessment, planning, construction, management and sustainable development of the ecological service function of urban greenbelt

Chewing Gum for Intestinal Function Recovery after Colorectal Cancer Surgery: A Systematic Review and Meta-Analysis

Background. This meta-analysis was performed to assess the efficacy and safety of chewing gum in intestinal function recovery after colorectal cancer surgery. Methods. A systematic search was conducted in PubMed, Embase, Science Direct, and Cochrane library for relevant randomized controlled trials (RCTs) published until April 2017. Summary risk ratios or weighted mean differences with 95% confidence intervals were used for continuous and dichotomous outcomes, respectively. Results. 17 RCTs with a total number of 1845 patients were included. Gum chewing following colorectal cancer surgery significantly reduced the time to first passage of flatus (WMD −0.55; 95% CI −0.94 to −0.16; P=0.006), first bowel movement (WMD −0.60; 95% CI −0.87 to −0.33; P<0.0001), start feeding (WMD −1.32; 95% CI −2.18 to −0.46; P=0.003), and the length of postoperative hospital stay (WMD −0.88; 95% CI −1.59 to −0.17; P=0.01), but no obvious differences were found in postoperative nausea, vomiting, abdominal distention, pneumonia, and mortality, which were consistent with the findings of intention to treat analysis. Conclusions. Chewing gum could accelerate the recovery of intestinal function after colorectal cancer surgery. However, it confers no advantage in postoperative clinical complications. Further large-scale and high-quality RCTs should be conducted to confirm these results

A lightweight identity-based cloud storage auditing supporting proxy update and workload-based payment

Cloud storage auditing allows the users to store their data to the cloud with a guarantee that the data integrity can be efficiently checked. In order to release the user from the burden of generating data signatures, the proxy with a valid warrant is introduced to help the user process data in lightweight cloud storage auditing schemes. However, the proxy might be revoked or the proxy’s warrant might expire. These problems are common and essential in real-world applications, but they are not considered and solved in existing lightweight cloud storage auditing schemes. In this paper, we propose a lightweight identity-based cloud storage auditing scheme supporting proxy update, which not only reduces the user’s computation overhead but also makes the revoked proxy or the expired proxy unable to process data on behalf of the user any more. The signatures generated by the revoked proxy or the expired proxy can still be used to verify data integrity. Furthermore, our scheme also supports workload-based payment for the proxy. The security proof and the performance analysis indicate that our scheme is secure and efficient

Modality of the Probability Judgment in Japanese and Chinese - focus on 「kamosirenai」「yexu」 and 「nitigainai」「yiding」 -

蓋然性判断のモダリティにおいて，｢かもしれない｣と｢也讦｣，｢にちがいない｣と｢一定｣は，話し手が事態に対し不確実性があると判断する形式である。｢かもしれない｣と｢也讦｣は話し手が命題内容の成立･実現に可能性があると判断する。｢にちがいない｣と｢一定｣は命題内容の成立･実現に必然性があると判断する。四つの形式は推し量る形式で，蓋然性の程度によって，｢かもしれない｣と｢也讦｣は蓋然性が低く，｢にちがいない｣と｢一定｣ は蓋然性が高い。｢かもしれない，にちがいない｣は疑問化できないのに対し，｢也讦，一定｣は疑問文と共起できるが，純然たる質問文と共起できない点から，四つの形式が基本的な意味で同じである。また，｢かもしれない｣と｢也讦｣において，｢かもしれない｣の意味スコープが単一で，｢也讦｣が文頭，文中および文末に現れるため，意味スコープがその位置変化によって，変化することが分かる。｢にちがいない｣と｢一定｣において，人称制限があるが，｢一定｣は話し手の意志と推測する事態が重なっている場合，主語が一人称である。考察を通して｢かもしれない｣と｢也讦｣「にちがいない｣と｢一定｣が意味上にも構文上にも共通点が多く，相違点が少ない

Dissymmetrical U-Shaped π-Stacked Supramolecular Assemblies by Using a Dinuclear CuI Clip with Organophosphorus Ligands and Monotopic Fully π-Conjugated Ligands

International audienceReactions between the U-shape binuclear CuI complex A bearing short metal-metal distances and the cyano-capped monotopic π-conjugated ligands 1-5 carrying gradually bulkier polyaromatic terminal fragments lead to the formation of π-stacked supramolecular assemblies 6-10 respectively in 50-80 % yields. These derivatives have been characterized by multinuclear NMR spectroscopy and X-ray diffraction studies. Their solid state structures show the selective formation of U-shaped supramolecular assemblies in which two monotopic π-conjugated systems present large (6,7,9) or medium (8,10) intramolecular π-overlap revealing π-π interactions. These assemblies self-organize into head-to-tail π-stacked dimers that in turn self-assemble affording infinite columnar π-stacks. The nature, the extentand the complexity of the intermolecular contacts within the head-to-tail π-stacked dimer depends on the nature of the terminal polyaromatic fragment carried by the cyano-capped monotopic ligand but it does not alter the result of the self-assembling process. These results demonstrate that the dinuclear molecular clip A bearing short metal-metal distance allows selective supramolecular assembling processes driven by the formation of intra- and intermolecular short π-π interactions in the resulting self-assembled structures demonstrating that their shape is not only dictated by the symmetry of the building blocks. This approach opens perspectives toward the formation of extented π-stacked columns based on dissymmetrical and functionnal π-conjugated systems

Two-Stream Regression Network for Dental Implant Position Prediction

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods