IoT database forensics : an investigation on HarperDB Security

The data that are generated by several devices in the IoT realmrequire careful and real time processing. Recently, researchers haveconcentrated on the usage of cloud databases for storing such datato improve efficiency. HarperDB aims at producing a DBMS that isrelational and non-relational simultaneously, to help journeymendevelopers creating products and servers in the IoT space. Much ofwhat the HarperDB team has talked about has been achieved, butfrom a security perspective, a lot of improvements need to be made.The team has clearly focused on the problems that exist from adatabase and data point of view, creating a structure that is unique,fast, easy to use and has great potential to grow with a startup.The functionality and ease of use of this DBMS is not in question,however as the trade-off triangle to the right suggests, this doesentail an impact to security. In this paper, using multiple forensicmethodologies, we performed an in-depth forensic analysis onHarperDB and found several areas of extreme concern, such as lackof logging functionalities, basic level of authorisation, exposure ofusers’ access rights to any party using the database, There had to bea focus on preventative advice instead of reactive workarounds dueto the nature of the flaws found in HarperDB. As such, we providea number of recommendations for the users and developers

Evaluation of service quality from patients' viewpoint

Background: Measuring patients' perception from health service quality as an important element in the assessment of service quality has attracted much attention in recent years. Therefore, this study was conducted to find out how the patients evaluated service quality of clinics at teaching hospitals affiliated with Tehran University of Medical Sciences in Iran. Methods: This cross-sectional study was conducted in Tehran in 2017 and 400 patients were randomly selected from four hospitals. Data were collected using a questionnaire, the validity and reliability of which were confirmed in previous study. In order to analyze the data, T-test, ANOVA, and Pearson correlation coefficient were calculated using SPSS 23. Results: The results indicated that among eight dimensions of health service quality, the patients were more satisfied with physician consultation, services costs and admission process. The highest and lowest mean scores were related to physician consultation (Mean = 4.17), and waiting time (Mean = 2.64), in that order. The total mean score of service quality was 3.73 (± 0.51) out of 5. Outpatient services were assessed as good, moderate and weak by 57.5, 40 and 2.5 of the patients, respectively. There was a significant relationship between the positive perception of service quality and reason for admission, source of recommendation, gender, education level, health status, and waiting time in the clinics (p < 0.05). Conclusion: The majority of the patients had a positive experience with visiting clinics and perceived service provision as good. In fact, patients' perceptions of physician consultation, provision of information to patients and the environment of delivering services, are the most important determinants of service quality in clinics. © 2019 The Author(s)

Design a fuzzy rule-based expert system to aid earlier diagnosis of gastric cancer

Introduction: Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50 of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer. Objective: to develop a fuzzy expert system that canidentify gastric cancer risk levels in individuals. Methods: This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert's opinion. Results: 50 case scenarios were usedto evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert's diagnosis. Results revealed that sensitivity was 92.1 and the specificity was 83.1. Conclusions: The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate. © 2018 Reza Safdari, Hadi Kazemi Arpanahi, Mostafa Langarizadeh, Marjan Ghazisaiedi, Hossein Dargahi, Kazem Zendehdel

A hierarchical key pre-distribution scheme for fog networks

Security in fog computing is multi-faceted, and one particular challenge is establishing a secure communication channel between fog nodes and end devices. This emphasizes the importance of designing efficient and secret key distribution scheme to facilitate fog nodes and end devices to establish secure communication channels. Existing secure key distribution schemes designed for hierarchical networks may be deployable in fog computing, but they incur high computational and communication overheads and thus consume significant memory. In this paper, we propose a novel hierarchical key pre-distribution scheme based on “Residual Design” for fog networks. The proposed key distribution scheme is designed to minimize storage overhead and memory consumption, while increasing network scalability. The scheme is also designed to be secure against node capture attacks. We demonstrate that in an equal-size network, our scheme achieves around 84% improvement in terms of node storage overhead, and around 96% improvement in terms of network scalability. Our research paves the way for building an efficient key management framework for secure communication within the hierarchical network of fog nodes and end devices. KEYWORDS: Fog Computing, Key distribution, Hierarchical Networks

Ultra-soft 100 nm Thick Zero Poisson’s Ratio Film with 60% Reversible Compressibility

About a 100 nm thick multilayer film of nanoparticle monolayers and polymer layers is shown to behave like cellular-foam with a modulus below 100 KPa. The 1.25 cm radius film adhered to a rigid surface can be compressed reversibly to 60% strain. The more than four orders of magnitude lower modulus compared to its constituents is explained by considering local bending in the (nano)cellular structure, similar to cork and wings of beetles. As the rigidity of the polymer backbone is increased in just four monolayers the modulus of the composite increases by over 70%. Electro-optical map of the strain distribution over the area of compression and increase in modulus with thickness indicates the films have zero Poisson’s ratio

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

© The Author(s) 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/ genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.Peer reviewedFinal Published versio