Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

<p><span>In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.</span></p>

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review

This study aimed to systematically compare three representative observational methods for assessing musculoskeletal loadings and their association with musculoskeletal disorders (MSDs): Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on a literature review without time limitations and was conducted on various factors related to observational methods. The comparisons showed that although it has a significant limitation of comprising only two classifications for the leg postures, (1) the RULA is the most frequently used method among the three techniques; (2) many studies adopted the RULA even in evaluation of unstable lower limb postures; (3) the RULA assessed postural loads as higher risk levels in most studies reviewed in this research; (4) the intra- and inter-reliabilities for the RULA were not low; and (5) the risk levels assessed by the RULA were more significantly associated with postural load criteria such as discomfort, MHTs and % capable at the trunk, and MSDs.

Testing the Four Basic Language Skills in the English Language Component of the Mature Students' Entrance Examinations: The Case of Six Ghanaian Universities

This study focused on the Mature Students’ Entrance Examinations (MSEE) which is a commonly used Ghanaian university placement examination. The fundamental aim was to evaluate the comprehensiveness of the English language component of the examination in the area of the four basic language skills (Reading, Writing, Speaking and Listening) tested. A second objective of the study was to explore the reasons behind the choices of the basic language skills tested in the MSEE. The multiple case study design was employed for this study, and the sources of data used were responses from in-depth interviews and the past questions. The data were subjected to analysis via thematic content analysis and document analysis respectively. The analysis highlights the specific contents of the past questions and their related basic language skills tested, as well as the reasons underlying the basic language skills tested. The results revealed that only two of the basic language skills (Reading and Writing) were tested, and time limitations and logistical challenges informed lecturers’ decisions not to test Speaking and Listening. The study recommends that Listening and Speaking tasks be incorporated into the examination to make it comprehensive. Received: 2 September 2021 / Accepted: 16 November 2021 / Published: 5 January 2022

Efficiency of Iranian Hospitals Before and After Health Sector Evolution Plan: A Systematic Review and Meta-Analysis Study

Purpose: Aging, chronic diseases, and development of expensive and advanced technologies has increased hospitals costs which have necessitated their efficiency in utilization of resources. This systematic review and meta-analysis study has assessed the efficiency of Iranian hospitals before and after the 2011 Health Sector Evolution Plan (HSEP).Methods: Internal and external databases were searched using specified keywords without considering time limitations. The retrieved articles were entered into EndNote considering inclusion and exclusion criteria, and the final analysis was performed after removing duplicates. Heterogeneity between the studies was assessed using Q and I2 tests. A forest plot with 95% confidence intervals (CI) was used to calculate different types of efficiency. The data were analyzed using STATA 14.Results: Random pooled estimation of hospitals technical, managerial, and scale efficiencies were 0.84 (95%CI = 0.78, 0.52), 0.9 (95%CI = 0.85, 0.94), and 0.88 (95%CI = 0.84, 0.91), respectively. Sub-group analysis on the basis of study year (before and after HSEP in 2011) indicated that random pool estimation of technical (0.86), managerial (0.91), and scale (0.90) efficiencies of Iranian hospitals for 2011 and before were better than technical (0.78), managerial (0.86), and scale (0.74) efficiencies after 2011.Conclusion: Type of hospital ownership was effective on hospital efficiency. However, HSEP has not improved hospital efficiency, so it is necessary for future national plans to consider all aspects.

Deep learning based MRI contrast synthesis using full volume prediction

In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

Thin Film and Nanostructured Pd-Based Materials for Optical H2 Sensors: A Review

In this review paper, we provide an overview of state-of-the-art Pd-based materials for optical H2 sensors. The first part of the manuscript introduces the operating principles, providing background information on the thermodynamics and the primary mechanisms of optical detection. Optical H2 sensors using thin films (i.e., films without any nanostructuring) are discussed first, followed by those employing nanostructured materials based on aggregated or isolated nanoparticles (ANPs and INPs, respectively), as well as complex nanostructured (CN) architectures. The different material types are discussed on the basis of the properties they can attribute to the resulting sensors, including their limit of detection, sensitivity, and response time. Limitations induced by cracking and the hysteresis effect, which reduce the repeatability and reliability of the sensors, as well as by CO poisoning that deteriorates their performance in the long run, are also discussed together with an overview of manufacturing approaches (e.g., tailoring the composition and/or applying functionalizing coatings) for addressing these issues.

Effects of Zeolite as a Drug Delivery System on Cancer Therapy: A Systematic Review

Zeolites and zeolitic imidazolate frameworks (ZIFs) are widely studied as drug carrying nanoplatforms to enhance the specificity and efficacy of traditional anticancer drugs. At present, there is no other systematic review that assesses the potency of zeolites/ZIFs as anticancer drug carriers. Due to the porous nature and inherent pH-sensitive properties of zeolites/ZIFs, the compounds can entrap and selectively release anticancer drugs into the acidic tumor microenvironment. Therefore, it is valuable to provide a comprehensive overview of available evidence on the topic to identify the benefits of the compound as well as potential gaps in knowledge. The purpose of this study was to evaluate the potential therapeutic applications of zeolites/ZIFs as drug delivery systems delivering doxorubicin (DOX), 5-fluorouracil (5-FU), curcumin, cisplatin, and miR-34a. Following PRISMA guidelines, an exhaustive search of PubMed, Scopus, Embase, and Web of Science was conducted. No language or time limitations were used up to 25th August 2021. Only full text articles were selected that pertained to the usage of zeolites/ZIFs in delivering anticancer drugs. Initially, 1279 studies were identified, of which 572 duplicate records were excluded. After screening for the title, abstract, and full texts, 53 articles remained and were included in the qualitative synthesis. An Inter-Rater Reliability (IRR) test, which included a percent user agreement and reliability percent, was conducted for the 53 articles. The included studies suggest that anticancer drug-incorporated zeolites/ZIFs can be used as alternative treatment options to enhance the efficacy of cancer treatment by mitigating the drawbacks of drugs under conventional treatment.

Advanced Hough-based method for on-device document localization

The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the docu-ment content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.