Word-based encryption algorithm using dictionary indexing with variable encryption key length

<p>This paper proposes a new algorithm for text encryption utilizing English words as a unit of encoding. The algorithm vanishes any feature that could be used to reveal the encrypted text through adopting variable code lengths for the English words, utilizing a variable-length encryption key, applying two-dimensional binary shuffling techniques at the bit level, and utilizing four binary logical operations with randomized shuffling inputs. English words that alphabetically sorted are divided into four lookup tables where each word has assigned an index. The strength of the proposed algorithm concluded from having two major components. Firstly, each lookup table utilizes different index sizes, and all index sizes are not multiples of bytes. Secondly, the shuffling operations are conducted on a two-dimensional binary matrix with variable length. Lastly, the parameters of the shuffling operation are randomized based on a randomly selected encryption key with varying size. Thus, the shuffling operations move adjacent bits away in a randomized fashion. Definitively, the proposed algorithm vanishes any signature or any statistical features of the original message. Moreover, the proposed algorithm reduces the size of the encrypted message as an additive advantage which is achieved through utilizing the smallest possible index size for each lookup table.</p>

Principles of Accommodation and Technology Update of Presbyopia Corrections using IR and UV Lasers

Purpose: To update and review the technology and principles of laser presbyopia reversal (LAPR) via sclera ablation and thermal shrinkage using infrared and UV lasers. Recent clinical data and new methods are also summarized. Study Design: LAPR using laser sclera ablation for increased accommodation of presbyopic eyes. Place and Duration of Study: New Taipei City, Taiwan, between June, 2021 and July, 2021. Methodology: Accommodation gain was obtained by laser scleral ablation of an eye using Er:YAG laser (at 2.94 um) using either line-pattern or dots-pattern outside the limbus in the oblique quadrants of an eye. The principles of accommodation and the key factors influencing the outcomes are discussed. The accommodation gain (AG) after the surgery is mainly due the change in anterior curvature and anterior shift of the lens. The effectiveness of ciliary body contraction for lens relaxation (or accommodation) may be influenced by the combined aging factors, including lens property changes (index, size, thickness and curvature), tissue elastic changes (in sclera and ciliary) and the zonular tension change. Classical theories of accommodation include Helmholtz and Schachar hypothesis. The key issues and new directions to overcome the drawbacks of the existing LAPR procedure (based on scleral ablation) are proposed. Clinical outcomes from two major groups, SurgiLight and Ace Vision, with two years follow are summarized. Results: Clinical outcomes during 2000 to 2020 are summarized showing an average Accommodation gain about 2.0 D, and postoperative egression about 0.25 D (after two years). Conclusion: Laser presbyopia reversal (LAPR) via sclera ablation using infrared laser is safe and effective, but suffers drawbacks of being invasive and procedures are too slow. New directions are required for improved outcomes.

Investigating PLGA microparticle swelling behavior reveals an interplay of expansive intermolecular forces

This study analyzes the swelling behavior of native, unmodified, spherically uniform, monodisperse poly(lactic-co-glycolic acid) (PLGA) microparticles in a robust high-throughput manner. This work contributes to the complex narrative of PLGA microparticle behavior and release mechanisms by complementing and extending previously reported studies on intraparticle microenvironment, degradation, and drug release. Microfluidically produced microparticles are incubated under physiological conditions and observed for 50 days to generate a profile of swelling behavior. Microparticles substantially increase in size after 15 days, continue increasing for 30 days achieving size dependent swelling indices between 49 and 83%. Swelling capacity is found to correlate with pH. Our study addresses questions such as onset, duration, swelling index, size dependency, reproducibility, and causal mechanistic forces surrounding swelling. Importantly, this study can serve as the basis for predictive modeling of microparticle behavior and swelling capacity, in addition to providing clues as to the microenvironmental conditions that encapsulated material may experience.

On querying historical k-cores

Many real-world relationships between entities can be modeled as temporal graphs, where each edge is associated with a timestamp or a time interval representing its occurrence. K -core is a fundamental model used to capture cohesive subgraphs in a simple graph and have drawn much research attention over the last decade. Despite widespread research, none of the existing works support the efficient querying of historical k -cores in temporal graphs. In this paper, given an integer k and a time window, we study the problem of computing all k -cores in the graph snapshot over the time window. We propose an index-based solution and several pruning strategies to reduce the index size. We also design a novel algorithm to construct this index, whose running time is linear to the final index size. Lastly, we conducted extensive experiments on several real-world temporal graphs to show the high effectiveness of our index-based solution.

Big Data Full-Text Search Index Minimization Using Text Summarization

An efficient full-text search is achieved by indexing the raw data with an additional 20 to 30 percent storagecost. In the context of Big Data, this additional storage space is huge and introduces challenges to entertainfull-text search queries with good performance. It also incurs overhead to store, manage, and update the largesize index. In this paper, we propose and evaluate a method to minimize the index size to offer full-text searchover Big Data using an automatic extractive-based text summarization method. To evaluate the effectivenessof the proposed approach, we used two real-world datasets. We indexed actual and summarized datasets usingApache Lucene and studied average simple overlapping, Spearman’s rho correlation, and average rankingscore measures of search results obtained using different search queries. Our experimental evaluation showsthat automatic text summarization is an effective method to reduce the index size significantly. We obtained amaximum of 82% reduction in index size with 42% higher relevance of the search results using the proposedsolution to minimize the full-text index size.

WEB APP: String Similarity Search - A Hash-based Approach

During this Project, we study string similarity search based on edit distance that is supported by many database management systems like Oracle and PostgreSQL. Given the edit distance, ed(s, t), between two strings, s and t, the string similarity search is to search out each string t in a string database D which is almost like a query string s such that ed(s, t) = t for a given threshold t. Within the literature, most existing work takes a filter-and-verify approach, where the filter step is introduced to reduce the high verification cost of 2 strings by utilizing an index engineered offline for D. The two up-to-date approaches are prefix filtering and native filtering. We have a tendency to propose 2 new hash- primarily based labeling techniques, named OX label and XX label, for string similarity search. We have a tendency to assign a hash-label, H s , to a string s, and prune the dissimilar strings by comparing 2 hash-labels, H s and H t , for two strings s and t within the filter step. The key idea is to take the dissimilar bit- patterns between 2 hash-labels.Our hash-based mostly approaches achieve high efficiency, and keep its index size and index construction time one order of magnitude smaller than the present approaches in our experiment at the same time.

Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

Nanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject "non-target" DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing with the help of efficient pangenome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics (half-maximal exact matches) in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONI's index and peak memory footprint are also 15 to 4 times smaller than minimap2, respectively. These improvements become even more pronounced with even larger reference databases; SPUMONI's index size scales sublinearly with the number of reference genomes included. This could enable accurate targeted sequencing even in the case where the targeted strains have not necessarily been sequenced or assembled previously. SPUMONI is open source software available from https://github.com/oma219/spumoni.

Multinomial Logistic Regression Model to Identify Factors Associated with Food Insecurity in Rural Households in Nepal

Background: Food is basics of our lives and many people experiences food insecurity at some time because of food deprivation and lack of access to food due to different resource constraints. It is a global challenge and threatens the rural people in developing countries like Nepal. Objective: The objective of the study is to identify the factors associated with food insecurity in rural area of Nepal. Materials and Methods: The analysis is based on rural household data extracted from the data of Nepal Demographic and Health Survey 2016. The dependent variable food insecurity status was measured in four levels namely food secure, mildly food insecure, moderately food insecure and severely food insecure household using Household Food Insecurity Access Scale. Independent variables were categorical and quantitative variables. In order to identify the factors associated with food insecurity, ordinal logit model was fitted initially. Due to violation of test of parallel lines by overall as well as some of the independent variables, multinomial logistic regression model was finally adopted by examining the model adequacy test. Results: The fitted multinomial logistic regression satisfied the diagnostic test including tests of goodness of fit, multicolinearity diagnostic criteria and minimum criteria of utilization of the model with about 29% predictive power. The variables ecological region, wealth index, size of agriculture land, any member(s) having saving account in any financial institution, any member(s) had gone to foreign employment in last 5 years other than India, family size, number of members completed secondary education and household member rearing cattle(s) were found to be significant. The poorest households (HHs) had 3.14 (CI: 1.88-5.26) times, poorer HHs 2.51 (CI: 1.55-4.07) times and moderate HHs 1.42 times higher chances of being severely food insecure relative to rich HHs. Conclusion: The study revealed that food insecurity of the rural HHs increases with decrease in the wealth index, size of land and number of members of the HHs with completed secondary education. The food insecurity of the households decreases with increase in the access to bank service.