Generalizations of Length Limited Huffman Coding for Hierarchical Memory Settings

In this paper, we study the problem of designing prefix-free encoding schemes having minimum average code length that can be decoded efficiently under a decode cost model that captures memory hierarchy induced cost functions. We also study a special case of this problem that is closely related to the length limited Huffman coding (LLHC) problem; we call this the soft-length limited Huffman coding problem. In this version, there is a penalty associated with each of the n characters of the alphabet whose encodings exceed a specified bound D(? n) where the penalty increases linearly with the length of the encoding beyond D. The goal of the problem is to find a prefix-free encoding having minimum average code length and total penalty within a pre-specified bound P. This generalizes the LLHC problem. We present an algorithm to solve this problem that runs in time O(nD). We study a further generalization in which the penalty function and the objective function can both be arbitrary monotonically non-decreasing functions of the codeword length. We provide dynamic programming based exact and PTAS algorithms for this setting

Prediction Of Strength Properties Of Geopolymer Concrete Using Artificial Intelligence Techniques

Several studies have successfully used fly-ash (FA)-like waste material for the manufacturing of geopolymer concrete (GPC). This study uses gene expression programming (GEP), a type of soft computing approach, to produce an empirical equation that estimates the compressive strength fc0 of GPC using FA. Through a thorough analysis of the published research, a consistent, large, and trustworthy data set is assembled in order to develop a model. 298 fc0 experimental outcomes make up the collected data set. The following are considered explanatory variables: the amount of extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the specimen's age (A), the curing duration (t), the ratio of fine aggregate to total aggregate (F/AG), the percentage of total aggregate by volume (%AG), the molarity of the NaOH solution, the activator or alkali to FA ratio (AL/FA), the ratio of sodium oxide (Na2O) to water (N/W) for preparing Na2SiO3 solution, and the ratio of Na2SiO3 to NaOH (Ns/No). An empirical GEP equation is put forth to calculate the fc0 of GPC using FA. The suggested model's precision, applicability, and forecasting capacity were assessed using parametric analysis, statistical verification, and a comparison with both linear and non-linear regression equations

Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement

Background: Common carotid artery lumen diameter (LD) ultrasound measurement systems are either manual or semi-automated and lack reproducibility and variability studies. This pilot study presents an automated and cloud-based LD measurements software system (AtheroCloud) and evaluates its: (i) intra/inter-operator reproducibility and (ii) intra/inter-observer variability. Methods: 100 patients (83 M, mean age: 68 ± 11 years), IRB approved, consisted of L/R CCA artery (200 ultrasound images), acquired using a 7.5-MHz linear transducer. The intra/inter-operator reproducibility was verified using three operator's readings. Near-wall and far carotid wall borders were manually traced by two observers for intra/inter-observer variability analysis. Results: The mean coefficient of correlation (CC) for intra- and inter-operator reproducibility between all the three automated reading pairs were: 0.99 (P < 0.0001) and 0.97 (P < 0.0001), respectively. The mean CC for intra- and inter-observer variability between both the manual reading pairs were 0.98 (P < 0.0001) and 0.98 (P < 0.0001), respectively. The Figure-of-Merit between the mean of the three automated readings against the four manuals were 98.32%, 99.50%, 98.94% and 98.49%, respectively. Conclusions: The AtheroCloud LD measurement system showed high intra/inter-operator reproducibility hence can be adapted for vascular screening mode or pharmaceutical clinical trial mode

A survey of big data classification strategies

Big data plays nowadays a major role in finance, industry, medicine, and various other fields. In this survey, 50 research papers are reviewed regarding different big data classification techniques presented and/or used in the respective studies. The classification techniques are categorized into machine learning, evolutionary intelligence, fuzzy-based approaches, deep learning and so on. The research gaps and the challenges of the big data classification, faced by the existing techniques are also listed and described, which should help the researchers in enhancing the effectiveness of their future works. The research papers are analyzed for different techniques with respect to software tools, datasets used, publication year, classification techniques, and the performance metrics. It can be concluded from the here presented survey that the most frequently used big data classification methods are based on the machine learning techniques and the apparently most commonly used dataset for big data classification is the UCI repository dataset. The most frequently used performance metrics are accuracy and execution time

Comparative evaluation of antiplaque and antigingivitis effects of an herbal and chlorine dioxide mouthwashes: A clinicomicrobiological study

Aim: The aim of the present study was to compare the efficacy of herbal mouthwash and chlorine dioxide mouthwash in reduction of plaque and gingivitis. Settings and Design: In a randomized clinical trial, forty patients were randomly selected and divided equally into two groups. Materials and Methods: After professional oral prophylaxis, the clinical parameters plaque index, gingival index, and modified sulcular bleeding index were recorded at baseline, 7th day, 14th day, and 21st day. The plaque samples were collected from gingival sulcus with an absorbent sterile paper point and were stored in a thioglycollate broth, then sent for microbiological examination. The microbial colony-forming units were assessed at baseline, 7th day, 14th day, and 21st day for Streptococcus mutans, Tannerella forsythia, and Fusobacterium nucleatum. Results: There was a statistical significant reduction in both clinical and microbiological parameters were observed with use of both the mouthwashes. However, herbal mouthwash was more effective in reducing the plaque and gingivitis than chlorine dioxide mouthwash. Conclusion: Herbal mouthwash was statistically efficacious in controlling plaque and gingivitis with potent antimicrobial activity