Numerical analysis of spray-dic modeling for fruit concentration drying process into powder based on computational fluid dynamic

The drying process is most popular preservation methods. It is important in producing the powder and natural dye by concentration fruit drying. Spray-DIC is one of the concentration techniques for drying process using the nozzle flow application in computational fluid dynamic. The mathematical modeling for drying process in this paper includes mass conservation and energy conservation of fruit concentration based on partial differential equation. The discretization of mathematical model will use the finite difference method with the initial and boundary conditions of nozzle flow application. The mathematical modeling computes numerical in sequential algorithm. Jacobi and Gauss-Seidel scheme will use to solve the linear system of mathematical modeling. The execution time, no of iteration, accuracy, root mean square error and maximum error are measured for investigating the numerical analysis. The results show the Gauss Seidel method is the alternative method compared to Jacobi method for solving the Spray-DIC modeling

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

Comparison between controlled and uncontrolled spray-DIC modeling for dehydration process

The work reported here focuses on the controllability expressions in the mathematical modeling of dehydration process of food concentrates in producing powder using spray-DIC (spray-Détente Instantaneé Controlee or spray-instant controlled pressure drop). This paper presents the second-order partial differential equations for mathematical modeling of moisture and heat transfer in spray-DIC process. This paper proposes the enhancement in the simple model of DIC technique with controllability expression to be used in the spray-DIC. The controllability expression in the drying process models gives better results when compared to the models without the controllability expression. The results were computed and shown by MATLAB 2013 with Windows 8 operating systems. The controllability expression in dehydration process model using the spray-DIC drier manage to succesfully control the dehydration process

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

Characterisation of a highly potent and near pan-neutralising anti-HIV monoclonal antibody expressed in tobacco plants

Background HIV remains one of the most important health issues worldwide, with almost 40 million people living with HIV. Although patients develop antibodies against the virus, its high mutation rate allows evasion of immune responses. Some patients, however, produce antibodies that are able to bind to, and neutralise different strains of HIV. One such ‘broadly neutralising’ antibody is ‘N6’. Identified in 2016, N6 can neutralise 98% of HIV-1 isolates with a median IC50 of 0.066 µg/mL. This neutralisation breadth makes N6 a very promising therapeutic candidate. Results N6 was expressed in a glycoengineered line of N. benthamiana plants (pN6) and compared to the mammalian cell-expressed equivalent (mN6). Expression at 49 mg/kg (fresh leaf tissue) was achieved in plants, although extraction and purification are more challenging than for most plant-expressed antibodies. N-glycoanalysis demonstrated the absence of xylosylation and a reduction in α(1,3)-fucosylation that are typically found in plant glycoproteins. The N6 light chain contains a potential N-glycosylation site, which was modified and displayed more α(1,3)-fucose than the heavy chain. The binding kinetics of pN6 and mN6, measured by surface plasmon resonance, were similar for HIV gp120. pN6 had a tenfold higher affinity for FcγRIIIa, which was reflected in an antibody-dependent cellular cytotoxicity assay, where pN6 induced a more potent response from effector cells than that of mN6. pN6 demonstrated the same potency and breadth of neutralisation as mN6, against a panel of HIV strains. Conclusions The successful expression of N6 in tobacco supports the prospect of developing a low-cost, low-tech production platform for a monoclonal antibody cocktail to control HIV in low-to middle income countries

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Sentiment analysis tools should take account of the number of exclamation marks!!!

There are various factors that affect the sentiment level expressed in textual comments. Capitalization of letters tends to mark something for attention and repeating of letters tends to strengthen the emotion. Emoticons are used to help visualize facial expressions which can affect understanding of text. In this paper, we show the effect of the number of exclamation marks used, via testing with twelve online sentiment tools. We present opinions gathered from 500 respondents towards “like” and “dislike” values, with a varying number of exclamation marks. Results show that only 20% of the online sentiment tools tested considered the number of exclamation marks in their returned scores. However, results from our human raters show that the more exclamation marks used for positive comments, the more they have higher “like” values than the same comments with fewer exclamations marks. Similarly, adding more exclamation marks for negative comments, results in a higher “dislike”

Engineering Secretory IgA against Infectious Diseases

The dawn of antibody therapy was heralded by the rise of IgG therapeutics. However, other antibody classes are at our disposal—one of the most exciting is IgA and is the most abundant antibody class within humans. Unlike IgG, it is uniquely specialized for mucosal applications due to its ability to form complex Secretory IgA (SIgA) molecules. Since the mucosa is constantly exposed to potential infectious agents, SIgA is pivotal to disease prevention as an important component of the mucosal barrier. Compared to IgG, SIgA has proven superior effectiveness in mucosal surfaces, such as the airway epithelium or the harsh gut environment. Despite this, hurdles associated with low yield and challenging purification have blocked SIgA therapeutic advancement. However, as a result of new antibody engineering strategies, we are approaching the next generation of (IgA-based) antibody therapies. Strategies include fine-tuning SIgA assembly, exploring different production platforms, genetic engineering to improve purification, and glycoengineering of different components. Due to its stability in mucosal environments, SIgA therapeutics would revolutionize passive mucosal immunotherapy—an avenue still underexploited by current therapeutics. This chapter will focus on the current perspectives of SIgA engineering and explore different approaches to unlocking the full therapeutic potential of SIgAs