Tem_357 Harnessing the Power of Digital Transformation, Artificial Intelligence and Big Data Analytics with Parallel Computing

Traditionally, 2D and especially 3D forward modeling and inversion of large geophysical datasets are performed on supercomputing clusters. This was due to the fact computing time taken by using PC was too time consuming. With the introduction of parallel computing, attempts have been made to perform computationally intensive tasks on PC or clusters of personal computers where the computing power was based on Central Processing Unit (CPU). It is further enhanced with Graphical Processing Unit (GPU) as the GPU has become affordable with the launch of GPU based computing devices. Therefore this paper presents a didactic concept in learning and applying parallel computing with the use of General Purpose Graphical Processing Unit (GPGPU) was carried out and perform preliminary testing in migrating existing sequential codes for solving initially 2D forward modeling of geophysical dataset. There are many challenges in performing these tasks mainly due to lack of some necessary development software tools, but the preliminary findings are promising. Traditionally, 2D and especially 3D forward modeling and inversion of large geophysical datasets are performed on supercomputing clusters. This was due to the fact computing time taken by using PC was too time consuming. With the introduction of parallel computing, attempts have been made to perform computationally intensive tasks on PC or clusters of personal computers where the computing power was based on Central Processing Unit (CPU). It is further enhanced with Graphical Processing Unit (GPU) as the GPU has become affordable with the launch of GPU based computing devices. Therefore this paper presents a didactic concept in learning and applying parallel computing with the use of General Purpose Graphical Processing Unit (GPGPU) was carried out and perform preliminary testing in migrating existing sequential codes for solving initially 2D forward modeling of geophysical dataset. There are many challenges in performing these tasks mainly due to lack of some necessary development software tools, but the preliminary findings are promising.Traditionally, 2D and especially 3D forward modeling and inversion of large geophysical datasets are performed on supercomputing clusters. This was due to the fact computing time taken by using PC was too time consuming. With the introduction of parallel computing, attempts have been made to perform computationally intensive tasks on PC or clusters of personal computers where the computing power was based on Central Processing Unit (CPU). It is further enhanced with Graphical Processing Unit (GPU) as the GPU has become affordable with the launch of GPU based computing devices. Therefore this paper presents a didactic concept in learning and applying parallel computing with the use of General Purpose Graphical Processing Unit (GPGPU) was carried out and perform preliminary testing in migrating existing sequential codes for solving initially 2D forward modeling of geophysical dataset. There are many challenges in performing these tasks mainly due to lack of some necessary development software tools, but the preliminary findings are promising

Compactness measurement using fuzzy multicriteria decision making for redistricting

This paper presents a new method for compactness assessment in redistricting planning using Fuzzy Multicriteria Decision Making. An Enhanced Compactness Index (ECI) representing the overall plan with respect to each criterion is obtained by using triangular fuzzy number. The ECI is generated based on the synthesis of the concepts of fuzzy set theory, AHP, /spl alpha/-cuts concept and index of optimism of district planners to estimate the degree of satisfaction of the judgements on a district plan. The proposed method is more flexible, simple and comprehensive with easy computation and efficiency which facilitates its uses In compactness measurement in redistricting application like school redistricting, election boundary redistricting and others. A case study on forest blocking Is presented to demonstrate Its applicability in redistricting applications with respect to their redistricting goals and criteria

A Multiobjective Spatial-based Zone Design Model (MoSZoD)

This paper presents a multiobjective approach for a spatial-based zone design model to the division of a land surface into two or more pieces. The model employs multiobjective optimization technique and Geographic Information System (GIS) as its components. This paper defines the problem based on multiobjective because it considers relationship among objectives and it is much more realistic to solve a real-world spatial zoning problem. The multiobjective decision analysis has been used to approximate and handle the Pareto-optimal solution to get optimal solution set after this paper applies a heuristic method to generate nondominated alternatives. This paper also aggregates the decision-makers' preferences by allowing interactivity with decision-makers. The flow of the model and its implementation in the GIS environment is presented. The computation has resulted in improvements in spatial zoning

Blood-Based Biomarkers of Aggressive Prostate Cancer

Purpose: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. Materials and Methods: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR) analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. Results: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8) and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8) 55% of G6 samples, 49% of G7(3+4), 79% of G7(4+3) and 83% of G8-10, while rejecting 98% of controls. Conclusion: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from biopsy and immediate therapy versus those more suited to an “active surveillance” strategy

Fuzzy multiple criteria decision making method in integrating compactness measurement for shape-based redistricting

Redistricting is a process to divide a land surface into two or more pieces by partitioning geographical zones or districts into territories, subject to some side constraints. Redistricting is an important spatial optimisation problem because it decides the space management of a particular region and is related to location dat. In some specific and scientific way, redistricting is multiple objectives combinatorial optimisation as it involves optimal arrangement of a group of discrete entities to satisfy various criteria for evaluating of the quantity of the given arrangement or solution

Multiple objectives hybrid metaheuristic for spatial-based redistricting : the framework and algorithms

This research has designed and developed a generic multiple objectives decision support framework for redistricting to provide a more realistic perception of redistricting problems. This thesis considers the multiple objective definition, identifies a wider range of alternatives, and describes the relationship between alternatives

Influence of Precursor Concentration and Temperature on the Formation of Nanosilver in Chemical Reduction Method

Nanosilver particles (NSPs) were produced by the reduction of silver nitrate using glucose as reducer, poly (vinyl pyrrolidone) as stabilizer and sodium hydroxide as reaction enhancer. Two parameters were investigated which are silver nitrate concentration (0.1 M, 0.5 M and 1.0 M) and reaction temperature (60°C and 80°C). Through spectral analysis using ultraviolet-visible spectrophotometer (UV-vis), all the samples recorded the maximum peak in the range of 384-411 nm which verified the formation of NSPs. TEM images showed the nanoparticles have spherical shape with the size range of 25-39 nm. Particle size and zeta potential analysis recorded the hydrodynamic size of nanoparticles in the range of 85-105 nm and the zeta potential ranging from -25 to -30 mV, under the pH value of 8. X-ray diffraction analysis showed that the NSPs have face center cubic (FCC) structure. All the produced NSPs surprisingly showed ferromagnetic-like behaviour based on the magnetization curves. FTIR result confirmed the presence of poly (vinyl pyrrolidone) on the NSPs surface. Furthermore, at the reaction temperature 60°C, the crystallite size, physical size as well as hydrodynamic size increased as the precursor concentration increased from 0.1 M to 0.5 M. However, as the precursor concentration further increases to 1.0 M, the size become smaller due to incomplete reduction process. In contrast, at 80°C, the sizes was gradually increased as the precursor concentration increases up to 1.0 M. In terms of controlled precursor concentration, the crystallite size and physical size become smaller as the temperature increases

Microalgae Cultivation in Palm Oil Mill Effluent (POME) Treatment and Biofuel Production

Palm oil mill effluent (POME) is the wastewater produced during the palm oil sterilization process, which contains substantial amounts of nutrients and phosphorous that are harmful to the environment. High BOD and COD of POME are as high as 100,000 mg/L, which endanger the environment. Effective pre-treatment of POME is required before disposal. As microalgae have the ability of biosorption on nutrients and phosphorous to perform photosynthesis, they can be utilized as a sustainable POME treatment operation, which contributes to effective biofuel production. Microalgae species C. pyrenoidosa has shown to achieve 68% lipid production along with 71% nutrient reduction in POME. In this study, a brief discussion about the impacts of POME that will affect the environment is presented. Additionally, the potential of microalgae in treating POME is evaluated along with its benefits. Furthermore, the condition of microalgae growth in the POME is also assessed to study the suitable condition for microalgae to be cultivated in. Moreover, experimental studies on characteristics and performance of microalgae are being evaluated for their feasibility. One of the profitable applications of POME treatment using microalgae is biofuel production, which will be discussed in this review. However, with the advantages brought from cultivating microalgae in POME, there are also some concerns, as microalgae will cause pollution if they are not handled well, as discussed in the last section of this paper

High grade prostate cancer (Gleason score 8 and above) biomarker gene list and differential expression ratio in Cohort II verification sample set (80 disease and 102 controls).

<p># The 7 biomarkers were picked up from the 10 that were verified in Cohort II samples, using gene-ratio algorithm, based on the best AUC of combined gene-pair.</p>†<p>Determined by qRT-PCR analysis using SAMSN1 as a partner gene, gene ratio was calculated using delta delta Ct calculation.</p>‡<p>Calculated by Mann-Whitney test.</p>*<p>area under receiver-operating-characteristic curve.</p