Daytime precipitation estimation using bispectral cloud classification system

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society

Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

Pengaruh Price Earning Ratio (Per), Price to Book Value (Pbv), Debt to Equity Ratio (Der), Return on Asset (Roa), Terhadap Return Saham Dalam Jakarta Islamic Index

The stock market is the place to look for funding activities of the company to fund its operations . The main function of the capital market is as a means of capital formation and accumulation of funds for the financing of a company . In order to facilitate the needs of Muslims in Indonesia who have interest in investment in capital market products , then developed Islamic capital markets in accordance with the basic principles of Islam which is expected to add an alternative means of investment in Indonesia in addition to the investment , which is already known and growing banking sector . Islamic stocks are stocks that the operation does not conflict with Islamic law , either the product or its management . Grouping Islamic stocks are in the Jakarta Islamic Index ( JII ) at the Indonesian Stock Exchange ( BEI ) . Getting a return ( profit ) is the main objective of the trading activity of investors in the capital market . Analysis of the company is one of the means used to analyze a stock investor to do by looking at the financial performance assessed with financial ratios . This study aimed to analyze the effect of Price Earning Ratio ( PER ) , Price to Book Value ( PBV ) Debt To Equity Ratio ( DER ) and Return on Assets ( ROA ) on stock returns is incorporated in the Jakarta Islamic Index ( JII ) the period of 2008 – 2012

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society

Integrating AI into engineering education: Leveraging CDIO for enhanced assessment strategies

The recent advancements of generation artificial intelligence (Gen AI) and large language model (LLM AI) in content creation and manipulation have brought significant challenges to teaching and learning in various disciplines. These challenges have called for a transformative change in traditional teaching and assessment strategies to accommodate the latest technological advancements, without compromising the integrity of assessments and evaluations. Many higher education institutions (HEIs) have employed critical thinking tasks within their evaluation methods to stimulate a thought process that would be difficult to simulate by AI-assisted technologies. However, it has been repeatedly observed that even analytical topics such as mathematics and core engineering modules were susceptible to the corruptive use of AI-assisted technologies in their assessments, which fundamentally demeans the educational qualifications’ quality across the HEIs. Equally well researched and developed machine learning AI (ML AI) can assist in data processing, pattern recognition and analysis. Having witnessed the advantages of CDIO (conceive, design, implement, operate)-based curricula in fostering innovation, critical thinking, and analytical skills across engineering, technology and design courses, this paper designs a modern strategy that harnesses the novelties of AI technologies within a CDIO-based pedagogy. This is as Gen AI has the potential to assist students in evaluating their conceived ideas at “C” stage, feedback on “D” and machine learning AI (ML AI) analysis of “O” stages, shortening the project lifecycle. Using the existing case-studies on CDIO-based teaching and learning, the intersection of CDIO principles and AI technologies have been mapped to identify opportunities and interferences. The findings demonstrated the empowerment of each CDIO stage, conceive, design, implement and operation, through the effective and optimum use of technology, both in teaching methods and in assessments. Therefore, this paper presents a modern approach to teaching and learning, acknowledging the opportunities and risks of AI within the engineering curriculum. It demonstrates the potential benefits of AI in CDIO pedagogy, to not only reduce the risks but also harness the potential benefits as a stimulating tool rather than a replicating technology. The output of this work offers rich insights to HEIs who seek to embrace the positive aspects of AI technologies while preserving the resilience and integrity of their practices in this era of technology

Antimicrobial Activity of 3 Substituted 6 Nitrobenzo xazolinones 2, 6 Chlorobenzoxazolinones 2 and Benzo xazolin 2 Thiones

The results of antimicrobial screening of several 3- substituted 6- nitrobenze xazolinones-2(I), 6-chlorobenzoxazolinones-2(II) and benzoxazolin-2 thiones(III) have been reported

Pengaruh Partisipasi Penganggaran, Job Relevant Information (JRI) dan Volatilitas Lingkungan terhadap Kinerja Manajerial pada Perusahaan Manufaktur di Indonesia

The study attempts to examine the relationship between budgetary participation and individual performance. In so doing the study include the level of Job Relevant Information (JRI) is treated as an intervening variable, and at the same time considers environmental volatility as a moderating variables.Data for this study were collected by using questionnaires, which were mailed to 500 department managers chosen from Indonesian Capital Market Directory. There were 74 responses of which 68 were complete and hence USAble.The study concludes that the budgetary participation effects the individual performance through JRI. Environmental volatility has no significant effects on budgetary participation and did not moderate the effects of participative budgeting on JRI. Interactions between environmental volatility and JRI had no significant effect on managerial performance

Seroprevalence of Hepatitis B and C among health care workers in Omdurman, Sudan

Background: Health care workers in developing countries including Sudan are at serious risk of infection from blood-borne pathogens particularly HBV and HCV, because of high prevalence of such pathogens in these countries. Methods: A cross sectional study was conducted during November 2007 to determine the seroprevalence of hepatitis B virus ( HBV ) and C ( HCV ) and their associated risk factors among the health care workers ( HCW ) of an urban referral hospital in central Sudan . Enzyme Linked Immunosorbent Assay technique was used to test the blood samples and a questionnaire to collect socio - demographic data of the study participants ( n = 211 ) . Results: The seroprevalence of HBsAg was 2.4%. None of the study participants had HCV antibodies in their blood samples. Age and past history of jaundice were significantly associated with HBsAg infection. The categories of HCW with higher risk of occupational transmission for HBsAg were nurses and non professional staff. Conclusion: The occupation risk of HBV infection among the HCW in this study was high for the nurses and cleaning staff. Effective prevention of HBV infection is mainly by vaccination to unexposed HCW, however acceptance of vaccine should be promoted for such high risk categories. Keywords: Hepatitis B&C; Health care workers; Sudan.Sudan Journal of Medical Sciences Vol. 3 (3) 2008: pp. 201-20

Couching in Nigeria: prevalence, risk factors and visual acuity outcomes.

PURPOSE: Couching is an ancient treatment for cataract which is still practiced in some of the poorer developing countries, particularly in sub-Saharan Africa. The purpose of this study is to describe risk factors for couching and visual acuity outcomes in a nationally representative sample of adults aged 40 years and above in Nigeria. METHODS: Probability in proportion size methods were used to identify a representative sample. Of the 15,375 adults enumerated, 13,582 were interviewed and examined. Examination included logMar acuities, slit lamp examination and dilated fundoscopy with digital fundus imaging. RESULTS: Almost half of the 583 eyes undergoing a procedure for cataract had been couched (249 eyes, 42.7%). Individuals living in rural areas (P = 0.033) and in the two underserved northern administrative zones (P = 0.33; P = 0.002) were more likely to have been couched. Visual outcomes were poor according to World Health Organization categories, with 55.8% of people and 73.1% of eyes having a presenting visual acuity of less than 3/60 and only 9.7% and 2.4% of people and eyes respectively having a good outcome (6/18 or better). None were wearing an aphakic correction, and with correction acuities improved but 42.6% of eyes were still blind (< 3/60). CONCLUSIONS: Couching is still widely practiced in Nigeria and visual outcomes are very poor. The population needs to be made aware of the risks associated with the procedure, and services for high quality, affordable cataract surgery need to be expanded, particularly in rural areas and in the north of the country

Performance Models for Split-execution Computing Systems

Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardware limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.Comment: Presented at 18th Workshop on Advances in Parallel and Distributed Computational Models [APDCM2016] on 23 May 2016; 10 page