Biomass estimation and effect of meteorological parameters on Penaeus merguiensis stocks in the Hormuzgan province coastal waters

This study designed with objective on shrimp population structure biomass, management aspects and meteorological factors that affect on them. This investigation was carried out monthly by swept area method from Bandar Abbas to Sirik area waters. The optimum time for opening fishing season in Hormuzgan province waters in years 1999,2000, 2001 and 2002 respectively were about 9/10/1999, 4/10/2000, 2/10/2001, 28/9/2002 and total biomass for years 1999 up to 2002 were 731, 1350, 636 and 1708 tonnes, respectively. The Pearson correlation test between catch per unit of effort (CPUE) and meteorological parameters showed the significant correlation for mean temperature and moisture (P < 0.05) and for rainfall and evaporation did not show any significant correlation

Population structure of banana shrimp, Penaeus merguiensis De Man, 1888 in the Strait of Hormoz, Persian Gulf

Monthly length frequency data of banana shrimp, Penaeus merguiensis were collected from the coastal waters of the north east of the Persian Gulf, from January 2012 to January 2013. Software package FiSAT was used to analyze the length frequency data. Growth parameters, mortalities, exploitation rate and recruitment pattern were estimated from the carapace length in the area. Based on length – weight relationships, the estimated parameter a was 0.0015 for males and females and b was 2.8015 for males and 2.7852 for females. The estimated growth parameters: L∞=39.5 mm, K=1.8 year^−1 and t0=-0.08 year^−1 for males and L∞=50 mm, K=1.5 year^−1 and t0=-0.09 year^−1 for females. Longevity was estimated 1.67 and 2.00 years for males and females, respectively. The estimated mortality coefficients, were Z=5.9 (4.89 – 6.92) year−1 (α=0.05), M=2.9 year^−1 and F=3.0 year^−1 for males and Z=5.7 (4.94–6.37) year^−1 (α=0.05), M=2.5 year^−1 and F=3.2 year^−1 for females. The length at first capture, Lc estimated from the probability of capture routine was 23.3 mm for males and 24.2 mm for females. Sex ratio (M:F=1.1:1) was significantly different to 1:1. The exploitation rates of shrimp were 0.49 and 0.56 for males and females, respectively which could get higher because the survival of spawning stock after the fishing season is related to environmental factors and has less effect on the next year’s biomass. Recruitment was continued throughout the year but with one major peak in July to November

Association Between Common Carotid Artery Intima-Media Thickness and Proteinuria in Type 2 Diabetic Patients

Introduction. Proteinuria is the most reliable marker of diabetic nephropathy and an index of atherosclerosis and cardiovascular mortality in diabetic patients. In addition, common carotid artery intima-media thickness (CIMT) is a sensitive marker of early atherosclerosis and cardiovascular risk. The aim of this study was to evaluate the association between proteinuria and CIMT in type 2 diabetic patients. Materials and Methods. In a cross-sectional study, 154 patients with type 2 diabetes mellitus were enrolled. The CIMT was measured for all of the patients by one researcher. The 24-hour urine protein was measured using trichloroacetic acid method. Results. A total of 154 type 2 diabetic patients were enrolled with a mean diabetes mellitus duration of 8.91 +/- 6.99 years (95 women and 55 men). The mean urinary protein in the patients was 294.70 +/- 525.85 mg/24 h. The mean CIMT in all of the patients was 0.84 +/- 0.19 mm, and it was greater in the men than in the women (P = .03). The CIMT significantly correlated with patients' age (P < .001), systolic blood pressure (P < .001), and urinary protein excretion (P = .001). There was a marginal positive correlation between diabetes mellitus duration and the CIMT (P = .049). Conclusions. This study showed a significant association between CIMT, as a sensitive marker of macrovascular complication of diabetes mellitus, and proteinuria as an important index of microvascular complication of the disease

Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data

This work introduces a set of scalable algorithms to identify patterns of human daily behaviors. These patterns are extracted from multivariate temporal data that have been collected from smartphones. We have exploited sensors that are available on these devices, and have identified frequent behavioral patterns with a temporal granularity, which has been inspired by the way individuals segment time into events. These patterns are helpful to both end-users and third parties who provide services based on this information. We have demonstrated our approach on two real-world datasets and showed that our pattern identification algorithms are scalable. This scalability makes analysis on resource constrained and small devices such as smartwatches feasible. Traditional data analysis systems are usually operated in a remote system outside the device. This is largely due to the lack of scalability originating from software and hardware restrictions of mobile/wearable devices. By analyzing the data on the device, the user has the control over the data, i.e. privacy, and the network costs will also be removed

Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

The ubiquity and affordability of mobile and wearable devices has enabled us to continually and digitally record our daily life activities. Consequently, we are seeing the growth of data collection experiments in several scientific disciplines. Although these have yielded promising results, mobile and wearable data collection experiments are often restricted to a specific configuration that has been designed for a unique study goal. These approaches do not address all the real-world challenges of “continuous data collection” systems. As a result, there have been few discussions or reports about such issues that are faced when “implementing these platforms” in a practical situation. To address this, we have summarized our technical and user-centric findings from three lifelogging and Quantified Self data collection studies, which we have conducted in real-world settings, for both smartphones and smartwatches. In addition to (i) privacy and (ii) battery related issues; based on our findings we recommend further works to consider (iii) implementing multivariate reflection of the data; (iv) resolving the uncertainty and data loss; and (v) consider to minimize the manual intervention required by users. These findings have provided insights that can be used as a guideline for further Quantified Self or lifelogging studies