Study of Ammonia-Nitrogen and Phosphorus in Parit Rasipan Canal During the Wet Season

The Parit Rasipan Canal's deteriorating water quality and eutrophication are both blamed on ammonia-nitrogen and phosphorus. In order to conserve freshwater resources, it is crucial to understand the relationship between land use and water quality. It is also crucial to evaluate how land use affects the pollutants load. In this study, eutrophication along the Parit Rasipan drainage system will be identified, water quality will be investigated in terms of phosphorus and ammonia nitrogen concentration and classified according to land use type during the wet season, and ammonia-nitrogen and phosphorus concentrations will be compared with Normalized Difference Vegetation Index (NDVI) using unmanned aerial vehicles (UAV). At a specific location along the Parit Rasipan drainage system, samples were taken. The USEPA PhosVer 3 with Acid Persulfate Digestion Procedure (Method 8190) and Nessler's Method (Method 8038) were used, respectively, to measure phosphorus and ammonia nitrogen. Ammonia nitrogen and phosphorus final effluent concentrations ranged from 3.21 mg/L to 5.96 mg/L and 0.36 mg/L to 1.55 mg/L, respectively. The residential area's water, on the other hand, had significant concentrations of ammonia, nitrogen, and phosphorus, which contributed to eutrophication in the wake of industrial, agricultural, and farming activities

Determination of Nitrogen, Phosphorus and Potassium in Soil and Plant Due to Husbandry Farming in Parit Rasipan Drainage System

Nutrient flow into waterways and soils has rapidly causing eutrophication, which causes ecological instability, dissolved oxygen (DO) depletion, extinction of aquatic species, and perhaps public health risks. Therefore, this study focuses on the determination of nitrogen, phosphorus, and potassium (NPK) in soil and plants in the Parit Rasipan drainage system due to the livestock farming area. The water quality of the drainage system is being monitored. Soil and plant at the study area have been collected by grid sampling method at 4 sampling points on wet and dry seasons for N determination using APHA 4500 NORG-B and PK using US EPA 6010B (ICP OES) methods, respectively. In addition, water quality has been monitored in situ for pH, DO, and temperature by a HI 98192 HANNA multiparameter instrument, while water samples were collected and analyzed for selected parameters including total nitrogen (TN), total phosphorus (TP) using the HACH DR6000 Spectrophotometer and potassium (K) by atomic absorption spectroscopy (AAS). The results of this study show that the concentrations of TN (3380–6290 mg/kg), TP (450-820 mg / kg) and K (381–931 mg/kg) in the soil are classified as moderate to very high. Due to the high concentration, TN (5270–6870 mg/kg), TP (262-769 mg / kg) and K (10200–16200 mg/kg) concentrations in plants are also high. For water quality monitoring, the data shows (pH 6.1-6.4), (DO 0.8–1.1 mg/L), and (temperature 26.6-29.2˚C) both in wet and dry seasons. The concentrations of TN, TP and K (average ± s.d) concentrations in water during the wet season are (TN 43 ± 1mg/L), (TP 0.4 ± 1mg/L) and (K 3.9 ± 0.2mg/L) while during the dry season are (TN 49 ± 1mg/L), (TP 0.7 ± 1mg/L) and (K 4.2 ± 0.2mg/L). In conclusion, from the analysis of the results, the Parit Rasipan drainage system has a high level of NPK in both the wet and dry seasons due to the livestock activities in the area

Evaluation of two mobile health apps in the context of smoking cessation: qualitative study of cognitive behavioral therapy (CBT) versus non-CBT-based digital solutions.

BACKGROUND: Mobile health (mHealth) apps can offer users numerous benefits, representing a feasible and acceptable means of administering health interventions such as cognitive behavioral therapy (CBT). CBT is commonly used in the treatment of mental health conditions, where it has a strong evidence base, suggesting that it represents an effective method to elicit health behavior change. More importantly, CBT has proved to be effective in smoking cessation, in the context of smoking-related costs to the National Health Service (NHS) having been estimated to be as high as £2.6bn in 2015. Although the evidence base for computerized CBT in mental health is strong, there is limited literature on its use in smoking cessation. This, combined with the cost-effectiveness of mHealth interventions, advocates a need for research into the effectiveness of CBT-based smoking cessation apps. OBJECTIVE: The objective of this study was, first, to explore participants' perceptions of 2 mHealth apps, a CBT-based app, Quit Genius, and a non-CBT-based app, NHS Smokefree, over a variety of themes. Second, the study aimed to investigate the perceptions and health behavior of users of each app with respect to smoking cessation. METHODS: A qualitative short-term longitudinal study was conducted, using a sample of 29 smokers allocated to one of the 2 apps, Quit Genius or Smokefree. Each user underwent 2 one-to-one semistructured interviews, 1 week apart. Thematic analysis was carried out, and important themes were identified. Descriptive statistics regarding participants' perceptions and health behavior in relation to smoking cessation are also provided. RESULTS: The thematic analysis resulted in five higher themes and several subthemes. Participants were generally more positive about Quit Genius's features, as well as about its design and information engagement and quality. Quit Genius users reported increased motivation to quit smoking, as well as greater willingness to continue using their allocated app after 1 week. Moreover, these participants demonstrated preliminary changes in their smoking behavior, although this was in the context of our limited sample, not yet allowing for the finding to be generalizable. CONCLUSIONS: Our findings underscore the use of CBT in the context of mHealth apps as a feasible and potentially effective smoking cessation tool. mHealth apps must be well developed, preferably with an underlying behavioral change mechanism, to promote positive health behavior change. Digital CBT has the potential to become a powerful tool in overcoming current health care challenges. The present results should be replicated in a wider sample using the apps for a longer period so as to allow for generalizability. Further research is also needed to focus on the effect of greater personalization on behavioral change and on understanding the psychological barriers to the adoption of new mHealth solutions

Planning a cluster randomized trial with unequal cluster sizes: practical issues involving continuous outcomes

BACKGROUND: Cluster randomization design is increasingly used for the evaluation of health-care, screeening or educational interventions. At the planning stage, sample size calculations usually consider an average cluster size without taking into account any potential imbalance in cluster size. However, there may exist high discrepancies in cluster sizes. METHODS: We performed simulations to study the impact of an imbalance in cluster size on power. We determined by simulations to which extent four methods proposed to adapt the sample size calculations to a pre-specified imbalance in cluster size could lead to adequately powered trials. RESULTS: We showed that an imbalance in cluster size can be of high influence on the power in the case of severe imbalance, particularly if the number of clusters is low and/or the intraclass correlation coefficient is high. In the case of a severe imbalance, our simulations confirmed that the minimum variance weights correction of the variation inflaction factor (VIF) used in the sample size calculations has the best properties. CONCLUSION: Publication of cluster sizes is important to assess the real power of the trial which was conducted and to help designing future trials. We derived an adaptation of the VIF from the minimum variance weights correction to be used in case the imbalance can be a priori formulated such as "a proportion (γ) of clusters actually recruit a proportion (τ) of subjects to be included (γ ≤ τ)"

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

A systematic review of natural health product treatment for vitiligo

<p>Abstract</p> <p>Background</p> <p>Vitiligo is a hypopigmentation disorder affecting 1 to 4% of the world population. Fifty percent of cases appear before the age of 20 years old, and the disfigurement results in psychiatric morbidity in 16 to 35% of those affected.</p> <p>Methods</p> <p>Our objective was to complete a comprehensive, systematic review of the published scientific literature to identify natural health products (NHP) such as vitamins, herbs and other supplements that may have efficacy in the treatment of vitiligo. We searched eight databases including MEDLINE and EMBASE for vitiligo, leucoderma, and various NHP terms. Prospective controlled clinical human trials were identified and assessed for quality.</p> <p>Results</p> <p>Fifteen clinical trials were identified, and organized into four categories based on the NHP used for treatment. 1) L-phenylalanine monotherapy was assessed in one trial, and as an adjuvant to phototherapy in three trials. All reported beneficial effects. 2) Three clinical trials utilized different traditional Chinese medicine products. Although each traditional Chinese medicine trial reported benefit in the active groups, the quality of the trials was poor. 3) Six trials investigated the use of plants in the treatment of vitiligo, four using plants as photosensitizing agents. The studies provide weak evidence that photosensitizing plants can be effective in conjunction with phototherapy, and moderate evidence that <it>Ginkgo biloba </it>monotherapy can be useful for vitiligo. 4) Two clinical trials investigated the use of vitamins in the therapy of vitiligo. One tested oral cobalamin with folic acid, and found no significant improvement over control. Another trial combined vitamin E with phototherapy and reported significantly better repigmentation over phototherapy only. It was not possible to pool the data from any studies for meta-analytic purposes due to the wide difference in outcome measures and poor quality ofreporting.</p> <p>Conclusion</p> <p>Reports investigating the efficacy of NHPs for vitiligo exist, but are of poor methodological quality and contain significant reporting flaws. L-phenylalanine used with phototherapy, and oral <it>Ginkgo biloba </it>as monotherapy show promise and warrant further investigation.</p

A Seriation Approach for Visualization-Driven Discovery of Co-Expression Patterns in Serial Analysis of Gene Expression (SAGE) Data

Background: Serial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives. Principal Findings: Here we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs ’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data. Conclusions: We explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a usefu