THE USE OF A MULTI-OBJECTIVE GENETIC ALGORITHM FOR CALIBRATION OF WATER QUALITY NUMERICAL MODEL OF EAGLE CREEK RESERVOIR, IN

poster abstractWater quality models used for water resource management require large amounts of input parameters, whose values may or may not be readily available. The calibration of these models involves the adjustment of several input parameters. The credibility of calibrated models is judged based on their agreement with actual data. However, calibration of water quality numerical models can be an exceptionally computationally challenging process. In this research, the Environmental Fluid Dynamic Code’s (EFDC) HEM3D water quality model was developed for the Eagle Creek Reservoir in order to model three algal groups (cyanobacteria, diatoms, and greens) as well as reservoir nutrient dynamics. A multi-objective genetic algorithm was then used for calibration by adjusting predetermined input parameters within a certain range and based on the model’s agreement with observed data in the reservoir. The genetic algorithm was parallelized to work across a network of machines and on multiple threads. This presentation will demonstrate the advantages of using such a parallelized genetic algorithm for efficiently calibrating computationally expensive numerical models

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively

Super Annigeri 1 and improved JG 74: Two Fusarium wilt-resistant introgression lines developed using marker-assisted backcrossing approach in chickpea (Cicer arietinum L.)

Annigeri 1 and JG 74 are elite high yielding desi cultivars of chickpea with medium maturity duration and extensively cultivated in Karnataka and Madhya Pradesh, respectively. Both cultivars, in recent years, have become susceptible to race 4 of Fusarium wilt (FW). To improve Annigeri 1 and JG 74, we introgressed a genomic region conferring resistance against FW race 4 (foc4) through marker-assisted backcrossing using WR 315 as the donor parent. For foreground selection, TA59, TA96, TR19 and TA27 markers were used at Agricultural Research Station, Kalaburagi, while GA16 and TA96 markers were used at Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur. Background selection using simple sequence repreats (SSRs) for the cross Annigeri 1 × WR 315 in BC1F1 and BC2F1 lines resulted in 76–87% and 90–95% recurrent parent genome recovery, respectively. On the other hand, 90–97% genome was recovered in BC3F1 lines in the case of cross JG 74 × WR 315. Multilocation evaluation of 10 BC2F5 lines derived from Annigeri 1 provided one superior line referred to as Super Annigeri 1 with 8% increase in yield and enhanced disease resistance over Annigeri 1. JG 74315-14, the superior line in JG 74 background, had a yield advantage of 53.5% and 25.6% over the location trial means in Pantnagar and Durgapura locations, respectively, under Initial Varietal Trial of All India Coordinated Research Project on Chickpea. These lines with enhanced resistance and high yield performance are demonstration of successful deployment of molecular breeding to develop superior lines for FW resistance in chickpea

Effective modeling for integrated water resource management: a guide to contextual practices by phases and steps and future opportunities

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management. © 2019 Elsevier Lt

Climate Change and Heat Stress Tolerance in Chickpea

Chickpea (Cicer arietinum L.) is a cool-season food legume and suffers heavy yield losses when exposed to heat stress at the reproductive (flowering and podding) stage. Heat stress is increasingly becoming a severe constraint to chickpea production due to the changing scenario of chickpea cultivation and expected overall increase in global temperatures due to climate change. A temperature of 35 °C was found to be critical in differentiating heat-tolerant and heat-sensitive genotypes in chickpea under field conditions. Large genetic variations exist in chickpea for reproductive-stage heat tolerance. Many heat-tolerant genotypes have been identified through screening of germplasm/breeding lines under heat stress conditions in the field. A heat-tolerant breeding line ICCV 92944 has been released in two countries (as Yezin 6 in Myanmar and JG 14 in India) and is performing well under late-sown conditions. Heat stress during the reproductive phase adversely affects pollen viability, fertilization, pod set, and seed development, leading to abscission of flowers and pods, and substantial losses in grain yield. Studies on physiological mechanisms and genetics of heat tolerance, and identification of molecular markers and candidate genes for heat tolerance, are in progress. The information generated from these studies will help in developing effective and efficient breeding strategies for heat tolerance. The precision and efficiency of breeding programs for improving heat tolerance can be enhanced by integrating novel approaches, such as marker-assisted selection, rapid generation turnover, and gametophytic selection. Chickpea cultivars with enhanced heat tolerance will minimize yield losses in cropping systems/growing conditions where the crop is exposed to heat stress at the reproductive stage

Spermine oxidase (SMO) activity in breast tumor tissues and biochemical analysis of the anticancer spermine analogues BENSpm and CPENSpm

Background: Polyamine metabolism has a critical role in cell death and proliferation representing a potential target for intervention in breast cancer (BC). This study investigates the expression of spermine oxidase (SMO) and its prognostic significance in BC. Biochemical analysis of Spm analogues BENSpm and CPENSpm, utilized in anticancer therapy, was also carried out to test their property in silico and in vitro on the recombinant SMO enzyme. Methods: BC tissue samples were analyzed for SMO transcript level and SMO activity. Student’s t test was applied to evaluate the significance of the differences in value observed in T and NT samples. The structure modeling analysis of BENSpm and CPENSpm complexes formed with the SMO enzyme and their inhibitory activity, assayed by in vitro experiments, were examined. Results: Both the expression level of SMO mRNA and SMO enzyme activity were significantly lower in BC samples compared to NT samples. The modeling of BENSpm and CPENSpm complexes formed with SMO and their inhibition properties showed that both were good inhibitors. Conclusions: This study shows that underexpression of SMO is a negative marker in BC. The SMO induction is a remarkable chemotherapeutical target. The BENSpm and CPENSpm are efficient SMO inhibitors. The inhibition properties shown by these analogues could explain their poor positive outcomes in Phases I and II of clinical trials

High temperature tolerance in grain legumes

High temperature stress (or heat stress) during reproductive stages is becoming aserious constraint toproductivity of grain legumes as their cultivation is expanding to warmer environments and temperature variability is increasing due to climate change.Large genetic variations exist ingrainlegumesforheat tolerance whichcan be exploited for development of locally adapted heat tolerant cultivars. Heat tolerant cultivars will be more resilient to the impacts of climate change, allow flexibility in sowing dates and enhance opportunities for expanding area of grain legumes to new nichesand croppingsystems

Super Annigeri 1 and improved JG 74: two Fusarium wilt-resistant introgression lines developed using marker-assisted backcrossing approach in chickpea (Cicer arietinum L.)

Annigeri 1 and JG 74 are elite high yielding desi cultivars of chickpea with medium maturity duration and extensively cultivated in Karnataka and Madhya Pradesh, respectively. Both cultivars, in recent years, have become susceptible to race 4 of Fusarium wilt (FW). To improve Annigeri 1 and JG 74, we introgressed a genomic region conferring resistance against FW race 4 (foc4) through marker-assisted backcrossing using WR 315 as the donor parent. For foreground selection, TA59, TA96, TR19 and TA27 markers were used at Agricultural Research Station, Kalaburagi, while GA16 and TA96 markers were used at Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur. Background selection using simple sequence repreats (SSRs) for the cross Annigeri 1 × WR 315 in BC1F1 and BC2F1 lines resulted in 76–87% and 90–95% recurrent parent genome recovery, respectively. On the other hand, 90–97% genome was recovered in BC3F1 lines in the case of cross JG 74 × WR 315. Multilocation evaluation of 10 BC2F5 lines derived from Annigeri 1 provided one superior line referred to as Super Annigeri 1 with 8% increase in yield and enhanced disease resistance over Annigeri 1. JG 74315-14, the superior line in JG 74 background, had a yield advantage of 53.5% and 25.6% over the location trial means in Pantnagar and Durgapura locations, respectively, under Initial Varietal Trial of All India Coordinated Research Project on Chickpea. These lines with enhanced resistance and high yield performance are demonstration of successful deployment of molecular breeding to develop superior lines for FW resistance in chickpea

A Prospective Examination of Clinician and Supervisor Turnover Within the Context of Implementation of Evidence-Based Practices in a Publicly-Funded Mental Health System

Staff turnover rates in publicly-funded mental health settings are high. We investigated staff and organizational predictors of turnover in a sample of individuals working in an urban public mental health system that has engaged in a system-level effort to implement evidence-based practices. Additionally, we interviewed staff to understand reasons for turnover. Greater staff burnout predicted increased turnover, more openness toward new practices predicted retention, and more professional recognition predicted increased turnover. Staff reported leaving their organizations because of personal, organizational, and financial reasons; just over half of staff that left their organization stayed in the public mental health sector. Implications include an imperative to focus on turnover, with a particular emphasis on ameliorating staff burnout