A Study on Evolving Optimal Cropping Patterns in Groundwater Over-exploited Region of Perambalur District of Tamil Nadu

Falling groundwater tables and depletion of economically accessible groundwater resources would have major social and economic consequences. The present study has been taken up with the overall objective of evolving optimal crop plans to sustain the use of groundwater resources for irrigation. Perambalur district was purposively selected for the study as it mainly depends on groundwater for its irrigation. Linear programming technique was used to evolve optimal crop plans. The constraints identified were primarily irrigation water, besides land availability during the cultivating seasons and capital. Six typical farms were selected, one each for the open well, wells in tank command area and tubewell-irrigated farms in critical and over-exploited groundwater regime and also for semi-critical and safe groundwater regime. The results of the optimal crop plans derived showed that the irrigation water-use in the critical period could be reduced by 24.30, 4.54 and 51.71 hours of pumping in ordinary wells, wells in tank command area and tubewell-irrigated farms, respectively in critical and over-exploited groundwater regime sample farms. In the semi-critical and safe groundwater regime sample farms, the optimal crop plans revealed that the irrigation water-use in the critical period could be reduced by 4.61, 3.99, and 4.73 hours of pumping in ordinary wells, wells in tank command area and tubewell-irrigated farms, respectively. Area under high water intensive crops namely, paddy and sugarcane declined almost in all the optimal crop plans. Area under low water intensive crops (groundnut, gingelly and tapioca) showed an increasing trend in all optimal crop plans. The net income of the sample farms increased marginally or considerably in the optimal crop plans of both the critical and overexploited groundwater regime sample farms and semi-critical and the safe groundwater regime sample farmsAgricultural and Food Policy,

Generic task problem solvers in Soar

Two trends can be discerned in research in problem solving architectures in the last few years. On one hand, interest in task-specific architectures has grown, wherein types of problems of general utility are identified, and special architectures that support the development of problem solving systems for those types of problems are proposed. These architectures help in the acquisition and specification of knowledge by providing inference methods that are appropriate for the type of problem. However, knowledge based systems which use only one type of problem solving method are very brittle, and adding more types of methods requires a principled approach to integrating them in a flexible way. Contrasting with this trend is the proposal for a flexible, general architecture contained in the work on Soar. Soar has features which make it attractive for flexible use of all potentially relevant knowledge or methods. But as the theory Soar does not make commitments to specific types of problem solvers or provide guidance for their construction. It was investigated how task-specific architectures can be constructed in Soar to retain as many of the advantages as possible of both approaches. Examples were used from the Generic Task approach for building knowledge based systems. Though this approach was developed and applied for a number of problems, the ideas are applicable to other task-specific approaches as well

Patters of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey

Background: Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective: The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods: Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results: About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95White individuals (OR 1.65, 95incomes greater than US$75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions: The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society [Abstract copyright: ©Ranganathan Chandrasekaran, Vipanchi Katthula, Evangelos Moustakas. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.10.2020.

Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders’ willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36–1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89–7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37–2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16–2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03–3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing

Deteriorating Inventory Model For Two Parameter Weibull Demand With Shortages

In this paper a deteriorating inventory model have been developed for two parameter Weibull demand rate. Shortages are allowed and are completely backlogged .This inventory system follows an two-parameter exponnential distribution deterioration rate in which the holding cost is constant .The results are described with the numerical example and sensitivity analysis. Keywords: Deterioration, Exponential distribution, holding cost, Inventory, shortages, Weibull demand rate

Ecotoxicological Studies on the Bioaccumulation of the Heavy metals in the Vellore population,TamilNadu, India

The study was totally aimed to know the extent of bioaccumulation through the plants from the sediments. Total heavy metals concentration (chromium, lead, zinc, nickel and cadmium) were undertaken in the food composites collected from Puliyanthangal village near Ranipet of Vellore district, Tamilnadu, India. There are around 240 tanneries in these areas, besides number of  ceramic, refractory, boiler auxiliaries plant and chromium chemical factories. Since the ground water was heavily contaminated with heavy metals, studies were carried out to know if there was transfer of heavy metals in the food chain. Drinking water samples, sediment samples as well as food samples were  collected and analysed for Chromium, Lead, Nickel, Zinc and Cadmium levels. The Environmental risk assessment was undertaken in the foodchain for all the above metals through water, sediment and through the grains and the crops grown in that area. Bioaccumulation factor was calculated with respect to the above parameters and it was concluded that the heavy metals were found to be concentrated through the various levels of food chain but not  biomagnified through the food chain