A study of basal crop coefficient for wheat under humid regime from heat unit accumulations

A methodology has been presented to compute basal crop coefficient from soil moisture and heat unit accumulations, for wheat in the humid region of northeast India. In developing the method data from 1976-77 to 1981-82 crop seasons for the Sonalika variety of wheat from germination to maturity have been used and tested on independent data set for 1982-83 and 1985-86 crop seasons. Milk stage to physiological maturity stage is found to use maximum fraction of heat unit totals. The largest value of basal crop coefficient is about I 5 occurring during milk stage of the crop growth. Very high correlation is noticed between the actual ET and those computed from the model.

Forecasting peak surface gust wind in association with thunderstorm activity during pre-monsoon season at Delhi

In this study, an attempt is made to develop an objective method for forecasting the direction and speed of the gusty winds associated with thunderstorms at Delhi. For this purpose, surface and upper-air data for April, May and June (AMJ) for the years 1985-90 are utilized. Multiple regression equations are developed for forecasting the direction and speed of the gusty winds, using stepwise screening method, for which a total of 181 potential predictors are utilized. The developed dynamical-statistical models are tested with independent data sets of 1994 and 1995 for April, May and June. The dynamical-statistical models give satisfactory results with the developmental as well as the independent data sets. The root mean square error of the direction vary between 58° and 77° and the speed forecast vary between 9 and 12 knots. Possible reasons for large deviations of the forecast, noticed on a very few occasions, have also been examined.

A method for forecasting visibility at Hindon

At present the approach to forecasting visibility is synoptic and personal experience of the weather forecaster. The month of December typically a winter month, is associated with poor visibility. Aviators require visibility forecast in terms of a definite quantitative value at a specific place in specific time frame. Therefore, in this study an attempt is made to develop a suitable model for forecasting visibility in December at a place Hindon near Delhi in a quantitative manner. In the development process of forecasting visibility, different approaches such as auto-regression, multiple regression, climatology and persistence have been attempted. The models are developed using seven years (1984-90) data of December. The model is evaluated with the independent data sets from the recent years 1994-95. It is found that climatology-persistence method provides better results as compared to the multiple regression and auto-regression methods. The developed model provided positive skill scores as high as 70% on development as well as independent data sets.

Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media

Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates competing in the 2018 Senate Midterms. The algorithm is able to identify the presence of political attacks (both in general, and specifically for character and policy attacks) and incivility. Due to the novel nature of the instrument, the article discusses the external and convergent validity of these measures. Results suggest that automated classifications are able to provide reliable measurements of campaign negativity. Triangulations with independent data show that our automatic classification is strongly associated with the experts’ perceptions of the candidates’ campaign. Furthermore, variations in our measures of negativity can be explained by theoretically relevant factors at the candidate and context levels (e.g., incumbency status and candidate gender); theoretically meaningful trends are also found when replicating the analysis using tweets for the 2020 Senate election, coded using the automated classifier developed for 2018. The implications of such results for the automated coding of campaign negativity in social media are discussed.

Implementing data-driven decision support system based on independent educational data mart

<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.</span>

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.

Multivariate Analysis and Validation of the Prognostic Factors for Skull Base Chordoma

Background: Skull base chordoma is a rare tumor with low-grade malignancy and a high recurrence rate, the factors affecting the prognosis of patients need to be further studied. For that, we investigated prognostic factors of skull base chordoma through the database of the Surveillance, Epidemiology, and End Results (SEER) program, and validated in an independent data set from the Xiangya Hospital.Methods: Six hundred and forty-three patients diagnosed with skull base chordoma were obtained from the SEER database (606 patients) and the Xiangya Hospital (37 patients). Categorical variables were selected by Chi-square test with a statistical difference. Survival curves were constructed by Kaplan–Meier analysis and compared by log-rank test. Univariate and multivariate Cox regression analyses were used to explore the prognostic factors. Propensity score matching (PSM) analysis was undertaken to reduce the substantial bias between gross total resection (GTR) and subtotal resection (STR) groups. Furthermore, clinical data of 37 patients from the Xiangya Hospital were used as validation cohorts to check the survival impacts of the extent of resection and adjuvant radiotherapy on prognosis.Results: We found that age at diagnosis, primary site, disease stage, surgical treatment, and tumor size was significantly associated with the prognosis of skull base chordoma. PSM analysis revealed that there was no significant difference in the OS between GTR and STR (p = 0.157). Independent data set from the Xiangya Hospital proved no statistical difference in OS between GTR and STR groups (p = 0.16), but the GTR group was superior to the STR group for progression-free survival (PFS) (p = 0.048). Postoperative radiotherapy does not improve OS (p = 0.28), but it can prolong PFS (p = 0.0037). Nomograms predicting 5- and 10-year OS and DSS were constructed based on statistically significant factors identified by multivariate Cox analysis. Age, primary site, tumor size, surgical treatment, and disease stage were included as prognostic predictors in the nomograms with good performance.Conclusions: We identified age, tumor size, surgery, primary site, and tumor stage as main factors affecting the prognosis of the skull base chordoma. Resection of the tumor as much as possible while ensuring safety, combined with postoperative radiotherapy may be the optimum treatment for skull base chordoma.

The Impermanence of Moves: Return and Onward Migration

Several, independent data analyses demonstrate that neither rural-urban nor urban-rural migrations are as permanent as one normally assumes; return is common within a few years. Return from rural-urban migration is more prevalent among men and the less-well-educated and is strongly associated with rejoining a spouse. Age of return to a rural area is bimodal, peaking around age 20 and among children; no evidence of return upon retirement is apparent. Across countries, more than half return to a district other than their origin. Returned migrants’ rural incomes are greater than those of people who remained at home, both on average and among measurably equivalent groups. Upward mobility in income in towns is affirmed, particularly for the less-well-educated. Seasonal migration is more common among men and the better educated and by individuals, not joint families. Seasonal migration in India as well as step and onward migration elsewhere are not as common as is popularly claimed.

Modeling the past, present, and future distributions of endangered white abalone (Haliotis sorenseni) to inform recovery efforts in California

White abalone (Haliotis sorenseni) was once commonly found in coastal waters of the Southern California Bight (SCB) and south to Punta Abreojos, Baja California, Mexico. During the 1970s, white abalone supported a commercial fishery, which reduced the population and resulted in the closure of the fishery in 1996. When population levels continued to decline, National Marine Fisheries Service (NMFS) listed the species as endangered under the Endangered Species Act. The California Department of Fish and Wildlife and NMFS began surveying the wild populations, propagating specimens in captivity, and protecting its seabed habitat. We modeled coarse-scale (17 x 17 km) historical (using fishery-dependent data [1955–1996]) and contemporary (using fishery-independent data [1996–2017]) distributions of white abalone throughout its historical domain using random forests and maximum entropy (MaxEnt), respectively, and its fine-scale (10 x 10 m) contemporary distribution (fishery-independent data) using MaxEnt. We also investigated potential outplanting habitat farther north under two scenarios of future climate conditions. The coarse-scale models identified potential regions to focus outplanting efforts within SCB while fine-scale models can inform population monitoring and outplanting activities in these particular areas. These models predict that areas north of Point Conception may become candidate outplant sites as seawater temperatures continue to rise in the future due to climate change. Collectively, these results provide guidance on the design and potential locations for experimental outplanting at such locations to ultimately improve methods and success of recovery efforts.

Digital twin predicting diet response before and after long-term fasting

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet-response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new clinical study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, and we can quantify personalized expected differences in outcome for any diet. This constitutes a new digital twin technology.