Implementation of Integrated Pest and Disease Management programme in irrigated cauliflower crop led to reduction in number of conventional pesticide sprays by 50-60 %. The safer biorational pesticides, insect growth regulators and cultural methods of pest management as introduced in the IPM programme were well received by the farmers in farmers' participatory trainings (FPT). Lower insect and disease incidence with higher curd production was observed in the IPM fields as compared to conventional non IPM fields. Furthermore the module was able to drag the cost of crop protection down by 45 percent resulting in higher benefit-cost ratio. The IPM module led to reinforcement of natural enemies resulting in sustainable and stable pest control regime warranting less pesticide application. Cotesia glomeratus L. was found parasitizing the larvae of Spodoptera litura F. in IPM fields whereas there was no parasitization in non IPM fields. Post implementation evaluation of the IPM programme revealed that the farmers were educated about the right choice of pesticides, proper time and dose of application, pest monitoring and application of pesticides based on action threshold. Increase in participation of women in the IPM programme was ensured by educating them about the mechanical management of S. litura.

The objective evaluation and screening for psychosocial development of infants living tin urban slums is necessary for early detection and intervention.

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Journal homepage: http://www.ijcmas.com Pest control practices in the vegetable crops have been heavily dominated by the routine use of broad-spectrum insecticides to control pests. Concerns have emerged about the adverse consequences of over use of pesticide. These consequences include short and longterm health hazards, contamination and environmental degradation. To minimize the pest losses farmers heavily depend on the chemical pesticides and accessing pest management information from the pesticide dealers due to weak state extension support system. Vegetable production plays a crucial role in agriculture by providing food, nutritional and economic security to the people of with higher returns per unit area to the producers. The study was conducted with objective to find out the adoption behavior of farmers towards pest management in western region of Uttar Pradesh. The studied adoption level of pest management practices in study area by following proportionate random sampling method 100 vegetable growers were selected and primary data was collected though personal interview method. Findings revealed that majority of the respondents had medium level of adoption of IPM practices while equal per cent of respondents (20%) had high and low level of adoption, respectively. With regard to cultural practices, majority of the farmers had adopted the practice of transplanting of recommended number of seedling. As mechanical control measures, the raised bed nursery had adopted by majority farmers. Among the cultural, mechanical, biological and chemical measures of Integrated Pest Management, respondents mainly followed cultural and mechanical methods for pest management of cauliflower crop. Utilization of locally available resources and promotion of the Farmers Participatory Approach by incorporating the vegetable growers' indigenous wisdom about the Bio-pesticides and the natural enemies of the pests of the vegetables in the sphere of IPM techniques of the vegetables grown by the farmers is very essential in this State.

Objective To investigate the safety and efficacy of goat lung surfactant extract (GLSE) compared with bovine surfactant extract (beractant; Survanta®, AbbVie, USA) for the treatment of neonatal respiratory distress syndrome (RDS). Study design We conducted a double-blind, non-inferiority, randomized trial in seven Indian centers between June 22, 2016 and January 11, 2018. Preterm neonates of 26 to 32 weeks gestation with clinical diagnosis of RDS were randomized to receive either GLSE or beractant. Repeat dose, if required, was open-label beractant in both the groups. The primary outcome was a composite of death or bronchopulmonary dysplasia (BPD) at 36 weeks postmenstrual age (PMA). Interim analyses were done by an independent data and safety monitoring board (DSMB). Result After the first interim analyses on 5% enrolment, the “need for repeat dose(s) of surfactant” was added as an additional primary outcome and enrolment restricted to intramural births at five of the seven participating centers. Following second interim analysis after 98 (10% of 900 planned) neonates were enroled, DSMB recommended closure of study in view of inferior efficacy of GLSE in comparison to beractant. There was no significant difference in the primary outcome of death or BPD between GLSE group ( n = 52) and beractant group ( n = 46) (50.0 vs. 39.1%; OR 1.5; 95% CI 0.7–3.5; p = 0.28). The need for repeat dose of surfactant was significantly higher in GLSE group (65.4 vs. 17.4%; OR 9.0; 95% CI 3.5–23.3; p < 0.001). Conclusions Goat lung surfactant was less efficacious than beractant (Survanta®) for treatment of RDS in preterm infants. Reasons to ascertain inferior efficacy of goat lung surfactant requires investigation and possible mitigating strategies in order to develop a low-cost and effective surfactant.

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In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90∘, –45∘, 0∘, 45∘, and 90∘. The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.

In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods.