M. M. Pathan

AIM: This study was conducted to evaluate the impact of summer and winter season on physiological, biochemical, hormonal, and antioxidant parameters in Indigenous sheep. MATERIALS AND METHODS: The research was carried out during summer and winter season. 8 adult apparently healthy female sheep (aged 2-4 years) of similar physiological status were selected. Daily ambient temperature and relative humidity were recorded to calculate the temperature-humidity index (THI). The THI value of summer and winter season were 82.55 and 59.36, respectively, which indicate extreme hot condition during summer season and extreme cold condition during winter season. Physiological parameters were recorded daily during the experimental periods. Blood samples were collected at weekly interval and analyzed for biochemical, hormonal, and antioxidant parameters. The results were analyzed using completely randomized design. RESULTS: ) were decreased significantly during summer season. CONCLUSION: .

The movie is one of the integral components of our everyday entertainment. The worldwide movie industry is one of the most growing and significant industries and seizing the attention of people of all ages. It has been observed in the recent study that only a few of the movies achieve success. Uncertainty in the sector has created immense pressure on the film production stakeholder. Moviemakers and researchers continuously feel it necessary to have some expert systems predicting the movie success probability preceding its production with reasonable accuracy. A maximum of the research work has been conducted to predict the movie popularity in the post-production stage. To help the movie maker estimate the upcoming film and make necessary changes, we need to conduct the prediction at the early stage of movie production and provide specific observations about the upcoming movie. This study has proposed a content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description. Using RS output and movie rating and voting information of similar movies, we created a new feature set and proposed a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. We also proposed a system to predict the popularity of the upcoming movie among different audience groups. We have divided the audience group into four age groups junior, teenage, mid-age and senior. This study has used publicly available Internet Movie Database (IMDb) data and The Movie Database (TMDb) data. We had implemented a multiclass classification model and achieved 96.8% accuracy, which outperforms all the benchmark models. This study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decisions.

To study the effect of vitamin E (VE), copper (Cu) and zinc (Zn) supplementation on the in vitro phagocytic activity (PA) and lymphocyte proliferation response (LPR) of blood neutrophils and lymphocytes, thirty Sahiwal pregnant cows (six in each group) in their late gestation at 30 days before the expected date of calving were selected from the NDRI experimental herd and supplemented with various micronutrients from 30 days before calving to 45 days after calving. Cows were supplemented individually with VE (1000 IU/cow/day), Cu (20 ppm/cow/day) and Zn (80 ppm/cow/day) and also with a combination of VE, Cu and Zn to study cumulative effect of all micronutrients. One group without any supplementation acted as a control. Blood neutrophils and lymphocytes were isolated and studied for their PA and LPR. Supplementation of micronutrients like VE, Cu, Zn and a combination of all these nutrients significantly (p < 0.01) increased the PA of experimental cows as compared to control (unsupplemented) cows during the pre-partum period. During post-partum, all the micronutrients (VE, Cu, Zn and their combination) showed a significant (p < 0.01) increase in the PA of experimental cows as compared to control cows. Of all the groups, significant (p < 0.01) and maximum PA was observed in the combination group followed by Zn-supplemented group during both the pre- and post-partum period. A significant (p < 0.01) increase in LPR of B lymphocytes was observed in combination-supplemented group during the pre-partum period and during both the pre- and post-partum period in the Cu-supplemented group.

Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.