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Forecasting is valuable to countries because it enables them to make informed business decisions and develop data-driven strategies. Fruit production offers promising economic opportunities to reduce rural poverty and unemployment in developing countries and is a crucial component of farm diversification strategies. After vegetables, fruits are the most affordable source of essential vitamins and minerals for human health. India's fruit production strategies should be developed based on accurate predictions and the best forecasting models. This study focused on the forecasting behavior of production of apples, bananas, grapes, mangoes, guavas, and pineapples in India using data from 1961 to 2015 (modelling set) and 2016–2020 (predicting set). Two unit root tests were used, the Ng–Perron (2001) test, and the Dickey–Fuller test with bootstrapping critical values depending on the Park (2003) technique. The results show that all variables are stationary at first differences. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were used and compared based on goodness of fit. The results indicated that the ETS model was the best in all the cases, as the predictions using ETS had the smallest errors and deviations between forecasting and actual values. This result was confirmed using three tests: Diebold–Mariano, Giacomini–White, and Clark–West. According to the best models, forecasts for production during 2021–2027 were obtained. In terms of production, an increase is expected for apples, bananas, grapes, mangoes, mangosteens, guavas, and pineapples in India during this period. The current outcomes of the forecasts could enable policymakers to create an enabling environment for farmers, exporters, and other stakeholders, leading to stable markets and enhanced economic growth. Policymakers can use the insights from forecasting to design strategies that ensure a diverse and nutritious fruit supply for the population. This can include initiatives like promoting small-scale farming, improving postharvest storage and processing facilities, and establishing effective distribution networks to reach vulnerable communities.

The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.

Online shopping can be done from our convenient places like home, office, etc., and the product will be delivered to the respective places. There are many factors influencing online shopping. The purpose of this study is to develop a statistical model that is used to determine the factors that influence online shopping. In this study, using factor analysis five main factors have been obtained from 15 variables that influence online shopping. These five factors have significant effects on satisfaction of customers and accounted up to 56% of total variation. Using the factor scores as independent variables, multiple regression model has been developed for predicting customers satisfaction in online shopping. Customer satisfaction has been used as dependent variable in the regression model. The five main factors that contribute online shopping are: preference of consumers towards online shopping, the risk involved in purchasing products through online, time effectiveness in online shopping, difficulties faced during online shopping and getting products from trustworthy websites.

Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.

The formation of biomolecular condensates in vivo is increasingly recognized to underlie a multitude of crucial cellular functions. Furthermore, the evolution of highly dynamic protein condensates into progressively less reversible assemblies is thought to be involved in a variety of disorders, from cancer over neurodegeneration to rare genetic disorders. There is an increasing need for efficient experimental methods to characterize the thermodynamics of condensate formation and that can be used in screening campaigns to identify and rationally design condensate modifying compounds. Theoretical advances in the field are also identifying the key parameters that need to be measured in order to obtain a comprehensive understanding of the underlying interactions and driving forces. Here, we review recent progress in the development of efficient and quantitative experimental methods to study the driving forces behind and the temporal evolution of biomolecular condensates. [ABSTRACT FROM AUTHOR]

Background: Many cross pollinated flowering plants rely on pollinators for reproduction and survival. The pigeon pea (arhar), Cajanus cajan L., is the India's most important pulse crop. It attracts pollinators because to its cross pollination nature, possible nectar and pollen source, attractive colour and scent of the seeds and timing of blossoming. As a result, a complete research of pigeon pea insect pollinator fauna shall be used to conserve those fauna for increasing pigeon pea pollination effectiveness. Methods: Diversity of different pollinating agents associated with the crop pigeon pea by observing their abundance, diversity, percentage contribution, dominance status and diversity following the statistical diversity indexes were recorded during Kharif, 2021-22 in the state of Odisha where the insect pollinators' fauna was recorded through fixed plot survey. Result: Megachille lanata (Fabricius) was found to be the most dominant pollinator throughout the flowering period among seven different pollinators of order Hymenoptera. M. lanata and Megachille disjuncta (Fabricius) shown higher foraging activity i.e. 6.02 bees/5 min/m2 and 4.25 bees/5 min/m2 respectively. Subdominant class represented maximum species among different dominant status classes exhibited. Very less diversity was observed throughout the season but still more diversity has been observed at mid flowering stags during 10:00-11:00 AM. [ABSTRACT FROM AUTHOR]

Biomolecular condensates have emerged as important structures in cellular function and disease, and are thought to form through liquid‐liquid phase separation (LLPS). Thorough and efficient in vitro experiments are therefore needed to elucidate the driving forces of protein LLPS and the possibility to modulate it with drugs. Here we present Taylor dispersion‐induced phase separation (TDIPS), a method to robustly measure condensation phenomena using a commercially available microfluidic platform. It uses only nanoliters of sample, does not require extrinsic fluorescent labels, and is straightforward to implement. We demonstrate TDIPS by screening the phase behaviour of two proteins that form biomolecular condensates in vivo, PGL‐3 and Ddx4. Uniquely accessible to this method, we find an unexpected re‐entrant behaviour at very low ionic strength, where LLPS is inhibited for both proteins. TDIPS can also probe the reversibility of assemblies, which was shown for both α‐synuclein and for lysozyme, relevant for health and biotechnology, respectively. Finally, we highlight how effective inhibition concentrations and partitioning of LLPS‐modifying compounds can be screened highly efficiently. [ABSTRACT FROM AUTHOR]

This article explores the relationship between weather conditions and the cultivation of finger millet in Keonjhar, Odisha. Finger millet is a popular crop in India, especially among tribal communities and farmers in areas with poor soil and no irrigation. The study found that finger millet is resilient to various stresses, such as warming, water, and nutrition stress, making it suitable for climate change conditions. The article also presents the results of a field experiment that examined the impact of sowing dates and varieties on crop yield. The study found that the crop sown on June 20th had the highest grain and straw yield, likely due to receiving the most rainfall during its growth period. The correlation analysis revealed that seed yield was negatively correlated with rainfall at certain stages of growth, but positively correlated with temperature, humidity, evaporation, and wind speed at other stages. The study also analyzed the heat unit requirements for different sowing dates and cultivars, with early sowing and the Bhairabi variety performing best in terms of yield and heat unit accumulation in the North Central Plateau Zone of Odisha. [Extracted from the article]

Twelve phyto products and two forms of cow dung were evaluated during 2021- 22 in the Entomology Research Laboratory, M S Swaminathan School of Agriculture, Centurion University of Technology and Management, Gajapati District of Odisha for their efficacy against Rhyzopertha dominica (F) in stored paddy. The experiment was laid out in a completely randomized design with each treatment replicated thrice. Individual treatments comprised 20 g disinfested paddy seeds treated with test products @ 5g/ kg seed separately and an untreated check and inoculated with 20 adults /treatment. All the treatments were effective in suppressing the R. dominica, with maximum adult mortality (92.1%) being in black pepper powder followed by custard apple seed powder (88.4%) at 28 days after release. The least population buildup (2 adults), grain damage (3.3%), and weight loss (2.7%) were observed with black pepper powder treated paddy at 120 days after storage. [ABSTRACT FROM AUTHOR]

Rice grains of 20 varieties were tested for their physical and biochemical basis of resistance against lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera, Bostrichidae). Physical parameters such as grain length x breadth, thousand seed weight, and grain hardness exhibited significant differences among tested varieties. Grain hardness conferred resistance significantly by resulting in delayed adult emergence (-0.519), and weight loss per cent (-0.603). Biochemical parameters such as total carbohydrates and proteins showed significant differences among the varieties. Total carbohydrate was positive and significantly correlated with adult emergence, but with median development period negatively correlated. Protein content was also found positively correlated with adult emergence and negatively correlated with the median development period. [ABSTRACT FROM AUTHOR]