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Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.

The current study was carried out during 2020 to 2022, at Horticulture Research Centre (HRC), Nagicherra, Agartala, Tripura to assess and compare the effects of various land use systems, including bamboo, tea, mango, lemon, rice-rice, wheat-millet, okra-onion and uncultivated soils, on soil organic carbon (SOC) dynamics. SOC is a critical component of terrestrial ecosystems, influencing soil health, fertility and carbon sequestration potential. The NEH region of India, Tripura characterized by its diverse agro-ecological zones and land use systems (LUS), presents a unique opportunity to investigate the various land use regimes' effects on SOC pools. Walkley and Black carbon (WBC) significantly vary among the selected LUS, ranging from 7.14–12.48 g/kg, with the maximum values in tea LUS. In 0–30 cm depth, very labile C (CVL) pools are very variable among the selected LUS (2.04–5.35 g/kg), which is the highest in tea and mango compared to the uncultivated system. The C pools in selected LUS indicated the deviation depth and land use pattern. Lability index (LI) varies from 1.50–1.63 and 1.40–1.74 in 0–30 cm and 30–60 cm depth, respectively. Carbon pool index (CPI) assessed highest in tea LUS, 1.78 and 2.1 from 0–30 and 30–60 cm, respectively. Carbon management index (CMI) was higher in selected LUS compared to uncultivated system.

Climate change has a considerable influence on agricultural output, raising farmers’ production risk. Nevertheless, the risk can be mitigated by selecting stable genotypes. In countries such as India, where significant proportions of farmers are smallholders or operate on marginal land, the minimization of risk is of paramount importance. Existing methods of stability measures often result in low-yielding varieties. Consequently, there is a need to develop more effective stability strategy to solve this problem without reducing yield. In light of the preceding, the Rank Based Stability Index (RSI) has been proposed for choosing genotypes based on the rank of interaction residuals to mitigate the influence of climatic changes without compromising yield. Through statistical analyses, the RSI approach demonstrates its ability to discern stable genotypes resilient to environmental fluctuations. By evaluating genotype performance across multiple environments and seasons, RSI identifies cultivars with consistent yield performance, thus offering a valuable tool for enhancing crop resilience and ensuring food security. The effectiveness of the proposed RSI approach for selecting stable genotypes from groundnut (Arachis hypogaea L.) data has been notably demonstrated in comparison to other methods. RSI emerges as a promising methodology for genotype selection in groundnut, offering a robust framework for mitigating the influence of climatic changes on crop yields.

The Baraichari Shale Formation, which is equivalent to the Boka Bil Formation, is well exposed within the Neogene sequence of the Sitapahar Anticline in the Chittagong-Tripura Fold Belt, Bengal Basin, Bangladesh. Regardless of its importance in hydrocarbon accumulation, a comprehensive facies analysis is essential to understand and explain the heterogeneity within different depositional architectural elements. The purpose of this work is to delineate the lithofacies characteristics, depositional environment heterogeneities and propose a generic depositional model for the Middle to Late Miocene shallow marine sediments of the formation. The sedimentary facies were investigated at three outcrops along the Kaptai–Chandraghona road cut sections of the Sitapahar anticline. The formation is composed of dark to bluish-gray shale, silty shale, yellowish brown siltstone, and gray to yellowish brown fine-to medium-grained sandstone. Eight distinct sedimentary facies have been identified and were categorized into three facies associations: subtidal or estuarine, mixed flats with tidal creeks, and tidal mud flats. The subtidal facies, predominant in the middle member, consists of fine to medium-grained ripple cross-stratified sandstone-siltstone, herringbone cross-stratified sandstone, and trough cross-stratified sandstone, indicating sub-tidal channel deposition. Mixed flats with tidal creeks exhibit heterolithic facies, suggesting energy level changes in intertidal to sub-tidal zones influenced by tidal currents. Tidal mud flats are predominantly laminated shale/mudstone and lenticular laminated sandstone-siltstone, reflecting deposition during tide transitions in the intertidal zone. Fining upward cycles within the formation indicate progressive progradation or changes in inner tidal flats conditions during regressive periods. These depositional settings serve as analogs for equivalent subsurface reservoirs and contribute to the construction of reservoir models with greater accuracy.

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting.

The Lateral Organ Boundaries Domain (LBD) containing genes are a set of plant-specific transcription factors and are crucial for controlling both organ development and defense mechanisms as well as anthocyanin synthesis and nitrogen metabolism. It is imperative to understand how methylation regulates gene expression, through predicting methylation sites of their promoters particularly in major crop species. In this study, we developed a user-friendly prediction server for accurate prediction of 6mA sites by incorporating a robust feature set, viz., Binary Encoding of Mono-nucleotide DNA. Our model,MethSemble-6mA, outperformed other state-of-the-art tools in terms of accuracy (93.12%). Furthermore, we investigated the pattern of probable 6mA sites at the upstream promoter regions of the LBD-containing genes in Triticum aestivum and its allied species using the developed tool. On average, each selected species had four 6mA sites, and it was found that with speciation and due course of evolution in wheat, the frequency of methylation have reduced, and a few sites remain conserved. This obviously cues gene birth and gene expression alteration through methylation over time in a species and reflects functional conservation throughout evolution. Since DNA methylation is a vital event in almost all plant developmental processes (e.g., genomic imprinting and gametogenesis) along with other life processes, our findings on epigenetic regulation of LBD-containing genes have dynamic implications in basic and applied research. Additionally, MethSemble-6mA (http://cabgrid.res.in:5799/) will serve as a useful resource for a plant breeders who are interested to pursue epigenetic-based crop improvement research.

Water, nutrients, and energy are the three main inputs in agricultural production and recently there has been a drop in the factor productivity of these inputs because of their improper management and deterioration of soil health. To maximize agricultural productivity while lowering strain on natural resources, the best synergistic combinations of tillage, residue, nitrogen, and water management should be identified for improving resource use efficiency of wheat. Hence, an attempt has been made to evaluate the impact of contrasting tillage, crop residue mulch, nitrogen, and irrigation interaction on yield, radiation productivity (RP), and water productivity (WP) of wheat in a split-factorial design. Results showed that wheat yield was higher under no-tillage (4.8%) than that of conventional tillage. Crop residue mulch (CRM) and higher nitrogen application enhanced RP, WP, and yield of wheat; although RP increased with increase in nitrogen application up to 100% recommended dose of nitrogen (RDN). CRM significantly reduced the seasonal evapotranspiration (6.0‒7.2%) as compared to residue removal treatment. Deficit irrigation enhanced the WP while it lowered the crop yield significantly. Therefore, wheat can be grown under no-tillage, CRM, 100% RDN with deficit irrigation to obtain higher WP but with full irrigation to obtain higher yield, and RP in the semiarid climate of India.

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

BackgroundPrice forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions.MethodsIn recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India.ResultsAn empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models.ConclusionsAmong the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

Oral health is a critical component of human health but is sometimes forgotten, particularly during humanitarian crises. This research aimed to ascertain the state of oral health among Rohingya refugees living in one of the largest refugee camps and evaluate their knowledge and practice of oral health. A multicenter cross-sectional survey was conducted among 477 participants from July to September 2021 using a structured questionnaire. There were 34 Rohingya camps and out of those 14 camps were accessible for data collection. The study participants were between 18-82 years residing in the refugee camps under Cox's Bazar. The majority of participants (53.88%) were female and between the ages of 25 and 45. Around 46.12% of respondents did not have basic oral health knowledge, while 53.67% were in need of dental care. Nearly half of the participants demonstrated poor oral health practices. Participants' age and educational level were positively associated with oral health knowledge (p = 0.02 and p

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people's physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals from eight divisions of Bangladesh diagnosed and confirmed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) from June 2020 to November 2020. Given a response rate of 60% in a pilot study, a random list of 6400 COVID-19 patients was generated to recruit approximately 3200 patients from eight divisions of Bangladesh and finally a total of 3244 participants could be recruited for the current study. The validated Bangla version of the World Health Organization Quality of Life Brief (WHOQOL-BREF) questionnaire was used to assess the QoL. Data were analyzed by STATA (Version 16.1) and R (Version 4.0.0). All the procedures were conducted following ethical approval and in accordance with the Declaration of Helsinki. The mean scores of QoL were highest for the physical domain (68.25±14.45) followed by social (65.10±15.78), psychological (63.28±15.48), and environmental domain (62.77±13.07). Psychological and physical domain scores among females were significantly lower than the males (p

In this article, gauge condition in elastodynamics is explored more to revive its potential capability of simplifying wave propagation problems in elastic medium. The inception of gauge condition in elastodynamics happens from the Navier-Lame equations upon application of Helmholtz theorem. In order to solve the elastic wave problems by potential function approach, the gauge condition provides the necessary conditions for the potential functions. The gauge condition may be considered as the superposition of the separate gauge conditions of Lamb waves and shear horizontal (SH) guided waves respectively, and thus, it may be resolved into corresponding gauges of Lamb waves and SH waves. The manipulation and proper choice of the gauge condition does not violate the classical solutions of elastic waves in plates; rather, it simplifies the problems. The gauge condition allows to obtain the analytical solution of complicated problems in a simplified manner.

This paper presents a new methodology for detecting and quantifying delamination in composite plates based on the high-frequency local vibration under the excitation of piezoelectric wafer active sensors. Finite-element-method-based numerical simulations and experimental measurements were performed to quantify the size, shape, and depth of the delaminations. Two composite plates with purpose-built delaminations of different sizes and depths were analyzed. In the experiments, ultrasonic C-scan was applied to visualize the simulated delaminations. In this methodology, piezoelectric wafer active sensors were used for the high-frequency excitation with a linear sine wave chirp from 1 to 500 kHz and a scanning laser Doppler vibrometer was used to measure the local vibration response of the composite plates. The local defect resonance frequencies of delaminations were determined from scanning laser Doppler vibrometer measurements and the corresponding operational vibration shapes were measured and utilized to quantify the delaminations. Harmonic analysis of local finite element model at the local defect resonance frequencies demonstrated that the strong vibrations only occurred in the delamination region. It is shown that the effect of delamination depth on the detectability of the delamination was more significant than the size of the delamination. The experimental and finite element modeling results demonstrate a good capability for the assessment of delamination with different sizes and depths in composite structures.

This paper focuses on impact localization of composite structures, which possess more complexity in the guided wave propagation due to the anisotropic behavior of composite materials. In this work, a composite plate was manufactured by using a compression molding process with proper pressure and temperature cycle. Eight layers of woven composite prepreg were used to manufacture the composite plate. A structural health monitoring (SHM) technique was implemented with piezoelectric wafer active sensors (PWAS) to detect and localize the impact on the plate. There were two types of impact event that were considered in this paper (a) low energy impact event (b) high energy impact event. Two clusters of sensors recorded the guided acoustic waves generated from the impact. The acoustic signals were then analyzed using a wavelet transform based time-frequency analysis. The proposed SHM technique successfully detected and localized the impact event on the plate. The experimentally measured impact locations were compared with the actual impact locations. An immersion ultrasonic scanning method was used to visualize the composite plate before and after the impact event. A high frequency 10 MHz 1-inch focused transducer was used to scan the plate in the immersion tank. Scanning results showed that there was no visible manufacturing damage in the composite plate. However, clear impact damage was observed after the high-energy impact event.

The validation of structural health monitoring (SHM) systems for aircraft is complicated by the extent and number of factors that the SHM system must demonstrate for robust performance. Therefore, a time- and cost-efficient method for examining all of the sensitive factors must be conducted. In this paper, we demonstrate the utility of using the simulation modeling environment to determine the SHM sensitive factors that must be considered for subsequent experiments, in order to enable the SHM validation. We demonstrate this concept by examining the effect of SHM system configuration and flaw characteristics on the response of a signal from a known piezoelectric wafer active sensor (PWAS) in an aluminum plate, using simulation models of a particular hot spot. We derive the signal responses mathematically and through the statistical design of experiments, we determine the significant factors that affect the damage indices that are computed from the signal, using only half the number of runs that are normally required. We determine that the transmitter angle is the largest source of variation for the damage indices that are considered, followed by signal frequency and transmitter distance to the hot spot. These results demonstrate that the use of efficient statistical design and simulation may enable a cost- and time-efficient sequential approach to quantifying sensitive SHM factors and system validation.

This study analyzes the depositional regime and textural properties of the sediments from the Shitalakshya River in Bangladesh, enabling us to comprehend how these sediments evolved in a river environment. For this investigation, 30 representative samples were taken from the Shitalakshya River, and their textures were analyzed. The cumulative frequency curve is obtained by using semilog graph paper to plot particle size (in phi scale) against cumulative percent. The statistical parameters such as median (Md), mode (Mo), mean (MZ), standard deviation (σi), skewness (SKi), and kurtosis (KG) were calculated using the percentile of phi values (1%, 5%, 16%, 25%, 50%, 75%, 84%, and 95%). The cumulative curves show that the sediments are deposited through the traction population (1.90%), saltation population (75.64%), and suspension population (22.46%). The median value varies between 0.7Ø and 4.85Ø, with an average of 2.738Ø indicating coarse sand to coarse silt. The analyzed samples have unimodal, bimodal, and polymodal distribution, which indicates the sediments are carried by different tributaries and distributaries in the Sitalakshya River system. The range of 1.46Ø–4.05Ø represented by the observed mean value suggests sand with medium to extremely fine grains. Most of the sediments indicate moderate sorting, which is shown by the standard deviation (sorting), which ranges from 0.399Ø to 1.48Ø. The skewness value ranges between −0.01Ø and 0.66Ø, suggesting near symmetrical skewed, while the kurtosis value ranges from 0.54Ø to 1.87Ø, indicating the sediments are 20% leptokurtic, 20% mesokurtic, and 60% platykurtic. According to the CM plot, the Shitalakshya River is mostly deposited in the area between the rolling and suspension fields, indicating a transportation regime of saltation. The scatter plots of skewness versus sorting and graphic mean versus sorting indicate that the sediments fall within the river sand zone. The energy process discriminant functions of the sediments show that they were deposited by a fluvial process. [ABSTRACT FROM AUTHOR]