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In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques. [ABSTRACT FROM AUTHOR]

The current article reports an effective and low-cost removal of Acid blue 93 dye using synthesised peanut (Arachis hypogaea) shells. In first step, the adsorbents were examined using pHpzc, TG-DTG, FTIR and SEM characterisation techniques. In the second phase, the parameters such as contact time (CT), pH, adsorbent dosage (AD) and initial concentration of dye (ICD) were assessed using one parameter at a time adsorption study (OPAT). Selecting the same parameters an effort was also made using design of experiment (DoE). It was found that, for maximum removal both the studies, OPAT and DoE delivered similar optimum conditions as CT 60 min, pH 7, AD 7 g l−1 and ICD 50 mg l−1. In addition, DoE added an edifying output in terms of percentage of contribution: ICD (36.35%) > AD (31.39%) > CT (27.03%)> pH (5.24%). In accordance with the above studies, kinetics, isotherm and thermodynamic analysis were also performed followed by error analysis. In kinetics, pseudo-second-order model was superior; in isotherm study, Langmuir model was dominant with a q m value of 23.36 mg g−1. Thermodynamic parameters such as Δ G ∘ , Δ H ∘ and Δ S ∘ stated the adsorption endothermic and spontaneous. The novelty of this work is in the development of the pre-treated peanut (A. hypogaea) shells for efficient and low-cost removal of Acid blue 93 dye from aqueous media. [ABSTRACT FROM AUTHOR]

The crop nitrogen (N) use efficiency has been recognized as a useful indicator to measure the overall progress of the country towards achieving the sustainable development goal. Moreover, national level N use efficiency indicators are essential for improved estimate of global N budget and environmental impact assessment. We made an attempt to calculate the indicators of N use efficiency for rice at national and regional level in India. We utilized the data from research papers (23 no) published in reputed scientific journals and theses (83 no) submitted to different state agricultural universities over a period of 46 years (between 1972 and 2018). The partial factor productivity of N for rice in India ranged from 39.8 to 68.0 kg kg−1 with a national average value of 52.8 kg kg−1. The national average agronomic N use efficiency and nitrogen recovery efficiency (REN) were 18 kg kg−1 and 42.6%, respectively. Conducive agro-climatic condition coupled with improved crop management resulted in higher REN (>50%) in northern zone states of India. Whereas, rain-fed eastern and central zone states have REN<40%. The partial N balance (PNB) in rice ranged from 0.72–1.42 in India. The states with lower PNB need to reconcile their N addition with crop N uptake by adopting N responsive varieties and advanced fertilizer management to prevent environment degradation and economic loss. The findings of this study have significant implications for N budget estimation and policy decisions on hunger, food security and environment at national and global level. [ABSTRACT FROM AUTHOR]

Rice is a major staple food crop for providing food security in Asian region. Rice crop mainly suffers from diseases like brown spot, leaf blast and hispa. Detecting rice crop disease in natural RGB images is a daunting task due the intricate texture of the region of the interest. The paper gives a novice approach to the detection of rice plant diseases. Here two different feature extraction methods were used one being Gray level co-occurrence matrix (GLCM) and the other is Intensity level based on the multi-fractal dimension (ILMFD) technique. The three different types of classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Twin Support Vector Machine (TWSVM) were used. Initially, the input rice crop images were processed, features extracted and finally the classification was done. The combination of ILMFD and TWSVM significantly improves the classification results as compared to existing models. The ILMFD method and TWSVM gives the highest Kernel accuracy of 100% detection of rice crop diseases (for the sample database used in this work) which validates the efficiency of the above mentioned techniques. [ABSTRACT FROM AUTHOR]

Microbial communities provide useful information about any chemical and physical changes in the environment and play an essential role in maintaining soil fertility. Biolog® eco-plates method was used to study the functional diversity of microbial communities, and their correlation with soil organic carbon (OC), microbial biomass and activities, under three different soil conditions of Sukinda chromite mining area of Odisha, India during August 2016. The OC, available nitrogen, available phosphorus and available potash were significantly (p < 0.05) lower in in situ and overburden soils as compared to forest soil. The average development rate of average well color development values decreased with incubation time in all soil conditions. The utilization of six categories of carbon sources by soil microbes decreased with the increase in chromium load and biplot analysis suggested that carbohydrate, polymer and amino acid utilizing microbes were dominant in mining soils. The ecotoxicological status of chromite mine soil would be useful for formulating strategies of possible bioremediation program. [ABSTRACT FROM AUTHOR]

A new adsorbent for removing copper ions from aqueous solutions has been developed and characterized. The present study deals with the sorption of Cu(II) from aqueous solution on chemically pretreated sodium carbonate-treated rice husk (SCRH). The physico-chemical characteristics of rice husks were investigated to analyze their suitability to adsorb Cu(II) ions from water and wastewater. The raw rice husk (RRH), SCRH and Cu(II) adsorbed rice husk were analyzed by SEM-EDAX analysis. FTIR spectroscopy was also applied to identify functional groups, capable of adsorbing metal ions. Batch kinetic studies were conducted for the adsorption of Cu(II) on SCRH. It has been observed that 92.9–96.0% removal of Cu(II) is achieved at 4.8 mg of Cu(II)/g of adsorbent, adsorbent dose of 10 g L–1 and initial Cu(II) concentration of 10 mg L–1 in a temperature range of 15–50 °C. It was observed that the adsorption of Cu(II) on SCRH followed pseudo second-order kinetic and time to achieve equilibrium was found to be 60 min. The maximum uptake (97%) of Cu (II) was observed at pH 6. In this paper, an attempt has also been made to develop simple and readily understandable thermodynamic parameters related to sorption process at the equilibrium for understanding the adsorption mechanism. The Gibbs free energy ΔG° values for the adsorption processes of Cu(II) at 15, 30, 40 and 50 °C were calculated as –6.16, –6.84, –8.01 and –8.53 kJ mol–1, respectively. The negative value of ΔG° indicates spontaneity of adsorption. The values of ΔH° and ΔS° for Cu(II) adsorption were calculated as 14.37 kJ mol–1 and 70.92 J K–1 mol–1, respectively. The activation energy for the adsorption of Cu(II) was found to be 9 kJ mol–1 which is a characteristic for diffusion limited processes. [ABSTRACT FROM AUTHOR]