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Pilot Study on Adoption and Usage of AI in Food Processing Industry by UTAUT2
Artificial intelligence (AI) improves the efficiency of work and effectiveness in the output. Currently, food processing industries have started using AI in their business operations. It is crucial to have an in-depth understanding of the adoption and usage of AI systems in food processing industries. Therefore, this paper validates the Unified Theory of Acceptance and Use of Technology (UTAUT2) in the context of the food processing industry. This study applied AI to the food processing industries in the Bengaluru region. The study's objective is to build a clear vision of the factors that affect the user acceptance and behaviour intention of the user by pilot test. The pilot survey collected 62 responses through the questionnaire. The respondents were employees from the food processing industries in Bengaluru. The reliability test of the questionnaire was done by using Jamovi 2.3.16 software. The questionnaire was tested in three ways: Cronbach Alpha, McDonalds Omega, and Inter-rater reliability. The results of the entire test were reliable since overall Cronbach Alpha of 0.874, which is within the range of 0.800.90, and considered good internal consistency. Similarly, McDonalds Omega is within the range of 0.800.90, which is excellent consistency, and Inter-rater reliability is within the range of moderately acceptable scores from 50 to 75%. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pink floyd's time: An aural metanarrative exploring time through form, lyric, and musical arrangement
The inability of language to capture the essence of time is a crisis that has been expressed by philosophers starting from St. Augustine to Paul Ricoeur. Appearing on their seminal album, Dark Side of the Moon, Pink Floyd's Time is a profound artistic attempt which transcends this language barrier by using music to bring the listeners to a more direct confrontation with time; doing so by juxtaposing time as calibrated and as experienced through the music and the lyrics, and by making the reader experience time-based affects such as impatience, expectation, monotony, and such. As a direct function of song, time is experienced as musical time in the song, thereby ensuring that the listener's confrontation with time is immersive, with lyrics that describe the nature of experienced and calibrated time working synchronously with the music to complete the image. In the context of its release in 1974, the 6:52 minute song was in engagement with the concept of time as well, in that it was among the pioneering ones which redefined radio broadcast time beyond the standard 3 minutes afforded to popular music tracks, with the commercially preferred listener span in mind. The matter of time thus becomes a multi-layered formal engagement in the song, at the level of lyric, recording, music and listening, thereby making possible an image of time that is polished and rounded. These aural, lyrical and production-based concepts will be addressed and expanded upon to show how Pink Floyd's Time functions as a metanarrative in how it uses and invokes the elements of time to talk about time. AesthetixMS 2020. -
Pioneering Security in Healthcare: Reversible Data Hiding for ECG Signals and Patient Information
A population in large across worldwide grieve from cardiac illnesses, leading towards an increase in the popularity of telecardiology. Consequently, a significant quantity of electrocardiogram (ECG) signals and sensitive patient information are communicated over the Internet. Using electrocardiogram (ECG) signals as the host medium, this system safeguards the patient's personal information. Nevertheless, it was not possible to replicate the original electrocardiogram (ECG) signal entirely. Therefore, any alterations that occur on EKG have the potential to lead physicians to make an inaccurate diagnosis, which is something that the patient cannot accept. According to the fundamental viewpoint presented in this study, reversible hidden data must completely hide the patient information and ECG signals when they are present. Discreet patient data leading to their personal attributes should be incorporated into the electrocardiogram which is ECG signal while preserving a high level of visibility. On the other hand, in order to safeguard the confidentiality of the patient and the ECG signal, we employ a single built-in encryption mechanism. The ECG instance which is watermarked is recreated in detail. The findings presented herein provide evidence that the proposed method can be reversed. 2026 IEEE. -
Piperine, an alkaloid of black pepper seeds can effectively inhibit the antiviral enzymes of Dengue and Ebola viruses, an in silico molecular docking study
Ebola and Dengue are the critical diseases caused by RNA viruses, especially in the tropical parts of the globe, including Asia and Africa, and no prominent therapeutic options are available so far. Here, an effort was made to evaluate the efficacy of black pepper (Piper nigrum L.) alkaloid Piperine as a potential drug through computational docking simulation. Eight structurally essential proteins of Dengue and Ebola virus were selected as in silico docking targets for Piperine. Absorption, Distribution, Metabolism, and Excretion profile showed that Piperine was safe and possessed significant drug-like properties. Molecular dynamic simulation and binding free energy calculation showed that Piperine could inhibit Methyltransferase (PDB id 1L9K) of Dengue and VP35 Interferon Inhibitory Domain (PDB id 3FKE) of Ebola virus in comparison with the commercial antiviral Ribavirin. Furthermore, statistical analysis based on multivariate and clustering approaches revealed that Piperine had more affinity towards viral proteins than that of Ribavirin. 2020, Indian Virological Society. -
Pixelated Pasts: Deepfakes as Instruments of Counter-memory in India
A new era of digital memory invites society to confront the silences of the archive and rethink the politics of collective remembrance. 2025, Economic and Political Weekly. All rights reserved. -
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. -
Place-based strategies, multichannel merger, and context-driven alerts for engagement with mobile marketing
Mobile marketing is essential for timely, personalized communication in the digital world. Engagement is increased by location-based strategies like geofencing and context-driven notifications. Integrating social media improves ties with customers. In the digital age, multi-channel integration guarantees a smooth and personalized experience. As per the authors, this chapter explores how location-based suggestions, context-driven notifications, and multi-channel integration enhance client connections while highlighting the significance of geolocation data for targeted content. For context-driven notifications to be effective, helpfulness and privacy must be balanced. Companies create stronger relationships with their consumers and improve the customer experience, which motivates both present and new customers to engage and connect with their brand. An analysis is conducted on the changing field of mobile marketing, emphasizing the use of location-based tactics, multi-channel integration, and context-driven notifications to increase user engagement. 2024, IGI Global. All rights reserved. -
Planetary Ball Milling and Tailoring of the Optoelectronic Properties of Monophase SnSe Nanoparticles
Downscaling of tin monoselenide (SnSe) samples to the nanometer regime (~8020nm) without affecting the structure, homogeneity, and optoelectronic properties was carried out by high-energy planetary ball milling (BM). The milling rate was varied from 200rpm to 800rpm by adopting a dry and wet-grinding top-down approach on customized stoichiometric SnSe precursors. The degree of crystallinity was assessed by powder x-ray diffraction (PXRD) and selected area electron diffraction. The lattice parameters, a = 4.435 b = 11.498 and c = 4.148 of the nanoparticles were calculated from the PXRD data. Energy-dispersive x-ray analysis confirmed the chemical homogeneity (49.88:51.12 at.%) of the samples. The effects of rotational velocity as well as mode of grinding on the morphology and the size of SnSe powders were investigated using electron microscopes. The direct optical transition with band gap varied from 1.75eV to 2.28eV was elucidated from UV-Vis-NIR data. Photoluminescence revealed an increase in the intensity of the emission peak at 462.97nm with angular velocities for both types of grinding. The variation of electrical resistivity (36107 ? cm) and mobility (3.451.12 cm2/Vs) with rotational speed was calculated for all the samples. The results obtained for the ball-milled nanoparticles pave the way towards the reduction of particle size, formation of stable morphology, and appreciable crystalline structure quality suitable for solar cell absorbers. Graphical Abstract: [Figure not available: see fulltext.] 2023, The Minerals, Metals & Materials Society. -
Planned fashion obsolescence in the light of supply chain uncertainty
Fast fashion has popularised the phenomenon of perceived obsolescence whereby customers try to stay in line with the current fashion trends in the market even though the apparel they own are in perfect condition. This has ultimately led the fashion industry to become the second largest polluter in the world. The primary objective of this research paper is to comprehend how the media manoeuvres customers to indulge in fast fashion and how that in turn leads to uncertainty in the supply chain. To understand this, a maximum variation sampling method was adopted which consisted of customers, supply chain partners and marketers. In order to draw a parallel between the variables researched in the past and the present day scenario, an interview schedule was employed. Through the variables selected with the help of Dedoose, a model was created to identify the hurdles faced by suppliers as well as the customer in the fast fashion cycle. The results found that the power to break the fast fashion phenomenon lay in the hands of the media as it is through them that customers' perception can be altered. The importance of artificial intelligence in SCM and the modern tools used in industry 4.0 have also been discussed. 2020 Allied Business Academies. -
Plano framework for graph indexing - A statistical analysis
Graph Mining is becoming one of the most dominant fields of research. There are plenty methods to index, re-index and to search the features throughout the index but still from the literature study there is no specific frame work which can sum up all three so that indexing and updating the index with new feature can be done in consistent intervals according to the arrival of new features. PLANO is the frame work which has the latest algorithms to look into the data and index. In this paper, Time and Memory efficiency of the proposed algorithms in the PLANO framework is tested statistically and compared with the existing algorithms memory and time usage. Research India Publications. -
Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)
Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. 2024 IEEE. -
Plant disease diagnosis and solution system based on neural networks
Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease. 2021, Engg Journals Publications. All rights reserved. -
Plant extract aided synthesis of iron sulphide/nickel sulphide type-II heterostructure for photochemical CO2 reduction and simultaneous degradation of dyes
The green synthetic route, solving issues in the energy sector and the removal of wastes for a clean environment are the major concerns across the globe for a sustainable future. The current work involves the synthesis of iron sulphide (FeS), nickel sulphide (NiS) and FeS/NiS heterostructure using a Calotropis procera leaf and flower extract as a reducing agent without any additional sulphur source. Structural optical, photo/electrochemical and morphological characterizations suggest the formation of a heterostructure between FeS and NiS of type II with tuned edge potentials. Due to which FeS/NiS showed enhanced activity in evolving CO and CH? through photoctalytic CO2 reduction reaction (CRR) and was found to be 2.5 and 2 times higher than FeS and NiS, respectively. Further, all three materials were studied for photocatalytic degradation of two cationic dyes (methylene blue: MB and safranin O: SO) under different light sources. The % degradation of dyes MB and SO was found to be 98 and 96 %, respectively, in the presence of FeS/NiS heterostructure under sunlight. The factors affecting the dye degradation (pH, initial concentration, catalyst dosage) were optimized to achieve maximum efficiency. The degradation study using FeS/NiS was additionally examined in industrial effluent and the simultaneous degradation of MB and SO and the results are satisfactory. Photocatalytic mechanism was predicted based on the degradation results using liquid chromatography mass spectrophotometry (LCMS). The decreased charge transfer resistance, superior photocurrent response, bandgap tuning, shift in edge potentials, and formation of heterostructure and effective charge separation could be attributed to the appreciable efficiency of FeS/NiS. This work may lead to further research on the formation of metal sulfide-based heterostructures using a green approach and their application towards waste reduction and converting them to wealth towards energy and environmental remediation. 2025 Elsevier Ltd -
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved. -
Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Plant Secondary Metabolites: The Weapons for Biotic Stress Management
The rise in global temperature also favors the multiplication of pests and pathogens, which calls into question global food security. Plants have developed special coping mechanisms since they are sessile and lack an immune system. These mechanisms use a variety of secondary metabolites as weapons to avoid obstacles, adapt to their changing environment, and survive in less-than-ideal circumstances. Plant secondary metabolites include phenolic compounds, alkaloids, glycosides, and terpenoids, which are stored in specialized structures such as latex, trichomes, resin ducts, etc. Secondary metabolites help the plants to be safe from biotic stressors, either by repelling them or attracting their enemies, or exerting toxic effects on them. Modern omics technologies enable the elucidation of the structural and functional properties of these metabolites along with their biosynthesis. A better understanding of the enzymatic regulations and molecular mechanisms aids in the exploitation of secondary metabolites in modern pest management approaches such as biopesticides and integrated pest management. The current review provides an overview of the major plant secondary metabolites that play significant roles in enhancing biotic stress tolerance. It examines their involvement in both indirect and direct defense mechanisms, as well as their storage within plant tissues. Additionally, this review explores the importance of metabolomics approaches in elucidating the significance of secondary metabolites in biotic stress tolerance. The application of metabolic engineering in breeding for biotic stress resistance is discussed, along with the exploitation of secondary metabolites for sustainable pest management. 2023 by the authors. -
Plant- based Metabolites as Source of Antimicrobial Therapeutics: Prospects and Challenges
Plants are used as traditional medicines from ancient times to today as they are the largest living storehouses of bio- chemicals and pharmaceuticals known on Earth (Abdallah, 2011). The World Checklist of Vascular Plants (WCVP) database reported in April 2021 that there are 1,383,297 plant names with 996,093 plants identified at species level, constituting 342,953 accepted vascular plant species (Govaerts et al., 2021). Around 10% of the reported vascular plants are used as medicines (Salmer- Manzano et al., 2020). According to the MPNS, 33,443 species are recorded as being used for medicinal purpose (MNPS, 2021). Medicinal plants are those that have therapeutic properties which can pose pharmacological effect on the human or animal body (Namdeo, 2018). About 80% of the world's population depends on plant- based medicine for treatment of diseases (Okoye et al., 2014). The medicinal property of a plant is attributed to rich and diverse secondary metabolites (Allemailem, 2021). Secondary metabolites are intermediates or products of primary metabolism that are not involves directly in the growth and development of the plant (Jain et al., 2019). Plants generate secondary metabolites in response to stresses posed by biotic factors (bacteria, fungi, viruses, parasites, pests, weeds, and herbivore animals) and abiotic environmental factors (temperature, salinity, drought, UV radiation etc.) so as to adapt and survive in response to environmental stimuli during their life time (Yang et al., 2018). 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Plant-Derived Nanocellulosic Material: A Promising Technology Application in Environmental Bioremediation
Nanocellulose (NC) polymers derived from plant sources are gaining enormous interest in environmental remediation owing to their low cost and potential for renewable adsorption. Plant-derived nanocellulose is applied in waste water treatment because of its unique features and functionality. The word nanocellulose refers to cellulosic materials having a dimension of nanoscopic scale/or nanoscale. One such nanomaterial is a cellulose-based material with a well-aligned nanocellulose composition indicating its structural hierarchy. Nanocellulose has been recognized as a remarkable natural biomaterial adsorbent which is obtained from renewable sources such as wood, plants, fruit peel, can be found abundantly on earth, and biodegradable and can be easily used in the surface fabrication. Due to its increased surface area, nanocellulose has gained considerable advantage over conventional cellulose fibers. Application of nanocellulosic material in environmental remediation and wastewater treatment has recently emerged as a potential adsorbent generating, and aroused much attention in addressing the environmental issue. Nanocellulose may adsorb a wide range of contaminants, such as heavy metals, dissolved pollutants (organic), dyes, petroleum oil, and unwanted effluents. This review provides focus on the structure, properties, isolation, and adsorbent classes of nanocellulosic materials, as well as their applications in environmental remediation. 2025 by Apple Academic Press, Inc. -
Plant, Animal, and Microbial Sources of Dyes and Mordants
Synthetic dyes and mordants have been used by various industries, including food, cosmetics, textiles, and pharmaceuticals, for many decades. However, their potential hazards to the environment and human health, such as carcinogenicity and teratogenicity, have raised global concerns. In earlier decades, people used naturally extracted dyes and mordants from plants and insects for purposes like painting, dyeing clothes, and enhancing skin and hair, using substances like henna, turmeric, and saffron. However, chemically synthesized dyes quickly replaced natural dyes due to their easy availability and low cost. Currently, consumers are becoming more conscious of the use of synthetic dyes and their effects, which can cause allergies and toxicity. This has led to a resurgence of eco-friendly dyes and biocolors, which have gained importance. There has been advanced and increased development in utilizing naturally occurring bioresources to produce sustainable biocolors with multifunctional applications. Natural colors have not only increased their market value due to their aesthetic appeal but also for their various properties, including antibacterial, antiviral, anticancer, anti-inflammatory, and antioxidant effects. Indeed, biocolors derived from plants, animals, and microorganisms have better degradability and compatibility with the environment. These naturally occurring pigments need to be explored from various natural sources to meet the increasing global demand, using suitable techniques for their extraction. 2025 Apple Academic Press, Inc. -
Plasma sprayed magnesium aluminate and alumina composite coatings from waste aluminum dross
The absence of structured waste management practices for tons of black aluminum dross (Al-dross) when land-filled affects the ecosystem we live in. Researchers and technologists are now working towards three goals (a) minimization of Al-dross production (b) reducing its toxic effects on the environment and (c) treating the Al-dross to beneficiate useful materials from it in an environmentally friendly manner and to generate useful industrial products. The third aspect has been addressed in this study. Al-dross is an aluminum industry generated waste that mainly contains Al metal (oxidized during processing), Aluminum Nitride (AlN), ?-aluminum oxide (?-Al2O3) and magnesium aluminate (MgAl2O4). The oxides are highly suitable for refractory and thermally insulating material applications, but AlN is detrimental for two reasons - (a) thermal conductivity higher than the oxides and (b) carcinogenic gas evolution during processing. Hence AlN must be removed from Al-dross for further processing into refractories. In this work, AlN with minor quantities of halides were removed from Al-dross to extract the major useful refractory oxide constituents in an environmentally friendly manner. The process methodology involved sieving Al-dross to < 600 m particles, aqueous media treatment to remove the nitrides in the form of NH3 gas, oven drying and calcination at 10001150 C for 2 h (in an electrical muffle furnace in ambient air atmosphere) to obtain a mixture of the composite oxide powder of ? 99.0% purity. The calcined compound was mixed with suitable organic binders and sieved to obtain plasma sprayable powder and plasma spray-coated onto bond coated (commercial NiCrAlY) steel substrates. XRD and SEM with EDS facility were used to characterize the powders and coatings. A polished metallographic cross-section was prepared to study the microstructure and interface characteristics. The findings are presented. 2022