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Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.

Crops grown in areas contaminated by industrial and agricultural fluorine (F) have gained increasing attention, however F levels in different vegetables and lettuce cultivars are rarely reported. In situ-field experiment was designed to investigate the concentration, translocation, and health risks of F in 20 vegetable species and 25 lettuce cultivars. After the growth of 150 d for vegetables and 60 d for lettuce, F concentration (12.83-138.07 mg kg

Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace.

Foxtail millet (Pennisetum glaucum L.) is a vital crop that is planted as food and fodder crop around the globe. There is only limited information is present for abiotic stresses on the physiological responses to atrazine. A field experiment was conducted to investigate the effects of different atrazine dosages on the growth, fluorescence and physiological parameters i.e., malonaldehyde (MDA) and reactive oxygen species (ROS) (H2O2 and O2) in the leaves to know the extent of atrazine on oxidative damage of foxtail millet. Our experiment consisted of 0, 2.5, 12.5, 22.5 and 32.5 (mg/kg) of labeled atrazine doses on 2 foxtaill millet varieties. High doses of atrazine significantly enhanced ROS and MDA synthesis in the plant leaves. Enzymes activities like ascorbate peroxidase (APX) and peroxidase (POD) activities enhanced, while catalase (CAD) and superoxide dismutase (SOD) activities reduced with increasing atrazine concentrations. Finally atrazine doses at 32.5 mg/kg reduced chlorophyll contents, while chlorophyll (a/b) ratio also enhanced. Biomass, plant height, chlorophyll fluorescence parameters, minimal and maximal fluorescence (Fo, Fm), maximum and actual quantum yield, photochemical quenching coefficient, and electron transport rate are decreased with increasing atrazine doses.

Cloud computational service is one of the renowned services utilized by employees, employers, and organizations collaboratively. It is accountable for data management and processing through virtual machines and is independent of end users’ system configurations. The usage of cloud systems is very simple and easy to organize. They can easily be integrated into various storages of the cloud and incorporated into almost all available software tools such as Hadoop, Informatica, DataStage, and OBIEE for the purpose of Extraction-Transform-Load (ETL), data processing, data reporting, and other related computations. Because of this low-cost-based cloud computational service model, cloud users can utilize the software and services, the implementation environment, storage, and other on-demand resources with a pay-per-use model. Cloud contributors across this world move all these cloud-based apps, software, and large volumes of data in the form of files and databases into enormous data centers. However, the main challenge is that cloud users cannot have direct control over the data stored at these data centers. They do not even know the integrity, confidentiality, level of security, and privacy of their sensitive data. This exceptional cloud property creates several different security disputes and challenges. To address these security challenges, we propose a novel Quantum Hash-centric Cipher Policy-Attribute-based Encipherment (QH-CPABE) framework to improve the security and privacy of the cloud user’s sensitive data. In our proposed model, we used both structured and unstructured big cloud clinical data as input so that the simulated experimental results conclude that the proposal has precise, resulting in approximately 92% correctness of bit hash change and approximately 96% correctness of chaotic dynamic key production, enciphered and deciphered time as compared with conventional standards from the literature.

The prevalence of abiotic stresses hampers soil health and plant growth in most ecosystems. In this study, rice husk iron-enriched biochar (BC) was prepared and its superiority in terms of nutrients enrichment, porosity and different acidic functional group (O-H, C=O) relative to simple biochar was confirmed through scanning electron microscopic, X-ray fluorescence and Fourier transform infrared analysis. To further evaluate its nickel (Ni), salt (NaCl) and carbonate (CaCO3) stress mitigating impact on wheat physiology and biochemical attributes, a pot experiment was conducted using; BC (1%), Ni (0.5 mM NiNO3), Na (100 mM NaCl) and CO3 (100 mM CaCO3) and with twelve treatments; T1; Control, T2; NiNO3, T3; CaCO3, T4; NaCl, T5; BC, T6; Ni + BC, T7; CaCO3 + BC, T8; NaCl + BC, T9; Ni + CaCO3 + BC, T10; Ni + NaCl + BC, T11; CaCO3 + NaCl + BC, T12; Ni + NaCl + CaCO3 + BC. The Langmuir isotherm model revealed the maximum Ni adsorption capacity (2433 mg g−1) in treatments where Ni was applied with BC soil. Maximum soil DTPA-extractable Ni was found in the T9 treatment; however, Ni concentration was not reported in wheat roots while only trace amounts of Ni were found in wheat shoots with the T9 treatment. It was suggested that BC has the capacity to induce the immunization effect in plant roots by providing additional Fe so their ionic homeostasis and redox metabolism worked properly. This argument was further paved by the enhanced adsorption of these toxic ions in the presence of BC-favored wheat growth as indicated by maximum increases in shoot iron and potassium concentrations under Ni + CaCO3 + BC, relative to control. Furthermore, the decrease in shoot hydrogen peroxide (H2O2) (20%) and malondialdehyde (32%) concentrations and increase in shoot ascorbate peroxidase (81%) and catalase (three-fold) activities under Ni + BC relative to Ni + NaCl + CaCO3 + BC controlled the cell membrane damage. In conclusion, BC proved to be an excellent amendment to reduce the toxic effects of Ni, NaCl and CaCO3 stresses and enhance wheat growth and nutrition.

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method.

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

Previous studies have focused on total cadmium (Cd) accumulation in rice or its transformation in soil, but only a few have examined the entire soil-rice-human system. This study investigated the Cd bioaccessibility and bioavailability for humans from grains of early rice cultivars grown in a Cd-polluted field and further combined with multi-traits to discover and evaluate the optimum safe production and phytoremediation potential cultivars. The results revealed that Cd concentration in polished rice was0.20 mg kg

Potentially toxic metals have become a viable threat to the ecosystem due to their carcinogenic nature. Biochar has gained substantial interest due to its redox-mediated processes and redox-active metals. Biochar has the capacity to directly adsorb the pollutants from contaminated environments through several mechanisms such as coprecipitation, complexation, ion exchange, and electrostatic interaction. Biochar's electron-mediating potential may be influenced by the cyclic transition of surface moieties and conjugated carbon structures. Thus, pyrolysis configuration, biomass material, retention time, oxygen flow, and heating time also affect biochar's redox properties. Generally, reactive oxygen species (ROS) exist as free radicals (FRs) in radical and non-radical forms, i.e., hydroxyl radical, superoxide, nitric oxide, hydrogen peroxide, and singlet oxygen. Heavy metals are involved in the production of FRs during redox-mediated reactions, which may contribute to ROS formation. This review aims to critically evaluate the redox-mediated characteristics of biochar produced from various biomass feedstocks under different pyrolysis conditions. In addition, we assessed the impact of biochar-assisted FRs redox-mediated processes on heavy metal immobilization and mobility. We also revealed new insights into the function of FRs in biochar and its potential uses for environment-friendly remediation and reducing the dependency on fossil-based materials, utilizing local residual biomass as a raw material in terms of sustainability.

Nanoparticles have emerged as cutting-edge technology for the improvement of crops yield and safe cultivation of agricultural plants, especially in peripheral areas impaired with toxic heavy metals including chromium (Cr). The uncontrolled release of Cr mainly from anthropogenic factors is substantially polluting the surrounding environment, thereby extensively accumulated in soil-plant system. The excessive Cr-accretion in plant tissues disturbed the morph-physiological, biochemical, cellular, metabolic and molecular processes, and impaired the plants functionality. Therefore, it is obligatory to restrict the accumulation and toxic effects of Cr in plant organs. Recent studies on metallic nanoparticles (MNPs) such as iron oxide, silicon dioxide, copper oxide and zinc oxide have approved their efficacy as potent pool to curb the Cr-induced phytotoxicities and improved the plant tolerance. MNPs attenuated the bioaccumulation and phytotoxicity of Cr by utilizing key mechanisms such as improved photosynthetic machinery, regulation of cellular metabolites, greater chelation capacity to bind with Cr, release of corresponding metallic ions, upsurge in the uptake of essential nutrients, activation of antioxidants (enzymatic and non-enzymatic), reduction in oxidative stress, and cellular injuries, thus improvement in plant growth performances. We have briefly discussed the current knowledge and research gaps in existing literature along with possible recommendations for future research. Overall, Cr-detoxification by MNPs may depends upon the target plant species, Cr speciation, plant growth stages (seedling, vegetative and ripening etc.), treatment methods (foliar spray, seed priming and nutrient solution etc.), type, size, dose and coating of applied MNPs, and conditions (hydroponic and soil environment etc.). This review would help plant scientists to develop MNPs based strategies such as nano-fertilizers to alleviate the Cr-accumulation and its toxic impacts. This may leads to safe and healthy food production. The review outcomes can provide new horizons for research in the applications of MNPs for the sustainable agriculture.

Information is needed for comparative assessment and agronomic practices for phytoavoidation in multi-pollutant field. A field study was conducted to explore 97 Brassica pekinensis L. genotypes with permissible limit of contaminants growing in a severely Cd, moderately nitrate and slightly Pb multi-polluted field. Thirteen genotypes, i.e. KGZY, CXQW, CAIB, JINL, JQIN, JFEN, WMQF, XLSH, TAIK, BJXS, JUKA, XYJQ and GQBW, were identified with permissible limit for nitrate, Cd and Pb based on their resistance to heavy metal and nitrate accumulation in leaves when grown in co-contaminated soils. Furthermore, the correlation between essential and toxic elements concentrations in plant of B. pekinensis were inconsistent. Generally speaking, application of increasing Ca, K and S fertilizers in appropriate forms and dosages tended to increase the yield and quality of B. pekinensis cultivated in multi-pollutant field.

Cadmium (Cd) and fluorine (F) often coexist in environment and are toxic to organisms; however, their combined effects on plants are still not well documented. In this study, the co-effects of Cd and F on germination, biomass, photosynthesis, and nutrients uptake of lettuce were carried out in hydroponic culture. The results showed that the seed germination and seedling biomass decreased with an increase in Cd and F supplementation. The root morphology verified these effects as excess combined Cd and F diminished the root tips and surface area of lettuce, while single Cd and F inhibited the growth by decreasing root length and average diameter, respectively. These effects were also consistence with a reduction in photosynthesis which was mainly regulated by reducing the quantum yield of PS II, electron transport activity, stomatal conductance, intercellular CO

With the growing number of cloud users, shared data auditing is becoming increasingly important. However, these schemes have issues with the certificate management. Although there is a certificate-shared auditing scheme, it is ineffective in dealing with dynamic data and protecting data privacy. The verifier cannot access the data content to ensure data integrity due to security concerns. This paper proposes a novel technique to ensure the integrity and improve the access control. A novel enhanced storage retrieval mechanism is used to improve the performance of the cloud’s storage and retrieval mechanisms to achieve this. The technique is evaluated in concern of the upload, download, encryption, and decryption time. As the file size grows, so does the time it takes to upload it. Similarly, the time taken to encrypt files of various formats and sizes evidenced that it depends on the file size and format. Thus, the encryption time increases as the file sizes increases, demonstrating the performance of the proposed system.

The structure of biochar produced at various pyrolysis temperatures influences metal geochemical behavior. Here, the impact of wheat straw-derived biochar (300, 500, and 700 °C) on the immobilization and transformation of metals in the contaminated soil-plant system was assessed. The findings of the sequential extraction revealed that biochar additives had a substantial influence on the speciation of Cr, Ni, Pb, and Zn in the contaminated soil. The lowest F1 (exchangeable and soluble fraction) + F2 (carbonate fraction) accounted for Cr (44%) in WB-300, Ni (43.87%) in WB-500, Pb (43.79%), and Zn (49.78%) in WB-700 with applied amendments of their total amounts. The characterization results indicated that high pyrolysis temperatures (300-700 °C) increased the carbon-containing groups with the potential to adsorb metals from the soil-plant environment. The bioconcentration and translocation factors (BCF and TF) were less than 1, indicating that metal concentration was restricted to maize roots and translocation to shoots. Reactive oxygen species (ROS) intracellularly influence metal interactions with plants. Electron paramagnetic resonance (EPR) was performed to determine hydroxyl radical generation (•OH) in plant segments to assess the dominance of free radicals (FRs). Consequently, the formation of •OH significantly depends on the pyrolysis temperature and the interaction with a contaminated soil-plant environment. Thus, metal transformation can be effectively decreased in the soil-plant environment by applying WB amendments.