Terms Of Storage Research Articles

Surface energy balance-based surface urban heat island decomposition at high resolution

Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat storage term) using Sentinel-2 and Landsat-8 images in Beijing. The simulated SUHI intensity, derived by combining all five contribution terms, exhibited a good consistency but a higher spatial resolution, than SUHI intensity extracted from Landsat-8 land surface temperature product. SUHI intensity tended to decrease from the old city to outsides, attributed to the decrease of evapotranspiration, solar radiation and anthropogenic heat term. The convection and heat storage term play a positive role in reducing SUHI. Among urban morphological blocks, low-rise and high-density blocks had the strongest SUHI, with the evapotranspiration term contributing the most. The results highlighted the capacity of the urban surface to evaporate water in affecting Beijing SUHI. The proposed method provides one useful tool to analyze the drivers of SUHI from the aspect of heat formation, which can be potentially applied worldwide for large-scale comparisons of how urbanization affects UHI.

Blockchain and Cryptography Framework of E-Apps with Big Data

With the tremendous and rapid evolution taking place in the field of technology and considering the rise in data volume that is dealt with daily, managing this data, whether in terms of security or terms of storage especially if the data is huge, is considered a necessary issue. Therefore, in this research, we established a framework that provides both security and storage/repository management. Where the security issue in the suggested framework is supported by the use of lightweight hash functions and public-key encryption algorithms represented by SPONGENT and elliptic curve cryptography (ECC). Additionally, the fast random number generator is also used to support the security algorithms included in the framework, while managing the repository storage is controlled by the use of a hybrid Blockchain to manage storage for this type of big data. The process of storing this data in databases or any of the traditional centralized methods exposes the data to loss or penetration. After analyzing the proposed framework, it successfully addressed the prevention of malicious within the field of proposed research attacks. Moreover, the performance analysis of the framework proposed was quite effective with the lightweight SPONGENT and ECC results, while the creation of blocks in the storage phase was not more than 0.18 ns. Thus, we obtained an effective framework in terms of security, performance, and terms of data repository management and control.

Statistical analysis of the efficiency of customs warehouses use when applying customs regimes during the period of martial law in Ukraine

The article is devoted to the comparative characteristics of the organization of storage of goods in customs warehouses under the «customs warehouse» regime. A general scientific method of comparison was used to identify disparities between the volumes of customs payment receipts in the regional section by individual customs offices. An algorithm for determining the maximum term of storage of goods in a customs warehouse depending on the applied customs regime has been built. The grouping of customs offices by the area of customs warehouses in terms of their types was carried out on the basis of the register of enterprises that were granted permission to open and operate customs warehouses for 2022–2023. The possible options for placement of goods in the current customs warehouses at the customs of Ukraine are given, which allowed establishing the specialization of customs by types of customs warehouses. A statistical analysis of the structure and dynamics of customs payments by customs in 2021 and 2022 was carried out, which made it possible to determine the most effective and ineffective customs in terms of revenue to the State Budget of Ukraine. The comparative characteristics of the normative regulation of the order of organization of storage of goods in the customs warehouse under the conditions of the «customs warehouse» regime and other customs regimes in accordance with the Customs Code of Ukraine are given. It was put forward to test the hypothesis that one of the important factors affecting the receipt of customs payments to the State Budget of Ukraine is the area of customs warehouses. The coefficient of determination was applied as a method of correlation-regression analysis, which confirmed the existence of a significant relationship between the area of customs warehouses and expected customs payments to the State Budget of Ukraine. It has been established that among the factors affecting the receipt of customs payments, 38 percent is accounted for by the area of customs warehouses, and 62 percent by other factors.

Colorimetry method for nitrocellulose powder charge monitoring system

Nitrocellulose powders are unstable chemical compounds. Over time, the consumer properties of the powder charge can change so much that the charges will not only be unsuitable for their intended use, but also become dangerous for personnel and the barrel system. This is associated with the problem of establishing the terms of safe storage of powders and their operational suitability, the need for a constant system for monitoring their condition. However, in developed NATO countries, there is practically no system for monitoring powder charges throughout their entire shelf life. Some countries have some components of the above-mentioned system, but its elements do not fully cover the entire spectrum of the state of powder charges. For the monitoring system to work effectively, informative indicators are needed that allow determining the state of powder charges without the use of complex diagnostic equipment. One of such indicators is the color of the surface of the powder element. In this study, an automated method for assessing the quality of nitrocellulose powder charges by the color of their surface for further use as intended was developed. The use of this method will significantly reduce the time of assessment and decision-making by experts, and eliminate subjectivity. In addition, the method will allow creating a database of powder charges for further monitoring of their condition throughout the entire life cycle. To confirm the reliability and adequacy of the developed method, an experimental study was conducted. The study data showed that the proposed hypothesis is true, namely, during storage, nitrocellulose powder charges change the color of their surface, and it is possible to determine their shelf life

Nano-drug-based Targeted Therapy Alleviates Ferroptosis-induced Liver Toxicity

Abstract: Iron is an essential inorganic element for an organism, with several metabolic activities. The glycoproteins ferritin and transferrin, which assist in carrying iron to various body parts, are used to store iron. In terms of iron uptake, storage, and excretion, equilibrium should be preserved. Ferroptosis is an iron-dependent form of cell death with traits like lipid peroxidation buildup and ROS generation. It is distinct from other forms of cell death visually and biochemically. Many cancer cells block ferroptosis by controlling different cell survival pathways. Compared to healthy, normal cells, cancer cells are more dependent on iron. A subgroup of tumor cells known as cancer stem cells has stem-like characteristics. These are in charge of metastasis and recurrence. The liver plays a significant part in the body's detoxifying process and is the primary iron storage organ. Numerous liver disorders are frequently accompanied by excessive iron accumulation. Due to excessive iron deposits, the liver is more vulnerable to oxidative damage, which can occasionally result in liver failure. Chemotherapy, which involves administering several medications to treat cancer, may be hazardous to the body's other cells. The ferroptosis condition and high iron accumulation can potentially impair liver function. A tailored drug delivery method may ameliorate the impact of excessive iron accumulation and favorably correlate with liver damage, consequently enhancing liver function.

A moisture budget perspective on Australian rainfall variability

AbstractRainfall variability over Australia is revisited from the viewpoint of the atmospheric moisture budgets in three regions: the extratropics, Subtropics, and Tropics. The budgets are calculated using three‐hourly European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) and ERA5‐Land data between 1979 and 2022. The use of the moisture budget at short time‐scales enables the investigation of the relationship between synoptic weather‐scale processes and the longer term variability of the rainfall climate. The total variability in the vertically integrated moisture flux divergence (VIMD) is significantly larger than the evaporation minus precipitation (E − P), to a large extent due to the sub‐daily time‐scales. E − P is related more closely to moisture flux convergence in winter (summer) over south (north) Australia, suggesting a clear seasonality in the relationship between the two budget terms. The E − P–VIMD relationship is nearly in phase in the Tropics, whereas VIMD leads E − P by 9–15 hr with eastward‐propagating signals in the extratropics and Subtropics. Such seasonal and regional discrepancies in the relationship are attributed to the background state of moisture availability and temperature as represented by relative humidity and lifting condensation levels. The variability of the budget imbalance and its seasonality are dominated by the variability in VIMD. The imbalance reduces rapidly with temporal smoothing, with the storage term approaching zero at approximately 20 days, which can be thought of as making a transition time‐scale from high‐frequency weather‐related variability into slow‐varying background conditions. Weather‐related variability (cyclones, fronts, and thunderstorms) dominates the overall E − P variability in the extratropics and Subtropics, whereas slow‐varying background conditions contribute equally to the total variability in the Tropics.

Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in ‘Yali’ pear

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in ‘Yali’ pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm−2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm−2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in ‘Yali’ pear, which would be helpful for supporting postharvest decision systems.

A U-Net architecture as a surrogate model combined with a geostatistical spectral algorithm for transient groundwater flow inverse problems

Characterizing groundwater flow parameters is crucial for understanding complex aquifer systems, and inverse techniques play a fundamental role in modeling hydrogeological parameters and assessing their uncertainties. Nonetheless, the use of a forward model in these methods can be highly time-consuming, especially with an increasing number of model parameters. To address this issue, we propose a surrogate model based on a U-Net architecture that replaces the transient groundwater flow model, reducing runtime and enabling a fast quantification of uncertainties related to key parameters, including heterogeneous hydraulic conductivity, boundary conditions, specific storage, and pumping rate. The surrogate is trained using limited evaluations of the forward model to learn the physical relationship between hydraulic conductivity fields and transient hydraulic heads measured on-site. The physical principles of the studied problem, including boundary conditions, specific storage, and source terms, are also mapped and introduced as inputs to the model to enhance its understanding of the governing equation of transient groundwater flow. To speed up learning using image–image regression, the previously predicted transient hydraulic heads also serve as an input to predict the transient heads at the current time step. Once the model is trained, we use a spectral geostatistical method to solve the inverse problem, a pumping test of 12 h, using the surrogate model in place of the forward model. Our study demonstrates that the trained U-Net accurately reproduces the state variables corresponding to a specific parameter field, and in terms of computational demand, using U-Net as a surrogate model reduces the required computational time by approximately an order of magnitude for the defined problem. The proposed approach offers an efficient and accurate method for groundwater flow parameter characterization and uncertainty quantification in complex aquifer systems.

River plastic transport and storage budget

Rivers are one of the main conduits that deliver plastic from land into the sea, and also act as reservoirs for plastic retention. Yet, our understanding of the extent of river exposure to plastic pollution remains limited. In particular, there has been no comprehensive quantification of the contributions from different river compartments, such as the water surface, water column, riverbank and floodplain to the overall river plastic transport and storage. This study aims to provide an initial quantification of these contributions. We first identified the main relevant transport processes for each river compartment considered. We then estimated the transport and storage terms, by harmonizing available observations on surface, suspended and floodplain plastic. We applied our approach to two river sections in The Netherlands, with a focus on macroplastics (≥2.5 cm). Our analysis revealed that for the studied river sections, suspended plastics account for over 96% of item transport within the river channel, while their relative contribution to mass transport is only 30%–37% (depending on the river section considered). Surface plastics predominantly consisted of heavier items (mean mass: 7.1 g/#), whereas suspended plastics were dominated by lighter fragments (mean mass: 0.1 g/#). Additionally, the majority (98%) of plastic mass was stored within the floodplains, with the river channel accounting for only 2% of the total storage. Our study developed a harmonized approach for quantifying plastic transport and storage across different river compartments, providing a replicable methodology applicable to different regions. Our findings emphasize the importance of systematic monitoring programs across river compartments for comprehensive insights into riverine plastic pollution.

Numerical investigation about effects of flow sediment-storage variation on capacity modelling of morphological evolutions

The capacity model based on the Exner equation has been widely used to simulate bed load sediment transport. However, most of their governing equations neglect a sediment storage term (∂hCe/∂t), of which the impact on bed load transport simulation is unknown and needs to be better comprehended. This paper has split the capacity model into two sub-models: one with the sediment storage term and the other without the sediment storage term. The two sub-models are applied to simulate a series of typical bed load transport processes, such as dam-break flooding and swash flows. A comparison of their results is used to shed light on the effect of sediment storage term on flows and morphological evolutions. It is shown that for both dam-break flooding and swash flows, neglection of the inflow sediment storage may lead to under-estimation of bed erosion or over-estimation of bed deposition, which further affects the computed hydrodynamics greatly. Moreover, it is shown that including this factor can improve quantitative accuracy of sediment transport estimation. These indicate an important role of this factor and accordingly the present work provide a numerical justification for using sediment storage terms in capacity models.

A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies

Abstract. Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21 568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from the internet. In addition to annual and monthly streamflow indices, each basin's daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily–weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties in all meteorological variables are estimated with independent data sources. Our analyses reveal the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the revealed pattern should be accounted for by LSH users; (ii) ∼6 % watersheds shifted between human-managed and natural states during 2001–2015, e.g., basins with environmental recovery projects in northeast China, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account; and (iii) GSHA watersheds showed a more widespread declining trend in runoff coefficient than an increasing trend, pointing towards critical water availability issues. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.1 can be accessed at https://doi.org/10.5281/zenodo.8090704 and https://doi.org/10.5281/zenodo.10433905 (Yin et al., 2023a, b).

A Combination of Three Scales for Measuring User-Perceived Usability of a Clinical Information System: Which Approach Produces the Most Informative Results?

Introduction: For the first time in Iran, a kidney stone clinical information system (CIS) has been designed to manage and calculate and visualize patients’ kidney stone risk profiles. Nevertheless, the usability of this system has not been evaluated yet. Medically, this study aims to evaluate the user-perceived usability of the kidney stone CIS. Technically, the current study aims to determine which user-perceived usability testing approach produces the most informative results about the usability of this system.Material and Methods: Three questionnaires, including system usability scale (SUS), software usability measurement inventory (SUMI), and post-study system usability questionnaire, were applied to carry out the study. A total of 15 users of the kidney stone CIS participated.Results: The findings revealed that the system is of medium usability. Moreover, of the three methods used, the SUMI echoes the comments of the end-users. Despite the medium usability of the system, it was comprehensive in terms of proper data collection and storage, as well as reporting. The interface design, the lack of appropriate guidance, the time-consuming data entry, and the slow reporting system were aspects needed improvement.Conclusion: Using a combination of tools is recommended for usability evaluation. Since there is much space in the Global and each sub-scale, whereby measures may improve with SUMI, this research recommends its use for future evaluation of the kidney stone CIS.

Detection of doping substances in paired dried blood spots and urine samples collected during doping controls in Danish fitness centers.

The use of dried blood spot (DBS) in anti-doping can be advantageous in terms of collection, transportation, and storage compared with the traditional anti-doping testing matrices urine and venous blood. There could, nonetheless, be disadvantages such as shorter detection windows for some substances compared with urine, but real-life comparison of the detectability of prohibited substances in DBS and urine is lacking. Herein, we present a liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based screening method for simultaneous detection of 19 target analytes from the doping substance categories S1-S5 in a single spot. Ninety-eight urine and upper-arm DBS (Tasso-M20) sample pairs were collected from fitness centers customers notified for doping control by Anti Doping Denmark, and three sample pairs were collected from active steroid users undergoing clinical evaluation and treatment at a Danish hospital. The analytical findings were cross compared to evaluate the applicability of the developed DBS testing menu in terms of feasibility and analytical performance. To our knowledge, this is the first study to compare the detectability of prohibited substances in DBS and urine samples collected in a doping control setting. Twenty-seven of the urine samples and 23 DBS samples were positive, and we observed a very high concordance (95%) in the overall analytical results (i.e., positive or negative samples for both urine and DBS). Collectively, these results are very promising, and DBS seems suitable as a stand-alone matrix in doping control in fitness centers likely because of the high analyte concentration levels in these samples.

Assessment of vegetation change using NDVI, LST, and carbon analyses in Çankırı Karatekin University, Turkey.

Due to a rising population and urbanization, the green areas have been decreasing in cities, with a negative impact on air pollution, human health, and ecosystem. As part of the urban environment, university campuses contribute to urban ecosystem with their vegetation. This study aims to (1) assess the change of vegetative land cover of the Çankırı Karatekin University in Turkey and (2) evaluate its benefits to the ecosystem in terms of carbon sequestration, storage, and improvement of air quality by means of a simulation. In the study, the density and vegetation change were assessed with NDVI and LST analyses in ArcGIS; carbon emissions and air pollution benefits were estimated in i-Tree Canopy tool. The study showed that the healthy vegetation consisting of trees/shrubs and grass/herbaceous, which was 32.2% (28ha) in 2000, increased to 85% (74ha) in 2020 NDVI maps, and the surface temperature also increased between 2000 and 2020 in LST maps.The rise in vegetation as grass/herbaceous areas instead of trees/shrubs and the use of impervious buildings/roads on the land surface increased the land surface temperature. As a result of the analyses in the i-Tree-Canopy tool, it was estimated that the trees/shrubs and grass/herbaceous vegetation canopy covering 31.42% of the study area removed a total of 512,845.65g of pollutant gas and particles from the air, 20.79 tonnes of carbon sequestered annually, and 522.01 tonnes of carbon stored by vegetative land cover. In the simulation, where 32.62% soil/bare ground areas were converted to trees/shrubs in order to improve vegetation cover in the area, it was determined that it contributed 5 times more to the ecosystem service value for removing pollutants from the air, carbon storage, and improving the ecosystem. It was revealed that the vegetative land cover formed by tree/shrub species should be increased in the campus in the future. The study method model serves as a tool for planning and designing eco-friendly urban environment.

A secure and privacy preserved data aggregation scheme in IoMT

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

Dickson polynomial-based secure group authentication scheme for Internet of Things

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli’s Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

A review of time-memory trade-off techniques

This paper delves into the discussion of time-memory trade-off techniques in the field of cryptanalysis. The method was initially introduced by Hellman in 1980, subsequently, DP trade-off, rainbow trade-off, and checkpoint trade-off have been proposed to enhance the efficiency of cryptographic attacks. This paper elaborates on the concept of rainbow trade-off and their variants and presents optimizations in terms of storage and runtime speed for time-memory trade--off methods. Ingenious storage optimization significantly reduces the storage overhead of pre-computed tables, and the rapid advancement of implementation platforms achieves speed optimization for the online phase. Through these optimization measures, time-memory trade-off methods exhibit even more remarkable performance in practical applications. For researchers and practitioners in the field of cryptography, the content of this paper provides valuable references and insights for their work.

Assessing carbon storage and sequestration benefits of urban greening in Nador City, Morocco, utilizing GIS and the InVEST model

In the 21st century, tackling climate change is an important challenge, and the absorption of CO2 via urban greening employing photosynthesis offers a potential answer to the climate issue. Within this work, the city of Nador is chosen as a case study. The initiative in question proposes an enhanced technique focused on estimating the advantages of urban greening in terms of carbon storage and sequestration. This technique combines the use of Geographic Information Systems (GIS) tools and the InVEST model, enabling the study of land use and land cover (LULC) scenarios within the location. These scenarios contain numerous alternatives, ranging from the adoption of intense and extensive green roofs to the establishment of gardens and woodland areas. The carbon storage modeling technique depends on the exploitation of land use maps, reinforced by a bibliographic examination of the key carbon reservoirs. The analysis of results from biophysical modeling indicates a considerable increase of over 45.36 % compared to the existing condition when the option of extensive green roofs is chosen. In the setting of extensive green roofs, this rise reaches a rate of 64.20 %. Furthermore, the restoration of the Gourougou forest north of the city leads to an increase of 70.30 %. The adoption of diverse combinations, such as the integration of green roofs with urban vegetation, greatly helps to further enhancing carbon storage. From a financial perspective, the additional value can range from $2,012,825.16 to $3,601,025.53, depending on the chosen scenario. This financial assessment highlights the possible advantages that might be converted into carbon credits in return for emission reduction, partially offsetting the expenses associated with the adoption of each greening solution. Ultimately, these findings give a chance for urban planners and green architecture professionals to perform in-depth planning analyses.

A Tasty Twist: Exploring the Potential of Edible Vaccines

A biological preparation known as a vaccine offers active acquired immunity to a specific infectious or malignant disease. There are various vaccine varieties, such as: inactivated vaccines. live-attenuated immunizations. mRNA (messenger RNA) vaccines. vaccinations include subunits, recombinants, polysaccharides, and conjugates. Genetically engineered crops with antigens for particular diseases are known as edible vaccines. Due to the vaccine's simplicity of culture, this lowers the product cost. Since they don't require adjuvants that strengthen the susceptible response, edible vaccines are a viable method of immunization delivery. In terms of vacuity, storage, medication, product, and transportation, edible vaccines are also economical. Foods under such operation include potato, banana, lettuce, soybean, rice, lettuce, apple, peas, soyabean, cherry tomatoes, alfalfa, tomatoes, carrot etc. This review focuses on the development of edible vaccines over the years and the various lookouts it holds as technology keeps developing. The evolution of vaccines has led to the discovery of new forms of vaccination that are efficacious and cover a broad array of disease.

Machine learning for effective EHR management in blockchain-cloud integration

<p>Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.</p>