Federated learning (FL) is a recent development in artificial intelligence, which is typically based on the concept of decentralized data. As cyberattacks are frequently happening in the various ...applications deployed in real time, most industrialists are hesitating to move forward in adopting the technology of the Internet of Everything. This article aims to provide an extensive study on how FL could be utilized for providing better cybersecurity and prevent various cyberattacks in real time. We present an extensive survey of the various FL models currently developed by researchers for providing authentication, privacy, trust management, and attack detection. We also discuss few real-time use cases that have been deployed recently and how FL is adopted in them for preserving privacy of data and improving the performance of the system. Based on the study, we conclude this article with some prominent challenges and future directions on which the researchers can focus for adopting FL in real-time scenarios.
Acute liver failure (ALF) is classically defined by coagulopathy and hepatic encephalopathy (HE); however, acute liver injury (ALI), i.e., severe acute hepatocyte necrosis without HE, has not been ...carefully defined nor studied. Our aim is to describe the clinical course of specifically defined ALI, including the risk and clinical predictors of poor outcomes, namely progression to ALF, the need for liver transplantation (LT) and death.
386 subjects prospectively enrolled in the Acute Liver Failure Study Group registry between 1 September 2008 through 25 October 2013, met criteria for ALI: International Normalized Ratio (INR)≥2.0 and alanine aminotransferase (ALT)≥10 × elevated (irrespective of bilirubin level) for acetaminophen (N-acetyl-p-aminophenol, APAP) ALI, or INR≥2.0, ALT≥10x elevated, and bilirubin≥3.0 mg/dl for non-APAP ALI, both groups without any discernible HE. Subjects who progressed to poor outcomes (ALF, death, LT) were compared, by univariate analysis, with those who recovered. A model to predict poor outcome was developed using the random forest (RF) procedure.
Progression to a poor outcome occurred in 90/386 (23%), primarily in non-APAP (71/179, 40%) vs. only 14/194 (7.2%) in APAP patients comprising 52% of all cases (13 cases did not have an etiology assigned; 5 of whom had a poor outcome). Of 82 variables entered into the RF procedure: etiology, bilirubin, INR, APAP level and duration of jaundice were the most predictive of progression to ALF, LT, or death.
A majority of ALI cases are due to APAP, 93% of whom will improve rapidly and fully recover, while non-APAP patients have a far greater risk of poor outcome and should be targeted for early referral to a liver transplant center.
The entire computing paradigm is changed due to the technological advancements in Information and Communication Technology (ICT). Due to these advancements, various new communication channels are ...being introduced, out of which the Internet of Things (IoT) plays a significant role. The Internet of Medical Things (IoMT) is a special category of IoT in which the medical devices communicate with each other for sharing sensitive data. These advancements help the healthcare industry to have better contact and care towards their patients. But they too have certain drawbacks since there are so many security and privacy issues like replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing, denial of service (DoS) attacks and malware attacks. When the sensitive data is being attacked by any of these attacks, there is a chance of losing the authorized data to the attacker or getting altered due to which the data is not available for the authorized users and customers. Machine learning algorithms are widely used in the Intrusion Detection System (IDS) for detecting and classifying the attacks at the network and host level in a dynamic manner. Many supervised and unsupervised algorithms have been designed by researchers from the area of machine learning and data mining to identify the reliable detection of an anomaly. However, the main challenge in the IDS models are changed in dynamic and random behavior of malicious attacks and designing a scalable solution that can handle this behavior. The rapid change in network behavior and the fast evolution of various attacks paved the way for evaluating various datasets that are generated over the years and to design different dynamic approaches. In this paper, a deep neural network (DNN) is used to develop effective and efficient IDS in the IoMT environment to classify and predict unforeseen cyberattacks. The network parameter are preprocessed, optimized and tuned by hyperparameter selection methods. A comprehensive analysis of experiments in DNN with other machine learning algorithms are compared on the benchmark intrusion detection dataset. Through rigorous testing, it has proved that the proposed DNN model performs better than the existing machine learning approaches with an increase in accuracy by 15% and decreases in time complexity by 32%, which helps in faster alerts to avoid post effects of intrusion in sensitive cloud data storage.
Removing decolorizing acid blue 113 (AB113) dye from textile wastewater is challenging due to its high stability and resistance to removal. In this study, we used an artificial neural network (ANN) ...model to estimate the effect of five different variables on AB113 dye removal in the sonophotocatalytic process. The five variables considered were reaction time (5–25 min), pH (3–11), ZnO dosage (0.2–1.0 g/L), ultrasonic power (100–300 W/L), and persulphate dosage (0.2–3 mmol/L). The most effective model had a 5-7-1 architecture, with an average deviation of 0.44 and R2 of 0.99. A sensitivity analysis was used to analyze the impact of different process variables on removal efficiency and to identify the most effective variable settings for maximum dye removal. Then, an imaginary sonophotocatalytic system was created to measure the quantitative impact of other process parameters on AB113 dye removal. The optimum process parameters for maximum AB 113 removal were identified as 6.2 pH, 25 min reaction time, 300 W/L ultrasonic power, 1.0 g/L ZnO dosage, and 2.54 mmol/L persulfate dosage. The model created was able to identify trends in dye removal and can contribute to future experiments.
•Artificial neural networks (ANN) predict AB113 dye removal from wastewater accurately.•ANN model extracts vast knowledge of the sonophotocatalytic process from a limited data.•An easy, user-friendly ANN software was created to analyze the sonophotocatalytic process.
Broadening of the genetic base for identification and transfer of genes for resistance to insect pests and diseases from wild relatives of rice is an important strategy in resistance breeding ...programs across the world. An accession of Oryza nivara, International Rice Germplasm Collection (IRGC) accession number 105710, was identified to exhibit high level and broad-spectrum resistance to Xanthomonas oryzae pv. oryzae. In order to study the genetics of resistance and to tag and map the resistance gene or genes present in IRGC 105710, it was crossed with the bacterial blight (BB)-susceptible varieties 'TN1' and 'Samba Mahsuri' (SM) and then backcrossed to generate backcross mapping populations. Analysis of these populations and their progeny testing revealed that a single dominant gene controls resistance in IRGC 105710. The BC(1)F(2) population derived from the cross IRGC 105710/TN1//TN1 was screened with a set of 72 polymorphic simple-sequence repeat (SSR) markers distributed across the rice genome and the resistance gene was coarse mapped on chromosome 7 between the SSR markers RM5711 and RM6728 at a genetic distance of 17.0 and 19.3 centimorgans (cM), respectively. After analysis involving 49 SSR markers located between the genomic interval spanned by RM5711 and RM6728, and BC(2)F(2) population consisting of 2,011 individuals derived from the cross IRGC 105710/TN1//TN1, the gene was fine mapped between two SSR markers (RMWR7.1 and RMWR7.6) located at a genetic distance of 0.9 and 1.2 cM, respectively, from the gene and flanking it. The linkage distances were validated in a BC(1)F(2) mapping population derived from the cross IRGC 105710/SM//2 × SM. The BB resistance gene present in the O. nivara accession was identified to be novel based on its unique map location on chromosome 7 and wider spectrum of BB resistance; this gene has been named Xa33. The genomic region between the two closely flanking SSR markers was in silico analyzed for putatively expressed candidate genes. In total, eight genes were identified in the region and a putative gene encoding serinethreonine kinase appears to be a candidate for the Xa33 gene.
New value-added uses for solid municipal waste are needed for environmental and economic sustainability. Fortunately, value-added biochars can be produced from mixed solid waste, thereby addressing ...solid waste management issues, and enabling long-term carbon sequestration. We hypothesize that soil deficiencies can be remedied by the application of municipal waste-based biochars. Select municipal organic wastes (newspaper, cardboard, woodchips and landscaping residues) individually or in a 25% blend of all four waste streams were used as feedstocks of biochars. Three sets of pyrolysis temperatures (350, 500, and 750 °C) and 3 sets of pyrolysis residence time (2, 4 and 6 h) were used for biochar preparation.
The biochar yield was in the range of 21–62% across all feedstocks and pyrolysis conditions. We observed variations in key biochar properties such as pH, electrical conductivity, bulk density and surface area depending on the feedstocks and production conditions. Biochar increased soil pH and improved its electrical conductivity, aggregate stability, water retention and micronutrient contents. Similarly, leachate from the soil amended with biochar showed increased pH and electrical conductivity. Some elements such as Ca and Mg decreased while NO3-N increased in the leachates of soils incubated with biochars. Overall, solid waste-based biochar produced significant improvements to soil fertility parameters indicating that solid municipal wastes hold promising potential as feedstocks for manufacturing value-added biochars with varied physicochemical characteristics, allowing them to not only serve the needs for solid waste management and greenhouse gas mitigation, but also as a resource for improving the quality of depleted soils.
•Paper and wood waste can serve as feedstocks for the production of value-added biochars.•Biochars can alleviate economic and environmental problems associated with solid waste disposal.•Soils amended with solid organic waste derived biochars can improve soil in fertility.•Biochars prepared from solid municipal waste can also be used for soil-carbon sequestration.
Pigeonpea (Cajanus cajan) is one of the important grain legume crops cultivated in the semi-arid tropics, playing a crucial role in the economic well-being of subsistence farmers. India is the major ...producer of pigeonpea, accounting for over 75% of the world’s production. Sterility mosaic disease (SMD), caused by Pigeonpea sterility mosaic virus (PPSMV) and transmitted by the eriophyid mite (Aceria cajani), is a major constraint to pigeonpea cultivation in the Indian subcontinent, leading to potential yield losses of up to 100%. The recent characterization of another Emaravirus associated with SMD has further complicated the etiology of this challenging viral disease. This review focuses on critical areas, including the current status of the disease, transmission and host-range, rapid phenotyping techniques, as well as available disease management strategies. The review concludes with insights into the future prospects, offering an overview and direction for further research and management strategies.
In the present work, a nonlinear thermo-electro-mechanical response of functionally graded piezoelectric material (FGPM) actuators is investigated. The theoretical formulation is based on the ...Timoshenko beam theory with the von Kármán nonlinearity (in the form of midplane stretching), and a microstructural length scale is incorporated by means of the modified couple stress theory. A power-law distribution of thermal, electrical, and mechanical properties through beam thickness (or height) is assumed. The governing equations are derived using the principle of virtual displacements. A displacement finite element model of the theory is developed, and the resulting system of nonlinear algebraic equations is solved with the help of Newton's iteration method. Numerical results are presented for transverse deflection as a function of load parameters and out-of-plane boundary conditions. The parametric effects of microstructural length scale parameter, power-law index of the material distribution across the thickness, boundary conditions, beam geometry, and applied actuator voltage on the beam response are investigated through various numerical examples. The results reveal the existence of bifurcation (or critical states) for certain types of in-plane loads. For other load types, including out-of-plane loads, the beam undergoes a unique and stable deflection path that does not contain any critical point.
•Accounts for shear deformation with geometric nonlinearity and size effects.•Buckling analysis with identification of critical states is carried out.•Includes the effects of microstructure dependency, and material distribution.•Includes the effects of boundary conditions, beam geometry, and applied voltage.
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