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A PID controller design using an internal model control (IMC) approach is a well-established method for controller tuning in a DC-DC boost converter. This study introduces an innovative implementation of a novel indirect Internal Model Control (IMC) strategy for PID controller design, tailored specifically for a DC-DC boost converter. While the indirect IMC approach has been documented in prior research, its application to boost converters signifies a substantial contribution to the field. The proposed method simplifies the tuning process by focusing exclusively on the plant shifting parameter ψ, thereby eliminating the need for an IMC filter. Optimal tuning is achieved through the Grey Wolf Optimization (GWO) method, which enhances the controller’s stability, robustness, and transient response in the presence of disturbances commonly encountered in boost converter operation. Extensive simulations are performed in a MATLAB Simulink environment to compare the performance of the GWO-based indirect IMC-PID controller with traditional PID and IMC-PID designs. Performance is assessed based on transient response parameters and performance indices, such as IAE, ISE, ITAE, and ITSE. Results reveal that the GWO-optimized indirect IMC-PID controller significantly outperforms conventional controllers, demonstrating enhanced servo and regulatory behaviors.

The present study makes a qualitative and quantitative assessment of inundation limit, the structural damage and shoreline change due to very severe cyclonic storm Phailin and also estimates the rate of recovery along different parts of south Odisha coast. At the time of landfall of Phailin along Odisha and adjoining Andhra Pradesh coast on October 12, 2013, maximum sustained surface wind speed reached up to 200–210 kmph gusting to 220 kmph with an estimated pressure drop 66 mbar at the center. Significant wave heights reached up to more than 7 m with a mean period range between 4 and 12 s from southeast direction during the event. Strong gale wind and high wave followed by massive rainfall brought irreparable damage to the coastal structures and shoreline along south Odisha. Real-time kinematics global positioning system and differential global positioning system Arcpad were used to monitor the shoreline change before and after Phailin and the inundation limit. The study indicates maximum inundation at southernmost part of Odisha coast (Ramayapatnam) followed by inside the Gopalpur Port area and minimum near Gopalpur port north. Shoreline change from pre- (September 2013) to post-storm period (October 2013) is landward all along south Odisha coast with maximum (48.7 m) near Rushikulya turtle nesting beach and minimum (12.6 m) near south of Gopalpur tourist beach. The recovery of the beach and dune areas assessed during October 2014 (after 1 year from the post-storm observation) is uneven. Percentage of recovery is maximum at south side of Gopalpur port, while recovery is minimum on north of the port.

Depleting fossil fuel, soaring price, growing demand and global climate change concerns have driven research for finding alternative source of sustainable fuel. Microalgae have emerged as a potential feedstock for biofuel production as many strains accumulate high amounts of lipid, with faster biomass growth and higher photosynthetic yield than that of their land plant counterparts, without needing agricultural land or ecological landscapes, and offering opportunities for mitigating global climate change allowing waste water treatment and CO2 sequestration. Despite these benefits, microalgae pose many challenges, including low lipid yield under limiting growth conditions and slower growth in high lipid content strains. Biotechnological interventions can make major advances in strain improvement for commercial scale production of biofuel. We discuss various strategies including efficient transformation toolbox, and to increase lipid accumulation and its quality through regulation of key enzymes involved in lipid production, blocking the competing pathways, pyramiding genes, enabling high cell biomass under nutrient deprived conditions and other environmental stresses, and controlling the upstream regulators of targets, the transcription factors and microRNAs. We highlight the opportunities emerging from the current progress in application of genome editing in microalgae for accelerating strain improvement program.

Background Role of Th9 cells and interleukin-9 (IL-9) in human autoimmune diseases such as psoriasis and ulcerative colitis has been explored only very recently. However, their involvement in human rheumatoid arthritis (RA) is not conclusive. Pathogenesis of RA is complex and involves various T cell subsets and neutrophils. Here, we aimed at understanding the impact of IL-9 on infiltrating immune cells and their eventual role in synovial inflammation in RA. Methods In vitro stimulation of T cells was performed by engagement of anti-CD3 and anti-CD28 monoclonal antibodies. Flow cytometry was employed for measuring intracellular cytokine, RORγt in T cells, evaluating apoptosis of neutrophils. ELISA was used for measuring soluble cytokine, Western blot analysis and confocal microscopy were used for STAT3 phosphorylation and nuclear translocation. Results We demonstrated synovial enrichment of Th9 cells and their positive correlation with disease activity (DAS28-ESR) in RA. Synovial IL-9 prolonged the survival of neutrophils, increased their matrix metalloprotienase-9 production and facilitated Th17 cell differentiation evidenced by induction of transcription factor RORγt and STAT3 phosphorylation. IL-9 also augmented the function of IFN-γ + and TNF-α + synovial T cells. Conclusions We provide evidences for critical role of IL-9 in disease pathogenesis and propose that targeting IL-9 may be an effective strategy to ameliorate synovial inflammation in RA. Inhibiting IL-9 may have wider impact on the production of pathogenic cytokines involved in autoimmune diseases including RA and may offer better control over the disease.

Fiber-reinforced polymers (FRPs) have been widely used to strengthen steel structures, which could suffer from corrosion or the introduction of web openings, for utilities such as ductwork, plumbing, electrical conduits, and HVAC systems. The present numerical study involves the application of unidirectional carbon FRP (CFRP) sheets to steel I-beams, damaged due to corrosion or web openings, to regain their lost load-carrying capacity. Finite element analysis (FEA) was utilized to develop and validate three beam models against existing experimentally tested specimens. Subsequently, a parametric study was conducted investigating the effect of various corrosion levels and the number of circular web openings on the yield and ultimate load capacities of the beams. The optimum number of CFRP layers needed to strengthen corroded beams was determined and six CFRP strengthening scenarios were adopted to determine the best configurations to retrofit steel beams with openings (SBWOs). The results revealed that corrosion, introduced by thinning the bottom flange, reduced both yield and ultimate load capacities, with a nearly perfect linear reduction in ultimate load for each 2.5% thickness loss. The optimum number of CFRP layers depended on the level of corrosion damage. Furthermore, while maintaining a constant total opening area, beams with a greater number of smaller circular web openings demonstrated higher yield and ultimate load capacities than those with fewer larger openings. Out of the six adopted CFRP strengthening scenarios, three configurations that involved applying CFRP sheets to both flanges and the web effectively restored the strength of SBWOs, when adequate CFRP layers were used.

This paper proposes a novel compressed domain robust watermarking scheme which embeds watermark by altering the intra prediction modes of 4 × 4 intra prediction blocks of the most recent high-definition video standards H.265/HEVC. Due to different compression architecture and higher number of intra prediction mode, the existing intra prediction mode based watermarking strategies for previous video standards such as H.264/AVC are not robust when those are directly applied for H.265/HEVC. This proposed work overcomes this shortcoming by reducing the synchronization error of watermark after re-compression attack in two stage. First, a spatial texture analysis is done based on number of non-zero transform coefficients of embedding blocks. Then, suitable candidate blocks for watermark embedding are selected based on 4 × 4 intra luma PB’s sustainably and watermarked mode sustainability while maintaining visual quality and bit rate. In next stage, the robustness of the proposed method has been enhanced by grouping of intra prediction modes such a way that mode change due to re-compression can be closed within a group. Finally, each group is represented with two bits of watermark sequence and embedding have been done by altering prediction modes of selected 4 × 4 intra prediction block to the representative mode of the group denoted by the watermark bit pair. Experimental results on various test sequences show that the scheme is robust against re-compression with high QP values and robustness has been increased twice compared to existing intra prediction mode based watermarking schemes. Also, the proposed scheme has very low effect on the visual quality having least peak to signal ration of 28 dB for the watermarked test sequences and also has very similar bit increase rate compared to existing scheme.

Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions.

Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.

The field of data ethics is concerned with the ethical considerations surrounding data, algorithms, and associated practices, with the aim of identifying ethical solutions. The application of ethical principles to the handling of data, algorithms, and practices can facilitate the identification and delineation of ethical quandaries within the domain of data science. The present study focuses on the topic of data ethics, specifically pertaining to the processes of data collection, data model construction, evaluation, and deployment. This study introduces a comprehensive framework designed to facilitate the management of ethical considerations in data science projects. In order to authenticate the framework, a case study was conducted and our perspectives on its practical implementation were presented. The description of the scope of future research is also provided.

Background Jatropha curcas is an important biofuel crop due to the presence of high amount of oil in its seeds suitable for biodiesel production. Triacylglycerols (TAGs) are the most abundant form of storage oil in plants. Diacylglycerol O-acyltransferase (DGAT1) enzyme is responsible for the last and only committed step in seed TAG biosynthesis. Direct upregulation of TAG biosynthesis in seeds and vegetative tissues through overexpression of the DGAT1 could enhance the energy density of the biomass, making significant impact on biofuel production. Results The enzyme diacylglycerol O-acyltransferase is the rate-limiting enzyme responsible for the TAG biosynthesis in seeds. We generated transgenic Jatropha ectopically expressing an Arabidopsis DGAT1 gene through Agrobacterium-mediated transformation. The resulting AtDGAT1 transgenic plants showed a dramatic increase in lipid content by 1.5- to 2 fold in leaves and 20-30 % in seeds, and an overall increase in TAG and DAG, and lower free fatty acid (FFA) levels compared to the wild-type plants. The increase in oil content in transgenic plants is accompanied with increase in average plant height, seeds per tree, average 100-seed weight, and seed length and breadth. The enhanced TAG accumulation in transgenic plants had no penalty on the growth rates, growth patterns, leaf number, and leaf size of plants. Conclusions In this study, we produced transgenic Jatropha ectopically expressing AtDGAT1. We successfully increased the oil content by 20-30 % in seeds and 1.5- to 2.0-fold in leaves of Jatropha through genetic engineering. Transgenic plants had reduced FFA content compared with control plants. Our strategy of increasing energy density by enhancing oil accumulation in both seeds and leaves in Jatropha would make it economically more sustainable for biofuel production.