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Initial Public Offerings (IPOs) provide great opportunities for companies to grow and expand, and they allow investors to invest their money wisely to get a decent Return on Investment (ROI) in the short term. Nevertheless, the intricate nature of the stock market is susceptible to several influences such as a company's financial statement, governmental regulations, and public sentiment, which hinders the attainment of a satisfactory ROI. This study aims to address this challenge by developing a model that combines public sentiment analysis and machine learning approaches to optimize the ROI for IPO trends. The study gives a novel approach that uses multiple different features associated with IPOs like the public opinion, grey market price (GMP), issue prices, lot size etc. and leverages the use of various machine learning techniques like Random Forest, Decision Tree, Naive Bayes, and K-Nearest Neighbour (KNN) to make well-informed investment recommendations. The testing results demonstrate that the Decision Tree method surpasses the other algorithms, with an accuracy rate of 82.3%. This discovery emphasizes the effectiveness of our method in forecasting the success of IPOs by utilizing a combination of sentiment analysis and crucial financial indicators.

This paper contributes to the growing body of literature exploring the ramifications of AI-driven technological singularity and its economic implications. The exploration unfolds in three key segments. First, it sheds light on the concepts of artificial general intelligence (AGI), AI superintelligence, and singularity itself. Subsequently, it discusses the AI alignment problem, addressing the potential outcomes of superintelligent AI on human civilisation. Further, Giddens’ structuration theory is used to highlight the prominent role of AI-based “authoritative resources” in determining the allocation of resources and ensuring distributive justice in a techno-utopian society. The paper also explores the idea of utopia and the “end of history” and concludes with the suggestion that achieving a technological utopia with superintelligent AI is a mechanism design problem.

The shift of conventional vehicles to electric vehicles (EVs) is on pace nowadays. Increment in EVs adoption eventually results as increment of load on the existing power distribution system. This increment can adversely affect the performance of the distribution system. Location of electric vehicles charging station (EVCS) in the distribution system requires more technical finding before moving on into it. Implementation needs attention for several aspects like electric profile, economics, and traffic management and so, this work considers all of them. The impact of charging stations (EVCS) on operating parameters of the distribution network such as voltage stability, reliability and power losses are investigated by the proposed formulation of a Voltage Stability-Reliability-Power Loss (VRP) indices in this work. This index has the capabilities of considering voltage stability, reliability, and power loss under a single frame. The capabilities of the system with random load changes are compared with the base system without increments in load. This presented work gives assurance about the EV based load increment capabilities in the existing system without compromising with distribution system constraints. The main contribution of the proposed work is to examine the impact of voltage sensitivity, system stability, power losses, reliability measures, and a complex VRP index on choosing locations for EV charging stations and its effects on the existing system. Also, to ensure accurate results, these factors are assessed across seven distinct load scenarios, contributing significantly to the ongoing research in this field.

Abstract Hepatic fibrosis is driven by the activation of hepatic stellate cells (HSCs). The Hippo pathway and its effectors, YAP and TAZ, are key regulators of HSC activation and fibrosis. However, there is a lack of mechanistic understanding of YAP/TAZ regulation in HSCs. Here we show that AMPK activation leads to YAP/TAZ inhibition and HSC inactivation in vitro, while the expression of a kinase-inactive mutant reversed these effects compared to wild type AMPKɑ1. Notably, the depletion of LATS1/2, an upstream kinase of YAP/TAZ signaling, rescues YAP/TAZ activation, suggesting that AMPK may be mediating YAP/TAZ inhibition via LATS1/2. In the carbon tetrachloride mouse model of fibrosis, pharmacologic activation of AMPK in HSCs inhibits YAP/TAZ signaling and reduces fibrosis. The findings implicate AMPK as a critical regulator of YAP/TAZ signaling and HSC inactivation and highlight AMPK activation as a therapeutic target for the treatment of hepatic fibrosis.

Abstract Boron nitride (BN) is a promising 2D material as well as a potential wide‐bandgap semiconductor. Chemical vapor deposition (CVD) is commonly used to deposit single layers or thin films of BN, but the deposition process is insufficiently understood at an atomic scale. the CVD of BN is studied using two boron precursors, the organoboranes, triethylborane, and trimethylborane. Using high resolution (scanning) transmission electron microscopy and electron energy loss spectroscopy, this study shows that hexagonal‐BN (h‐BN) nucleates and grows epitaxially for ≈4 nm before it either polytype transforms to rhombohedral‐BN (r‐BN), turns to less ordered turbostratic‐BN or is terminated by a layer of amorphous carbon. this study proposes that the carbon in the organoboranes deposits on the epitaxially growing h‐BN surface and this either leads to the polytype transition to r‐BN, the transition to less ordered BN growth, or complete surface poisoning with carbon terminating BN growth. These results question the use of organoboranes in CVD of epitaxial BN films, and the polytype stability of h‐BN growing on graphene.

Due to advancements in human society and lifestyle changes, the escalating demand for electric energy, along with the expansion of industrialization, highlights the growing importance of green energy such as renewable energy sources (RESs). Nevertheless, the combination of these sources introduces various operational and control challenges for existing power systems. A pivotal concern in power systems involves achieving the essential load frequency control (LFC) to mitigate the deviations in system frequency and unscheduled power exchanges among control areas. This paper conducts a thorough review of the progression of LFC research, spanning historical, present, and prospective power systems. The analysis encompasses considerations of traditional power system models, the integration of RESs into traditional models, associated constraints, deregulated LFC, the application of power electronics devices, energy storage implementations, and LFC strategies tailored for microgrids or multi-microgrids. This comprehensive literature review aims to bridge the research gap by addressing contemporary challenges, such as uncertainties in RES development, and provides insights into future directions for LFC of the power systems.

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

Medical workers can assess disease progression and create expedient treatment plans with the help of automated and accurate 3Dsegmentation of medical images. DCNNs (Deep convolution neural networks) have been widely used in this work, but their accuracy still needs to be increased, mostly due to their insufficient understanding of 3D environments. This study proposed three dimensional residual networks, ResUNet++, for precise segmentations of three-dimensional medical images where encoders, segmentation decoders, and context residual decoders are used. Two decoders are connected at scale utilizing context attention maps and context residual, the former explicitly learns inter-slice context data and the latter utilizes contexts as attention to increase segmentation accuracy. This model was assessed by using MICCAI 2018 BraTS dataset and, the Pancreas-CT dataset. The BrasTS and Pancreas-CT dataset scales were compared in terms of ET, WT, TC. Moreover, the proposed model was compared with/without boundary loss and validation dice score. The outcomes not only show how effective the suggested 3D residual learning approach is, but also show that the suggested ResUNet++ offers better accuracy compared to six of the top-ranking techniques used for segmenting tumors in the brain.

Fossil fuel pollution has contributed to dramatic changes in the Earth’s climate, and this trend will continue as fossil fuels are burned at an ever-increasing rate. Many countries around the world are currently making efforts to reduce greenhouse gas emissions, and one of the methods is the Tradable White Certificate (TWC) mechanism. The mechanism allows organizations to reduce their energy consumption to generate energy savings certificates, and those that achieve greater energy savings can sell their certificates to those that fall short. However, there are some challenges to implementing this mechanism, such as the centralized and costly verification and control of energy savings. Moreover, the verification process is not transparent, which could lead to fraud or manipulation of the system. Therefore, in this paper, we propose a blockchain-based TWC mechanism to automatically create, verify, and audit the TWC certificates. In addition, we propose a smart-contract-based TWC trading mechanism that enables traders to trade their TWCs without exposing their private information in an untrusted environment. Evaluations show that the proposed TWC framework is scalable for 1000 TWC traders simultaneously, and optimization problem can be solved in less than 120ms. Moreover, it has been shown that Polygon Matic incurs least gas cost compared to other blockchain-based solutions.

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

We report on the first detailed beam tests attesting the fundamental principle behind the development of high-current-efficiency ultrafast electron microscope systems where a radio frequency (RF) cavity is incorporated as a condenser lens in the beam delivery system. To allow for the experiment to be carried out with a sufficient resolution to probe the performance at the emittance floor, a new cascade loop RF controller system is developed to reduce the RF noise floor. Temporal resolution at 50 fs in full-width-at-half-maximum and detection sensitivity better than 1% are demonstrated on exfoliated 1T-TaSe2 system under a moderate repetition rate. To benchmark the performance, multi-terahertz edge-mode coherent phonon excitation is employed as the standard candle. The high temporal resolution and the significant visibility to very low dynamical contrast in diffraction signals via high-precision phase-space manipulation give strong support to the working principle for the new high-brightness femtosecond electron microscope systems.

This study explores the enhancement of thermal performance by disrupting the laminar sublayer through the use of turbulence promoters on the surface of a heated plate. The focus is on investigating how changes in several geometrical parameters of a V-rib combined with a collective ring impact fluid flow and heat transfer properties within a flat plate solar heat collector. Computational methods validated turbulence models, and a range of Reynolds numbers (4000–20000) were employed to conduct a comprehensive CFD analysis, examining flow dynamics and heat transport properties. The study specifically examines the influence of various parameters, including the relative V combined ring ratio (WD/WVR) ranging from 3.0 to 6.0, the Ratio of pitch V and ring to height V and ring (PVR/HVR) from 45.33 to 78, and the relative V and ring height (HVR/Dh) from 0.0238 to 0.0368. High friction factor (f) values were observed at settings of WD/WVR, PVR/HVR, and HVR/Dh at 6.0, 45.33, and 0.0368, respectively. Of significance, the findings indicate that both the thermohydraulic performance (THP) and Nusselt number (Nu) peak when WD/WVR, PVR/HVR, and HVR/Dh are set at 5.0, 63.33, and 0.0325, respectively. This optimal configuration demonstrates the potential to maximize heat transfer and overall system efficiency.

Purpose The essay aims to critically examine different theories proposed in the literature on international trade. Design/methodology/approach The essay is based on theoretical literature on the theories of international trade and working policies behind modern world trade globally. Findings The essay starts with traditional theories of international trade and an account of the mercantilist pattern of trade is provided, and comments are made on the use of simplified models in the classical theories. Significant focus is laid on the new trade theory and the use of strategic trade policies by advanced countries. A helpful supplement is provided regarding the role of FDI and technology in trade. A further effect of trade on growth is also analyzed. Theories of the 21st century are discussed with a call for models that can incorporate income inequality, political strategies, and other real-world factors. Research limitations/ implications The essay deals with only theoretical literature on international trade theories and policies. Findings and interpretation are subject to the theoretical development of international trade. Originality/ value The essay provided a critical explanation of international trade theories and tried to explain the paradigm shift in the theoretical framework of international trade.

Purpose – The study aims to explore the argument of implementing the lean method to part or whole of an operation by examining the moderating impact of varying levels of the extent of implementation of four different lean methods, along with their functionalities, in predicting productivity improvement (PI). Design/methodology/approach – As the focus of understanding the efficacy of lean principles is shifting from process to industry level, this study tried to generalize the approach by gathering data from 132 large Indian auto component manufacturers. This involves an assessing/monitoring approach rather than measurement. Findings – Results highlighted the interdependence or individuality of the extent of implementation of lean methods and their functionalities. Findings revealed a significant moderating effect in improving productivity to a greater extent of 50%. Research limitations/implications – Adopting an assessment approach to a measurement study provides a noteworthy contribution to bridging theory and practical consequences. The findings can be appropriately extrapolated to medium and small enterprises forming a critical connection in the entire automobile manufacturing ecosystem. Practical implications – The study showed that even if a lean method is applied to a certain extent of operations the chances of PI are significant. This is important for decision makers as they confront problems of optimum resource allocation. Social implications – PI, reduced cost and generalization of results would enable the auto component industry to become more competitive. Originality/value – The examination of the moderation effect of a lean principle implementation extent, along with that of its functionalities to predict the improvement in productivity from its existing level, is a major outcome of this study.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

Abstract In this study, acrylic-epoxy-based nanocomposite coatings loaded with different concentrations (0.5–3 wt.%) of graphene oxide (GO) nanoparticles were successfully prepared via the solution intercalation approach. The thermogravimetric analysis (TGA) revealed that the inclusion of GO nanoparticles into the polymer matrix increased the thermal stability of the coatings. The degree of transparency evaluated by the ultraviolet–visible (UV–Vis) spectroscopy showed that the lowest loading rate of GO (0.5 wt.%) had completely blocked the incoming irradiation, thus resulting in zero percent transmittance. Furthermore, the water contact angle (WCA) measurements revealed that the incorporation of GO nanoparticles and PDMS into the polymer matrix had remarkably enhanced the surface hydrophobicity, exhibiting the highest WCA of 87.55º. In addition, the cross-hatch test (CHT) showed that all the hybrid coatings exhibited excellent surface adhesion behaviour, receiving 4B and 5B ratings respectively. Moreover, the field emission scanning electron microscopy (FESEM) micrographs confirmed that the presence of the functional groups on the GO surface facilitated the chemical functionalization process, which led to excellent dispersibility. The GO composition up to 2 wt.% showed excellent dispersion and uniform distribution of the GO nanoparticles within the polymer matrix. Therefore, the unique features of graphene and its derivatives have emerged as a new class of nanofillers/inhibitors for corrosion protection applications.

Citrus tristeza virus (CTV), one of the casual agents of citrus decline, is responsible for the death of millions of citrus trees and reduced production and productivity of citrus orchards worldwide. CTV epidemics has been recorded from several parts of the world where mainly steam pitting (SP) and quick decline (QD) strains of CTV have induced severe disease reactions. Identification and characterization of CTV isolates primarily has been focused on the biological assaying in indicator plants, serology-based ELISA and molecular PCR tests. Controlling the presence and spread of CTV where it is absent or establishment is limited heavily relies upon preventive measures, quarantine and legislations. Cross protection is an appealing technique especially for controlling CTV – Stem Pitting strains and use of CTV resistant rootstocks largely prevent infection by CTV – Quick Decline strains. More reliable and effective way to control CTV is breeding for resistant or tolerant cultivars. Advances in molecular biology have lead scientists to find out genes and map genetic loci of CTV resistant citrus and related species that could be exploited in breeding. However incorporation of resistant ability offered by a handful of citrus and its related species into the susceptible cultivars containing other desirable agronomical traits is challenging through classical plant breeding approaches. The following review work is based on Citrus tristza virus and its management practices.

Using a uniquely configured glow discharge-based Shivalik Plasma Device-I, we demonstrate a variety of collective phenomena in dusty plasma away from the glow discharge region. The cylindrical glass device produces plasma using parallel disc-shaped electrodes with a smaller anode size than the cathode. The dust microparticles are initially sprinkled over the grounded cathode. These particles acquire a significant negative charge upon plasma formation, resulting in their levitation due to the balance between the Coulomb force and gravity. The new device supports the levitation of a big-sized (10 × 8 × 5 cm3) three-dimensional dust cloud over the glass surface. It contrasts the dusty plasma formations in-between electrodes reported earlier. As the discharge voltage varies from high to low, the dust cloud travels from over the glass surface to between the electrodes. A complex interplay of dust void over the cathode, a sharp density gradient, and gravity lead to self-excitation of collective dust phenomena. It includes dust density waves (phase velocity, vph ∼ 4 cm/s), dust cloud oscillation (frequency, f = 5 Hz), sheared flow (flow velocity, vf ∼ 1 cm/s), and multiple-sized dust vortices. These dust vortices provided an excellent platform for studying turbulent mixing phenomena. The power spectrum analysis agreed with two-dimensional Kolmogorov power-law scaling. This is an ideal dusty plasma apparatus where we can create or move the dust cloud to a location of choice from the glass surface to in-between the electrodes and excite one among many collective dust dynamics.

Structural tailoring of the flavone framework (position 7) via organopalladium-catalyzed C–C bond formation was attempted in this study. The impact of substituents with varied electronic effects (phenyl ring, position 2 of the benzopyran scaffold) on the antitumor properties was also assessed. Resultantly, the efforts yielded a furyl arm bearing benzopyran possessing a 4-fluoro phenyl ring (position 2) (14) that manifested a magnificent antitumor profile against the Ishikawa cell lines mediated through dual inhibition of PARP and tubulin [(IC50 (PARP1) = 74 nM, IC50 (PARP2) = 109 nM) and tubulin (IC50 = 1.4 µM)]. Further investigations confirmed the ability of 14 to induce apoptosis as well as autophagy and cause cell cycle arrest at the G2/M phase. Overall, the outcome of the study culminated in a tractable dual PARP-tubulin inhibitor endowed with an impressive activity profile against endometrial cancer.

Air pollution poses a significant threat to human health, though a clear understanding of its mechanism remains elusive. In this study, we sought to better understand the effects of various sized particulate matter from polluted air on Alzheimer's disease (AD) development using an AD mouse model. We exposed transgenic Alzheimer's mice in their prodromic stage to different sized particulate matter (PM), with filtered clean air as control. After 3 or 6 months of exposure, mouse brains were harvested and analyzed. RNA-seq analysis showed that various PM have differential effects on the brain transcriptome, and these effects seemed to correlate with PM size. Many genes and pathways were affected after PM exposure. Among them, we found a strong activation in mRNA Nonsense Mediated Decay pathway, an inhibition in pathways related to transcription, neurogenesis and survival signaling as well as angiogenesis, and a dramatic downregulation of collagens. Although we did not detect any extracellular Aβ plaques, immunostaining revealed that both intracellular Aβ1–42 and phospho-Tau levels were increased in various PM exposure conditions compared to the clean air control. NanoString GeoMx analysis demonstrated a remarkable activation of immune responses in the PM exposed mouse brain. Surprisingly, our data also indicated a strong activation of various tumor suppressors including RB1, CDKN1A/p21 and CDKN2A/p16. Collectively, our data demonstrated that exposure to airborne PM caused a profound transcriptional dysregulation and accelerated Alzheimer's-related pathology.

Background: Artificial intelligence (AI) is revolutionizing medical diagnosis and healthcare, providing constant support to medical practitioners. Intelligent systems alleviate workload pressure while optimizing practitioner performance. AI and deep learning have also improved medical imaging and audio analysis. Material and Methods: This research focuses on predicting respiratory diseases using audio recordings from an electronic stethoscope. A convolutional neural network (CNN) was trained on a Respiratory Sound Database, augmented to generate 1,428 audio files. Techniques such as pitch shifting, time stretching, noise addition, time and frequency masking, dynamic range compression, and resampling were employed to increase the diversity and size of the training data. Result: Features were extracted from mono audio files, creating a four layer CNN with 90% accuracy. The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhancing their performance. The study highlights AI's potential in respiratory disease detection through audio analysis. Conclusion: The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhancing their performance.

BACKGROUND: Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. MATERIALS AND METHODS: This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol’s resilience against attacks. RESULTS: The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol’s communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol’s security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. CONCLUSIONS: As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.

This numerical study aims to determine the exergy analysis for a modified solar heat collector (SHC) having wavy delta winglets as turbulent promoters. These winglets disrupt the viscous layer to improve the heat transfer rate from the heat-absorbing surface to the fluid medium in the rectangular channel. The analysis considers following parameters: the relative longitudinal pitch (P/H), number of waves (φ), and flow angle of attack (α0) and flow Reynolds number range of 4000–18000. The maximum exergy efficiency of 29.18% is achieved with a longitudinal pitch of 5, an angle of attack of 60°, and a relative number of waves of 5. To evaluate the system's feasibility and sustainability, the study uses a Sustainability Index (SI), which reflects the system's Waste Energy Ratio (WER) and Improvement Potential (IP). This assessment helps identify opportunities for improvement in the thermal system.

This research paper presents an experimental analysis on a heat exchanger tube using a newly designed perforated conical ring combined with twisted tape as inserts. The investigation analyzes various geometric and flow parameters in the experimental setup, including a wide range of Reynolds numbers, varying from 6000 to 30000, nanoparticle volume concentration (φnp) of 0.25-1.0%, the ratio of inlet flow dia to inner print dia of the ring (DIR/DBR) of 1.33-2.33, ring pitch ratio (RP/ DED) of 1.41-2.51, twist ratio (TL/ WT) of 3.33-4.38, with fixed values for other parameters: relative ring height ((DED/ WR) of 2.33 and nanoparticle diameter (dnp) of 30 nm. The optimal result of the thermal-hydraulic performance parameter (ηTT) is 1.45 at DIR/DBR = 1.83, TL/ WT = 3.50 and RP/ DED = 1.76. For the CuO/H2O nanofluids flow heat exchanger tube with perforated conical rings, empirical correlations were developed for the Nusselt number and the friction factor. These correlations were established with a range of 9.0% and 8.0%, respectively.

The flat plate solar heat collector captures solar thermal energy for agricultural drying, heating houses and businesses, cooling food, and producing steam. Adding twisted tape inserts to the absorbent surface of a flat plate solar heat collector increases its efficiency. This article describes the absorber plate of twisted tape inserts and discusses the thermal and hydraulic performance achieved by varying the twist pitch and twist height. Computational fluid dynamics is performed for Reynolds numbers ranging from 5000 to 17000, relative twist height (TH/Dh) of 0.011-0.044, relative twisted tape inserts pitch (PTT/TH) of 49.95-73.2 and relative twist pitch (TP/TH) of 8-15. Heat transmission and friction factors owing to twisted tape inserts have been analyzed in depth using computational fluid dynamics, with analysis performed using the k-epsilon model in commercial ANSYS Fluent 19.2 software. A maximum thermal-hydraulic performance value of 2.41 is observed for the many twisted tape parameters, with, TH/Dh of 0.022, PTT/TH of 64.60, and TP/TH of 12.

Abstract Unprecedented efforts of the researchers have been witnessed in the recent past towards the development of vaccine platforms for the control of the COVID-19 pandemic. Albeit, vaccination stands as a practical strategy to prevent SARS-CoV-2 infection, supplementing the anti-COVID19 arsenal with therapeutic options such as small molecules/peptides and antibodies is being conceived as a prudent strategy to tackle the emerging SARS-CoV-2 variants. Noteworthy to mention that collective efforts from numerous teams have led to the generation of a voluminous library composed of chemically and mechanistically diverse small molecules as anti-COVID19 scaffolds. This review article presents an overview of medicinal chemistry campaigns and drug repurposing programs that culminated in the identification of a plethora of small molecule-based anti-COVID19 drugs mediating their antiviral effects through inhibition of proteases, S protein, RdRp, ACE2, TMPRSS2, cathepsin and other targets. In light of the evidence ascertaining the potential of small molecule drugs to approach conserved proteins required for the viral replication of all coronaviruses, accelerated FDA approvals are anticipated for small molecules for the treatment of COVID19 shortly. Though the recent attempts invested in this direction in pursuit of enrichment of the anti-COVID-19 armoury (chemical tools) are praiseworthy, some strategies need to be implemented to extract conclusive benefits of the recently reported small molecule viz. (i) detailed preclinical investigation of the generated anti-COVID19 scaffolds (ii) in-vitro profiling of the inhibitors against the emerging SARS-CoV-2 variants (iii) development of assays enabling rapid screening of the libraries of anti-COVID19 scaffold (iv) leveraging the applications of machine learning based predictive models to expedite the anti-COVID19 drug discovery campaign (v) design of antibody–drug conjugates.

The cell bodies of postganglionic sympathetic neurons innervating the heart primarily reside in the stellate ganglion (SG), alongside neurons innervating other organs and tissues. Whether cardiac-innervating stellate ganglionic neurons (SGNs) exhibit diversity and distinction from those innervating other tissues is not known. To identify and resolve the transcriptomic profiles of SGNs innervating the heart, we leveraged retrograde tracing techniques using adeno-associated virus (AAV) expressing fluorescent proteins (GFP or Td-tomato) with single cell RNA sequencing. We investigated electrophysiologic, morphologic, and physiologic roles for subsets of cardiac-specific neurons and found that three of five adrenergic SGN subtypes innervate the heart. These three subtypes stratify into two subpopulations; high (NA1a) and low (NA1b and NA1c) neuropeptide-Y (NPY) -expressing cells, exhibit distinct morphological, neurochemical, and electrophysiologic characteristics. In physiologic studies in transgenic mouse models modulating NPY signaling, we identified differential control of cardiac responses by these two subpopulations to high and low stress states. These findings provide novel insights into the unique properties of neurons responsible for cardiac sympathetic regulation, with implications for novel strategies to target specific neuronal subtypes for sympathetic blockade in cardiac disease.

Phase change materials (PCMs) used for thermal energy storage (TES) have shown to be particularly promising, especially in light of the growing need for a wide variety of energy-related applications. This article discusses the usage of packed beds made from phase change materials in solar energy storage devices for variable temperature environments. The primary heating/cooling technologies and associated thermal energy storage difficulties have also been examined from the perspective of engineering applications. The packed bed’s history from a thermodynamic perspective, the temperature dispersion inside the packed bed, and several design factors that influence its performance are all covered in the brief description of the packed bed. Energy and exergy efficacy are the foundations of its analysis. The article delves into numerous thermal energy storage methods, with phase change materials being the primary topic and a beneficial means of storing thermal energy. In addition, phase change materials also keep latent thermal energy during phase transitions at almost constant temperatures.

Abstract Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.

Convalescent plasma therapy (CPT) is used to treat patients with Covid-19, but evidence for clinical benefit from clinical trials is divergent. Here the authors report in that CPT treatment does not affect mortality in patients with severe Covid-19 in phase 2 single center open label randomised controlled trial.

This dataset provides detailed information on rice production practices being applied by farmers during 2018 rainy season in India. Data was collected through computer-assisted personal interview of farmers using the digital platform Open Data Kit (ODK). The dataset, n = 8355, covers eight Indian states, viz., Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Odisha, Punjab, Uttar Pradesh and West Bengal. Sampling frames were constructed separately for each district within states and farmers were selected randomly. The survey was deployed in 49 districts with a maximum of 210 interviews per district. The digital survey form was available on mobile phones of trained enumerators and was designed to minimize data entry errors.Each survey captured approximately 225 variables around rice production practices of farmers’ largest plot starting with land preparation, establishment method, crop variety and planting time through to crop yield. Detailed modules captured fertilizer application, irrigation, weed management, biotic and abiotic stresses. Additional information was gathered on household demographics and marketing. Geo-points were recorded for each surveyed plot with an accuracy of

The defect centers in solid-state materials especially the nitrogen-vacancy (NV) centers in diamond have shown a tremendous potential for their utilization in quantum technology applications. However, they exhibit certain drawbacks such as the feeble zero phonon line with huge phonon contribution and the higher lifetime values. Here, we present a novel approach to control the spontaneous emission from NV centers in nanodiamond using engineered self-assembled photonic crystals. Using two complimentary emission measuring geometries at room temperature, we show a 63% suppression and 17% enhancement of NV center emission intensity using photonic stopgap, supported with simulations. The emission rates are modified in a broad spectral range of NV center emission and are consistent with the Barnett–Loudon sum rule. The results are crucial for emerging quantum technologies using NV centers in diamond.

Non-alcoholic fatty liver disease (NAFLD) is a common cause of liver disease worldwide, and is characterized by the accumulation of fat in the liver. Non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD, is a leading cause of liver transplantation. Fibrosis is the histologic feature most associated with liver-related morbidity and mortality in patients with NASH, and treatment options remain limited. In previous studies, we discovered that acid ceramidase (aCDase) is a potent antifibrotic target using human hepatic stellate cells (HSCs) and models of hepatic fibrogenesis. Using two dietary mouse models, we demonstrate that depletion of aCDase in HSC reduces fibrosis without worsening metabolic features of NASH, including steatosis, inflammation, and insulin resistance. Consistently, pharmacologic inhibition of aCDase ameliorates fibrosis but does not alter metabolic parameters. The findings suggest that targeting aCDase is a viable therapeutic option to reduce fibrosis in patients with NASH.

Globally, researchers have devoted consistent efforts to producing excellent coating properties since coating plays an essential role in enhancing electrochemical performance and surface quality. In this study, TiO2 nanoparticles in varying concentrations of 0.5, 1, 2, and 3 wt.% were added into the acrylic-epoxy polymeric matrix with 90:10 wt.% (90A:10E) ratio incorporated with 1 wt.% graphene, to fabricate graphene/TiO2 -based nanocomposite coating systems. Furthermore, the properties of the graphene/TiO2 composites were investigated by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), ultraviolet-visible (UV-Vis) spectroscopy, water contact angle (WCA) measurements, and cross-hatch test (CHT), respectively. Moreover, the field emission scanning electron microscope (FESEM) and the electrochemical impedance spectroscopy (EIS) tests were conducted to investigate the dispersibility and anticorrosion mechanism of the coatings. The EIS was observed by determining the breakpoint frequencies over a period of 90 days. The results revealed that the TiO2 nanoparticles were successfully decorated on the graphene surface by chemical bonds, which resulted in the graphene/TiO2 nanocomposite coatings exhibiting better dispersibility within the polymeric matrix. The WCA of the graphene/TiO2 coating increased along with the ratio of TiO2 to graphene, achieving the highest CA of 120.85° for 3 wt.% of TiO2. Excellent dispersion and uniform distribution of the TiO2 nanoparticles within the polymer matrix were shown up to 2 wt.% of TiO2 inclusion. Among the coating systems, throughout the immersion time, the graphene/TiO2 (1:1) coating system exhibited the best dispersibility and high impedance modulus values (Z0.01 Hz), exceeding 1010 Ω cm2.

Abstract The activated hepatic stellate cells (HSCs) are the major cells that secrete the ECM proteins and drive the pathogenesis of fibrosis in chronic liver disease. Targeting of HSCs by modulating their activation and proliferation has emerged as a promising approach in the development of anti-fibrotic therapy. Sorafenib, a multi-kinase inhibitor has shown anti-fibrotic properties by inhibiting the survival and proliferation of HSCs. In present study we investigated sorafenib induced cytoplasmic vacuolation mediated decreased cell viability of HSCs in dose and time dependent manner. In this circumstance, sorafenib induces ROS and ER stress in HSCs without involvement of autophagic signals. The protein synthesis inhibitor cycloheximide treatment significantly decreased the sorafenib-induced cytoplasmic vacuolation with increasing cell viability. Antioxidant human serum albumin influences the viability of HSCs by reducing sorafenib induced vacuolation and cell death. However, neither caspase inhibitor Z-VAD-FMK nor autophagy inhibitor chloroquine could rescue the HSCs from sorafenib-induced cytoplasmic vacuolation and cell death. Using TEM and ER organelle tracker, we conclude that the cytoplasmic vacuoles are due to ER dilation. Sorafenib treatment induces calreticulin and GPR78, and activates IRE1α-XBP1s axis of UPR pathway, which eventually trigger the non-apoptotic cell death in HSCs. This study provides a notable mechanistic insight into the ER stress directed non-apoptotic cell death with future directions for the development of efficient anti-fibrotic therapeutic strategies.

Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade’s research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems’ operational issues and thus improve stability and reliability. This paper’s findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration.

The emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the Internet. Using SDN, network devices can be controlled from a centralized, programmable control plane that is decoupled from their data plane, whereas with NFV, network functions (such as network address translation, firewall, and intrusion detection) can be virtualized instead of being implemented on proprietary hardware. In addition, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key to automating network operations and enhancing customer service. Many of the challenges behind SDN and NFV are currently being investigated in several projects all over the world using AI and ML techniques, such as AI- and software-based networking, autonomic networking, and policy-based network management. Contributions to this Special Issue come from the above areas of research. Following a rigorous review process, four excellent articles were accepted that address and go beyond many of the challenges mentioned above.

Telemedicine is the use of technology to provide healthcare services and information remotely, without requiring physical proximity between patients and healthcare providers. The coronavirus disease 2019 (COVID-19) pandemic has accelerated the rapid growth of telemedicine worldwide. Integrating artificial intelligence (AI) into telemedicine has the potential to enhance and expand its capabilities in addressing various healthcare needs, such as patient monitoring, healthcare information technology (IT), intelligent diagnosis, and assistance. Despite the potential benefits, implementing AI in telemedicine presents challenges that can be overcome with physician-guided implementation. AI can assist physicians in decision-making, improve healthcare delivery, and automate administrative tasks. To ensure optimal effectiveness, AI-powered telemedicine should comply with existing clinical practices and adhere to a framework adaptable to various technologies. It should also consider technical and scientific factors, including trustworthiness, reproducibility, usability, availability, and cost. Education and training are crucial for the appropriate use of new healthcare technologies such as AI-enabled telemedicine. This article examines the benefits and limitations of AI-based telemedicine in various medical domains and underscores the importance of physician-guided implementation, compliance with existing clinical practices, and appropriate education and training for healthcare providers.

Introduction: Dermatophytosis form approximately 15-75% of all the mycological infections. Dermatophytes are closely related keratinolytic fungi with ability to degrade keratin and invade the skin, hair and nails causing dermatophytosis. Aim: To evaluate the Minimum Inhibitory Concentration (MIC) of antifungal drug against the isolated dermatophytes by broth microdilution method. Materials and Methods: The present cross-sectional study was conducted in the Department of Microbiology, Muzaffarnagar Medical College, Muzaffarnagar, Uttar Pradesh, India. The duration of the study was August 2018-August 2020 and included 245 patients of which 165 samples were culture positive. A 10-20% Potassium Hydroxide (KOH) mount was prepared from the skin scrapings, nail clippings, and hair bits to look for fungal elements. The specimens were also inoculated on Mycosel media and Sabouraud Dextrose Agar (SDA) with chloramphenicol. The dermatophytes were identified on the basis of colony characteristics, Lacto Phenol Cotton Blue (LPCB) mount, nutritional requirement, temperature tolerance, urease production, and in-vitro hair perforation test. Antifungal susceptibility testing was done for all fungal isolates and performed by broth microdilution method. The descriptive statistics were reported as means with their Standard Deviation (SD). Data were statistically evaluated with International Business Machines (IBM) Statistical Package for the Social Sciences (SPSS) Statistics for Mac, Version 25.0., IBM Corp., Chicago IL. Results: In this study, the MIC range for all the 165 isolates for dermatophytes tested for antifungal susceptibility showed that itraconazole, terbinafine and voriconazole showed the lowest MIC range of 0.0019-0.5 μg/mL followed by griseofulvin and fluconazole at MIC range of 0.125-32 μg/mL. The MIC50 of itraconazole and terbinafine was seen lowest at 0.0313 μg/mL followed by voriconazole at 0.0625 μg/mL, griseofulvin at 0.25 μg/mL for all isolated dermatophytes. Highest MIC50 with 4 μg/mL was found for fluconazole against T. mentagrophytes and T. violaceum. MIC90 of terbinafine, itraconazole and voriconazole was seen lowest at 0.25 μg/mL followed by griseofulvin at 1 μg/mL for all isolated dermatophytes. Highest MIC90 of fluconazole was recorded at 16 μg/mL for all isolated dermatophytes. Conclusion: Highest MIC50 with 8 μg/mL was found for fluconazole against all isolated dermatophytes. MIC90 of terbinafine, itraconazole and voriconazole was seen lowest at 0.25 μg/mL followed by griseofulvin at 1 μg/mL for all isolated dermatophytes. Highest MIC90 of fluconazole was recorded at 16 μg/mL for all isolated dermatophytes.

The farmland visitor Bath White, Pontia daplidice moorei is recorded for the first time in Meghalaya. The butterfly is documented in an opportunistic survey on 17.v.2015 along the GuwahatiShillong road, near Shangbangla village at an elevation of 520 meters. This record will help in updating the status of butterfly fauna in Meghalaya, India.

Oscillations in control loops are very common and primarily responsible for product quality variations and therefore may reduce profitability of the plant. Further, there can also be multiple oscillations at the same time due to multiple sources such as improper tuned controllers, disturbances and nonlinearity etc. Detection and estimation of oscillation is desired for their effective compensation which becomes a challenging task particularly in presence of noise. This paper presents a novel oscillation detection method based on linear predictive coding (LPC). In the proposed technique roots of the linear predictive polynomial are used to detect the oscillations. Further, for the quantification spectrum of cross-correlation of linear predictive residual and original data is used. The effectiveness of the proposed oscillation detection technique has been demonstrated with several simulation examples and actual plant data. The comparative investigation with the empirical mode decomposition method, generally employed for this task, revealed that proposed technique is simple, fast, robust and capable of handling slow varying trends. Keywords: Oscillation detection, Linear predictive coding, Stiction, Linear predictive polynomial, Cross-correlation

Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.

The present study emphasizes on a comparative assessment of solar air heater (SAH) roughened with six different baffles viz. Transverse, inclined transverse, dimple, inclined dimple, arc and sine wave, using computational fluid dynamics (CFD) analysis. The comparison has performed to investigate the augmentation in the heat transfer and friction factor of SAH with different baffles. The CFD study has been accomplished by varying Reynolds number (Re) from 3000 to 18000 whereas, Relative roughness pitch (P/e=10) and Relative roughness height (e/Dh= 0.271) were kept fixed. Flow patterns by varying (Re), local and average Nusselt number, local and average friction factor, thermo-hydraulic performance (THP) have reported and elaborated. The computational results have verified by performing experiments on the same set of operating and geometric parameters. The deviation among numerical and experimental results of THP have found +11%, which are in acceptable range. The results reveals that the positioning and shape of baffle unswervingly affect the THP of the solar air heater. The maximum THP of 2.05 has been recorded with a sine wave shape baffles at Re = 15000. The sine wave baffle shape has judged as the best among the studied baffles on a given range of operating and geometric parameters.

The application of nanofluids for enhancing the heat transfer rate is widely used in various heat exchanger applications. The selection of oil as the base to prepare nanofluids significantly enhances the thermal performance, due to its high heat carrying capacity as compared to conventional base fluid. A review is performed of various heat exchanger conduits having base fluid as nanoparticles with oil. It is reported that the heat transfer rate of a heat exchanger is significantly increased with the use of oil-based nanofluids. The rate of heat transfer depends on the type of nanoparticle, its concentration and diameter, the base fluid, as well as factors like the mixture of more than two nanoparticles (hybrid nanofluids) and stability. A review is also performed of the thermal performance of the different nanofluids analyzed by various investigators. The heat transfer system reviewed in this work includes triangular, square, and circular conduits, as well as rib surface conduits. The review of various applications viz. solar thermal systems, heat exchangers, refrigerators, and engines, is carried out where the inclusion of the oil base is used. It is reported that the amalgamation of the nanomaterial with the oil as base fluid is a prolific technique to enhance thermal performance. The performance of the reviewed research work is comparatively analyzed for different aspects viz. thermal oil, mineral oil, hybrid, and conventional nanoparticles, concentration of nanoparticles, etc. The novelty of the present work is the determination of the effective performing oil-based nanofluid in various applications, to figure out the selection of specific mineral oil, thermal oil, nanoparticle concentration, and hybrid nanofluids.

The sustainability index, waste energy ratio and improvement potential of a staggered air jet impingement on the staggered spherical protrusions of a roughened absorber plate were derived for the present study to evaluate exergy losses and irreversibility in the system. The experimental analysis was carried out for selected parameters: relative streamwise pitch, relative spanwise pitch and relative jet diameter to hydraulic diameter ratio. The flow Reynolds number ranged from 4000–18,000. The augmentation in Nusselt number and friction factor compared to a smooth surface was 4.9 and 12.4 times, respectively. The statistical correlation developed determined the maximum thermohydraulic performance parameter and exergetic efficiency be 3.02 and 3.87%, respectively. The magnitude of the sustainability index, waste energy ratio and improvement potential was found to be 1.0347, 0.962 and 10.84, respectively, for the entire range of tested parameters. A cost analysis was also performed to evaluate the cost-effectiveness of the solar thermal system with and without turbulent promoters.

Human host and pathogen interaction is dynamic in nature and often modulated by co-pathogens with a functional role in delineating the physiological outcome of infection. Co-infection may present either as a pre-existing pathogen which is accentuated by the introduction of a new pathogen or may appear in the form of new infection acquired secondarily due to a compromised immune system. Using diverse examples of co-infecting pathogens such as Human Immunodeficiency Virus, Mycobacterium tuberculosis and Hepatitis C Virus, we have highlighted the role of co-infections in modulating disease severity and clinical outcome. This interaction happens at multiple hierarchies, which are inclusive of stress and immunological responses and together modulate the disease severity. Already published literature provides much evidence in favor of the occurrence of co-infections during SARS-CoV-2 infection, which eventually impacts the Coronavirus disease-19 outcome. The availability of biological models like 3D organoids, mice, cell lines and mathematical models provide us with an opportunity to understand the role and mechanism of specific co-infections. Exploration of multi-omics-based interactions across co-infecting pathogens may provide deeper insights into their role in disease modulation.

High penetration of wind power into the grid necessitates the coordinated action of wind energy conversion systems and the grid. A suitable generation control is required to fulfill the grid integration requirements, especially during faults. A system using a pair of voltage source converters with a squirrel cage induction generator coupled to a wind turbine is proposed to provide fault ride-through during grid faults. A threefold action is used for providing the effective fault ride-through via coordinated action of the machine side and the grid side converter. The entire wind energy conversion system is controlled such that the wind turbine remains connected even during the faults. To implement the threefold action: (i) A decoupled current controller is placed in the grid side converter, which separately controls the positive and negative sequence currents arising during faults. The grid side converter controller is capable of eliminating the double frequency oscillations at the dc-link voltage and, hence, real power, which arises during the unsymmetrical faults; (ii) Reactive power injection is additionally provided by the grid side converter for better grid support; and (iii) The vector control technique is used in machine side converter along with the droop control to adjust the generator speed and the torque resulting in actuation of the pitch control mechanism to limit power generation without shutdown of the turbine.

Co-infection with ancillary pathogens is a significant modulator of morbidity and mortality in infectious diseases. There have been limited reports of co-infections accompanying SARS-CoV-2 infections, albeit lacking India specific study. The present study has made an effort toward elucidating the prevalence, diversity and characterization of co-infecting respiratory pathogens in the nasopharyngeal tract of SARS-CoV-2 positive patients. Two complementary metagenomics based sequencing approaches, Respiratory Virus Oligo Panel (RVOP) and Holo-seq, were utilized for unbiased detection of co-infecting viruses and bacteria. The limited SARS-CoV-2 clade diversity along with differential clinical phenotype seems to be partially explained by the observed spectrum of co-infections. We found a total of 43 bacteria and 29 viruses amongst the patients, with 18 viruses commonly captured by both the approaches. In addition to SARS-CoV-2, Human Mastadenovirus, known to cause respiratory distress, was present in a majority of the samples. We also found significant differences of bacterial reads based on clinical phenotype. Of all the bacterial species identified, ∼60% have been known to be involved in respiratory distress. Among the co-pathogens present in our sample cohort, anaerobic bacteria accounted for a preponderance of bacterial diversity with possible role in respiratory distress. Clostridium botulinum, Bacillus cereus and Halomonas sp. are anaerobes found abundantly across the samples. Our findings highlight the significance of metagenomics based diagnosis and detection of SARS-CoV-2 and other respiratory co-infections in the current pandemic to enable efficient treatment administration and better clinical management. To our knowledge this is the first study from India with a focus on the role of co-infections in SARS-CoV-2 clinical sub-phenotype.