Publication2

This study examines the stress spillover of the stock market, bond market and exchange market among the BRICS countries during the COVID-19 pandemic. Following the principal component analysis approach, a composite stress index is developed for each country to measure the stress level in BRICS. We use the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) approach to investigate the dynamic connectedness among the countries. While the stress in the stock market and exchange market is measured by their respective volatility, the bond market fluctuation is examined by using the yield spreads between the 10-year government bonds yield of BRICS countries and 10-year government bond yield of the United States. The study finds that among all the BRICS countries, India and China have been major transmitters as well as receivers of the stress spillover. The findings of our study contribute to the literature by highlighting the importance of understanding the behaviour and interconnectedness of the economies in a group. The study also provides valuable insights to policymakers who need to be more vigilant about the financial crisis and spillover among the countries.

In this paper, a 2 inverted U-slot integrated rectangular patch antenna array is proposed that can be used for S band applications i.e., radar applications, communication and wireless networks. In the proposed antenna design 2 inverted U slots are introduced in the patch to improve bandwidth providing a bandwidth of 150 MHz and gain of 4.08 dBi resonating at 3.3 GHz. Furthermore, 4 X 4 antenna array is fabricated on 1.6 mm thick FR4 substrate with dielectric constant 4.4 providing a gain of 14.2 dBi. Ansys HFSS is used for antenna simulation and a prototype is fabricated and tested.

27-hydroxycholesterol (27-HC) is the first known endogenous selective estrogen receptor modulator (SERM), and its elevation from normal levels is closely associated with breast cancer. A plethora of evidence suggests that aberrant epigenetic signatures in breast cancer cells can result in differential responses to various chemotherapeutics and often leads to the development of resistant cancer cells. Such aberrant epigenetic changes are mostly dictated by the microenvironment. The local concentration of oxygen and metabolites in the microenvironment of breast cancer are known to influence the development of breast cancer. Hence, we hypothesized that 27-HC, an oxysterol, which has been shown to induce breast cancer progression via estrogen receptor alpha (ERα) and liver X receptor (LXR) and by modulating immune cells, may also induce epigenetic changes. For deciphering the same, we treated the estrogen receptor-positive cells with 27-HC and identified DNA hypermethylation on a subset of genes by performing DNA bisulfite sequencing. The genes that showed significant DNA hypermethylation were phosphatidylserine synthase 2 (PTDSS2), MIR613, indoleamine 2,3-dioxygenase 1 (IDO1), thyroid hormone receptor alpha (THRA), dystrotelin (DTYN), and mesoderm induction early response 1, family member 3 (MIER). Furthermore, we found that 27-HC weakens the DNMT3B association with the ERα in MCF-7 cells. This study reports that 27-HC induces aberrant DNA methylation changes on the promoters of a subset of genes through modulation of ERα and DNMT3B complexes to induce the local DNA methylation changes, which may dictate drug responses and breast cancer development.

Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.

Generation of current or potential at nanostructures using appropriate stimuli is one of the futuristic methods of energy generation. We developed an ambient soft ionization method for mass spectrometry using 2D-MoS2, termed streaming ionization, which eliminates the use of traditional energy sources needed for ion formation. The ionic dissociation-induced electrokinetic effect at the liquid-solid interface is the reason for energy generation. We report the highest figure of merit of current generation of 1.3 A/m2 by flowing protic solvents at 22 μL/min over a 1 × 1 mm2 surface coated with 2D-MoS2, which is adequate to produce continuous ionization of an array of analytes, making mass spectrometry possible. Weakly bound ion clusters and uric acid in urine have been detected. Further, the methodology was used as a self-energized breath alcohol sensor capable of detecting 3% alcohol in the breath.

Photovoltaic Thermal (PVT) systems generate hot water and electricity simultaneously. The grade of thermal energy generated by such PVT systems is low, resulting in limited application. To overcome this challenge, the coupling of the PVT system to a secondary flat plate collector (FPC) has been explored in this work. For the system's performance estimation, a 3-D numerical model has been developed for both glazed and unglazed PVT-FPC collector. Based on the numerical model, PVT collectors have been fabricated, and each is then connected in series to a commercially available FPC collector. Experiments conducted on the fabricated PVT-FPC system showed a close match between simulation and experiments. Further, experiments conducted on the unglazed PVT – FPC collector showed a peak outlet water temperature of 60–63 °C at 30 LPH. For the unglazed PVT collector, the peak electrical and thermal efficiency of 16% and 25% is reported respectively, whereas for the FPC collector, the thermal efficiency of 35% is reported. Compared to an individual unglazed PVT collector, 17 °C higher outlet water temperature is reported for the coupled system. Similarly, for the case of glazed PVT-FPC collector, the peak outlet water temperature reported is 65–67 °C at 30 LPH with 14–15 °C higher outlet water temperature.

While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, where prediction or early detection of the onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos (i.e., two spatial dimensions for the image frames and the third dimension for time). We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

Corrosion in steel reinforcement has a severe impact on the performance of the structures. It is one of the important factors for the degradation of structures. It leads to volume expansion due to corrosion products, cracking and splitting of concrete and degradation of bond between the steel reinforcement and concrete. Most of the numerical studies carried out are based on artificial corrosion but there is lack of study with respect to the 3D nonlinear finite element analysis of a naturally corroded beam. The concrete and reinforcement is modelled based on a constitutive model which is based on nonlinear fracture mechanics and von-mises plasticity yield criterion respectively. Bond and corrosion model which was previously developed is used in this analysis. Interface 2D elements are used for the surface between the concrete and steel reinforcement. The analysis results indicates that the beam analyzed shows a variation of around 20% with respect to the existing literature for a naturally corroded beam.

The sudden spike in the Covid-19 pandemic cases around the world caused an urgent demand for Personal Protective Equipment (PPE) such as N95 face mask, face shield, ventilator valve, and non-contact door hook. The current situation requires a huge cost involvement and hard to solve the huge demand in a short period by using the traditional fabrication process. Therefore, the three-dimensional (3D) printing technology emerged as a suitable alternative to produce cost-effective production methods for these components. The present research work focuses on the design, types of filament material, and 3D printing process to print the required PPE kits which facilitates to reduce the economic cost, environmentally hazardous, and failure of the product (reusable). Overall the initial investment needed for the 3D printing technology to print the polymer medical accessories is reduced and the material can be reused after appropriate treatment.

The limited availability of conventional energy carriers has increased the demand for renewable energy sources to cater to the ever increasing energy demand. The present chapter focusses on phosphoric acid fuel cells (PAFCs) detailing their working principles and application potential. The different materials used in PAFCs, including electrolyte, catalysts and bipolar plates and their importance in the ultimate performance are summarized. The degradation behavior due to acid leaching which affect several components of the cell is outlined. Researchers’ contributions towards early identification of degradation of catalyst and catalyst support in particular due to the presence of excess quantity of phosphoric acid, is also described. The importance of having anticorrosive coatings on bipolar plates is outlined in view of achieving life enhancement of the cell operation. Significant work has been carried out in PAFC modeling (1D–3D, steady state and dynamic models) and these are reviewed. The important design considerations during scale-up such as gasket selection and its arrangement, planarity of electrodes, fabrication method of membrane electrode assemblies (MEAs), thermal stress management etc. are described in detail. The chapter outlines various applications explored from power ratings of 20–500kW and also future requirements to be addressed in material as well as engineering issues.

The majority of anti-cancer drugs fail to reach clinical trials due to their low water solubility. A biocompatible drug delivery system that encapsulates and efficiently delivers hydrophobic drugs to the target site is the need of the hour. This study addresses the issue by focusing on a polymeric polyglycerol sebacate (PGS) nanoparticles loaded with 5-fluorouracil (5FU), a primary line chemotherapy drug for many types of cancers. The generated nanoparticle (PGS-NP) was biocompatible and had minimal cytotoxicity against the MDA-MB-231 and A549 cell lines, even at a high concentration of 100 μg mL−1. The cell viability post treatment with PGS nanoparticles encapsulated with 5FU (PGS-5FU) decreased to as low as around 40% whereas, in the case of treatment with 5FU, the viability percentage increased. The nanoparticles also showed controlled drug release when encapsulated with 5FU. This striking observation suggested that these nanoparticles can improve the efficacy of drug delivery to tumor sites. Apoptosis assay and caspase-3 activity quantification supported these data wherein PGS-5FU treatment showed almost three times caspase-3 activity as compared to control cells. Additionally, throughout all the experiments, MDA-MB-231 cells were more sensitive to PGS-5FU than A549 cells, indicating that these nanoparticles are ideal for breast cancer treatment. In summary, 5FU encapsulated PGS nanoparticles are a potential drug carrier to deliver 5FU efficiently to cancer cells.

Application of intelligent data analytics using machine learning in management of 5G networks can enable autonomous networking capabilities in 5G networks. This paper describes the design and implementation of CygNet MaSoN, a management system supporting advanced aggregation and analytics features combined with machine learning. The system supports detection of anomalous network behaviour, detection of degradation in network performance and service quality and also supports resource optimization. The main objective is to achieve self-organizing and closed loop automation functionalities expected as part of autonomous functioning of 5G networks. Details of the system architecture and components are presented. Three real-life use cases implemented on this system are then described. Machine learning models built and synthetic data generation methods adopted are presented with the features considered. The results obtained using the MaSoN system are also presented to demonstrate the effectiveness of the system in 5G network operations.

CTDSMs with high resolution and bandwidth greater than 200kHz are needed in industrial, medical, and automotive applications. Such high performance demands very low noise and distortion. The noise and distortion have to be suppressed even further in advanced technologies due to the low voltage headroom. A major challenge of low noise and distortion design is the large area cost of DAC and loop filters. The main feedback RDAC occupies a large area in [1]. 1st-order data weighted average (DWA) is used but has limited mismatch error suppression. There is also a kink in the SNDR plot of [1] at low input amplitudes due to tones caused by DWA. To reduce the area, [2], [3] use DWA for the MSB bits and mismatch error shaping (MES) for the LSB bits. MES enables the binary coded DAC to save the LSB DAC area. However, the overall DAC's mismatch-induced distortion is dominated by the MSB bits. Thus, the approach of [2], [3] yields limited performance benefits due to the relatively mild 1st-order mismatch error shaping obtained from the DWA operation on the MSB bits.

We present a supply supervisory circuit that makes use of an adaptively biased current comparator achieving fast response time (1.8 μ s) while consuming very low quiescent current (Iq). A Burst mode charge-pump is used to ensure monitoring down to 0.75V. This is the lowest reported common monitoring and operating supply for supervisory circuits. Fabricated in 130nm CMOS, this work achieves an Iq(nA) x Delay (μ s) FoM of 29, a 70x improvement over state-of-the-art supply supervisory circuits.

A new one-dimensional (1D) coordination polymer of composition [Ag(L)(NO3)]n was synthesized by a reaction of silver(I) nitrate with a new hydrazone-hydrazide ligand (L) derived from 2-acetyl furan and ethyl hydrazine carboxylate. Both the ligand, L, and the silver(I) complex were characterized using elemental analysis, electron impact mass spectrometry (EI-Mass), infrared (IR), proton nuclear magnetic resonance (1H NMR), ultraviolet–visible (UV–Vis) spectroscopy, and emission spectroscopy analyses. Single-crystal X-ray diffraction study shows that the structure of the silver(I) complex consists of 1D polymeric chains in which silver(I) ion is in four coordination complexes arising from one N, O-chelating L and two O, O′-bridging nitrate anions. The complex emitted energy around 360 nm and was used as a single precursor for synthesis of silver oxide nanoparticles (Ag2O NPs). From transmission electron microscopy (TEM) and higher-resolution TEM (HR-TEM) images, the as-prepared Ag2O NPs were nearly spherical in shape with average particle sizes of approximately 22–24 nm. Anti-bacterial properties of both the ligand and the metal complex were studied.

This research presents a rule-based Anti-lock Braking System (ABS) algorithm towards active vehicle safety in Heavy Commercial Road Vehicles (HCRVs). Wheel Slip Regulation (WSR) algorithms, that are constituents of an ABS, are typically either model-based or rule-based. Model-Based Algorithms (MBAs) utilise mathematical models that characterise vehicle dynamics and are intensive in their demand for real-time information. On the other hand, Rule-Based Algorithms (RBAs) operate on set rules with pre-defined thresholds and utilise data from sensors that are typically available on the vehicle. A great deal of advancement has been reported in literature related to MBAs. However, most commercially available RBAs are proprietary in nature and the finer details are seldom revealed. Hence, this work proposes an RBA, which is a combined Slip and Wheel Acceleration Threshold Algorithm (SWATA), including a framework for identifying the importance of thresholds and their magnitudes. SWATA was tested on a Hardware-in-Loop (HiL) setup across varying road and loading conditions, and it provided a maximum of 36% braking distance improvement compared to a case where it was inactive. An adaptive version–ASWATA–is also proposed that can adapt the thresholds according to the particular tyre-road interface. Additionally, a quantitative comparison of the proposed RBA with a Sliding Mode Control (SMC) based MBA, which was developed and tested on the same HiL setup, is presented. It was observed that the performance of the RBA was on par with that of the MBA for most test cases, but with minimal data requirements.

Coordinate Measuring Machines (CMM) are widely used in form inspection of free-form surfaces. Generally, the form error at each measured point is estimated using the widely known and accurate point-inversion method. This method has relatively high time complexity and cannot be preferred for fast inspection. Hence in this work, an alternative two-stage methodology based on the concept of the Voronoi diagram is proposed. In the first stage, the poles data is extracted from the Voronoi diagram of the discretized surface. In the second stage, the form-error-estimate algorithm executing in O(m log n) time estimates the errors using the poles data and the discretized surface. Numerical and experimental implementations are executed using NURBS surfaces. The proposed method's accuracy is on par with the point-inversion method and is 94.97% faster than the latter. Hence this method can be used for fast and accurate CMM and CNC based (in-situ) free-form surface inspection.

This paper proposes a transmission-line based bidirectional reflection-type phase shifter for the 2.5 to 3.2 GHz frequency band. Open and short reflective loads are used to double the available phase shift and reduce insertion loss. The transmission line is implemented using inductors and MOS switches in a 65 nm RF CMOS process. This work achieves a total phase shift of 180° with phase resolution of 22.5° at 2.85 GHz and rms error of 1.2°. The insertion loss of the phase shifter is 5.7 dB and the loss variation is less than 0.3 dB.

This work presents a 5 Gb/s PAM4 hybrid voltage-mode transmitter with current mode continuous-time linear equalization. The transmitter achieves a large output signal swing with a PAM4 CMOS output driver. CTLE is embedded in the transmitter's output stage to compensate a range of channel loss without reducing the signal swing at the receiver front end. Fabricated in 65 nm CMOS process, the transmitter dissipates 20.4 mW in the output driver at 5 Gb/s while transmitting 1.1 V peak-to-peak differential output swing across 2 m UTP cable.

This paper presents a baseline repositioning controller for floating offshore wind farms. It is assumed that each turbine in the farm receives a lateral position reference command by a high-level wind farm controller. A low-level controller for each wind turbine in the farm manipulates the aerodynamic force direction and magnitude to achieve the reference command, while either power maximization or power regulation at the rated power is sought depending on the generator speed feedback signal. The low-level repositioning controller is a combination of the existing controller of blade pitch angle and generator torque, and the gain-scheduled proportional-integral nacelle yaw controller. To validate the repositioning controller, a previously-developed low-fidelity dynamic wind farm simulator, called FOWFSimDyn, will be utilized. The simulation results demonstrate that repositioning wind turbines in a wind farm has a great potential in increasing the total energy capture of the wind farm with sufficiently long mooring lines. The result in this paper can be a baseline to be compared with other advanced wind farm repositioning control in future research.