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Bayesian network based probabilistic modelling of earthquake induced landslides.
No full textLarge earthquakes cause slope instability in hilly areas, resulting in landslides. Landslides can potentially kill people and seriously harm infrastructure like highways and railroads. Because of the severity of the immediate and long-term effects of landslide destruction, more reliable methods of failure estimation are required. In this study, a unique approach is used to calculate the likelihood of the safety factor and permanent displacement of natural slopes under earthquake shaking. The proposed approach was constructed using probabilistic modeling of landslide instability based on the Bayesian Network technique. First, the pseudo-static factor of safety was computed, considering it an uncertain parameter. Then, the permanent displacement of failure mass was estimated through probabilistic analysis considering the effect of critical and peak horizontal acceleration. In the process of probabilistic analysis, soil and slope properties (cohesion, friction angle, unit weight, slope angle, and failure depth) and peak horizontal acceleration were considered as random variables distributed as normal and exponential functions, respectively. To illustrate the applicability of the proposed approach, a hypothetical infinite slope was adopted from past literature. The results showed that due to the event of an earthquake, the slope might experience permanent displacement. Finally, based on the variation of permanent displacement, the likelihood of landslide occurrences was estimated. Validation of the study was established by comparing the outcomes with the results obtained using the analytical joint probability method. The methodology presented in this study would lead to an estimation of landslide failures by taking uncertainties into account, which would increase the safety of city dwellers
Corrigendum to “Second round statewide sentinel-based population survey for estimation of the burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population of Karnataka, India, during January-February 2021” [IJID Regions Vol 1(2021) pages 107–116]
No full textThe authors regret the affiliation correction of Dr. Giridhara R. Babu. The correct affiliation should be amended to read as follows: Giridhara R Babu, Department of Population Medicine, College of Medicine, QU Health, Qatar University. The authors would like to apologise for any inconvenience caused
Electrochemical engineering of anode with zincophilic polymer interface for reversible zinc battery
No full textAqueous rechargeable zinc-ion batteries are emerging as green and safe energy devices owing to their high energy density and eco-friendliness. The bottleneck is the inhomogeneous nucleation resulting in the growth of Zn dendrites and the parasitic side reactions at the anode. Herein, an electrochemical anode engineering strategy is demonstrated to curb the uncontrolled dendritic growth and side reactions. It involves the in situ generation of a solid-electrolyte interface based on a Zn nanoparticle-embedded poly(acrylic acid) hybrid layer (PAA-nZn) on the anode. The homogeneously distributed polar functionalities and porous nature of the polymer regulate the uniform zinc ion flux, and the embedded zinc particle serves as nucleation sites for zinc plating. The hybrid layer reduces the nucleation overpotential and promotes instantaneous nucleation. The symmetrical cell made with PAA-nZn@Zn has a lifecycle of >860 h with a voltage gap of 28 mV. The full cell fabricated by pairing the engineered anode with α-MnO2 cathode showed a high discharge specific capacity of 238.3 mAh g−1 at 0.2 A g−1 with a capacity retention of 81.76% after 200 cycles and a long lifecycle. The electrochemical engineering of the anode suppresses the dendritic growth and protects the anode from unwanted side reactions and passivation
Supply chain digitisation and management
No full textIn the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem
Fast list decoding of univariate multiplicity and folded reed-solomon codes
No full textWe show that the known list-decoding algorithms for univariate multiplicity and folded Reed-Solomon (FRS) codes can be made to run in ̂O(n) time. Univariate multiplicity codes and FRS codes are natural variants of Reed-Solomon codes that were discovered and studied for their applications to list decoding. It is known that for every ε>0, and rate rε(0,1), there exist explicit families of these codes that have rate r and can be list decoded from a (1-r-ε) fraction of errors with constant list size in polynomial time. In this work, we present randomized algorithms that perform the above list-decoding tasks in ̂O(n), where n is the block-length of the code. Our algorithms have two main components. The first component builds upon the lattice-based approach of Alekhnovich (IEEE Trans. Inf. Theory 2005), who designed a ̂O(n) time list-decoding algorithm for Reed-Solomon codes approaching the Johnson radius. As part of the second component, we design ̂O(n) time algorithms for two natural algebraic problems: given a (m+2) -variate polynomial Q(x,y0,…,ym)=̂Q(x)+Σmi=0Qi(x)⋅yi the first algorithm solves order-m linear differential equations of the form Q(x,f(x),df÷dx,…,dmf÷dxm)≡0 while the second solves functional equations of the form Q(x,f(x),f(γx),…,f(γmx))≡0, where m is an arbitrary constant and γ is a field element of sufficiently high order. These algorithms can be viewed as generalizations of classical ̂O(n) time algorithms of Sieveking (Computing 1972) and Kung (Numer. Math. 1974) for computing the modular inverse of a power series, and might be of independent interest.
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Designed nanoarchitectures of a BiOBr/BiOI nanosheet heterojunction anchored on dendritic fibrous nanosilica as visible-light responsive photocatalysts
No full textHeterojunctions, particularly those involving BiOBr/BiOI, have attracted significant attention in the field of photocatalysis due to their remarkable properties. In this study, a unique architecture of BiOBr/BiOI was designed to facilitate the rapid transfer of electrons and holes, effectively mitigating the recombination of electron–hole pairs. Accordingly, the BiOBr/BiOI nanosheet heterojunction was anchored on dendritic fibrous nanosilica (DFNS) by the immobilization of Bi2O3 nanodots in DFNS and the subsequent reaction with HBr and then HI vapors at room temperature. The 4 nm-Bi2O3 nanodots acted as a sacrificial template to form BiOX nanosheets by reaction with HX vapors (X = Br, I). The BiOBr/BiOI nanosheet heterojunction with the lateral size remained in the range of 90 to 110 nm and a thickness of 15 nm formed on DFNS, where the BiOBr:BiOI ratio in the product was controlled by the exposure time to HX vapors. The reaction sequence (HBr → HI vapors) was a key for the formation of BiOBr/BiOI nanosheet heterojunction with controlled composition. When the reaction of Bi2O3 nanodots with HI vapor was performed in the reverse sequence (HI→ HBr), the substitution of I– with Br– occurred to form BiOBr sheets on DFNS. The BiOBr/BiOI nanosheet heterojunction anchored on DFNS was used as a visible-light-driven photocatalyst for the decomposition of benzene in water under solar light, and its activity was superior to that of single BiOX nanosheets on DFNS
Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography
No full textDevelopment of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum classification performance is obtained with gated recurrent unit with accuracy and F1-score of 89.2% and 89.0%, respectively with raw temperature data as input of window 300. The study demonstrates that the proposed DAI approach effectively predicts the age of thermal barrier coatings under isothermal heat-treatment conditions by correlating the thermal response with coating degradation
Multi modal data fusion of PAUT with thermography assisted by Automatic Defect Recognition System (M-ADR) for NDE Applications
No full textIn this paper, we proposed a method for detecting and characterizing defects in metals by combining the techniques of phased-array ultrasonic testing (PAUT) with pulsed thermography (PT) using a data fusion coordinate transformation technique to combine the capabilities of the two modalities into a volumetric dataset. PAUT inspection is limited to internal defects, whereas PT inspection is limited to surface and near-surface defects. The data fusion technique combines complementing information from both modalities, allowing one to comprehend defects that would otherwise be invisible using either technique alone. To enhance the defect detection process, we developed a multimodal automatic defect detection (M-ADR) system that includes a Deep Neural Network (DNN) and a Bi-Planar Medial Axis Transform (Bi-MAT) algorithm. Convolution operations are performed in all three orthogonal planes using the DNN architecture to learn the defect feature in the fused volumetric training dataset. M-ADR uses the Bi-MAT method to size defects based on the output features of the DNN model. The integrated DNN system with fused volumetric information achieves a remarkable flaw detection accuracy of 91.46%, outperforming conventional DNN models and single-modality inspection techniques. M-ADR allows extraction of precise defect geometries, which sizes the smallest defect of λ/4
Regulation of chloroplast biogenesis, development, and signaling by endogenous and exogenous cues
No full textChloroplasts are one of the defining features in most plants, primarily known for their unique property to carry out photosynthesis. Besides this, chloroplasts are also associated with hormone and metabolite productions. For this, biogenesis and development of chloroplast are required to be synchronized with the seedling growth to corroborate the maximum rate of photosynthesis following the emergence of seedlings. Chloroplast biogenesis and development are dependent on the signaling to and from the chloroplast, which are in turn regulated by several endogenous and exogenous cues. Light and hormones play a crucial role in chloroplast maturation and development. Chloroplast signaling involves a coordinated two-way connection between the chloroplast and nucleus, termed retrograde and anterograde signaling, respectively. Anterograde and retrograde signaling are involved in regulation at the transcriptional level and downstream modifications and are modulated by several metabolic and external cues. The communication between chloroplast and nucleus is essential for plants to develop strategies to cope with various stresses including high light or high heat. In this review, we have summarized several aspects of chloroplast development and its regulation through the interplay of various external and internal factors. We have also discussed the involvement of chloroplasts as sensors of various external environment stress factors including high light and temperature, and communicate via a series of retrograde signals to the nucleus, thus playing an essential role in plants' abiotic stress response
A histidine-functionalized ROS scavenging hybrid nanozyme for therapeutic application in Parkinson's disease pathogenesis
No full textOxidative stress-mediated neurotoxicity is one of the significant molecular consequences in the progression of neurodegenerative diseases like Parkinson's disease (PD). Although many genetic mutations are implicated in developing familial and sporadic PDs, the progressive degeneration of dopaminergic neurons still lacks therapeutic intervention. Biocompatible nanomaterials with enzyme-like characteristics, especially SOD1 activity to scavenge excessive reactive oxygen species, can serve as an effective tool to treat PD pathogenesis. Here we report novel histidine-functionalized zinc oxide nanoparticles (ZnNPs-H) and the corresponding copper-doped nanoparticles (ZnCuNPs-H) mimicking SOD1 activity. The functionalized NPs exhibit superior nanozyme activity by robust reactive oxygen species (ROS) scavenging potential compared to previously reported NPs in vitro. The functionalized NPs exhibited excellent biocompatibility in the neuronal cellular model and were targeted efficiently into mitochondrial compartments. The NPs showed robust superoxide dismutase nanozyme activity and scavenged the excess ROS upon rotenone and lung cancer screen-1 (LCS-1) treatment. The nanozyme effectively substituted the endogenous functions of SOD1 and reinstated the cellular redox homeostasis. Most importantly, the NPs rescued neuronal cells from the toxic effects of MPP+ conditions and restored the mitochondrial health associated with cellular morphology, highlighting their neuroprotective role in preventing Parkinson's disease pathogenesis, thus opening a therapeutic strategy for disease management