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The novel SARS-CoV-2 coronavirus that emerged in the city of Wuhan, China, last year has since become the COVID-19 pandemic across all continents. To restrict the spread of the virus pandemic, the Government of India imposed a lockdown from 25 March 2020. In India, Kannur district was identified as the first “hotspot” of virus transmission and a “triple-lockdown” was implemented for a span of twenty days from 20 April 2020. This article highlights the variations of surface O 3 , NO, NO 2 , CO, SO 2, NH 3 , VOC’s, PM 10 , PM 2.5 and meteorological parameters at the time of pre-lockdown, lockdown and triple-lockdown days at Kannur town in south India using ground-based analyzers. From pre-lockdown days to triple-lockdown days, surface O 3 concentration was found to increase by 22% in this VOC limited environment. NO and NO 2 concentrations were decreased by 61% and 71% respectively. The concentration of PM 10 and PM 2.5 were observed to decline significantly by 61% and 53% respectively. Reduction in PM 10 during lockdown and triple-lockdown days enhanced the intensity of solar radiation reaching the lower troposphere, and increased air temperature and reduced the relative humidity. Owing to this, surface O 3 production over Kannur was found to have increased during triple-lockdown days. The concentration of CO (67%), VOCs (61%), SO 2 (62%) and NH 3 (16%) were found to decrease significantly from pre-lockdown days to triple-lockdown days. The air quality index revealed that the air quality at the observational site was clean during the lockdown.

The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow‐based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.

Scalability, leakage, short-channel effects, and reliability problems are some of the difficulties facing the semiconductor industry as it continues to experience a reduction in size. Heavy charged particles striking an integrated circuit (IC) cause a Single Event Upset (SEU). As devices are scaled down, it usually causes Single and Multiple Node Upset. To overcome the upsets, device and CMOS circuit radiation hardening by design (RHBD) techniques are adopted. In the resent times, Carbon nanotubes have emerged as a feasible technology capable of addressing CMOS problems while maintaining performance and reliability. This manuscript proposes a Low power High speed Quadrate Node Upset Carbon Nanotube latch (LHQCNT). The LHQCNT latch contains three Dual Interlocked cells with a delta interconnection design, supplying enough redundant nodes to ensure robustness to Multi Node Upsets due to charge sharing. The investigation includes simulation, and performance comparison of a LHQCNT latch with an existing hardened latch. The LHQCNT latch obtained the power, delay, PDP and APDP as 4.4 µW, 1.23 ps, 5.41e-18 and 5.19e-2 respectively with a supply voltage of 1 V. Results from simulations show that the proposed LHQCNT latch archives low power, delay, and APDP of any latch with a comparable soft error tolerance level.

Hot flow behavior of hot isostatically processed experimental nickel-based superalloy is investigated over temperature and strain rate ranging from 1000–1200 °C and 0.001–1 s⁻¹, respectively by carrying out constant true strain rate isothermal compression tests up to true strain of 0.69. True stress–true strain curves corrected for adiabatic temperature rise exhibited rapid strain hardening followed by flow softening behavior irrespective of temperature and strain rate regimes investigated, although anomalous flow behavior is observed at 1200 °C. Variation of peak flow stress with temperature is corroborated to the microstructural changes pertaining to the morphology and relative volume fraction of the phases present. From the experimental results, constitutive model incorporating the effects of strain rate, strain, and temperature is established to describe the hot flow behavior of investigated alloy. Dependence of peak flow stress on strain rate and temperature described by Zener–Hollomon (Z) parameter indicated increase in peak flow stress with Z. Additionally Cingara-Queen equation is employed to predict flow curve up to peak stress. The reliability of developed constitutive models is validated statistically and the results indicate reasonable agreement with experimental findings.

Possibilities of enhancing mechanical properties of brittle intermetallic containing high entropy alloys (HEAs) using novel processing and microstructural design strategies were investigated in the present work. For this purpose, homogenized CoCrFeNi 2.1 Nb 0.2 HEA consisting of FCC matrix and complex Laves phase particles was successfully processed by severe cold- or cryo-rolling to 90% reduction in thickness followed by annealing (800 °C/1 hour(h)). As compared to cold-rolling, cryo-rolling resulted in a finer lamellar nanostructure and decidedly greater fragmentation of the Laves phase. Upon annealing, the cold-rolled HEA showed a recrystallized FCC matrix dispersed with D0 19 structured ε nano-precipitates. In contrast, the finer nanostructure and greater driving force for accelerated precipitation of profuse nano-precipitates at the early stages of annealing inhibited recrystallization in the cryo-rolled HEA and resulted in the formation of heterogeneous microstructure consisting of retained deformed and recrystallized regions. The novel heterogeneous microstructure of the cryo-rolled and annealed HEA resulted in a remarkable enhancement in strength-ductility synergy. The present results indicated that cryo-rolling could be used as an innovative processing strategy for tailoring heterogeneous microstructure and achieving novel mechanical properties.

Lung cancer poses a serious health risk, making early diagnosis essential for better survival outcomes. Detection of lung cancer involves a series of medical evaluations and imaging techniques to identify cancerous cells in the lungs. Computed Tomography (CT) images are most frequently used to recognize lung cancer since it has high resolution, enhanced clarity, and minimal noise and distortions. However, accurate detection of lung cancer is complex owing to variations in nodule size, shape, and boundary definition. Therefore, an innovative model named Wide Slice Residual Kronecker Network (WISeRKNet) has been developed to diagnose lung cancer from CT images. Initially, image pre-processing is applied by using homomorphic filtering. Subsequently, the extraction of nodules in the lung is performed by the Link-Net model. Subsequently, augmentation of the image is conducted, and then the process of feature extraction is applied to refine shape-based features. At last, diagnosing lung cancer is executed by the WISeRKNet and which combines the Wide Slice Residual Network (WISeR) and the Deep Kronecker Network (DKN). Moreover, the developed WISeRKNet model demonstrated superior performance, by achieving improved value in accuracy as 91.686%, True Positive Rate (TPR) as 90.485%, True Negative Rate (TNR) as 92.727%, Precision as 90.980% and F1 score as 90.484% on the Lung Cancer Computed Tomography Images database using 90% of the data for training.

Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed.

In recent times, the search for innovative material to fabricate smart textiles has been increasing to satisfy the expectation and needs of the consumers, as the textile material plays a key role in the evolution of human culture. Further, the textile materials provide an excellent environment for the microbes to grow, because of their large surface area and ability to retain moisture. In addition, the growth of harmful bacteria on the textile material not only damages them but also leads to intolerable foul odour and significant danger to public health. In particular, the pathogenic bacteria present in the fabric surface can cause severe skin infections such as skin allergy and irritation via direct human contact and even can lead to heart problems and pneumonia in certain cases. Recently, nanoparticles and nanomaterials play a significant role in textile industries for developing functional smart textiles with self-cleaning, UV-protection, insect repellent, waterproof, anti-static, flame-resistant and antimicrobial-resistant properties. Thus, this review is an overview of various textile fibres that favour bacterial growth and potential antibacterial nanoparticles that can inhibit the growth of bacteria on fabric surfaces. In addition, the probable antibacterial mechanism of nanoparticles and the significance of the fabric surface modification and fabric finishes in improving the long-term antibacterial efficacy of nanoparticle-coated fabrics were also discussed.

Maturity stage of fresh banana fruit is an important factor that affects the fruit quality during ripening and marketability after ripening. The ability to identify maturity of fresh banana fruit will be a great support for farmers to optimize harvesting phase which helps to avoid harvesting either under-matured or over-matured banana. This study attempted to use image processing technique to detect the maturity stage of fresh banana fruit by its color and size value of their images precisely. A total of 120 images comprising 40 images from each stage such as under-mature, mature and over-mature were used for developing algorithm and accuracy prediction. The mean color intensity from histogram; area, perimeter, major axis length and minor axis length from the size values, were extracted from the calibration images. Analysis of variance between each maturity stage on these features indicated that the mean color intensity and area features were more significant in predicting the maturity of banana fruit. Hence, two classifier algorithms namely, mean color intensity algorithm and area algorithm were developed and their accuracy on maturity detection was assessed. The mean color intensity algorithm showed 99.1 % accuracy in classifying the banana fruit maturity. The area algorithm classified the under-mature fruit at 85 % accuracy. Hence the maturity assessment technique proposed in this paper could be used commercially to develop a field based complete automatic detection system to take decision on the right time of harvest by the banana growers.

Individual attitudes vastly affect the transformations we are experiencing and are vital in mitigating or intensifying climate change. A socio-climate model by coupling a model of rumor dynamics in heterogeneous networks to a simple Earth System model is developed, in order to analyze how rumors about climate change impact individuals’ opinions when they may choose to either believe or reject the rumors they come across over time. Our model assumes that when individuals experience an increase in the global temperature, they tend to not believe the rumors they come across. The rumor rejectors limit their CO 2 emissions to reduce global temperature. Our numerical analysis indicates that, over time, the temperature anomaly becomes less affected by the variations in rumor propagation parameters, and having larger groups (having more members) is more efficient in reducing temperature (by efficiently propagating rumors) than having numerous small groups. It is observed that decreasing the number of individual connections does not reduce the size of the rejector population when there are large numbers of messages sent through groups. Mitigation strategies considered by the rejectors are highly influential. The absence of mitigative behavior in rejectors can cause an increase in the global average temperature by 0.5°C. Our model indicates that rumor propagation in groups has the upper hand in controlling temperature change, compared to individual climate-denying propagation.