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"Kumar, Sumit"
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Intermolecular charge-transfer complex between solute and ionic liquid: experimental and theoretical studies
Ground-state intermolecular donor–acceptor complex ([MCP][NTf
2
]-MN; 1:1) is formed between
π
-electron of 1-methyl-naphthalene (MN) as solute (electron-rich) and
π
+
electron of 1-methyl-4-cyanopyridinium bis((trifluoromethyl)sulfonyl)amide ([MCP][NTf
2
]) as solvent (electron deficient), observed in solid state. Intermolecular charge-transfer (IMCT) band is observed, indicating the formation of stable [MCP][NTf
2
]-MN complex. The IMCT process of [MCP][NTf
2
]-MN complex depends on relative strength of
π
–
π
+
stack between cation of [MCP][NTf
2
] IL and aromatic unit of MN. From DFT studies, it is clear that the geometry and interactions in [MCP][NTf
2
]-MN complex are also influenced by NTf
2
anion. This solute–solvent interaction shows the deviation of inertness nature of [MCP][NTf
2
] IL. AIM analysis, electron localization function (ELF) and localized orbital locator (LOL) surface maps are obtained to achieve information regarding intermolecular interactions in the complex. Hirshfeld surface analysis and its fingerprint maps are used to identify pairwise interactions between atoms in order to avail molecular packing of the complexes from crystallographic data. NCI plots display combination of specific atom–atom interactions through hydrogen bond and vdW interactions. AIMD study shows that the complex attains a lower energy of − 2630.72 hartree at 125 and 445 fs.
Journal Article
An Overview on the Role of Relative Humidity in Airborne Transmission of SARS-CoV-2 in Indoor Environments
COVID-19 disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China and spread with an astonishing rate across the world. The transmission routes of SARS-CoV-2 are still debated, but recent evidence strongly suggests that COVID-19 could be transmitted via air in poorly ventilated places. Some studies also suggest the higher surface stability of SARS-CoV-2 as compared to SARS-CoV-1. It is also possible that small viral particles may enter into indoor environments from the various emission sources aided by environmental factors such as relative humidity, wind speed, temperature, thus representing a type of an aerosol transmission. Here, we explore the role of relative humidity in airborne transmission of SARS-CoV-2 virus in indoor environments based on recent studies around the world. Humidity affects both the evaporation kinematics and particle growth. In dry indoor places i.e., less humidity (< 40% RH), the chances of airborne transmission of SARS-CoV-2 are higher than that of humid places (i.e., > 90% RH). Based on earlier studies, a relative humidity of 40–60% was found to be optimal for human health in indoor places. Thus, it is extremely important to set a minimum relative humidity standard for indoor environments such as hospitals, offices and public transports for minimization of airborne spread of SARS-CoV-2.
Journal Article
Barriers to accessing health care services: a qualitative study of migrant construction workers in a southwestern Indian city
Background
This study examined access to health care in an occupational context in an urban city of India. Many people migrate from rural areas to cities, often across Indian states, for employment prospects. The purpose of the study is to explore the barriers to accessing health care among a vulnerable group – internal migrants working in the construction sector in Manipal, Karnataka. Understanding the lay workers’ accounts of access to health services can help to comprehend the diversity of factors that hinder access to health care.
Methods
Individual semi-structured interviews involving 15 migrant construction workers were conducted. The study applied theory-guided content analysis to investigate access to health services among the construction workers. The adductive analysis combined deductive and inductive approaches with the aim of verifying the existing barrier theory in a vulnerable context and further developing the health care access barrier theory.
Results
This study’s result is a revised version of the health care access barriers model, including the dimension of trust. Three known health care access barriers – financial, cognitive and structural, as well as the new barrier (distrust in public health care services), were identified among migrant construction workers in a city context in Karnataka, India.
Conclusions
Further qualitative research on vulnerable groups would produce a more comprehensive account of access to health care. The socioeconomic status behind access to health care, as well as distrust in public health services, forms focal challenges for any policymaker hoping to improve health services to match people’s needs.
Journal Article
Deep learning-based approach for identification of diseases of maize crop
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.
Journal Article
Recent advancements in multifaceted roles of flavonoids in plant–rhizomicrobiome interactions
The rhizosphere consists of a plethora of microbes, interacting with each other as well as with the plants present in proximity. The root exudates consist of a variety of secondary metabolites such as strigolactones and other phenolic compounds such as coumarin that helps in facilitating communication and forming associations with beneficial microbes in the rhizosphere. Among different secondary metabolites flavonoids (natural polyphenolic compounds) continuously increasing attention in scientific fields for showing several slews of biological activities. Flavonoids possess a benzo-γ-pyrone skeleton and several classes of flavonoids have been reported on the basis of their basic structure such as flavanones, flavonols, anthocyanins, etc. The mutualistic association between plant growth-promoting rhizobacteria (PGPR) and plants have been reported to help the host plants in surviving various biotic and abiotic stresses such as low nitrogen and phosphorus, drought and salinity stress, pathogen attack, and herbivory. This review sheds light upon one such component of root exudate known as flavonoids, which is well known for nodulation in legume plants. Apart from the well-known role in inducing nodulation in legumes, this group of compounds has anti-microbial and antifungal properties helping in establishing defensive mechanisms and playing a major role in forming mycorrhizal associations for the enhanced acquisition of nutrients such as iron and phosphorus. Further, this review highlights the role of flavonoids in plants for recruiting non-mutualistic microbes under stress and other important aspects regarding recent findings on the functions of this secondary metabolite in guiding the plant-microbe interaction and how organic matter affects its functionality in soil.
Journal Article
A Highly Compact Antipodal Vivaldi Antenna Array for 5G Millimeter Wave Applications
This paper presents a compact 1 × 4 antipodal Vivaldi antenna (AVA) array for 5G millimeter-wave applications. The designed antenna operates over 24.19 GHz–29.15 GHz and 30.28 GHz–40.47 GHz frequency ranges. The proposed antenna provides a high gain of 8 dBi to 13.2 dBi and the highest gain is obtained at 40.3 GHz. The proposed antenna operates on frequency range-2 (FR2) and covers n257, n258, n260, and n261 frequency bands of 5G communication. The corrugations and RT/Duroid 5880 substrate are used to reduce the antenna size to 24 mm × 28.8 mm × 0.254 mm, which makes the antenna highly compact. Furthermore, the corrugations play an important role in the front-to-back ratio improvement, which further enhances the gain of the antenna. The corporate feeding is optimized meticulously to obtain an enhanced bandwidth and narrow beamwidth. The radiation pattern does not vary over the desired operating frequency range. In addition, the experimental results of the fabricated antenna coincide with the simulated results. The presented antenna design shows a substantial improvement in size, gain, and bandwidth when compared to what has been reported for an AVA with nearly the same size, which makes the proposed antenna one of the best candidates for application in devices that operate in the millimeter frequency range.
Journal Article
Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network
Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers.
Journal Article
Fuzzy Logic with Deep Learning for Detection of Skin Cancer
Melanoma is the deadliest type of cancerous cell, which is developed when melanocytes, melanin producing cell, starts its uncontrolled growth. If not detected and cured in its situ, it might decrease the chances of survival of patients. The diagnosis of a melanoma lesion is still a challenging task due to its visual similarities with benign lesions. In this paper, a fuzzy logic-based image segmentation along with a modified deep learning model is proposed for skin cancer detection. The highlight of the paper is its dermoscopic image enhancement using pre-processing techniques, infusion of mathematical logics, standard deviation methods, and the L-R fuzzy defuzzification method to enhance the results of segmentation. These pre-processing steps are developed to improve the visibility of lesion by removing artefacts such as hair follicles, dermoscopic scales, etc. Thereafter, the image is enhanced by histogram equalization method, and it is segmented by proposed method prior to performing the detection phase. The modified model employs a deep neural network algorithm, You Look Only Once (YOLO), which is established on the application of Deep convolutional neural network (DCNN) for detection of melanoma lesion from digital and dermoscopic lesion images. The YOLO model is composed of a series of DCNN layers we have added more depth by adding convolutional layer and residual connections. Moreover, we have introduced feature concatenation at different layers which combines multi-scale features. Our experimental results confirm that YOLO provides a better accuracy score and is faster than most of the pre-existing classifiers. The classifier is trained with 2000 and 8695 dermoscopic images from ISIC 2017 and ISIC 2018 datasets, whereas PH2 datasets along with both the previously mentioned datasets are used for testing the proposed algorithm.
Journal Article
Energy consumption minimisation at edge node using$$C_cBPS$$approach in predicting sensor parameters in WSNs
Abstract Owing to limited storage and battery power, wireless sensor nodes often face challenges in maintaining long-term energy sustainability. To address this, only a subset of sensors remains active to monitor different sensor parameters while others get predicted to minimize sensor node energy consumption. In prediction, not all active parameters are equally important, as low-correlated parameters increase computational complexity and decrease accuracy. Researchers use highly correlated active parameters, though existing solutions often use polynomial time and don’t ensure optimal parameter set. This paper proposes a cross-correlation-based parameter selection $$(C_cBPS)$$ approach, ensuring the selected parameter set is stable and Pareto-optimal. Simulations are performed on nine publicly available datasets of environmental data collected from different places and at different sampling intervals to validate the effectiveness of the $$C_cBPS$$ approach. It has been observed that $$C_cBPS$$ approach selects a subset of active parameters faster than existing approaches and reduces energy consumption at the edge node ranges from $$6.5\\%$$ - $$34.2\\%$$ in the prediction of sleep sensor parameters on various datasets.
Journal Article