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Handloom weavers and lockdown in Sualkuchi Cluster of Assam
After demonetisation in 2016, followed by imposition of the goods and services tax in the subsequent year, the COVID-19 lockdown has turned out to be a final nail in the coffin for the handloom sector in Assam. It has special importance in the informal economy of Assam since it is next to agriculture in creating employment opportunities. An examination of the Sualkuchi weaving cluster in Assam shows the many challenges the weavers, most of them women, face. 2020 Economic and Political Weekly. All rights reserved. -
Reading Patterns, Engagement Style and Theory of Mind
Theory of mind (TOM) refers to a set of abilities which enables understanding of mental states including beliefs, emotions and intentions of self and others. The purpose of this paper is to study the effect of different reading patterns including frequency of reading fiction and genre preference on TOM performance. It also aims to compare the accuracy of TOM performance under explicit goal directed and non-directed reading conditions. To achieve this objective, a sample of 72 Indian college students were randomly allocated to two groups and were evaluated on the Reading the Mind in the Eyes Test (RMET) and the Short Story Task (SST). The two groups differed with respect to task instructions aimed at mobilizing different manner of engagement (goal directed and nondirected) with the prose in the SST. The individual reading habits and preferences of all the participants were recorded by a self report questionnaire. Scores on the novel SST showed significant positive correlation with RMET scores. No significant difference in TOM performance with respect to the different engagement styles was found, indicating that TOM abilities function continuously and equally effectively when being used in goal directed and nondirected conditions. Notably, participants who reported to prefer literary fiction performed significantly better on the SST task than the participants who prefer popular fiction. This positive link between literary fiction and TOM has important implications in clinical and developmental fields and necessitates further research. 2021, National Academy of Psychology (NAOP) India. -
Advances in detecting non-steroidal anti-inflammatory drugs (NSAIDs) using molecular receptors and nanostructured assemblies
The detection and quantification of non-steroidal anti-inflammatory drugs (NSAIDs) are crucial due to their widespread use and potential impact on human health and the environment. This review provides a comprehensive survey of the recent advancements in sensing technologies for NSAIDs, focusing on molecular receptors and nanostructured assemblies. Molecular receptors based on different fluorescent molecules such as anthracene, naphthalimide, squaraine, quinoline, BINOL, etc. offer high selectivity and sensitivity for NSAID detection. In parallel, nanostructured assemblies including CdSe/ZnS, Cd/S quantum dots (QDs), carbon dot-containing imprinted polymers, Ag and Au nanoparticles (NPs), hydrogel-embedded chemosensors, etc. were utilized for NSAID detection. This review highlights the different binding pathways with the change of various photophysical properties combining molecular recognition elements with nanomaterials to develop innovative sensors that achieve rapid, sensitive, and selective detection of NSAIDs. The review also discusses current challenges and future prospects in the field and based on reported designed receptors and nanostructured assemblies. To the best of our knowledge, no reviews have been reported on this topic so far. Thus, this review will fruitfully guide researchers to design various new molecular receptors and nanostructured materials to detect NSAIDs. 2024 RSC. -
Impedance and electrochemical studies of rGO/Li-ion/PANI intercalated polymer electrolyte films for energy storage application
The present manuscript describes the synthesis of reduced graphene oxide (rGO) from coke by using modified Hummers method. The synthesized emeraldine poly aniline (PANI) polymer was used as a polymer host matrix. A series of polymer electrolyte films were prepared by varying concentration of rGO, PANI and Lithium carbonate. The synthesized PANI and rGO were soluble in common polar solvent. The structural, Nyquist and cyclic voltammetry studies of polymer electrolyte were investigated. The XRD and FTIR investigation confirms the formation of rGO and PANI in view of structural and chemical compositions respectively. The electrical property of polymer electrolyte was obtained by Nyquist plot which represents the perfect semicircular pattern. It confirms the charge transport mechanism with the decreased concentration of rGO in polymer electrolyte. The cyclic voltammetry performed at different scan rate on potential window ranged between-0.5 to 0.6 V represents the oxidation and reduction peaks. The overall results describe that the present electrolyte material can be a potential candidate for energy storage application.. 2019 Elsevier Ltd. -
Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach
Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (?9.261 Kcal mol?1) and 3 (?9.554 Kcal mol?1) when compared with the control drug Givinostat (-8.789 and ?9.448 Kcal mol?1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol?1) to HDAC8 compared to Givinostat (-8.947 Kcal mol?1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study. Communicated by Ramaswamy H. Sarma. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Python programming for geospatial applications: Web mapping, interactive visualization, and beyond
Geospatial solutions represent a pivotal toolset for analyzing, interpreting, and visualizing spatial data across diverse domains, facilitating informed decision-making and fostering innovation. This book chapter provides a comprehensive overview of geospatial solutions, emphasizing their critical role in addressing spatially explicit challenges and driving efficiency, productivity, and innovation across various sectors. Furthermore, it explores the integration of Python programming in geospatial applications, highlighting its versatility and extensive ecosystem of libraries and tools tailored for spatial data analysis and visualization. The fundamentals of web mapping are discussed in depth, elucidating spatial representation, technologies, and tools commonly employed in web mapping applications. Also, the chapter explores Python's role in retrieving geospatial data with Python, visualization methods, and interactive web mapping. 2024, IGI Global. -
A proposed framework for an appropriate governance system to develop smart cities in India
The Government of India has undertaken a novel step towards building new smart cities as well as transforming some of its existing cities into smart cities. However, tension relating to the governance of smart cities has emerged. Therefore, a mixed-methods approach was used based on a perception survey, case studies, and discussions with stakeholders and experts, to examine the current governance challenges in transforming existing cities into smart cities, and to explore various perspectives to propose a framework for an appropriate governance system for developing smart cities in India. The findings suggested that the current executive-led governance system, with special-purpose vehicles (SPVs) under the control of the state governments as the promoters of smart city development, might not lead to the smart governance system envisaged but, rather, add confusion and conflict, and undermine the constitutionally mandated, legislative-led urban local bodies. The argument in this article is for a people-centric, balanced governance approach with strengthened urban local bodies, enabled by advanced digital technology and the constructive participation of different social solidarities, in which the SPVs would act as the intellectual and executive wing of the urban local bodies. 2023 Regional Studies Association. -
Ortho-Vanillin based multifunctional scaffold for selective detection of Al3+ and Zn2+ employing molecular logic with DFT study and cell imaging with live Grass pea
Ligand (E)-N-(2-hydroxy-3-methoxybenzylidene) acetohydrazide (HL) has been designed and synthesized from o-vanillin and acetohydrazide for selective sensing of Al3+ and Zn2+ ions. In the photoluminescence studies, the receptor HL itself shows very poor fluorescence but on incremental addition of Al3+ and Zn2+ ions in solution of the probe HL individually leads to a sharp increase in emission intensities at wavelengths 468 nm (?15 fold) and 504 nm (?8 fold) respectively. Due to excited state intramolecular proton transfer (ESIPT), HL exhibits weak emission in absence of any analytes but in presence of Al3+ and Zn2+, chelation-enhanced fluorescence (CHEF) with coordination of Al3+ and Zn2+ inhibits ESIPT, which results large increase of fluorescence enhancement. The ligand HL shows high selectivity and sensitivity to detect Al3+ and Zn2+ among various metal ions with LOD (Limit of Detection) 0.836 10?6 M and 1.01 10?6 M respectively. DFT calculation has been performed to study the binding phenomenon of ligand HL with metal ions. A molecular logic gate has been build-up with Al3+ and Zn2+ and EDTA as three chemical input. Simultaneously, cytotoxicity and cell biology for the probe and corresponding Al3+ sensing were observed. 2023 Elsevier B.V. -
Rendering View of Kitchen Design Using Autodesk 3Ds Max
The method of creating a 3D kitchen design model is clarified, including setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifier. The way materials are introduced to the model which is defined in addition to lighting the environment and setting up the renderer. Rendering methods and procedures are also defined. Multiple images were drawn to create the final rendering. The goal of our research is to produce a kitchen design that uses materials to enhance models. Cylinder, sphere, box, plane, and splines were the shapes employed. Editable poly, editable spline, and UVW map are the modifiers. Finally, we enhanced the model using a material editor and target lighting. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dhat syndrome and its perceived impact on psychological well-being
Background: Dhat syndrome is a culture-bound syndrome originating in the Indian subcontinent, primarily among men characterized by the fear of loss of semen. Objective: The article discusses the perceived impact of Dhat syndrome on the overall psychological well-being of the individual. Method: Four patients from hospitals in Kolkata, West Bengal, were screened using MINI and then interviewed using semi-structured interview to assess presenting concerns, interventions, psychological well-being, attitude toward sex and masturbation, and their sociodemographic details. The data were then categorized based on the dimensions of the questionnaire, which was then analyzed individually and separately based on the dimensions. The differences and commonalities between the dimensions as conveyed by the participants were then reported. Results: The analysis showed that the participants reported lower levels of psychological well-being based on the categories of Seligman's PERMA model and attributed it to the symptoms experienced by them. They traced the beginning of the hindrances to achieving optimal well-being to the onset of symptoms. Conclusion: This article proposes the incorporation of integrative therapeutic interventions and advocacy of sex education to address the psychological well-being over the current symptom reduction interventions used. 2019 Indian Journal of Social Psychiatry | Published by Wolters Kluwer - Medknow. -
Isolation and Characterization of Antidermatophytic Bioactive Molecules from Piper longum L. Leaves
Piper longum L. (Piperaceae) commonly known as "long pepper" is a well known medicinal plant in ayurveda. Different parts of this plant, such as root, seed, fruit, whole plant etc. are used traditionally in various ailments. Here we have investigated the antidermatophytic activity of sequentially extracted petroleum ether, chloroform, methanol and water extracts from P. longum leaf against Trichophytonmentagrophytes, T. rubrum, T. tonsurans, Microsporum fulvum and M. gypseum. Better activity of chloroform and methanol extracts was observed. The chloroform extract was selected for further study and the MIC value was recorded as 5.0 mg ml-1 against the test organisms. In the chloroform extract, tannins and phenolic compounds were detected. Further activity-guided fractionation of chloroform extract by silica gel column chromatography yielded nine major fractions. Among these, fraction-1, 4, 5 and 7 showed higher antidermatophytic activity. Fraction-4 on further purification by repeated column chromatography yielded a potential antidermatophytic fraction showing MIC value of 0.625 mg ml-1 against T. mentagrophytes and T. rubrum as determined by broth microdilution method. The major compounds were identified as 1,2-benzenedicarboxylic acid, bis(2-ethylhexyl) ester (C24H38O4] (41.45 %), 2,2-dimethoxybutane (C6H14O2] (13.6 %) and ?-myrcene (C10H16) (6.75 %) based on GC-MS data. 2012 Association of Microbiologists of India. -
A Review on Preprocessing Techniques for Noise Reduction in PET-CT Images for Lung Cancer
Cancer is one of the leading causes of death. According to World Health Organization, lung cancer is the most common cause of cancer deaths in 2020, with over 1.8 million deaths. Therefore, lung cancer mortality can be reduced with early detection and treatment. The components of early detection require screening and accurate detection of the tumor for staging and treatment planning. Due to the advances in medicine, nuclear medicine has become the forefront of precise lung cancer diagnosis. Currently, PET/CT is the most preferred diagnostic modality for lung cancer detection. However, variable results and noise in the imaging modalities and the lung's complexity as an organ have made it challenging to identify lung tumors from the clinical images. In addition, the factors such as respiration can cause blurry images and introduce other artifacts in the images. Although nuclear medicine is at the forefront of diagnosing, evaluating, and treating various diseases, it is highly dependent on image quality, which has led to many approaches, such as the fusion of modalities to evaluate the disease. In addition, the fusion of diagnostic modalities can be accurate when well-processed images are acquired, which is challenging due to different diagnostic machines and external and internal factors associated with lung cancer patients. The current works focus on single imaging modalities for lung cancer detection, and there are no specific techniques identified individually for PET and CT images, respectively, for attaining effective and noise-free hybrid imaging for lung cancer detection. Based on the survey, it has been identified that several image preprocessing filters are used for different noise types. However, for successful preprocessing, it is essential to identify the types of noise present in PET and CT images and the appropriate techniques that perform well for these modalities. Therefore, the primary aim of the review is to identify efficient preprocessing techniques for noise and artifact removal in the PET/CT images that can preserve the critical features of the tumor for accurate lung cancer diagnosis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey
Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information. The Electrochemical Society -
Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges
Cancer is a life-threatening disease, resulting in nearly 10 million deaths worldwide. There are various causes of cancer, and the prognostic information varies in each patient because of unique molecular signatures in the human body. However, genetic heterogeneity occurs due to different cancer types and changes in the neoplasms, which complicates the diagnosis and treatment. Targeted drug delivery is considered a pivotal contributor to precision medicine for cancer treatments as this method helps deliver medication to patients by systematically increasing the drug concentration on the targeted body parts. In such cases, nanoparticle-mediated drug delivery and the integration of artificial intelligence (AI) can help bridge the gap and enhance localized drug delivery systems capable of biomarker sensing. Diagnostic assays using nanoparticles (NPs) enable biomarker identification by accumulating in the specific cancer sites and ensuring accurate drug delivery planning. Integrating NPs for cancer targeting and AI can help devise sophisticated systems that further classify cancer types and understand complex disease patterns. Advanced AI algorithms can also help in biomarker detection, predicting different NP interactions of the targeted drug, and evaluating drug efficacy. Considering the advantages of the convergence of NPs and AI for targeted drug delivery, there has been significantly limited research focusing on the specific research theme, with most of the research being proposed on AI and drug discovery. Thus, the study's primary objective is to highlight the recent advances in drug delivery using NPs, and their impact on personalized treatment plans for cancer patients. In addition, a focal point of the study is also to highlight how integrating AI, and NPs can help address some of the existing challenges in drug delivery by conducting a collective survey. 2023 Das and J. -
A review on the efficacy of artificial intelligence for managing anxiety disorders
Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy. Copyright 2024 Das and Gavade. -
A survey on artificial intelligence for reducing the climate footprint in healthcare
The primary mission of the healthcare sector is to protect from various ailments with improved healthcare services and to use advanced diagnostic solutions to promote reliable treatments for complex diseases. However, healthcare is among the significant contributors to the current climate crisis. Therefore, research is underway to identify various measures to reduce the emissions from advanced healthcare systems. Modern healthcare facilities invest significantly in renewable energy, efficient energy solutions, and intelligent climate cooling and control technologies. Furthermore, innovative technologies like artificial intelligence (AI) are proposed to enable automation for patient health monitoring. With the advances in AI, there are green AI goals for potentially reducing emissions through data-driven and well-optimized models for healthcare. Furthermore, novel machine learning and deep learning techniques are continually proposed for improved efficiency to reduce emissions. Therefore, the scope of the research is to review the potential of AI in healthcare for lowering emission rates and its methodologies, current approaches, metrics, challenges, and future trends to attain a straightforward pathway. 2022 -
Nontoxic photoluminescent tin oxide nanoparticles for cell imaging: Deep eutectic solvent mediated synthesis, tuning and mechanism
Non-toxic and photoluminescent (PL) tin oxide nanoparticle synthesis in Deep Eutectic Solvents (DESs) is being reported herein. Both radiation (electron beam and ? radiation) and solvothermal methods were employed for the synthesis. An electron beam radiation technique proved to be more appropriate in tuning the size and morphology compared to the solvothermal process. Addition of any external oxido-reductive or stabilizing agent could be avoided by the use of Reline (choline chloride?:?urea; 1?:?2) as the host matrix. Detailed analysis of the PL behaviour of the nanoparticles is another important aspect of this study. The oxygen vacancies and tin interstitials responsible for photoluminescence have been identified from the de-convoluted PL spectra of the nanoparticles. Time dependent PL kinetics depicts PL decay at ?1.2 ns due to near band edge emission and at ?3.15 ns due to defect state emission. The synthetic process has been standardized focusing on the size of the particles by varying all possible experimental parameters such as the temperature, concentration of the precursors, reaction time, dose of irradiation and dose rate. Synthesized nanoparticles have been characterized using XRD, XPS and EDX. TEM images illustrate nanomorphological differences obtained in the two methods. The probable mechanism of synthesis (both radiation and thermal) has been proposed based on the results obtained from transient studies using electron pulses and FTIR experiments. Cytotoxicity data demonstrate that the nanoparticles are suitable for application in biological studies involving cells up to a concentration of 10 ?M. Imaging experiments with these photoluminescent nanoparticles exhibit their ubiquitous distribution including the nucleus of the tumour cells, which signifies potential application of these NPs for targeted drug delivery in cancer chemotherapy. Furthermore, the nanoparticles exhibited excellent antioxidant properties in vitro. The findings herein can open up enormous possibilities for more advanced and dedicated research towards using this cheap and versatile nanomaterial in a variety of biomedical applications. 2021 The Royal Society of Chemistry. -
Partner betrayal trauma and trust: Understanding the impact on attachment style and self-esteem
Dismissal of an individual's emotional experience by their significant others can have a massive impact on the psychological well-being of the individual. Betrayal trauma discusses the prevalent social phenomenon and its short- as well as long-term impacts on an individual. This study focused on betrayal trauma in romantic relationships. It aimed to find its relation with an individual's self-esteem and attachment styles, with trust as a mediating variable. The tools used in the study- were the partner betrayal trauma trust scale, adult attachment scale and self-esteem scale, each of which was a self-report measurement scale circulated among young adults in the Indian population. The study consisted of 140 participants (n = 140) with a mean age of 21.7 and a standard deviation (SD) of 2.05. The participants included 85% female, 16% male, 3% of the participants identified as genderfluid, and 2% of the participants preferred not to mention their gender. The results from the study show that betrayal trauma in romantic relationships is related to an individual's attachment style and self-esteem. A positive significant correlation was found between betrayal trauma, self-esteem and attachment style, which reveals the impact of betrayal trauma on the psychological well-being of an individual. These findings may aid mental health practitioners in helping young adults resolve their relationship crises and enhance their lifestyles in India. 2024 Elsevier Masson SAS -
A Review of the Detection of Pulmonary Embolism from Computed Tomography Images Using Deep Learning Methods
Medical imaging has been evolving at a steady pace generating enormous amounts of health data, and the use of deep learning (DL) has helped a great deal in processing the detailed data. Deep learning-based methods are used in different medical imaging tasks to detect and diagnose diseases. For example, medical imaging is used to diagnose pulmonary embolism (PE), a commonly occurring cardiovascular disease with high mortality and prevalence and a low diagnosis rate. According to medical experts, PE has resulted in many deaths because of missed diagnoses for the medical condition. Another critical aspect of the disease is the possibility of permanent lung damage if left untreated. The use of deep learning methods in medical imaging is attributed to their ability to use learning-based methods to process enormous amounts of data. However, there are some unique challenges in the detection of PE. PE is not specific in its clinical presentation and is easily ignored, making it difficult to diagnose. Deep learning-based detection methods help a great deal in the disease detection in miniature sub-branches of the alveoli, and images with noisy artifacts easily compared to manual diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Humanising History through Graphic Narratives: Exploring Stories of Home and Displacement from the North-East of India
Literature from the North-East has responded to national, global and local issues, including questions on immigration and ethnic violence. They have resisted the colonial framework of representation and have invoked a sense of cultural and ethnic particularity (Sarma, 2013). This literature has adopted a multilingual register to respond to 1) patriarchal and 2) ethnonationalist discourses that have a forced and overbearing presence in the everyday lives of people and their stories. These writings evoke an ethno-critical approach that engages otherness and difference in such a way as to provoke an interrogation of and a challenge to our familiar realms of experience and is consistent with a recognition and legitimation of heterogeneity (Sarma, 2013). Select stories from First Hand (Volume II, 2018) - The Lonely Courtyard (2018), My Name is Jahanara (2018), and A Market Story (2019) by Kumdo Yumnam provide the heterogeneity that is characteristic of the works of literature emerging from the North-East, thereby resisting the homogeneity often indicative of the term 'North-East'. The analysis will explore how the selected texts negotiate textuality and visuality in a specific manner to present an archive of everyday life that humanises history. 2022 Aesthetics Media Services. All rights reserved.