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Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Fractional and memory effects on wave reflection in pre-stressed microstructured solids with dual porosity
The present work investigates the influence of fractional-order derivative and memory-dependent derivative on the behavior of various waves reflected at the free surface of a size-dependent, pre-stressed, microstructured thermoelastic solid with a dual porosity framework. A generalized MooreGibsonThomson (MGT) model, incorporating higher-order terms and memory effects, is adopted to describe the complex heat transfer behavior within the material. A nonlocal framework based on Eringen's theory is utilized to derive the basic relations of the considered medium. An examination of the non-dimensionalized governing equations is conducted employing the normal mode technique to provide accurate solutions. The research demonstrates the presence of six separate wave modes that travel at varying speeds within the medium. The energy and amplitude ratios of reflected waves are determined by applying suitable boundary conditions. The influence of varying incidence angles on the reflected wave energy distribution is investigated numerically and visualized using MATLAB software. The study reveals that the energy ratios of the reflected waves are sensitive to the fractional-order parameter, kernel functions, initial stress, and nonlocality parameter. The analysis suggests a conservative reflection process, indicating minimal energy loss during reflection. Key findings and their implications for relevant scenarios are presented in the conclusion. Comparisons with existing models for certain cases demonstrate good agreement, supporting the validity of the present model. 2025 Elsevier Masson SAS -
Crowd Monitoring System Using Facial Recognition
The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and manymore. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The importance of strategic agility and resilience in work-life balance
This chapter's objective is to analyze agility and resilience which are essential qualities of work-life balance. Similarly, both enable individuals to play the important role of both professional and personal responsibilities effectively. In this chapter, the author has mentioned the importance and the role of strategic agility which describes the ability to predict and respond quickly to changes and challenges in the work environment. Also, all these involve being acceptable, adaptable, flexible, and open to new ideas and approaches. In the work-life balance framework, strategic agility supports individuals to be proactive, positive, and efficient enough to manage their time and energy. It helps individuals prioritize their tasks, allocate resources properly, and enhance their understanding of where to invest their efforts. 2024, IGI Global. All rights reserved. -
Therapeutic potential of marine macrolides: An overview from 1990 to 2022
The sea is a vast ecosystem that has remained primarily unexploited and untapped, resulting in numerous organisms. Consequently, marine organisms have piqued the interest of scientists as an abundant source of natural resources with unique structural features and fascinating biological activities. Marine macrolide is a top-class natural product with a heavily oxygenated polyene backbone containing macrocyclic lactone. In the last few decades, significant efforts have been made to isolate and characterize macrolides' chemical and biological properties. Numerous macrolides are extracted from different marine organisms such as marine microorganisms, sponges, zooplankton, molluscs, cnidarians, red algae, tunicates, and bryozoans. Notably, the prominent macrolide sources are fungi, dinoflagellates, and sponges. Marine macrolides have several bioactive characteristics such as antimicrobial (antibacterial, antifungal, antimalarial, antiviral), anti-inflammatory, antidiabetic, cytotoxic, and neuroprotective activities. In brief, marine organisms are plentiful in naturally occurring macrolides, which can become the source of efficient and effective therapeutics for many diseases. This current review summarizes these exciting and promising novel marine macrolides in biological activities and possible therapeutic applications. 2022 The Authors -
Engineering the functionality of porous organic polymers (POPs) for metal/cocatalyst-free CO2 fixation at atmospheric conditions
Carbon dioxide (CO2) utilization as C1 feedstock under metal/co-catalyst-free conditions facilitates the development of eco-friendly routes for mitigating atmospheric CO2 concentration and producing value-added compounds. In this regard, herein, we designed a bifunctional porous organic polymer (POP-1) by incorporating acidic (-CONH) and CO2-philic (-NH/N) sites by judicious choice of organic precursors. Indeed, POP-1 exhibits high heat of interaction for CO2 (40.2 kJ/mol) and excellent catalytic performance for transforming carbon dioxide to cyclic carbonates, a high-value commodity chemical with high selectivity and yield under metal/cocatalyst/solvent-free atmospheric pressure conditions. Interestingly, an analogous polymer (POP-2) that lacks basic (-NH/N) sites showed lower CO2 interaction energy (31.6 kJ/mol) and catalytic activity than that of POP-1. The theoretical studies further supported the superior catalytic activity of POP-1 in the absence of Lewis acidic metal and cocatalyst. Notably, POP-1 showed excellent reusability with retention of catalytic performance for multiple cycles of usage. Overall, this work presents a novel approach to metal/cocatalyst/solvent-free utilization of CO2 under eco-friendly atmospheric pressure conditions. 2024 Elsevier Ltd -
Efficient chemical fixation of CO2from direct air under environment-friendly co-catalyst and solvent-free ambient conditions
The capture and conversion of CO2from direct air into value-added products under mild conditions represents a promising step towards environmental remediation and energy sustainability. Consequently, herein, we report the first example of a Mg(ii)-based MOF exhibiting highly efficient fixation of CO2from direct air into value-added cyclic carbonates under eco-friendly co-catalyst and solvent-free mild conditions. The bifunctional MOF catalyst was rationally constructed by utilizing an eco-friendly Lewis acidic metal ion, Mg(ii), and a nitrogen-rich tripodal linker, TATAB. The MOF possesses a high BET surface area of 2606.13 m2g?1and highly polar 1D channels decorated with a high density of CO2-philic sites which promote a remarkably high CO2uptake of 50.2 wt% at 273 K with a high heat of adsorption value of 55.13 kJ mol?1. The high CO2-affinity combined with the presence of a high density of nucleophilic and Lewis acidic sites conferred efficient catalytic properties to the Mg-MOF for chemical fixation of CO2from direct air under environment-friendly mild conditions. The remarkable performance of the Mg-MOF for the fixation of CO2from direct air was further supported by in-depth theoretical calculations. Moreover, the computational studies provided an insight into the mechanistic details of the catalytic process in the absence of any co-catalyst and solvent. Overall, this work represents a rare demonstration of carbon capture and utilization (CCU) from direct air under eco-friendly mild conditions. The Royal Society of Chemistry 2021. -
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. -
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. -
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 -
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. -
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 -
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. -
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. -
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 -
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. -
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. -
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. -
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. -
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.