Faculty publications @ CTUAP

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A Comprehensive Exploration of the Synergistic Relationship between DMSO and Peroxide in Organic Synthesis
(2024) Kishor Padala
In the realm of organic synthesis, reagents can serve not only as solvents but also as synthons. Dimethyl sulfoxide (DMSO) is recognized for its efficiency in this dual capacity, enabling diverse chemical transformations. DMSO can generate various synthons, including methyl, methylene, methine, oxygen, and methyl sulfoxide, broadening the accessible compound repertoire. Activation of DMSO as a reagent relies heavily on synergies with secondary agents like peroxide, persulfate, or iodine. Recent years have witnessed a surge in innovative synthetic techniques harnessing the synergistic interplay of DMSO and peroxide, leading to environmentally friendly and cost-effective reactions with mild conditions. This review highlights the synergistic effects of DMSO and peroxides (up to 2023), detailing their activation mechanisms and the generation of various synthons, along with numerous reported derivatives. Although this topic has received considerable attention in recent years, there are numerous discrepancies and a plethora of possibilities yet to be explored. We anticipate that this review will significantly support researchers in advancing their innovations to a greater extent in the future.
A Comprehensive Review of Course Recommendation Systems for MOOCs
(2024) Bonthu Kotaiah N
In recent years, many students have accepted Massive Open Online Courses (MOOCs) as a means of education. Due to the enormous number of courses available through MOOC, students need help in identifying and selecting an appropriate course based on their profile and interests. To address this issue, MOOCs incorporate a course recommendation system that generates a list of courses based on the student’s prerequisites. This literature review attempts to detect and assess trends, processes employed, and developments in MOOC course RS through an exhaustive analysis of academic literature published between January 1, 2016, and November 31, 2023. The study includes the various methodologies employed, the datasets used for evaluations, the performance measures used, and the many issues encountered by Recommendation Systems. Literature published in ScienceDirect, Wiley, Springer, ACM, and IEEE, were chosen for review. After applying inclusion and exclusion criteria, 76 articles from the aforementioned databases, including journals, conferences, and book chapters, were selected. The investigation found that methods from Machine Learning and Deep Learning were widely deployed. Traditional approaches like ”content-based filtering, collaborative filtering, and hybrid filtering” were frequently employed in conjunction with other algorithms for more accurate and precise suggestions. It also underlines the need to take data sparsity, the cold start problem, data overload, and user preferences into account when designing a course recommendation system. This paper contributes to examining the cutting-edge course Recommendation System in depth, examining recent developments, difficulties, and future work in this field.
A study of sleep disorders, mental distress, and depression among students during COVID pandemic
(2023) Mandala, Gangu Naidu
The present study has attempted to study the effects of depression, mental distress, and sleep disorders among students during the COVID-19 pandemic. Stress, mental distress, depression, sleep disorders, headaches, loneliness, screen fatigue, and high distress levels are common symptoms observed across the population. The study focused on the students of higher education who have been attending online classes since the inception of COVID-19 virus. The detailed questionnaire was circulated online to 450 students, out of which 323 responded. After filtering the incomplete responses, 286 sample sizes were taken into consideration. The data were analysed using SPSS software, and hypotheses and model testing were performed using the AMOS software. A signifi cant relationship was found between depression, distress, sleep disorders, and student behaviour. Loneliness, lack of physical interaction, and overexposure to screens were found to be major trigger elements affecting students’ mental health. To dilute the effect on students’ behaviour and enhance their mental health, the authors recommend taking precautionary measures by the concerned stakeholders.
A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models
(2023) Bonthu Kotaiah N
Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods. © 2023 IEEE.
Access to Healthcare Among Tribal Population in India: A Cross‐Sectional Household Survey
(2025) Parikipandla, Sridevi
This study addresses significant healthcare access challenges faced by India's 104 million‐strong tribal population, who are among the most disadvantaged and typically live in hilly rural and remote areas with poor health infrastructure and resources. The study aims to examine healthcare access patterns in six tribal areas, focussing on primary health centres (PHCs), to develop a strategy that improves healthcare service accessibility, quality, and utilization for tribal communities. Data were collected from 9837 participants from 24 PHC areas across six states. Most respondents (78.8%; CI: 77.98–79.61) reported monthly visits of government health workers to their habitations, indicating regular healthcare access. Two‐thirds confirmed house visits in the last 3 months, primarily receiving immunisation services. A significant portion (57.38%; CI: 56.39–58.36) received health ed ucation, and a majority (64.29%; CI: 63.33–65.24) were satisfied with the services. About 77% depend on PHC and its healthcare staff, though state‐wise variations exist. Common reasons for not using these services included distance (17.45%; CI: 16.71 18.22) and lack of trust (4.57%; CI: 4.17–5.01). Most respondents were examined by a doctor (60.32%; CI: 59.35–61.29) and received diagnostic tests (27.50%; CI: 26.62–28.39). Walking (21.88%; CI: 21.6–22.71) and auto‐rickshaw/cab (20.23%; CI: 19.44 21.04) were the most common travel modes, with a mean travel time of 34 min. The data highlights the tribal population's preferences and experiences with primary healthcare services. Understanding these patterns can guide the design imple mentation research to further improve accessibility and utilisation of primary healthcare services among these vulnerable populations.
Acidic graphene organocatalyst for the superior transformation of wastes into high-added-value chemicals
(2022) Kalidindi, Suresh Babu
Our dependence on finite fossil fuels and the insecure energy supply chains have stimulated intensive research for sustainable technologies. Upcycling glycerol, produced from biomass fermentation and as a biodiesel formation byproduct, can substantially contribute in circular carbon economy. Here, we report glycerol’s solvent-free and room-temperature conversion to high added-value chemicals via a reusable graphene catalyst (G-ASA), functiona lized with a natural amino acid (taurine). Theoretical studies unveil that the superior performance of the catalyst (surpassing even homogeneous, indus trial catalysts) is associated with the dual role of the covalently linked taurine, boosting the catalyst’s acidity and affinity for the reactants. Unlike previous catalysts, G-ASA exhibits excellent activity (7508 mmolg−1 h−1)andselectivity (99.9%) for glycerol conversion to solketal, an additive for improving fuels’ quality and aprecursorofcommodityandfinechemicals.Notably,thecatalyst is also particularly active in converting oils to biodiesel, demonstrating its general applicability.
Adoption of Artificial Intelligence for Manufacturing Companies
(2024) Appasaba L.V.
Artificial intelligence is gaining popularity in every aspect of business and development of organizations. In manufacturing sector, application of this technology is constantly evolving. However, application of artificial intelligence technology in the manufacturing sector is in the various sectors of manufacturing such as the supply chain management, production testing, quality assurance and engineering. The present study is undertaken to understand the areas of application of artificial intelligence in the manufacturing sector by considering the areas of quality assurance, product design and development, purchase, order-level management, maintenance, logistics and supply chain management. The results of the study show areas of application of artificial intelligence and future directions for enhancing this technology in the manufacturing sector. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Advances in nano silver-based biomaterials and their biomedical applications
(2024) Birudu Ravi Babu
Silver nanoparticles are among the most widely researched and used for nanotechnology-derived structures due to their extraordinary inherent optical properties, chemical stability, catalytic activity, and high conductivity. These idiosyncratic properties can be attributed to their unique physico-chemical characteristics, such as ultrafine sizes, high surface area, diverse shapes, and strong localized surface plasmon resonance. These distinctive features can be tailored using various physical, chemical, and biological synthesis methods. Various physical techniques are viable for producing silver nanoparticles on a large scale, but they suffer from drawbacks such as high-power consumption, expensive set-up, and limited control over nanoparticle size distribution. Chemical methods provide benefits like high yield, consistent shape and size distribution, and cost efficiency, but the residual toxicity of the chemicals involved hinders their biological applications. Biological synthesis approaches effectively overcome the limitations of both physical and chemical methods by eliminating the need for hazardous chemicals, requiring less energy, enabling diverse nanoparticle morphologies, and offering eco-friendliness and exceptional biocompatibility. The novel and promising properties of nanosilver-based biomaterials have been demonstrated to be suitable for a wide range of pharmacological and therapeutic biomedical applications. Their extensive application in wound healing, dentistry, cardiovascular disease treatment, nerve tissue engineering, cancer treatment, and biosensing can be attributed to their inherent antimicrobial and antibiofilm activity, antithrombotic properties, potential for nerve regeneration, photothermal conversion efficiency and sensitivity, respectively. This review discusses the different methods employed for synthesising silver nanoparticles and focuses on using nanosilverbased biomaterials for various biomedical applications.
An exfoliated redox active imide covalent organic framework for metal free hydrogen gas sensing†
(2023) Kalidindi, Suresh Babu
A two dimensional (2D) redox active donor–acceptor COF made of triphenylamine (TPA) and naphthalenediimide (NDI) acted as an efficient hydrogen chemisresistor and performed better than traditional metal oxides. Calculations have shown that the charge transfer interaction between H2 and an NDI linker through a carbonyl functionality enables a change in the resistance of the material upon exposure to H2 gas.
An exploratory study on Indian traditional hospitality "atithi Devo Bhawah" in Uttarakhand's rural areas post pandemic using data science
(2024) Kusum, Kusum
India, the land of art and culture, has always been the center of tourism since ages and people welcome and serve their guest as a god as they believed in "Atithi Devo Bhawah"which means "Guest is like God". Also, the government provides many subsidies and incentives on making people entrepreneurs and encouraging the hotel and tourism industry to come up with the best ambience and hospitality for their guests. Hospitality industry provides lots of opportunity in terms of jobs as well as revenue for development of states like Sikkim, Himachal Pradesh, Uttarakhand etc. Tourist places are mostly situated in the extreme interiors of rural areas led by local entrepreneurs, where life is itself a challenge to deal with the extreme climatic conditions, not well-structured accommodations, poor safety guards and lack of medical facilities that are all intrinsic to meet the basic living standards. In Uttarakhand, such rural areas include religious sites Yamunotri, Gangotri, Kedarnath and Badrinath, Chopta, Chakarata, etc along with some semi and well-developed cities like Haridwar, Rishikesh, Dehradun, Nainital, etc which all remain flooded with tourists in the season. In this COVID-19 phase, the whole industry has suffered badly and left everyone bewildered to make any decision for the future. Central and the state governments had updated some and will update several guidelines and policies for the tourism industry in the coming days. Hotels with good financial conditions and market values can easily adopt new SOP's and technologies to deal with the situations after the pandemic, but it will not be the same for the small or local entrepreneurs. This study covers the current and post COVID-19 problems faced by small and local entrepreneurs of Uttarakhand and proposals and plans needed by them to boost their business and tourism with the same theme of Atithi Devo Bhawah. © 2024 Author(s).
Analyzing the impact of security risk for securing healthcare information systems
(2025) Bonthu, Kotaiah
In recent years, the world has witnessed a dramatic shift towards the digital realm, with the increasing popularity of web-based applications taking center stage [1-3]. These applications have not only transformed the way we interact with technology but have also beckoned the attention of a shadowy and sophisticated counterpart-the hacker community [4-7]. In this dynamic landscape, the security risks associated with web-based services, particularly within the critical domain of healthcare, are mounting at an alarming rate [8]. © 2025 selection and editorial matter, Suhel Ahmad Khan, Mohammad Faisal, Nawaf Alharbe, Rajeev Kumar and Raees Ahmad Khan. All rights reserved.
Community-based tourism as a sustainable direction for the tourism industry: Evidence from the Indian sundarbans
(2024) Chatterjee, Prama
The evolving global tourism industry increasingly emphasizes sustainable practices benefiting both the environment and local communities. Community-Based tourism (CBT) in the Indian Sundarbans exemplifies this approach, preserving cultural and natural heritage while fostering grassroots socio-economic development. Through qualitative research methods including in-depth interviews, participant observations, and document analysis, this study reveals CBT's positive impact. It showcases successful cultural heritage preservation, community engagement in conservation, local empowerment, and economic diversification through tourism activities. Furthermore, CBT facilitates cultural exchange, education, and responsible tourism practices. This research provides nuanced insights into CBT's multifaceted benefits, offering valuable lessons for sustainable tourism development in ecologically sensitive areas worldwide. By raising awareness among communities, tourism planners, and policymakers, this study aims to support successful implementation of CBT initiatives in diverse tourist destinations. © 2024, IGI Global. All rights reserved.
COVID-19 vaccine acceptance and hesitancy in Indian context: a systematic review and meta analysis
(2024) Parikipandla, Sridevi
Vaccination against COVID-19 is vital for achieving herd immunity, and the Government of India has adopted several strategies to achieve coverage. Vaccine hesitancy was identified as a potential obstacle in combating COVID-19. This study aimed to review the COVID-19 vaccine acceptance and hesitancy, and factors associated with vaccine hesitancy based on studies conducted in Indian populations. The data sources (PubMed, Scopus, and Google Scholar) were searched by following PRISMA guidelines, and the search was done in September 2022. We performed a meta-analysis through a random effect model to estimate pooled hesitancy rate with 95% confidence intervals (CI). A total of 3,339 records were searched, of which 46 studies were found to be eligible for inclusion in the review. The included studies covered 65,551 respondents, 55% were female. Studies reported COVID-19 vaccine acceptance rate of 65.7% in January-February 2021, which increased to 92.8% in May-August 2021. Likewise, the rate of vaccine hesitancy in December 2020 was 37%, dropping to 12.1% through November 2021. The estimated pooled COVID-19 vaccine hesitancy was 31% [95% CI: 27% - 36%, I2 = 99.3%]. Most studies highlighted that fear of the vaccine's side effects, efficacy, and safety were major barriers to vaccine acceptance. However, as the review indicates, it is important to consider and address all factors contributing to vaccine hesitancy.
Cross-Cultural Adaptation, Reliability and Validity of MOS Short Form Health Survey (SF-36) in Sickle Cell Disease Patients in India
(2025) Parikipandla, Sridevi
Purpose Patient-reported outcomes are critical for chronic illnesses like sickle cell disease (SCD), as its clinical manifestation is symptom-based and subjective. In order to conduct this assessment, valid and reliable self-reported tools are required. There is a lack of evidence for the reliability and validity assessment of even generic quality of life tools such as SF-36 in SCD patients. Hence, this study was conducted to address this knowledge gap. Methods This study utilised cross-sectional data from 125 SCD patients selected for a multi-centric study conducted across five SCD-endemic districts. Cross-cultural adaptation and translation of the tool were done in the local languages of these districts. Statistical analyses for floor and ceiling effects, item-internal consistency, interscale correlations and internal consistency were performed. Results Role Limitations due to Physical Problems and Social Functioning are the most affected and least affected areas of SCD patients’ lives, respectively. Item internal consistency was established for all subscales except General Mental Health, which recorded the highest distribution of responses. For subscale Vitality, Energy and Fatigue, only Cronbach’s α coefficient value (0.69) was marginally less than the cutoff. Conclusion Item level and scale level findings found SF-36 to be a reliable and valid tool to use in SCD patients in India. Further research with an adequate sample size is recommended to report conclusive evidence for other local languages.
Deep learning based Identification of Solid Waste Management in Smart Cities through Garbage Separation and Monitoring
(2023) Suryanarayana N.V.S.
The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques. © 2023 IEEE.
Design, Synthesis, Antimicrobial and Anticancer Activity of some Novel Benzoxazole-Isatin Conjugates
(2022) PV. Parvati Sai Arun
A series of novel benzoxazole-isatin conjugates were synthesized by treating 2-amino benzoxazole with 5 and 7 substituted isatin derivatives and were screened for in vitro antimicrobial and cytotoxic activities. The results showed that all the synthesized compounds shown mild to potent antibacterial activity. The MIC values were found between 10 and 100 µg/ml against tested bacterial and fungal organisms. Among all the compounds, 3d & 3c showed good antimicrobial. In vitro cytotoxic activities were evaluated by MTT assay of all the test compounds against the different human cancer cell lines. The compounds having substitution with electron-withdrawing groups (halides) at the 5th position on the isatin ring showed the most significant biological activity than substituted at the 7th position. The molecular docking interactions have shown good binding interactions with the protein targets glucosamine-6-phosphate synthase (GlcN-6-P synthase) and telomerase.
Editorial: Climate change and stress mitigation strategy in plants
(2023) Kumar, Anirudh
Facial emotion recognition using geometrical features based deeplearning techniques
(2023) Bonthu Kotaiah N
In recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. As a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. Researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. Hence, this paper proposes novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using VGG-19s (FCNN). Here, we utilize Haarcascade to detect the subject face and determine the distance and angle measurements. The entire process is to classify the facial ex-pressions based on extracting relevant features with the normalized angle and distance measures. The experimental analysis shows high accuracy on the MUG dataset of 94.22% and 86.45% on GEMEP datasets, respectively
Gender inclusivity in tourism sector and measures towards an equitable workplace
(2024) Mishra, Jitendra Mohan
As tourism is a growing sector in all developing and developed nations, ensuring gender equality in tourism will contribute to reducing gaps between gender roles and responsibilities that exist in the regions. The SOGIESC (Sexual Orientation, Gender Identity, Gender Expression & Sex Characteristics), identities like LGBTQ+ (Lesbians, Gay, Bisexual, Transgenders, Queer.), and women are underrepresented in all forms of development operations and seeking reservation in employment. The tourism and hospitality industry has yet to take measures to include women and LGBTQ+ by providing a dignified and supportive employment environment (Badgett et al., 2013). Women are known for their natural closeness to nature, maintaining and conserving resources and preserving culture. Despite their innate nature to adopt sustainable practices, empirical evidence claims that the participation of women compared to men in the tourism sector is disproportionate. Though skilled, semi-skilled, and unskilled people can be easily absorbed in this sector, women and SOGIESC identities still face difficulties engaging in tourism jobs, which becomes a hurdle in the nation's social and economic sustainability aspirations. This study examines the present scenario of sectoral inclusivity issues, i.e., formal and informal sectors in tourism. The scenario of gender and sustainability has been examined through case studies and literature reviews, and few observations have been made based on empirical reasoning. Also, the study identifies the issues and challenges in mobilising women and SOGIESC identities in the tourism sector and examines linkages between inclusive tourism and sustainable development. Accordingly, the study suggests future directions addressing gender inequality in the sector and incorporating more equal opportunities for women and SOGIESC identities to accomplish the goal of sustainability in all dimensions. © 2024 Nova Science Publishers, Inc. All rights reserved.
Global Sickle Cell Disease and Impact on School Education in Children: A Focus on Interventions for Improving Academic Performance
(2025) Parikipandla, Sridevi
Sickle cell disease (SCD) is a heritable (genetic) disorder and is marked by sporadic episodes of severe pain and hosts other health issues. About 5% of people worldwide suffer from this most common haemoglobinopathy.More than 7% of expectant mothers have haemoglobinopathies in their genetic composition. Each year 330,000 newborns are born with haemoglobinopathies. Nearly 80% of them are born in Africa, Nigeria, Congo, and India. Children with SCD have health challenges that impact their education. Absence from school, grade retention, academic performance and dropouts from school are major issues for children with SCD in the context of academic goals. The main objective of this paper is to understand how SCD affects the school performance of children globally, impact on their education and possible interventions and strategies to reduce school absenteeism. Studies suggest that parents, patients, educators and healthcare professionals can work together to comprehend and manage the effects of SCD on student attendance. Awareness campaigns for educators and care givers, remedial classes for SCD children in schools and policies aligning to the education of children with major disabilities like SCD may compensate the attendance and academic performance of the SCD children.

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