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Platinum decorated polythiophene modified stainless steel for electrocatalytic oxidation of benzyl alcohol
Abstract: Platinum nanoparticles were electrochemically deposited on conducting polymer polythiophene (PTh)-coated stainless steel (SS) substrate. A thin layer of PTh on the steel substrate facilitates uniform deposition of Pt nanoparticles on the substrate, thereby improving the surface area to a great extent. The electrochemical properties of the modified electrodes were analyzed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The physicochemical properties of the modified electrodes were investigated by Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction spectroscopy (XRD), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR). The proposed method has been applied for the electrocatalytic oxidation of benzyl alcohol in the presence of a mediator, 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO). Cyclic voltammetric studies reveal that the electrocatalytic activity of PtPTh/SS electrode is higher than that of PTh/SS electrode toward the conversion of benzyl alcohol to benzaldehyde. Graphic abstract: [Figure not available: see fulltext.]. 2019, Springer Nature B.V. -
Platt number of total graphs
The degree of an edge uv is defined as the number of edges incident on vertices u and v other than itself. The Platt number of a graph is the sum of degrees of all its edges. In this paper, the concept of degree of an edge is analysed in social networks. The Platt number is investigated in certain classes of graphs and their total graphs. Also related bounds are proposed on connected graphs. An algorithm developed to determine the Platt number of any connected graph is presented. 2018 Academic Publications. -
Play and Play Spaces for Global Health, Happiness, and Well-Being
Play has a significant role in an individuals learning and holistic development. Play and recreation are a need and right. Research on play indicates that the significance of play is neglected among the current generation. Play spaces are shrinking, and physical play is becoming extinct in most communities. This current scenario may or have led to poor physical and mental health outcomes. The proposed book chapter aims to present play and play spaces in physical and mental health. The literature of play theories in child development shows the role of play in socioemotional, physical, and cognitive development. The current paper brings together literature on play across the lifespan, highlighting how play and recreation impacts children, youth, adults, and older adults physical and mental health. The change in lifestyle patterns has contributed to the neglect of play and recreation. The paper throws light on the need for the attention of professionals and policymakers for interventions and advocacy at both local and global levels in promoting play and preserving natural play spaces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022. -
Play therapy as a rehabilitative measure among child survivors of bonded labour
A bonded labour condition is customarily because of relocation of persons due to the situations that are obligatory in nature. Bonded labour, which is characterized by a long-term affiliation between employer and employee, is usually congealed over a loan, and is entrenched complexly in India’s socio-economic culture - a culture that is a creation of class relations, a colonial history, and tenacious scarcity of resources among many citizens. The children living in such conditions face a lot of mistreatment and go through undeniable exploitation. The children present at the facility may or may not work with the parents yet go through a lot of pain, distress and abuse as the journey of cruelty and suffering is just the same as what their parent’s ordeal with. Children as young as 4 Years old are molested, beaten and abused on an average basis traumatizing the children before and after the rescue. These children are not permitted to enroll in schools as they do not have identity proofs or birth certificates. The only way of addressing the subject of emotional, physical and mental turbulence of the child in this perimeter is by enforcing play as a rehabilitative measure to help with past experiences and impending consequences. Play is essential to development because it contributes to the cognitive, physical, social, and emotional well-being of children and youth. Play also offers an ideal opportunity for parents or facilitators to engage fully with the children. Play allows children to use their creativity while developing their imagination, dexterity, and physical, cognitive, and emotional strength. When forms of play like puppetry, art, story-telling etc are added the child conjectures unpleasant feelings which she cannot hide the child is better able to act differently in relation to what he/she is feeling. The study aims to see if play therapy is a technique of rehabilitating child survivors of bonded labour. -
Playing With Differences: Social-Emotional Learning to Reduce Bullying and Promote Inclusivity
Inclusivity is a key indicator for the achievement of Sustainable Development Goals by 2030. At the school level, bullying mars the appreciation of individual differences and acts as a barrier to inclusivity. The use of social-emotional learning is recommended to promote inclusivity and reduce bullying. Play is an enjoyable form of learning social-emotional skills for all age groups. It is also known to promote positive peer relationships and enable learners to develop a wide range of skills. Hence, educators can incorporate play through digital pedagogical tools and grade-wise play activities to engage students. Resultantly, learners can become emotionally intelligent individuals, sensitive to and respectful of differences. 2023 by IGI Global. All rights reserved. -
Plugged in, tuned out: How earbuds on roads are becoming a silent menace
[No abstract available] -
Pluronic F127 and Dopamine Functionalized Fe2O3 Nanocomposites: A Multifunctional Polymer-Based Platform for Anticancer, Antibacterial, and Antioxidant Applications
Cancer, bacterial infections, and oxidative stress continue to pose serious global health challenges, necessitating the development of multifunctional therapeutic agents. Iron oxide (Fe2O3) nanoparticles were selected as the core material owing to their intrinsic biocompatibility, redox activity, and established biomedical relevance. To overcome the limitations of particle aggregation and poor solubility, pluronic F127 (a biocompatible triblock copolymer) was employed as a stabilizer, while dopamine was introduced as a surface modifier to enhance functionalization, improve dispersion, and facilitate cellular uptake. The resulting Fe2O3-PF127-DOP nanocomposites were thoroughly characterized using XRD, FTIR, SEM, TEM, PL, and XPS analyses, confirming successful functionalization and enhanced stability. Antioxidant assays revealed 79.24% activity at 20 ?g/mL, comparable to Vitamin C, highlighting its antioxidant activity. Antibacterial studies against multiple pathogenic strains, including Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Shigella dysenteriae, and Vibrio cholerae, showed markedly larger inhibition zones for Fe2O3-PF127-DOP than for Fe2O3, confirming its broad-spectrum antibacterial potential. Fe2O3-PF127-DOP exhibited superior cytotoxicity against HCT-116 colon cancer cells (IC50 = 15.3 ?g/mL) compared to Fe2O3 (IC50 = 17.2 ?g/mL), attributed to improved uptake and ROS-mediated apoptosis. Importantly, cytocompatibility studies on L929 fibroblast cells revealed high cell viability of 83% and 86% for Fe2O3 and Fe2O3-PF127-DOP, respectively, demonstrating the nanocomposite's biocompatibility. Overall, this study demonstrates that strategic functionalization of Fe2O3 with pluronic F127 and dopamine yields a stable, multifunctional nanocomposite with significant anticancer, antioxidant, and antibacterial applications. 2025 John Wiley & Sons Ltd. -
Pluronic f127 encapsulated titanium dioxide nanoparticles: Evaluation of physiochemical properties for biological applications
The infections caused by bacteria that are resistant to drugs are very bad for human health and kill thousands of people every year. Also, both human actions and natural processes make surface waters more likely to have drug-resistant bacteria grow and spread in the environment. Because of this, researchers need to find new ways to treat bacterial infections quickly becoming resistant to drugs as soon as possible. Drug delivery systems based on nanoparticles have enhanced biocompatibility, biocidal properties, pharmacokinetics, tumor targeting, and stability while exhibiting non-toxicity to normal cells and overcoming drug resistance. In the present work, the pluronic-F127 encapsulated titanium dioxide (PF127/TiO2) nanoparticles (NPs) were prepared by a green process using Morinda citrifolia leaf extract. X-ray diffraction patterns (XRD) revealed that synthesized NPs exhibit an anatase structure. FESEM and TEM images of synthesized PF127/TiO2 NPs showed a polymorphic structure and an average particle size of 5060 nm. The chemical composition of the prepared NPs, which included elements like carbon, titanium, and oxygen, was identified using the EDAX spectrum. With the DLS spectrum, the hydrodynamic sizes of PF127/TiO2 NPs were 176 nm. In the FTIR spectrum, the metal oxide stretching bands like O-Ti-O were located at 664 cm?1 for PF127/TiO2 NPs. The surface defects, including Ti and O vacancies, were studied using the photoluminescence spectrum. The prepared PF127/TiO2 NPs exhibited significant microbial activity for inhibiting hospital pathogenic bacterial and fungal strains, specifically (Staphylococcus aureus) S. aureus, (Streptococcus pneumoniae) S. pneumonia, (Klebsiella pneumoniae) K. pneumonia, (Shigella dysenteriae) S. dysenteriae and (Candida albicans) C. albicans. In addition, PF127/TiO2 NPs had highly anti-cancer properties against human blood cancer (MOLT-4) cell lines. Furthermore, we found that synthesized PF127/TiO2 NPs exhibited anti-inflammatory activity. 2023 -
Pluronic F127-functionalized cerium fluoride nanocomposite: synthesis, characterization, and its enhanced antibacterial activities
The persistence of pathogenic bacteria, rising antibiotic resistance, and the ongoing need for effective anticancer agents necessitate the development of advanced multifunctional therapeutic strategies. In this study, CeF? nanoparticles and PF127-functionalized CeF? (CeF?PF127) nanocomposites were synthesized via a facile wet chemical route and systematically characterized for their structural, optical, and biological properties. XRD confirmed the formation of phase-pure hexagonal CeF? with crystallite sizes of 31nm (CeF?) and 27nm (CeF?PF127), while SAED revealed lattice fringes of approximately 0.27nm (CeF?) and 0.29nm (CeF?PF127). EDAX and XPS validated the Ce/F stoichiometry and the successful surface functionalization with PF127. Optical analyses showed a slight reduction in band gap from 3.15 to 3.09eV upon polymer coating, and PL spectra indicated enhanced defect-related emission in CeF?PF127, suggesting stabilization of Ce3? ions and oxygen vacancy sites. Biological evaluations demonstrated that CeF?PF127 exhibited superior antioxidant activity (DPPH assay) and enhanced anticancer efficacy against MG-63 osteosarcoma cells, with lower IC?? values over 2472h. Antibacterial studies against S. aureus, B. subtilis, K. pneumoniae, and S. dysenteriae revealed larger inhibition zones (1820.5mm) and improved MIC/MBC values (600/1000gmL?1) compared to bare CeF?. Biocompatibility assessment using L929 fibroblasts confirmed cell viability exceeding 80% for both samples. Collectively, these results demonstrate that CeF?PF127 is a stable, multifunctional nanocomposite with promising potential for biomedical applications. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Pluronic F127Folic Acid Modified Nickel Oxide Nanocomposites via a Facile One-Pot Approach for InVitro Anticancer, Antibacterial, and DPPH Radical Scavenging Studies
Drug-resistant bacteria and cancer remain major challenges in healthcare, highlighting the need for multifunctional nanomaterials. In this study, folic acid- and Pluronic F127-modified nickel oxide nanocomposites (NiOPF127FA) were synthesized via a one-pot method, and their invitro antibacterial, antioxidant, and anticancer properties were evaluated. XRD analysis showed a crystallite size of 19.42 nm for NiOPF127FA, while PL spectra exhibited a green emission peak at 507 nm, indicative of structural defects in the NiO lattice. NiOPF127FA displayed enhanced antibacterial activity against MRSA and Candida albicans compared to bare NiO, as evidenced by larger inhibition zones and lower MIC and MBC values. The DPPH assay demonstrated improved radical scavenging activity of the modified nanocomposites, likely related to their smaller size, higher surface area, and surface defect-mediated electron transfer. Invitro anticancer studies using rat C6 glioblastoma cells revealed dose-dependent decreases in cell viability, with IC50 values of 12.3 ?g/mL for NiO and 9.6 ?g/mL for NiOPF127FA. Fluorescence staining with AO/EB and DAPI indicated morphological changes in nuclei and alterations in MMP, consistent with induction of cell death. Overall, these findings suggest that NiOPF127FA nanocomposites exhibit improved invitro biological activity, providing a foundation for further preclinical investigations of their potential biomedical applications. 2026 John Wiley & Sons Ltd. -
PM2.5 Prediction Models: A Systematic and Comparative Review
Airborne particulate matter (PM) is an amalgam of liquid droplets found in air and microscopic solid particles. The particles differ in size, shape, and chemical composition. PM has a significant impact on climate and precipitation and adversely affects human health as it can infiltrate the lungs and enter the cardiovascular system. This article explores the various PM2.5 prediction models proposed to date to predict a region's particulate matter (PM2.5) concentration. As prediction techniques evolve rapidly, this study aims to assess the various methodologies proposed for predicting PM2.5 concentration in different regions according to the factors that influence it. Various machine learning, deep learning, and statistical models have been proposed to predict hourly or daily PM2.5 concentrations in the air. The previously proposed models were compared using the RMSE, MAE, and R2 scores as the evaluation metrics. Since most of these models were region-specific and mostly used different parameters for the prediction, the comparison highlighted the need for a generalized model that could be fine-tuned based on the parameters of a particular region. Thus, this review points to the need for a high-accuracy generalized prediction model for PM2.5 that adapts to the diverse parameters region-wise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
PMFRO: Personalized Mens Fashion Recommendation Using Dynamic Ontological Models
There is a thriving need for an expert intelligent system for recommending fashion especially focusing on mens fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for mens recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the mens fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the mens fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending mens fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pneumonia classification from chest X-rays using significant feature selection and machine learning
The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%1, 96%2, and 94%2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Poetry writing can enhance character formation in school students through the application of rational emotive behavior theory
This study explores the role of poetry writing and the ABC model of Rational Emotive Behavior Therapy (REBT) in promoting character development and moral behavior among students. The study used DAS SF1 and SF2 and assessed poetic talent using a poetry attitude and knowledge test. Forty-eight students were selected using the purposive method from five Convent schools, along with five teachers. Following a two-month intervention of 30 sessions, the researcher conducted interviews to evaluate the methods efficacy. Content analysis of the integrated poetry writing and REBT program was performed using NVivo, while both quantitative and qualitative data were analyzed using SPSS. The study found significant positive correlations between the integration of REBT and improved behavior, suggesting that incorporating poetry writing into the curriculum can enhance students behavior and attitudes. 2024 National Association for Poetry Therapy. -
Poetry writing can enhance character formation in school students through the application of rational emotive behavior theory
This study explores the role of poetry writing and the ABC model of Rational Emotive Behavior Therapy (REBT) in promoting character development and moral behavior among students. The study used DAS SF1 and SF2 and assessed poetic talent using a poetry attitude and knowledge test. Forty-eight students were selected using the purposive method from five Convent schools, along with five teachers. Following a two-month intervention of 30 sessions, the researcher conducted interviews to evaluate the methods efficacy. Content analysis of the integrated poetry writing and REBT program was performed using NVivo, while both quantitative and qualitative data were analyzed using SPSS. The study found significant positive correlations between the integration of REBT and improved behavior, suggesting that incorporating poetry writing into the curriculum can enhance students behavior and attitudes. 2024 National Association for Poetry Therapy. -
Polarity detection on real-time news data using opinion mining
Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the users opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done. 2020 The authors and IOS Press. -
Policies and metrics for schedulers in cloud data-centers using CloudSim simulator
Todays cloud technology consumers must address escalating computing and storage demands for services and applications. However, decision-making on provisioning and scheduling is challenging due to varying workflow demands within Infrastructure as a Service (IaaS). This study formulates an optimization problem with multiple objectives to identify optimal policies, employing heuristic metrics through cloud simulation similar to AWS EC2 instances. Experiments involve two task scheduler types, time-shared and space-shared, aimed at minimizing execution time and cost. The study introduces two novel algorithms, SLB and MinMax, for comparison with standard algorithms. It emphasizes the importance of precise quantification of uncertainty in cloud storage allocation and highlights the state-of-the-art policies and metrics achieved through virtualization techniques. The studys novelty lies in simulating both policies at two levels and proposing a novel algorithm for multi-objective optimization while providing cost and time measurements. Contributions include experimenting with various combinations, applying heuristics to entire data center entities, proposing a novel algorithm, and offering cost and time measurements for the optimizations. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Policies and metrics for schedulers in cloud data-centers using CloudSim simulator
Todays cloud technology consumers must address escalating computing and storage demands for services and applications. However, decision-making on provisioning and scheduling is challenging due to varying workflow demands within Infrastructure as a Service (IaaS). This study formulates an optimization problem with multiple objectives to identify optimal policies, employing heuristic metrics through cloud simulation similar to AWS EC2 instances. Experiments involve two task scheduler types, time-shared and space-shared, aimed at minimizing execution time and cost. The study introduces two novel algorithms, SLB and MinMax, for comparison with standard algorithms. It emphasizes the importance of precise quantification of uncertainty in cloud storage allocation and highlights the state-of-the-art policies and metrics achieved through virtualization techniques. The studys novelty lies in simulating both policies at two levels and proposing a novel algorithm for multi-objective optimization while providing cost and time measurements. Contributions include experimenting with various combinations, applying heuristics to entire data center entities, proposing a novel algorithm, and offering cost and time measurements for the optimizations. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Policy Intervention Towards Ecological Balance Through Reduction of Carbon Footprint in India
India is experiencing unprecedented urbanisation and industrialisation with consequential carbon emission, which significantly increases the gap between international commitments and reality. This paper explores the recent policy initiatives aimed at achieving promised indicators and engage in recent developments on decarbonisation. The paper further identifies the gap between sustainable development and economic advancement. The paper reflects on the recent carbon-mitigating policies adopted by India. A doctrinal research strategy has been used to address this issue. International, foreign and domestic policies have been discussed to understand the impact of these policies on climate change crisis. Models from other nations were also evaluated, taking into consideration India's unique socio-economic context. This research aims to identify adaptable techniques to effectively manage emissions in critical sectors, including energy, transport and industry. The paper proposes a set of concrete policy interventions that can promote ecological balance while sustaining economic development. Once the regulatory bodies initiate appropriate implementation of these policies, the carbon emissions in India shall be more resilient and effective. 2026 International Union of Biochemistry and Molecular Biology, Inc.