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Fungal Mushrooms: A Natural Compound With Therapeutic Applications
Fungi are extremely diverse in terms of morphology, ecology, metabolism, and phylogeny. Approximately, 130 medicinal activities like antitumor, immunomodulation, antioxidant, radical scavenging, cardioprotective and antiviral actions are assumed to be produced by the various varieties of medicinal mushrooms. The polysaccharides, present in mushrooms like ?-glucans, micronutrients, antioxidants like glycoproteins, triterpenoids, flavonoids, and ergosterols can help establish natural resistance against infections and toxins. Clinical trials have been performed on mushrooms like Agaricus blazei Murrill Kyowa for their anticancer effect, A. blazei Murrill for its antihypertensive and cardioprotective effects, and some other mushrooms had also been evaluated for their neurological effects. The human evaluation dose studies had been also performed and the toxicity dose was evaluated from the literature for number of mushrooms. All the mushrooms were found to be safe at a dose of 2000mg/kg but some with mild side effects. The safety and therapeutic effectiveness of the fungal mushrooms had shifted the interest of biotechnologists toward fungal nanobiotechnology as the drug delivery system due to the vast advantages of nanotechnology systems. In complement to the vital nutritional significance of medicinal mushrooms, numerous species have been identified as sources of bioactive chemicals. Moreover, there are unanswered queries regarding its safety, efficacy, critical issues that affect the future mushroom medicine development, that could jeopardize its usage in the twenty-first century. Copyright 2022 Chugh, Mittal, MP, Arora, Bhattacharya, Chopra, Cavalu and Gautam. -
Spider Monkey Crow Optimization Algorithm with Deep Learning for Sentiment Classification and Information Retrieval
The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively. 2013 IEEE. -
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Presence or absence of Dunning-Kruger effect: Differences in narcissism, general self-efficacy and decision-making styles in young adults
The Dunning-Kruger effect is a cognitive bias in which individuals who are unskilled in certain domains overestimate their ability and are unaware of it. Past studies have focused on establishing the effect but have not looked into associated factors. This study aimed to see if the Dunning-Kruger effect has any influence on an individuals narcissism, general self-efficacy and decision making styles especially in young adults in the Indian population. The Dunning- Kruger effect was established using scores from the Cognitive Reflection Task and the Rationality scale from Rational Experiential Inventory, keeping the Unskilled and Unaware phrase under consideration, while establishing cut-offs. The participants were also divided into three groups - the group that was able to estimate their performance, the group that over-estimated their performance and the group that underestimated their performance. The dependent variables were measured using the NPI-16, General Self-Efficacy Scale and Flinders Decision-Making Styles Questionnaire. The Kruskal-Wallis H results showed that there is a significant difference between the group with Dunning-Kruger effect, without Dunning-Kruger effect and the group that underestimated their performance with reference to Narcissism, General Self-Efficacy, Vigilance and Hypervigilance decision-making styles. The Mann-Whitney U results further indicated a significant difference in Narcissism and Vigilance, between the groups that overestimated their performance and the group that accurately estimated their performance. However, there was no correlation between the CRT discrepancy scores of the individuals with Dunning-Kruger effect and the dependent variables. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
The Role of Gratitude as a Moderator of the Relationship Between Belief in a Just World and Forgiveness Among Middle-Aged Adults in India
This research explores the relationship between personal belief in a just world (PBJW), gratitude, and forgiveness within the context of middle-aged adults in India. While prior research has established links between PBJW and forgiveness, this investigation delves deeper, examining how gratitude moderates these relationships. The primary objective is to unveil how gratitude moderates the connection between PBJW and forgiveness, filling a significant research gap within the Indian context. The researchers collected data from 386 middle-aged Indian adults through online and offline surveys. The study reveals a positive but weak correlation between PBJW and forgiveness. Gratitude significantly moderates this relationship, amplifying the impact of PBJW on forgiveness. These discoveries offer fresh insights into the complex dynamics underlying forgiveness processes among middle-aged adults in India, addressing a critical gap in the existing research landscape within this cultural context. Practical implications are drawn for counselors and formators that support efforts to promote forgiveness and enhance interpersonal harmony and psychological health. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE. -
Nanomaterials for A431 Epidermoid Carcinoma Treatment
Malignancy is the ancient sickness that causes an increased rate of mortality worldwide. Traditional malignant growth treatments that are clinically utilized comprise chemotherapy, radiotherapy, and medical procedure. Despite the fact that there have been motivating enhancements in the nanotechnology and biomedical field, malignant growth remains the most urgent condition to treat, as the central reason for mortality. Nanotechnology has the possibility to improve medication transport and delivery by modifying pharmacokinetics and conveyance, resulting in reduced negative reactions and in this manner improving precision. Some issues exist regarding destinations and the difficulties that occur, and the potential for success becomes closer with every discovery. Nanomaterials are smaller in size than organic macromolecules. More correctly, they as a rule have a width of many nanometers (nm), which makes them from 100 up to multiple times smaller than even one malignancy cell. Nanoparticles can occur in sizes running from 10 up to 400nm, and can likewise be used with a simple set up or a blend of pharmacologically dynamic medications, depending on a superficial level of properties. The various aspect of nanotechnology for malignant growth treatment include exact targeting of the lively segments in cell/tissues, producing upgrades responsive medication discharge, defeating natural obstructions, interfacing against disease dynamic system with imaging atoms, improving disease examination, and imaging. For the most part, nanoparticles burdened with mending operators are conveyed experimentally for firm malignancy treatment. Todays nanotechnology is a magnificent platform for the treatment of differing malignant growths. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data-Driven Insights into Student Performance: Benchmarking Machine Learning Models for Grade Prediction using Regression and Classification Approaches
This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. 2025 IEEE. -
Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation in Cloud-SDN Environment
Cloud computing (CC) remains as a promising environment which offers scalable and cost effectual computing facilities. The combination of the SDN technique with the CC platform simplifies the complexities of cloud networking and considerably enhances the scalability, manageability, programmability, and dynamism of the cloud. This study introduces a novel Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation (MEDR-DDoSAD) technique in Cloud-SDN Environment. The major aim of the presented technique lies in the recognition of DDoS attacks from the cloud-SDN platform. The MEDR-DDoSAD technique transforms the input data into images and the features are derived via deep convolutional neural network based Xception model. 2022 IEEE. -
A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Amorphous versus crystalline Al2O3nanoparticles: A comparative study in photocatalytic dye degradation
This study focuses on the synthesis of aluminum oxide (Al2O3) nanoparticles and compares their amorphous and crystalline phases, emphasizing their suitability for photocatalytic dye degradation. The as-prepared Al2O3, synthesized using the sol-gel technique, is found to have an amorphous nature, which is later annealed at 1200C to obtain its ? phase of crystalline nature. Despite the widespread applications of aluminum oxide in various fields, the differences between its amorphous and crystalline phases are not well understood. This work bridges this gap by evaluating the amorphous and crystalline phases of Al2O3, particularly for dye degradation. As technologies advance to enhance aluminum-containing photocatalytic materials by doping, composites, and hybrids, understanding the impact of material phase on photocatalytic capabilities becomes crucial. The research comprehensively assesses structural, functional, morphological, optical, and dye degradation characteristics. Remarkably, amorphous Al2O3 demonstrates superior dye degradation efficacy compared with its crystalline counterpart, achieving an enhanced degradation efficiency of 87.2% for rhodamine B, a commonly used azo dye in the printing and textile industries. 2024 Emerald Publishing Limited: All rights reserved. -
A review on the electrochemical behavior of graphenetransition metal oxide nanocomposites for energy storage applications
Electrochemical energy storage devices like supercapacitors and rechargeable batteries require an improvement in their performance at the commercial level. Among them, supercapacitors are beneficial in sustainable nanotechnologies for energy conversion and storage systems and have high power rates compared to batteries. High chemical and mechanical stability, huge electrical conductivity, and high specific surface area have been beneficial for selecting graphene as a supercapacitor electrode material. The excellent properties of transition metal oxides are accountable for the application in the field of energy storage. The synergistic effects of the composites of graphene derivatives with transition metal oxides will boost the performance of the devices. Recently, several studies have been done for developing supercapacitor electrodes with these nanocomposites. This review article presents an analysis of the performance of these nanocomposites with an overview of their specific capacitance, energy density, and cycling stability for supercapacitor electrode application. A brief introduction of the theory and experimental analysis of supercapacitors is also given. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Effect of pH on the structural and optical properties of cobalt oxide nanoparticles synthesized by hydrothermal method
The paper focuses on the synthesis and characterization of cobalt oxide nanoparticles synthesized under different alkaline pH of the precursor solution by hydrothermal method. Cubic spinel Co3O4 crystallites were observed by X-ray diffraction pattern (XRD) and Raman spectrum. The crystallite size decreases as the pH value increases. The absorption spectrum exhibited two broad bands which are in good agreement with the cobalt oxide band structure. The change in bandgap was observed with pH of the precursor solution in agreement with size effects. Photoluminescence (PL) spectra consist of a broad emission with different peaks which are due to point defects. 2022 -
Effects of coronal mass ejection on PSR J1022+1001 and possible mode change of PSR J2145 - 0750 in the InPTA DR2
The Indian Pulsar Timing Array (InPTA) has recently published its second data release (DR2), comprising the timing analysis of seven years of data on 27 millisecond pulsars (MSPs), observed simultaneously in the 300 - 500 MHz (band 3) and 1260 - 1460 MHz (band 5), using the upgraded Giant Metrewave Radio Telescope (uGMRT). The low-frequency data, particularly in band 3, is highly sensitive to propagation effects such as dispersion measure (DM) fluctuations, which can be imprints of some astrophysical phenomena (scientific outliers). Here, we analyze the two outliers of possible astrophysical origin coming from the band 3 DM time series of two pulsars: PSR J1022+1001, with an ecliptic latitude of ?0.06?, and PSR J2145 - 0750, one of the brightest MSPs, with multi-component profile morphology. Our study reveals compelling evidence for a coronal mass ejection (CME) event traced in the data of PSR J1022+1001, and reports evidence for a potential mode-changing event in PSR J2145 - 0750. By contrasting these two cases, we show that DM fluctuations due to CME interacions and intrinsic mode-changing events produce distinct observational signatures, enabling a physically informed classification of scientific outliers in PTA datasets. Extending the analyses presented here to the full sample of InPTA-DR2 pulsars is expected to reveal additional CME events, and possible mode-changing events. Such detections will not only improve our understanding of solar and pulsar magnetospheric plasma interactions but will also enable more accurate modelling of DM variations, leading to improved pulsar timing solutions, which are crucial for high-precision Pulsar Timing Array (PTA) science. 2026 Elsevier B.V. -
Characterizing Ultimatum Game responders: a scoping review of factors that influence decision-making through an evolutionary lens
The Ultimatum Game is a widely used tool for studying conflict resolution within a bargaining framework. This scoping review aims to comprehensively examine the various internal and external factors influencing the responders behavior in this game and compile the status quo of the knowledge space. 31 pertinent research articles were identified from databases like Google Scholar, PubMed and JStor, using the following keywords ultimatum game, responder behavior, emotions and the ultimatum game, fairness in the ultimatum game, social norms and the ultimatum game, punishment game, impunity game, outside options in the ultimatum game. An analysis of the same yielded two broad domains of influencing factors: internal and external. Internal factors encompassed emotions, personality traits, and cognitive capabilities, showcasing their significant influence on decision-making. External factors, including ownership, social norms, power dynamics, outside options, gender, and attraction, revealed how the context of the game shaped responder choices. This review investigates how internal and external factors influence bargaining behavior within the Ultimatum Game, distinguishing between typical and atypical responder behavior. Invoking Kahnemans dual system theory offer insights into the evolutionary roots and modern cognitive processes guiding decision-making. The interplay between these systems reveals nuanced responses to fairness, reciprocity, and self-interest, challenging traditional economic models. While acknowledging the oversimplification of brain dynamics in these studies and also the need for cultural integration, the current review compiles a framework that advances our understanding of human behavior across disciplines, particularly for economics, psychology, and evolutionary biology. Refining this model promises deeper insights into decision-making processes amidst societal complexities. Copyright 2026 Chowdhury, Rangaswamy and Kolte. -
A study of pulsation & rotation in a sample of A-K type stars in the Kepler field
We present the results of time-series photometric analysis of 15106 A-K type stars observed by the Kepler space mission. We identified 513 new rotational variables and measured their starspot rotation periods as a function of spectral type and discuss the distribution of their amplitudes. We examined the well-established period-color relationship that applies to stars of spectral types F5-K for all of these rotational variables and, interestingly, found that a similar period-color relationship appears to extend to stars of spectral types A7 to early-F too. This result is not consistent with the very foundation of the period-color relationship. We have characterized 350 new non-radial pulsating variables such as A- and F-type candidate ? Scuti, ? Doradus and hybrid stars, which increases the known candidate non-radial pulsators in the Kepler field significantly, by ?20%. The relationship between two recently constructed observables, Energy and Efficiency, was also studied for the large sample of non-radial pulsators, which shows that the distribution in the logarithm of Energy (log (En)) can be used as a potential tool to distinguish between the non-radial pulsators, to some extent. Through visual inspection of the light curves and their corresponding frequency spectra, we found 23 new candidate red giant solar-like oscillators not previously reported in the literature. The basic physical parameters such as masses, radii and luminosities of these solar-like oscillators were also derived using asteroseismic relations. 2018, The Author(s). -
Future Battlefield System Using Graph Database and Internet of Things (IoT)
The Internet of Things (IoT) concept is rapidly evolving and is expected to influence each field of the computational realm. These advances have an impact on any nations defence force. The defense industrys solution mostly depends on detectors and their installations. The major goal of sensory statistics is to provide information that may be used for strategic choices and evaluation in future battling fields. Each piece of statistics, from documenting a soldiers essential health metrics to its ammunition, weapons, and position circumstance, has a function and is especially important to the strategic commander stationed in the control unit. This research proposes an innovative approach that combines the IoTs with the growing graph database to produce a contextual consciousness regarding each characteristic of the personnel on the battlefield. We show a projected future battlefield application condition in which we explore the graph database for contextual consciousness patterns to gain a strategic benefit over our competitors. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Clinical hypnosis and Patanjali yoga sutras
The trance states in yoga and hypnosis are associated with similar phenomena like relaxation, disinclination to talk, unreality, misrepresentation, alterations in perception, increased concentration, suspension of normal reality testing, and the temporary nature of the phenomena. While some researchers consider yoga to be a form of hypnosis, others note that there are many similarities between the trance in yoga and the hypnotic trance. The present study aimed to find similarities between the trance states of hypnosis and Patanjali?s yoga sutras. The trance states were compared with the understanding of the phenomena of trance, and the therapeutic techniques and benefits of both. An understanding of the concept of trance in Patanjali?s yoga sutras was gained through a thematic analysis of the book Four Chapters on Freedom by Swami Satyananda Saraswati. This led to an understanding of the concept of trance in the yoga sutras. The obtained concepts were compared to the concepts of trance in hypnosis (obtained through the literature on hypnosis) to investigate whether or not there exist similarities. The findings of the study show that there are similarities between the trance in hypnosis and the trance in Patanjali?s yoga sutras in the induction and deepening of the trance states in hypnosis and that of Samadhi, the phenomena present in hypnosis and the kinds of siddhis that are obtained through Samadhi, and the therapeutic techniques and the therapeutic process in Patanjali?s yoga sutra and hypnosis. -
Flexible Nanogenerators Based on Enhanced Flexoelectricity in Mn3O4 Membranes
Atomically thin, few-layered membranes of oxides show unique physical and chemical properties compared to their bulk forms. Manganese oxide (Mn3O4) membranes are exfoliated from the naturally occurring mineral Hausmannite and used to make flexible, high-performance nanogenerators (NGs). An enhanced power density in the membrane NG is observed with the best-performing device showing a power density of 7.99mWm?2 compared to 1.04Wm?2 in bulk Mn3O4. A sensitivity of 108mVkPa?1 for applied forces <10N in the membrane NG is observed. The improved performance of these NGs is attributed to enhanced flexoelectric response in a few layers of Mn3O4. Using first-principles calculations, the flexoelectric coefficients of monolayer and bilayer Mn3O4 are found to be 50100 times larger than other 2D transition metal dichalcogenides (TMDCs). Using a model based on classical beam theory, an increasing activation of the bending mode with decreasing thickness of the oxide membranes is observed, which in turn leads to a large flexoelectric response. As a proof-of-concept, flexible NGs using exfoliated Mn3O4 membranes are made and used in self-powered paper-based devices. This research paves the way for the exploration of few-layered membranes of other centrosymmetric oxides for application as energy harvesters. 2023 Wiley-VCH GmbH. -
Enhanced transport, dielectric and magnetic properties of Ni-doped (YFeO3)0.5(BaTiO3)0.5 perovskite for NTC thermistor and multifunctional applications
The solid-state reaction method was successfully employed to synthesize the environmentally friendly polycrystalline perovskite (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5. X-ray diffraction (XRD) analysis, complemented by Rietveld refinement, confirms its multiphase crystalline structure, comprising two cubic and one orthorhombic phase. Field-emission scanning electron microscopy (FE-SEM) reveals a well-defined surface morphology, while energy-dispersive spectroscopy (EDS) and elemental mapping validate the homogeneous distribution of constituent elements. Raman and FTIR spectroscopy further confirm the vibrational and atomic structural integrity of the material. Dielectric studies indicate a high dielectric constant (?338 at 100 Hz, room temperature), with strong frequency and temperature-dependent relaxation effects. Impedance spectroscopy reveals non-Debye relaxation behaviour, NTCR characteristics and impedance in the megaohm range at lower temperatures. AC conductivity results align well with Jonscher's power law. The thermistor coefficient (?) reaches 4778.61 at 450 C, demonstrating excellent potential for thermistor applications. Magnetic studies confirm a prominent ferromagnetic response at room temperature, with a saturation magnetization of 3.654 emu g?1 and coercive field of 196.4 Oe. These combined properties make (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5 a promising candidate for multifunctional applications. 2025 RSC.