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Comprehensive investigations on spectral and temporal features of GX 5-1 using AstroSat observations
Comprehensive spectrotemporal analyses of the Z-type neutron star low-mass X-ray binary GX 5-1 were performed using 10 broad-band observations from AstroSat/Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) instruments. The LAXPC-20 hardness-intensity diagram showed horizontal and normal branches (HBs and NBs) of the Z track which exhibited secular motion. The time-averaged spectra in the energy range 0.7-25.0 keV could be fitted with the model combination -Cconstant tbabs edge edge thcomp diskbb. This yielded ? ?2, kTe ?3.3 keV, and Fdisc/Ftotal ? 0.8 indicating the soft/intermediate spectral state of the source during the observations. Flux-resolved spectral analysis revealed a positive correlation between kTin and Fbol. However, a negative correlation was observed between them in one of the NBs. Time-averaged temporal analysis revealed multiple HB oscillations (HBOs) and NB oscillations (NBOs), and peaked noise components in the ?5-50 Hz range. Furthermore, flux-resolved temporal analysis showed that the frequency of the HBOs correlates positively whereas the strength of HBOs correlates negatively with Fbol, indicating their probable origin from the accretion disc. In contrast, the frequency and strength of NBOs remain fairly constant with Fbol, suggesting that they originate from a different region in the system. Using the relativistic precession model along with highest frequency of the HBO, the upper limits of the magnetic dipole moment (?) and field strength (B) at the poles of the neutron star in the system were found to be 25.60 1025G cm3 and 3.6408 G, respectively, for kA= 1. 2024 The Author(s). -
Comprehensive investigation on mechanical properties of mango seed shell short fiber-reinforced epoxy based polymer composites
The mechanical properties of discarded Mango Seed Shell Fiber (MSF)-reinforced epoxy composites are studied in this work. MSF, which was obtained through agricultural wastes, was added to the epoxy matrix in varying weight fractions viz., 5%, 10%, 15%, 20%, and 25% using the hand lay-up method. The outcome shows that the best mechanical performance is reached at the 15% MSF content, i.e., the tensile strength of 29.35?MPa and tensile modulus of 758?MPa, an improvement of 24% in comparison with the unreinforced (neat) epoxy. The modulus and flexural strength were 2962?MPa and 48.13?MPa for 15% MSF content which was 68% and 42% more than neat epoxy. The highest impact strength of 75.93?J/m for 15% MSF which corresponds to 148% higher than the neat epoxy, and the hardness was between 47 RHN and 56 RHN and was maximum for 10% MSF. The novelty of the current study lies in the utilization of mango seed shell fiber, which is an underutilized agro-waste product that has been utilized systematically as a reinforcing element in epoxy composite and the determination of optimal fiber loading by thorough mechanical testing is accomplished in the present work. The results provide the base of mechanical performance data of MSF-reinforced eco-composites and confirm its opportunities as sustainable and cost-efficient reinforcement for lightweight and environmental-friendly structural applications. 2026 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the models robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the models predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The systems sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. The Author(s) 2024. -
Comprehensive Data Analysis of Anticorrosion, Antifouling Agents, and the Efficiency of Corrosion Inhibitors in CO2 Pipelines
This study explores the various methods that are being proposed for their anticorrosion and antifouling capabilities and also reviews the unique properties that make them suitable for such applications. Special attention has also been given to the problem of corrosion in CO2 pipelines, considering the corrosion inhibitors currently being used and performing statistical analysis about if and how various factors such as temperature, flow velocity, pH, and CO2 pressure affect the rate of corrosion of the CO2 pipelines. Tests including ANOVA, correlation, and graph analyses were conducted to explore their relationships, and suitable conclusions were drawn for the data collected. 2024 Scrivener Publishing LLC. -
Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods
This research leveraged machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to predict breast cancer from CT and MRI scans. A dataset comprising 2345 instances of malignant and benign cases was meticulously curated, with 80% allocated for training and 20% for testing. The experimental results revealed the DNN as the top-performing model, exhibiting remarkable accuracy (95.2%), precision (94.8%), recall (95.6%), and F1 score (95.2%). The ANN also demonstrated strong performance, achieving an accuracy of 93.6% with balanced precision and recall scores. In contrast, the SVM, while respectable, fell slightly behind the machine learning models in terms of overall accuracy and performance. Detailed confusion matrices further elucidated the models capabilities and limitations, providing valuable insights into their diagnostic prowess. These findings hold great promise for breast cancer diagnosis, offering a non-invasive and highly accurate means of early detection. Such a tool has the potential to enhance patient care, reduce the strain on healthcare systems, and alleviate patient anxiety. The success of this research highlights the transformative impact of advanced machine learning in medical imaging and diagnosis, signaling a path toward more efficient and effective healthcare solutions. Further research and clinical validation are essential to translate these promising results into practical applications that can positively impact patients and healthcare providers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comprehensive and comparative analysis of sorbitan ester (Span) niosomes as emerging vesicular drug delivery platform: Fabrication, characterization, release dynamics, biocompatibility profiling and toxicological implications
To overcome the limitations and related adverse side effects of conventional drug delivery, niosomes, aka non-ionic surfactant vesicles, have emerged as an effective vesicular drug delivery system (VDDS) for the past few years. This study represents a comparative analysis of physico-chemical characteristics, in vitro and in vivo biocompatibility of synthesized sorbitan ester (Span) niosomal vesicles. In brief, Span 20, Span 40, Span 60 and Span 80 surfactants, along with equimolar concentration of cholesterol, were used to synthesize blank, Biochanin A (model hydrophobic drug) and Crocin (model hydrophobic drug) loaded niosomes. Characterization techniques unveiled that all niosomes were polydispersed sphericles with hydrodynamic diameter of 300 nm to 650 nm and PDI < 0.550. Fourier transform infrared spectroscopy (FTIR) and UVvisible spectroscopy (UV-Vis) analysis of drug loaded niosomes showed respective characteristic peaks of Biochanin A and Crocin, indicating effective drug encapsulation with EE% varying from 58.975 to 90.050. Among all formulations, Span 60 and Span 40 niosomes sketched satisfactory yield, drug encapsulation (EE%), loading efficacy (LD%), drug release and stability. Results obtained from in vitro biocompatibility study depicted that all niosomes had marked drug delivery efficacy with minimum cytotoxicity (<25 %) and haemolysis (<27 %) at 500 g/mL concentration. After a consecutive 14-day exposure to blank niosomes (100 mg/kg body weight) by intraparetonial injection, treated swiss albino mice exhibited little to no significant changes in body weight, organ weight, haematological and biochemical parameters, with normal hepato-renal histological characteristics. Thus, the study portrayed a mechanistic and comparative evaluation in vitro and in vivo applicability of niosomes with detailed sub-acute toxicological profiling. 2025 Elsevier B.V. -
Comprehensive Analysis of Canine Parvovirus Outbreaks: Predictive Modeling and Evaluation Metrics
This paper addresses the persistent threat of Canine Parvovirus (CPV) to canine health, exploring a spectrum of outcomes from recovery to fatalities. Employing a fusion of machine learning techniques and comprehensive evaluation metrics, we present a robust analysis of CPV outbreaks. Our methodology involves the development of a deep learning-based predictive model designed to anticipate CPV case outcomes based on symptoms and diverse contributing factors, with performance monitoring through visualization techniques. The study delves into the intricacies of a dataset featuring diverse features such as age, breed, symptoms, treatment, and geographic location. Through meticulous preprocessing and feature encoding, we establish a powerful deep learning model proficient in discerning intricate patterns within the data. Model evaluation encompasses key metrics, including accuracy, precision, recall, F1-score, confusion matrix, Cohens Kappa, and Matthews Correlation Coefficient, providing a comprehensive assessment of predictive capabilities. Our findings highlight the models proficiency in anticipating CPV outcomes, suggesting potential enhancements in decision-making within veterinary practice. Insights derived from this research contribute to the refinement of CPV diagnosis, treatment, and prevention strategies, ultimately benefiting the well-being of canine companions. The projects results demonstrate the efficacy of the proposed models in forecasting the prevalence and survival rate of the CPV virus in dogs using basic parameters. This approach eliminates the need for costly and time-consuming laboratory tests, typically requiring 1224h for results, showcasing a practical and efficient solution for CPV management. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comprehending algorithmic bias and strategies for fostering trust in artificial intelligence
Fairness is threatened by algorithm bias, systematic and unfair disparities in machine learning results. Amazon's AI-driven hiring tool favoured men. AI promised data-driven, impartial decision-making, but it has revealed sector-wide prejudice, perpetuating systematic imbalances. The algorithm's bias is data and design. Biassed historical data and feature selection and pre-processing can bias algorithms. Development is harmed by human biases. Algorithm prejudice impacts money, education, employment, and crime. Diverse and representative data collection, understanding complicated "black box" algorithms, and legal and ethical considerations are needed to address this bias. Despite these issues, algorithm bias elimination techniques are emerging. This chapter uses secondary data to study algorithm bias. Algorithm bias is defined, its origins, its prevalence in data, examples, and issues are discussed. The chapter also tackles bias reduction and elimination to make AI a more reliable and impartial decision-maker. 2024, IGI Global. All rights reserved. -
Compounds and methods for the treatment of non-alcoholic steatohepatitis /
Patent Number: WO2019111225, Applicant: AVALIV THERAPEUTICS.
Compounds and compositions are provided having the structure of Formula (I) or a pharmaceutically acceptable salt, tautomer, or stereoisomer thereof, wherein T, T', U, U', V, W, R1, R2, R3', n, o, o', o'', and o''' are as defined herein. Suchcompounds are useful for treating liver diseases and abnormal liver conditions, including non-alcoholic steatohepatitis via inhibition of the lysosomal enzyme cathepsin D. -
Composting for a sustainable future: Turning waste into nutrient-rich soil
As the worldwide waste management problem has risen, composting has gained popularity. It turns organic trash into nutrient-rich soil for agriculture, landscaping, and environmental rehabilitation. This chapter on composting, "Composting for a Sustainable Future: Turning Waste into Nutrient-Rich Soil," is comprehensive. It covers decomposition science, composting methods, materials, and procedures. Composting's agricultural, landscaping, and environmental remediation benefits are also covered. The chapter also examines composting's role in climate change mitigation, its obstacles, and remedies. Decomposition can help people, businesses, and communities live more sustainably. It urges decomposition and trash reduction and provides information about tools to start composting, a simple yet efficient solution to worldwide waste management. 2024, IGI Global. All rights reserved. -
Compositionally Homogeneous Soft Wrinkles on Elastomeric Substrates: Novel Fabrication Method, Water Collection from Fog, and Triboelectric Charge Generation
Functionality and stimuli-response of natural and artificial elastomeric materials depend significantly on the morphology of their surfaces. Structural transformability and tunable responsiveness of wrinkles on elastomeric materials can enable numerous applications in flexible electronics, optics, and adhesives. Currently existing fabrication techniques rely on sophisticated instrumentation, complex experimental setups, and expensive reagents. These methods are limited in terms of mechanical robustness of the wrinkles produced. Here, a simple, inexpensive, scalable, and reproducible strategy, making use of buckling instability for the creation of soft surface wrinkles on polydimethylsiloxane (PDMS), is presented. PDMS with lower elastic modulus is spin-coated onto a mechanically stretched film of PDMS with a higher elastic modulus. Thermal curing followed by the release of prestrain resulted in the formation of wrinkles in the top layer of the PDMS. The hydrophobic soft surface wrinkles with compositional homogeneity exhibit efficient fog water collection and triboelectric charge generation useful for the preparation of triboelectric nanogenerator devices. Furthermore, the substrates show high mechanical stability and mechanoresponsive optical behaviors. The simplicity and general applicability of the method presented here is expected to establish a promising pathway toward the formation of soft wrinkles in other elastomeric systems also, facilitating important applications in various fields. 2022 Wiley-VCH GmbH. -
Composites based on natural polymers
Polymers are composed of macromolecules of high molecular weight formed by the repeated union of small molecules known as monomers. Polymer materials over other materials such as metals and ceramics, are of light weight and hence extensively used. The use of renewable raw materials can provide a substantial activity for a sustainable society and environment. Natural polymer composites can provide an alternative to increasingly scarce raw materials from plants and animals. 2017 Nova Science Publishers, Inc. -
Components of the diffuse ultraviolet radiation at high latitudes
We have used data from the Galaxy Evolution Explorer to study the different components of the diffuse ultraviolet background in the region between the Galactic latitudes 70?-80?. We find an offset at zero dust column density (E(B ? V) = 0) of 240 18 photon units in the FUV (1539 and 394 37 photon units in the NUV (2316 . This is approximately half of the total observed radiation with the remainder divided between an extragalactic component of 114 18 photon units in the FUV and 194 37 photon units in the NUV and starlight scattered by Galactic dust at high latitudes. The optical constants of the dust grains were found to be a = 0.4 0.1 and g = 0.8 0.1 (FUV) and a = 0.4 0.1 and g = 0.5 0.1 (NUV). We cannot differentiate between a Galactic or extragalactic origin for the zero-offset but can affirm that it is not from any known source. 2019 Oxford University Press. All rights reserved. -
Complicated Grief during COVID-19: An International Perspective
Cultures across the globe have evolved time-tested rituals to honor those who die and offer solace and support to survivors with the goal of helping them to accept the reality of the death, cope with the feelings of loss, adjust to life without the deceased, and find ways to maintain a connection to the memory of the deceased. The COVID-19 pandemic has disrupted these rituals and brought significant changes to the way we mourn. Specifically, public health responses to COVID-19 such as social distancing or isolation, delays or cancellations of traditional religious and cultural rituals, and shifts from in-person to online ceremonies have disrupted rituals and thus made it more difficult to access support and complete the psychological tasks typically associated with bereavement. This paper conceptualizes the common bereavement tasks including emotion-focused coping, maintaining a connection to the deceased, disengagement and reframing death and loss, and problemfocused coping. It provides examples of how the COVID-19 pandemic has altered mourning rituals across several cultures and religions and contributed to prolonged grief disorder as defined by the ICD-11 that includes depressive symptoms and post-traumatic stress. Early evidence suggested that the suddenness of loss, the social isolation, and the lack of social support often associated with COVID-19-related death are salient risk factors for complicated grief. As a consequence, psychological assessments, grief counseling, and mental health support are needed by families of patients who died from COVID-19. These services must be essential components of any comprehensive public health response to the pandemic. 2022 Hogrefe Publishing. -
Complex Systems Mapping of Fiscal Growth Dynamics at Strategic Maritime Chokepoints Using Time-Series Slopes
This study examines how maritime and trading states allocate public resources between defence, health, and economic growth around three strategic chokepoints the Strait of Malacca, the Strait of Hormuz, and the Suez Canal. The analysis extends the classic guns versus butter framing by treating defence and health spending as co-evolving components of an interconnected fiscal-growth system. Using World Development Indicators data (1999-2024), trend slopes are estimated for military spending (% of GDP), healthcare spending (% of GDP), and GDP growth (annual %). Two derived indicators are computed, a defence-to-health slope ratio (military slope/health slope) and a fiscal-balance proxy (health slope - military slope). Augmented Dickey-Fuller tests are used to assess stationarity (unit-root behaviour), and Granger causality tests to examine whether GDP growth temporally precedes changes in spending shares. Hormuz chokepoint states show non-negative health slopes (e.g., UAE +0.1199) alongside negative GDP growth slopes in some cases (e.g., Qatar -0.4754). Suez chokepoint states exhibit negative defence slopes (e.g., Egypt -0.0899) with comparatively small or negative health slopes (e.g., Egypt -0.0211). The United States is included as an external benchmark because it is the largest trading nation by monetary trade volume and is directly or indirectly coupled to chokepoint flows; it shows health +0.1758 and military -0.0116 (ratio -0.0657). These quantified configurations support chokepoint-specific fiscal regimes and provide a compact visual map of security, health, growth dynamics in a small integrated complex systems. @ Binghamton (The ORB), 2026. -
Complex Network Articulation Points Detection and Centrality Measures
To clearly understand how network structure and function interact is a basic difficulty in the study of large networked systems. An old-fashioned idea from graph theory, called articulation points, may be used to do this. In a network, a node If removing it causes the network to become disconnected or causes more network components to get linked, it is an articulation point (AP). Single points of collapse are represented as articulation points in networks. The major goal of this research is to provide a method for identifying the articulation points and centrality measures. We can locate the articulation points considerably more quickly and effectively by using TARJANS Algorithm, which uses depth-first search. It must fulfill two requirements to qualify as an articulation point. For the root node of a DFS traversal to be an articulation point, it must contain at least two offspring nodes that are members of various sub graphs. It has been discovered that articulation points (APS) are crucial for maintaining the reliability and connection of several real-world networks. By assigning each node in the graph a scalar value based on an assumption, centrality metrics may be used to quantify each nodes significance. A fundamental centrality metric is node degree. In terms of node neighbors, it is equivalent. Hence, the more neighbors a node has, the more central and densely linked it is, and the more it affects the network by having more neighbors. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025. -
Complex and Multifaceted Nature of Cryptocurrency Markets: A Study to Understand its Time-Varying Volatility Dynamics
Decentralised Finance (DeFi) provides a new way to perform complex financial transactions by exploiting blockchain's ability to maintain a decentralised ledger of transactions without being constrained by centralised systems or human intermediaries. DeFi provides alternative financial instruments that might lessen portfolio risk, especially given the erratic state of the financial markets today. This study analyses the association between the year of the coin in which it was introduced and the market capitalisation of the respective companies. Furthermore, the study also tries to understand the volatility associated with cryptocurrencies using EGARCH & GJR-GARCH models. The results reveal that market capitalisation is not similar for all three stages of the age of cryptocurrency. Also, negative news tends to impact Bitcoin more than positive news, and the volatility is persistent and long-lasting. Ethereum, BNB & Solana see more volatility from absolute past shocks; however, Tether exhibits low but persistent volatility as a stablecoin. 2024, Creative Publishing House. All rights reserved. -
Complete analysis of differential cross section in 7 Li + ? ? ? 6 Li + n at astrophysical energies
We have carried out complete analysis of differential cross section in 7 Li + ? ? 6 Li + n using model-independent theoretical formalism. A complete analysis of the reaction involves measurements of not only one state of linear polarization of the photon but also another state of linear polarization inclined to the first at 45? and two states of circular polarization of the photon. An analytical study of the differential cross section including all the photon polarization states is carried out at near-threshold energies of interest to Big Bang Nucleosynthesis. 2024 IOP Publishing Ltd -
Complete analysis of beam analyzing powers in d + ? ? ? n + p at near threshold energies
Focusing attention on the photon spin in d ( ? ? , n ) p at near threshold energies of interest to Big Bang Nucleosynthesis, a complete analysis of beam analyzing powers in d ( ? ? , n ) p reaction is carried out. A complete analysis of the reaction needs not only measurements using one state of linear polarization of photon but also measurements using another state of linear polarization inclined to the first at ?/4 and the two states of circular polarization of the photon. A discussion on the complete characterization of the states of photon polarization is presented. The beam analyzing powers with respect to photon polarization are discussed theoretically, using model independent irreducible tensor formalism. 2022 IOP Publishing Ltd. -
COMPETITIVE MARKETING IN INDIA: A CASE STUDY ON AD STRATEGIES OF SAMSUNG GALAXY AND NOKIA LUMIA
Today the first thing that we look for before moving out of our house is our cell phone. This gadget has made possible for us to keep in touch with the entire globe irrespective of where we are. The first ever mobile phone which was commercially available was DynaTAC 8000x but that was the era when not many people could afford it. Contrary to this, today is the time where every nine out of ten people have their personal cell phones. This to a certain limit was made possible by Reliance. It came out with affordable CDMA cell phones which even the lower class population of India could afford. Although these companies have created a benchmark for themselves, they have not really been the leading brands of the industry in terms of variation, style, preference, promotion etc. To a certain extent, it has been noticed that Nokia has been the preference of the people due to its variety, style and longitivity. Nokia has been the leading smart phone brand in the industry for several years. For long it has maintained its credibility, keeping itself connected with the clients. The loyalty that its customers had for the company was strong enough for them to not go in search for other brands. Samsung, on the contrary wasnt doing very well in the market. Its designs and applications were constantly failing. Therefore the fact that Samsung is now the leading brand in our country has a lot aspects attached to it. There have been uncompromising strategies put behind the success of Samsung. From a very nominal smart phone company, it has risen to the most popular smart phone company in India. Its has been noticed that with the incoming of new handsets and softwares, there has been a change even in its promotional activities. Nokia, which in the past was loaded with new launches like the N series or the E series, suddenly came to a halt. This was when the Samsung started coming up with its touch screen Corby phones. Therefore this study will be executed to mark the difference in the marketing strategies of Samsung at its stage of rival, also comparing it to the parallel Nokia Advertisements.