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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. -
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. -
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. -
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 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 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 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 Phytochemical, Anti-Oxidant and GC-MS Analysis of Strobilanthes jomyi P. Biju, Josekutty, Rekha & J.R.I.Wood
Background and Objective: Plant-based medication is one of the most established practices in the Indian medical field. Earlier, raw parts of plants were directly used to treat many health conditions. Later, the most valuable part was identified, separated the chemical compounds and treated various diseases. The plant Strobilanthes jomyi belongs to the family Acanthaceae, commonly called Elathumpadi. The study aimed to evaluate the physicochemical, mineral composition, phytochemical, anti-oxidant and GC-MS analysis of leaves stem and root of S. jomyi. Materials and Methods: Different vegetative parts of S. jomyi were extracted with the Soxhlet extraction method by using methanol as solvent. Physicochemical, phytochemical, mineral composition, anti-oxidant and GC-MS analyses were evaluated by different standard protocols. Results: The phytochemical analysis revealed that leaves contained more phenolic (87.40.44 mg gG1 of GAE), flavonoid (66.230.53 mg gG1 equivalent of QE), carbohydrate (44.71.28 mg gG1 of fresh weight), protein (17.70.76 mg gG1 of fresh weight), proline (46.80.15 mg gG1 of fresh weight) and chlorophyll (46.80.15 mg gG1 of fresh weight) content than the root and stem of methanolic extract. The non-enzymatic anti-oxidant assays of the methanolic extract showed the presence of higher anti-oxidant activities in leaves, followed by root and stem. The GC-MS study of the root, stem and leaves revealed medicinally important bioactive compounds like 2,4-di-tert-butyl phenol, phytol, squalene, phenol, neophytadiene and lupeol. Conclusion: Strobilanthes jomyi can be used as an alternative source of the ayurvedic system of medicine based on its phytochemical and antioxidant activity. 2023. -
Comprehensive Review on CdTe Crystals: Growth, Properties, and Photovoltaic Application
Abstract: Despite the deep interest of materials scientists in cadmium telluride (CdTe) crystal growth, there is no single source to which the researchers can turn towards for comprehensive knowledge of CdTe compound semiconductor synthesis protocols, physical properties and performance. Considering this, the present review work focuses to bridge this shortcoming. The direct band gap (Eg) CdTe crystals have been in limelight in photovoltaic application (PV) since the optoelectronic properties such as Eg (1.49 eV), absorption coefficient (~105 cm1), p-type conductivity, carrier concentration (6 1016 cm3) and mobility (1040 cm2/(V s)) at the room temperature are reported that optimum for solar cells. Additionally, Cd-based compounds such as CdTe and CdZnTe have also been widely studied in the field of ? and ?-ray radiation detector, because of their extraordinary advantages like large atomic number, low weight, high mechanical hardness, flexibility, and the availability of the constituent materials. CdTe has demerits like toxicity and high melting temperature, which will complicate the growth of stoichiometric cadmium telluride crystals at high temperatures. In this regard, the review work focused the periodic evolution of the growth protocols until now. The different synthesis methods, characterization, and recent progress in the field of crystalline CdTe were discussed briefly. Important optical and electrical characteristics are presented in the tables and remaining issues have discussed, this could be looked into for further research. The applications of CdTe crystals for photovoltaic fields are also discussed in this review paper. Pleiades Publishing, Ltd. 2023. ISSN 0031-918X, Physics of Metals and Metallography, 2023, Vol. 124, No. 14, pp. 17951812. Pleiades Publishing, Ltd., 2023. ISSN 0031-918X, Physics of Metals and Metallography, 2023. Pleiades Publishing, Ltd., 2023. -
Comprehensive Review on Video Watermarking Security Threats, Challenges and its Applications
Data is a crucial resource for every business, and it must be protected both during storage and transmission. One efficient way of securing data and transferring it is through digital watermarking, where data is hidden inside a medium like text, audio, or video. Video watermarking is visible or invisible embedded data on a video in a logo, text, or video copyright disclaimer. In this proposed paper, the goal is to analyze the characteristics of video watermarking algorithms and the different metrics used for them. It deals with the extent to which the different requirements can be fulfilled, taking into consideration the conflicts between them and the practical challenges of video watermarking in terms of attacks like geometric attacks and non-geometric attacks. It also focuses on the process of watermarking a video. Recent advances in data security indicate that employing a video watermarking technology to transmit private data will be an effective method of transmitting sensitive data. The Electrochemical Society -
Comprehensive spectro-temporal studies of GX 17+2 using AstroSat observations
We performed a comprehensive spectro-temporal study of the Z-type neutron star low-mass X-ray binary GX 17+2 using long term data from the AstroSat/Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counter (LAXPC). The hardnessintensity diagrams (HIDs) of the source revealed a positive correlation between the hardness and intensity, characteristic of soft spectral state. Additionally, the LAXPC-20 HID showed the presence of secular shifts in both hardness and intensity. Time-averaged spectral modelling in the 0.7 ? 30.0 keV energy range indicated that the spectra could be well fitted with the model combination: constant edge edge tbabs thcomp bbodyrad. This analysis yielded a blackbody radius (Rbb) of ?59 km, photon index (?) of ?2.84 and electron temperature (kTe) of ?4.84 keV. Time-averaged temporal analysis revealed normal branch oscillations (NBOs) at ? 7 Hz in Observations 1 and 3, flaring branch oscillation (FBO) at ?15 Hz in Observation 2, and horizontal branch oscillation (HBO) at ?36 Hz in Observation 5. Flux resolved spectro-temporal analysis indicated that the source remained in the soft spectral state throughout all observations. A positive correlation was observed between kTbb, Fbb and Fbol, whereas an anti-correlation was noted between kTe and Fbol. The constant frequency of NBOs with an increase in Fbol suggests that their origin lies in a region strongly influenced by the corona, as explained by the radiation-hydrodynamic model. The origin of FBOs may be attributed to the damped radiation-hydrodynamic mode of radial flow, while the origin of HBOs is supported by the beat-frequency model. 2024 Elsevier B.V. -
Comprehensive strategies of Lignocellulolytic enzyme production from microbes and their applications in various commercial-scale faculties
Activities of anthropological organisms leads to the production of massive lignocellulosic waste every year and these lignocellulolytic enzymes plays crucial role in developing eco-friendly, sustainable and economical methods for decomposing and pre-treating the biomass to produce biofuels, organic acids, feeds and enzymes. Lignocellulolytic enzymes sustainably hydrolyse the biomass and can be utilized in wide range of applications such as personal care, pharmaceutical, biofuel release, sewage treatment, food and beverage industries. Every year a significant ton of biomass waste is released and insight on these crucial enzymes could establish in all the industries. However, due to the increased demand for compost materials, biomass degradation has resulted in composting processes. Several methods for improving compost amount and quality have been explored, including increasing decomposer inoculums, stimulating microbial activity, and establishing a decomposable environment. All of these prerequisites are met by biotechnological applications. Biotechnological procedures are used to improve the activity of enzymes on biomass. It leads to an adequate supply of compost and base materials for enterprises. In terms of effectiveness and stability during the breakdown process, lignocellulolytic enzymes derived from genetically modified species outperformed naturally derived lignocellulolytic enzymes. It has the potential to increase the quality and output of byproducts. This review discussed the development of lignocellulolytic enzyme families and their widespread applications in a variety of industries such as olive oil extraction, carotenoid extraction, waste management, pollution control, second-generation bio-ethanol production, textile and dyeing, pharmaceuticals, pulp and paper, animal feed, food processing industries, detergent, and agricultural industries. 2022 Visagaa Publishing House. -
Comprehensive study of the physicochemical properties of three-component deep eutectic solvents and their implications for microbial and anticancerous activity
Sustainable chemistry centers on substituting perilous solvents and materials with eco-conscious alternatives. Deep eutectic solvents (DES) hold substantial potential in this arena. This inquiry includes the formulation of three-component eutectic solvents and an exhaustive scrutiny of their physical and chemical attributes. These encompass solubility, boiling point, pH, density, viscosity, surface tension, refractive index, contact angle, conductivity, Fourier-transform infrared spectroscopy, polarized optical microscopy, thermogravimetric analysis, and differential scanning calorimetry. Furthermore, a biological exploration featured two bacterial strains and two fungal strains. The entire spectrum of ten three-component DES was administered to these microorganisms to discern plausible impacts. In addition, the biomedical promise of these DES was unveiled through anticancer assays employing MCF-7 and HeLa cell lines. The outcomes were favorable, underscoring robust anticancer potency, thereby hinting at future oncological utility. These interdisciplinary endeavors envelop the progression of sustainable solvent innovation, meticulous physicochemical scrutiny, microbial analysis, and anticancer appraisal. This study propels inventive resolutions with ecological and biomedical reverberations by amalgamating these distinct yet interconnected facets. 2024 Indian Chemical Society -
Comprehensive study of the relationship between multiverse and big data
Studies linking two broader spectra of topics have fascinated scholars in many aspects. Here we tried associating two such far-reaching aspects which have finite connectivity between them. Multiverse has been the talk of the hour which explains the theory of multiple universes which exist in parallel. This is a topic in physics concerned with many relative matters. On the other side, Big data is the subject in computing and information science describing the volume, velocity, and variability of the data hitting computer-connected systems. Big data can only be handled with newer architectures, algorithms, and methodologies as its features are contradicting regular computer systems and networks. It is well known that multiple processors are required to handle big data existing in parallel performing a single job given by the data analyst. So as we know, multiverse consist of hypothetical concepts of several parallel universe having everything like information, energy, and time. However, we see this situation to draw an association connecting parallel universes of the multiverse with parallel processors of big data by incorporating the concepts of working of parallel universe in the processing of Big Data. We provide a comprehensive observation on both the topics and take positive lenience on bringing a newer terminology in data science. History of multiverse along with big data structures are brought in with related parameters. This aspect is novel in its nature and we complement the literature carried out by the researchers and scholars appropriate analysis. We also showcase a model of the school of thought mentioned above in drawing conclusions. 2023 The Authors -
Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs
Sentiment analysis can be considered a major application of machine learning, more particularly natural language processing (NLP). As there are varieties of applications, Sentiment analysis has gained a lot of attention and is one among the fastest growing research area in computer science. It is a type of data analysis which is observed from news reports, user reviews, feedbacks, social media updates etc. Responses are collected and analyzed by researchers. All sentiments can be classified into three categories-Positive, Negative and Neutral. The paper gives a detailed study of sentiment analysis. It explains the basics of sentiment analysis, its types, and different approaches of sentiment analysis. The recent tools and APIs along with various real world applications of sentiment analysis in various areas are also described briefly. 2020 IEEE. -
Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine
The rising human population is causing the utilization of enormous amounts of fossil fuels to fulfill energy needs. Various renewable sources are used as fossil fuels however those resources are not powerful in supplanting customary non-renewable energy sources like gasoline in vehicles. The depletion of conventional fossil fuel utilized in a vehicle contributes to an increased portion of air contamination and is a danger to human well-being. Also, to maintain the supply demand, many active types of research have been carried out in mixing a higher percentage of ethanol over gasoline and further moving towards flex-fuel vehicles. But there arises a problem of knocking and higher CO, and HC emissions from the engine. To overcome the above problem, ethanol could be mixed in a higher percentage over gasoline with the help of hydrogen assistance and can completely avoid the problem of knocking and reducing CO and HC emissions. In this research, the combustion, emission, and performance characteristics of a variable-speed gasoline engine fuelled with ethanol-blended gasoline along with hydrogen assistance are taken for investigation at variable speeds like 1800, 1600, 1400, and 1200 rpm. Hydrogen is added to blended fuel (E30) which has better combustion, emission, and performance than other blended fuels. Hydrogen addition is done at 2, 3, and 4 ms respectively. The outcomes showed that the E30 + H2 at 3 ms has better combustion, emission, and performance, still, the emission of NOx is higher in comparison with all the other blends due to complete combustion. Thus, a two-stage analysis has been done, one is making a comparison among various blends of ethanol, and the second one is the comparison among the various energy shares of hydrogen. 2023 Hydrogen Energy Publications LLC -
Comprehensive understanding of biomedical usages of metal and non metal doped carbon dots
In recent years, carbon dots have garnered significant attention, particularly within the biomedical realm, owing to their exceptional characteristics. The unique attributes of carbon dots can be further enhanced through the introduction of heteroatom via doping. Various techniques have been devised by researchers to facilitate the doping of carbon dots, with both metallic and non-metallic elements. Elements such as nitrogen (81%), sulfur (67%), and silicon (64.1%) have been successfully employed for doping carbon dots leading to heightened quantum yields. This review compiles the diverse methodologies and elements employed in doping of carbon dots, and their applications in the biomedical domain in recent times. This review discusses the uses of doped carbon dots, both metal and non-metal-doped variants, elucidating their manifold utilities in various biomedical sectors, notably bioimaging, wound healing, and cancer therapy. The discussion culminates by addressing present challenges and offering insights into future prospects of doped carbon dots. 2023 Elsevier Ltd -
Compressed unfired blocks made with iron ore tailings and slag
Growing demand for houses in urban India has increased the requirements for construction materials such as clay fired bricks and cement blocks. At the same time, conventional practice of brick manufacturing is not environment friendly due to high energy consumption and CO2 emissions during various stages of its production. Therefore, recent trend in research has been directed towards utilization of various industrial wastes and methods, which emerge as sustainable alternatives for environmental concerns arising in the construction industry. This study focused on utilizing mining waste, namely iron ore tailing (IOT) in development of stable blocks. It has reported various properties of compressed unfired blocks formed by IOT and ground granulated blast furnace slag (GGBS) in varying proportions and with a fixed amount of lime. The combination of GGBS and lime was found to be suitable in stabilizing IOT towards block production. Furthermore, a maximum compressive strength of 7.7 MPa was achieved for blocks after 28days of air curing. Also, the addition of GGBS has reduced the water absorption and apparent porosity of the IOT blocks, confirming the positive interaction between IOT, GGBS and lime. It also indicates the prospective of blended binders in improving the compactness of the blocks, which will have direct influence on the durability and service life of the blocks. Finally, the results show that most of the developed blocks satisfy the requirement of IS 1077 specification and can be used in various applications such as load and non-load bearing walls, framed structures, foundations and pedestrian walkways. 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Compression Based Modeling for Classification of Text Documents
Classification of text data one of the well known, interesting research topic in computer science and knowledge engineering. This research article, address the classification of text files issue using lzw text compression algorithms. LZW is a lossless compression technique which requires two pass on the input data. These two passes are treated separately as training stage and text stage for classification of text data. The proposed compression based classification technique is tested on publically available datasets. Results of the experiments shows the effectiveness of the proposed algorithm. 2019, Springer Nature Singapore Pte Ltd. -
COMPUTATION OF b-CHROMATIC TOPOLOGICAL INDICES OF SOME GRAPHS AND ITS DERIVED GRAPHS
The two fastest-growing subfields of graph theory are graph coloring and topological indices. Graph coloring is assigning the colors/values to the edges/vertices or both. A proper coloring of the graph G is assigning colors/values to the vertices/edges or both so that no two adjacent vertices/edges share the same color/value. Recently, studies involving Chromatic Topological indices that dealt with different graph coloring were studied. In such studies, the vertex degrees get replaced with the colors, and the computation is carried out based on the topological index of our choice. We focus on b-Chromatic Zagreb indices and b-Chromatic irregularity indices in this work. This paper discusses the b-Chromatic Zagreb indices and b-Chromatic irregularity indices of the gear graph, star graph, and its derived graphs such as the line and middle graph. 2023, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved.