Scientific Applications Research Articles

Deposition, dissipation, metabolism, and dietary risk assessment of chlorothalonil on pakchoi

For the scientific application of chlorothalonil, a thorough understanding of chlorothalonil deposition, distribution, and dissipation on pakchoi is required so as to reduce its application dosage. In this study, the effects of the application dosage, application season, planting environment, initial deposition, field dissipation and metabolism of pakchoi were investigated. The chlorothalonil deposition amount gradually increased with increasing dosage and application frequency. The deposition law in each application strategy was leaf > edible parts > petiole > planting soil. The half-lives of chlorothalonil in pakchoi in May, October, and December were 2.86–5.79, 3.08–5.87, and 6.41–11.32 days, respectively. The deposition concentration and dissipation half-life of chlorothalonil in different parts of pakchoi and the planting soil in greenhouse environments were higher than those in open-field environments. The initial deposition concentration and half-life of chlorothalonil in pakchoi gradually decreased from north to south: Beijing >Hefei >Changsha in April and Hefei >Sanya in December. Chlorothalonil can be converted to metabolite I (C8HCl3N2O) in pakchoi plants, which can gradually dechlorinate to produce metabolite II (C8HCl3N2), metabolite III (C8H2Cl2N2), and metabolite IV (C8H3ClN2). Within an application interval of 7 days, the long-term consumption of pakchoi leaves and edible parts poses unacceptable dietary risks. This study improves chlorothalonil utilization rates and provides technical guidance for its scientific application in the pakchoi planting ecosystem.

Deposition, dissipation, metabolism, and dietary risk assessment of chlorothalonil on pakchoi

For the scientific application of chlorothalonil, a thorough understanding of chlorothalonil deposition, distribution, and dissipation on pakchoi is required so as to reduce its application dosage. In this study, the effects of the application dosage, application season, planting environment, initial deposition, field dissipation and metabolism of pakchoi were investigated. The chlorothalonil deposition amount gradually increased with increasing dosage and application frequency. The deposition law in each application strategy was leaf > edible parts > petiole > planting soil. The half-lives of chlorothalonil in pakchoi in May, October, and December were 2.86–5.79, 3.08–5.87, and 6.41–11.32 days, respectively. The deposition concentration and dissipation half-life of chlorothalonil in different parts of pakchoi and the planting soil in greenhouse environments were higher than those in open-field environments. The initial deposition concentration and half-life of chlorothalonil in pakchoi gradually decreased from north to south: Beijing >Hefei >Changsha in April and Hefei >Sanya in December. Chlorothalonil can be converted to metabolite I (C8HCl3N2O) in pakchoi plants, which can gradually dechlorinate to produce metabolite II (C8HCl3N2), metabolite III (C8H2Cl2N2), and metabolite IV (C8H3ClN2). Within an application interval of 7 days, the long-term consumption of pakchoi leaves and edible parts poses unacceptable dietary risks. This study improves chlorothalonil utilization rates and provides technical guidance for its scientific application in the pakchoi planting ecosystem.

A comprehensive review of community detection in graphs

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based methods, spectral clustering, probabilistic modeling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.

Identification of material by X-ray fluorescence analysis with a pyroelectric X-ray generator

By changing the temperature of Lithium Tantalate (LiTaO3) single crystal at moderate vacuum conditions leads to generation of strong electric field. The uncompensated polarization during the heating or cooling of the crystal causes the ejection of electrons from either the dielectric layer on the surface of the crystal or from a metal target depending on the polarity. The electrons are accelerated and gain energy of up to 100 keV. The energy of these electrons can be determined by measuring the end-point energy of the X-ray spectrum that resulted from the electron interactions with the target. The conception of a pyroelectric accelerator enabled us to develop compact (portable) electron source, which does not require an external high-voltage and the use of hazardous materials. The compact and portable nature of pyroelectric-driven particle sources holds significant promise for applications in materials science, particularly for materials analysis methodologies. The research demonstrates the feasibility of utilizing the X-ray signal generated by irradiation with electrons to identify elements in each sample. It is revealed that employing only the electron beam enables the successful acquisition of quantitative information regarding the sample structure through pyroelectric driven PD-PIXE analysis. These findings set the stage for the development of a compact and versatile apparatus for elemental analysis of materials based on a pyroelectric source.

Stable aryl-armed dihydrophenazines-based radicals

Stable organic radicals have long been desired for applications in quantum information science and optoelectronic semiconductor. Weakening the reactivity of unpaired electrons is an efficient strategy for fabrication stable radicals. The dual aryl/alkyl-substituted dihydrophenazine (DArPZ/DAlPz) derivatives have attracted a lot of attention due to their potential radical stability characteristics. In this work, three stable DArPZ-based radical cation compounds are obtained through tuning peripheral substituted groups with different electron donating/withdrawing effect to discuss their reactive activity changes in different solvent and aggregate state. Employing systematic experiments from crystallography, spectroscopy and theoretical calculation analyses, some referenceable molecular structure design strategy and the analytical model of evaluating radical behavior are formed and summarized. In addition, radical cations have good masking ability without continuous electrical circulating, implying their application in light-shading electrochromic devices. This work is referable on the design of stable radicals, the establishment of dynamic evaluation strategies and theoretical analysis models for their reactivity.

Exploring the InSAR signature associated with river-sourced recharge in California’s San Joaquin Valley

Recharge is a critical component for understanding aquifer systems and the sustainable management of groundwater resources, yet this process is challenging to measure at policy-relevant spatiotemporal scales. Building upon previous research, we tested the hypothesis that InSAR can be used to observe river-sourced recharge if the underlying recharge pathways are associated with sufficient clay content. Our analysis leveraged the decomposition of InSAR time series with interpretations of 3D resistivity models derived from airborne electromagnetic (AEM) surveys. We focused our analysis on two study sites where high density AEM data were available and river-sourced recharge is determined to have occurred during wet years: (1) near Fresno, California and (2) near Visalia, California. Sediment type and hydrogeological structure from AEM supported our hypothesis with the InSAR signature attributed to river-sourced recharge occurring only in the study site with semi-confined to confined conditions and relatively high fraction of interbedded clay within recharge pathways. The timing to peak amplitude, the key feature we wanted to isolate in the InSAR data, near Visalia was interpreted as a pressure pulse front associated with river-sourced recharge propagating into the San Joaquin Valley. This study further validated the potential of InSAR, coupled with AEM data, to map and monitor river-sourced recharge in aquifer systems. As InSAR data become more accessible, this approach holds promise for broader applications in groundwater science and management worldwide.

Ultrafast Photoinduced Dynamics in 1,3-Cyclohexadiene: A Comparison of Trajectory Surface Hopping Schemes†.

Photoinduced nonadiabatic processes play a crucial role in a wide range of disciplines, from fundamental steps in biology to modern applications in advanced materials science. A theoretical understanding of these processes is highly desirable, and trajectory surface hopping (TSH) has proven to be a well-suited framework for a wide range of systems. In this work, we present a comprehensive comparison between two TSH algorithms, the conventional Tully's fewest switches surface hopping (FSSH) scheme and the Landau-Zener surface hopping (LZSH), to study the photoinduced ring-opening of 1,3-cyclohexadiene (CHD) to 1,3,5-hexatriene at the spin-flip time-dependent density functional theory (SF-TDDFT) level of theory. Additionally, we compare our results with a literature study at the extended multistate complete active space second-order perturbation theory method (XMS-CASPT2) level of theory. Our results show that the average population and lifetimes estimated with LZSH using SF-TDDFT are closer to the literature (using multireference methods) than those estimated with FSSH using SF-TDDFT. The latter speaks in favor of applying LZSH in combination with the SF-TDDFT method to study larger and more complex systems such as molecular photoswitches where the CHD molecule acts as a backbone. In addition, we present an implementation of Tully's FSSH algorithm as an extension to the PySurf software package.

Predicting Air Quality Index and Fine particulate matter in Bagdad city Levels Using Advanced Machine Learning and Deep Learning Techniques

Particulate matter pollution is recognized globally as one of the most hazardous forms of air pollution, profoundly impacting environmental integrity and public health. Key metrics for assessing this pollution include the Air Quality Index (AQI) and fine particulate matter with diameters ≤ 2.5 μm (PM2.5). These indicators are closely associated with severe health consequences, such as premature death from chronic exposure. While traditional statistical methods have been employed in some studies to evaluate AQI and PM2.5, the application of advanced machine learning techniques has been limited. This research employs deep learning and artificial neural networks (ANN) to forecast AQI and PM2.5 levels in Baghdad, Iraq. The study utilizes an extensive dataset from July 1, 2016, to December 12, 2021, comprising over 48,000 data points for AQI and PM2.5. Time serves as an independent input variable influencing these dependent variables. The analysis employs a diverse set of machine learning algorithms, including random forest, decision tree, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory networks (LSTM). The findings demonstrate that MLP and LSTM models outperform other methods, providing the most accurate predictions. The correlation coefficients were 0.977 and 0.983 for the prediction of AQI and 0.973 and 0.985 for the prediction of PM2.5 using MLP and LSTM, respectively. In addition, the outcomes showed that both AQI and PM2.5 were within the moderate to unhealthy ranges, and their distribution levels pointed to the need for addressing air quality in Baghdad city. Furthermore, this study contributes to the burgeoning field of machine learning applications in environmental science by establishing a robust and nuanced predictive framework for evaluating air quality. It highlights the potential of deep learning in public health applications and offers actionable insights for policymaking to mitigate air pollution and its adverse effects.

PyPI: An internet-enabled learning tool to boost learner motivation for crop protection

Instructors are always interested in methods to activate learner incentives and motivation to increase learning effectiveness. This paper introduces the Python Package Index (PyPI) as a powerful tool to maximize learner incentives on software and presents an example of its application in agriculture and science. The more useful the PyPI application is, the more it will be downloaded worldwide, providing an external review for the learner, and strengthening their incentive. However, many existing tutorials on PyPI, including the official site, are not updated on the twine library for uploading files to the PyPI site. This paper presents an updated tutorial on using PyPI for counting disaggregated objects such as bugs and pests, and for software reproducibility validation via Code Ocean. Additionally, generative AI is introduced as an indispensable assistant for tasks such as understanding technical terms and providing solutions for encountered problems.

Conjugated microporous polymer for solid-phase extraction of neonicotinoid insecticides from environmental water samples

Conjugated microporous polymers (CMPs) have unique characteristics and have been used in a range of fascinating applications in separation sciences. In this study, a CMP, designated as CMP-1, was synthesized via the Sonogashira-Hagihara coupling reaction using 1,3,5-triphenylbenzene and 1,4-dibromobenzene as building blocks. CMP-1 features a large surface area, abundant micropore structures, and excellent stability, making it a promising solid-phase extraction adsorbent for the efficient enrichment of neonicotinoid insecticides (NEOs). Under the optimized conditions, CMP-1 was combined with high-performance liquid chromatography and diode array detection to enable the detection of NEOs with a wide linear range (0.5–200 μg·L–1), a low detection limit (0.26–0.58 μg·L–1), and acceptable precision. The developed method was applied to determine spiked NEOs in three types of environmental water samples, with recoveries of 73.7%–112.0% and relative standard deviations of 0.6%–9.4%.

Wearable solid-phase microextraction sampling for enhanced detection of volatile analytes in human ears

BackgroundInvestigating ear at molecule level is challenging task, since there is a lack of molecular detection by traditional diagnosis techniques such as otologic endoscopy, ear swab culture, and imaging diagnostic technique. Therefore, new development of noninvasive, highly sensitive, and convenient analytical method for investigating human ears is highly needed. ResultsWe developed a wearable sampling device for extracting trace analytes in ear by fixing solid-phase microextraction fibers into modified earmuffs (SPME-in-earmuffs). After sampling, SPME fiber was coupled with gas chromatography-mass spectrometry (GC-MS) for identification and quantification of extracted analytes. Enhanced detection of various analytes such as volatile metabolites, exposures, and therapeutic drugs of ears were demonstrated in this work. Particularly, sport-induced metabolic changes such as fatty acids, aldehyde compounds and oxidative produces were found from human ears using this method. Acceptable analytical performances were obtained by using this newly developed method for detecting ear medicines, e.g., low limit of detection (LOD, 0.005–0.021 ng/mL) and limit of quantification (LOQ, 0.018–0.071 ng/mL), excellent linear dynamic responses (R2 > 0.99, ranging from 0.050–8.00 ng/mL), good relative standard deviations (RSDs, 13.19 % ∼ 21.40 %, n = 6) and accuracy (84.43–150.18 %, n = 6) at different concentrations. SignificanceFor the first time, this work provides a simple, convenient, and wearable microextraction method for enhanced detection of trace volatiles in human ears. The enclosed space between ear and earmuff allows headspace SPME sampling of volatile analytes, and thus provides a new wearable method for monitoring ear metabolites and human exposures, showing potential applications in human health, disease diagnosis, and sport science.

Identification of material by X-ray fluorescence analysis with a pyroelectric X-ray generator

By changing the temperature of Lithium Tantalate (LiTaO3) single crystal at moderate vacuum conditions leads to generation of strong electric field. The uncompensated polarization during the heating or cooling of the crystal causes the ejection of electrons from either the dielectric layer on the surface of the crystal or from a metal target depending on the polarity. The electrons are accelerated and gain energy of up to 100 keV. The energy of these electrons can be determined by measuring the end-point energy of the X-ray spectrum that resulted from the electron interactions with the target. The conception of a pyroelectric accelerator enabled us to develop compact (portable) electron source, which does not require an external high-voltage and the use of hazardous materials. The compact and portable nature of pyroelectric-driven particle sources holds significant promise for applications in materials science, particularly for materials analysis methodologies. The research demonstrates the feasibility of utilizing the X-ray signal generated by irradiation with electrons to identify elements in each sample. It is revealed that employing only the electron beam enables the successful acquisition of quantitative information regarding the sample structure through pyroelectric driven PD-PIXE analysis. These findings set the stage for the development of a compact and versatile apparatus for elemental analysis of materials based on a pyroelectric source.

The Efficiency of the Alu Insertion Sequence in Discrimination Among some Individuals

Background: The Alu element is a widely distributed short interspersed nuclear element (SINE) in the human genome and has important applications in forensic science. The current study focused on assessing the effectiveness of Alu insertion polymorphism in forensic DNA profiling to identifying samples of some individuals living in the Babylon Governorate. Materials and methods: DNA was extracted From frozen blood samples (60) individuals were collected from Babylon - Al-Hilla Governorate from 8/8/2022 until 8/9/2022. Results: purified then a PCR technique Alu insertions were A(2q21.1)(111-115bp) 0.75, Alu deletion (0.25), B(8q23.1) (0.375) (0.625) C(13q34) (0.5)(0.5), D(15q23) (0.775) (0.225), E(16q23.3) (0.692) (0.308) and F(19q13.12)(0.616)(0.384) respectively.The alleles with Alu insertions at (2q21.1) were the most prevalent, whereas the Alu insertions at (8q23.1) were the least common. The similarity coefficient among individuals varied from 0.4 to 1, based on the proportion of genetic relatedness between them. Conclusions: Alu elements have efficacy in families’ discrimination it can be used more than one site, set of Alu insertion sequences should be used to accurate results.

Analysis of High-Performance Parallel Computing using TOPSIS Method

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

A counter-gravity casting system for in situ high-speed synchrotron x-ray imaging characterization.

Counter-gravity casting (CGC) aims to eliminate turbulent melt flow and defect formation during filling and subsequent solidification by pushing high-temperature melt into the mold cavity against gravity with regulated pressure. However, limited by the opaqueness of molten metals and the complexity of the CGC apparatus, it is extremely difficult to directly quantify the high-velocity mold filling and pressurized solidification in real-time. Here, we report the design and characterization of a CGC system capable of in situ monitoring of mold filling and subsequent solidification processes in the synchrotron beamlines by deploying a high-energy, high-speed synchrotron x-ray imaging technique. The high-velocity melt flow and dendrite growth during pressurized solidification have been quantified for systematical process parameter analysis by investigating time-resolved x-ray images of an exemplary Al-Cu alloy. The high-speed imaging results demonstrate that the in situ CGC system provides a useful way to better understand the fundamentals of mold filling, pressurized solidification, and experimental inputs for high-fidelity modeling in scientific and industrial applications.

An exploration of online-simulation-driven portfolio scheduling in workflow management systems

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use.The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. We perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

Individual quantification of ozone and reactive nitrogen species in mixtures by broadband UV–visible absorption spectra deconvolution

Ozone (O3), nitrogen oxides (NO x ), and reactive nitrogen species (RNS) play critical roles in atmospheric-pressure plasma applications. Although it is crucial to individually quantify these species to understand atmospheric-pressure plasmas and increase their effectiveness, the lack of reliable and cost-effective diagnostics makes this difficult for many researchers. To address this problem, we introduce a new deconvolution method of broadband ultraviolet–visible absorption spectra for the simultaneous measurement of eight species—O3, NO, NO2, NO3, N2O4, N2O5, HONO, and HNO3. Processing of broadband spectra enables deconvolution of similar cross-section profiles and measurement of high densities exceeding the instrumental limit. Novel correction processes enable accurate analysis despite incomplete cross-section data and utilize a priori chemical knowledge to ensure theoretically reasonable results. Two case studies test the efficacy of the method: NO2 and N2O4 equilibria, and reactive species produced by a surface dielectric barrier discharge. With an analysis time of 15–20 ms per spectrum, the measured densities agree well with other theoretical and experimental results, and detection limits on the order of ppmv were achieved with a short path length of 15 cm. This spectral analysis method will facilitate the real-time monitoring of O3, NO x , and RNS in many scientific research and industrial applications of atmospheric pressure plasmas.

Novel Polypeptide Automated Synthesis Devices: A Review

Peptides are a unique class of proteins and one-dimensional biological nanomaterials that play an exceptionally important role in life science and industrial applications. At present, peptide synthesis equipment encounters issues like low synthetic efficiency, challenges in scaling up, and limited automation. This article provides an overview of the key technologies in peptide synthesis equipment, covering aspects such as peptide information screening, peptide synthesis, and detection. It analyzes and summarizes the principles, methods, and critical challenges of traditional solid-phase peptide synthesis technology, microwave-assisted solid-phase peptide synthesis technology, and continuous-flow solid-phase peptide synthesis technology. It also discusses microfluidic solid-phase peptide synthesis technology and future research trends, offering insights into the search for an efficient, high-yield, high-purity, scalable, and intelligent peptide synthesis method, which holds significant research significance.

The Use of Weather, Water, Ice, and Climate (WWIC) Information in the Polar Regions: What Is Known after the Decadelong Polar Prediction Project?

Abstract The polar regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services, and disaster response capabilities are limited in these remote environments. Relevant and usable weather, water, ice, and climate (WWIC) information is vital for safety, activity success, adaptation, and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its “Societal and Economic Research and Applications” (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints, and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–23), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles (n = 43), examining collaborations, topics, methods, and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics, and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness, and actionable knowledge provision for users and guide future research as the critical need for salient weather services across the polar regions remains beyond the PPP. Significance Statement Every day, people in the Arctic and Antarctic use weather, water, ice, and climate information to plan and carry out outdoor activities and operations in a safe way. Despite advances in numerical weather prediction, technology, and product development, barriers to accessing and effectively communicating high-quality usable observations, forecasts, and actionable knowledge remain. Poorer services, prediction accuracy, and interpretation are exacerbated by a lack of integrated social science research on relevant topics and a mismatch between the services provided and user needs. As a result, continued user engagement, research focusing on information use, risk communication, decision-making processes, and the application of science for services remain highly relevant to reducing risks and improving safety for people living, visiting, and working in the polar regions.

Hybrid fault tolerant cost aware mechanism for scientific workflow in cloud computing

Cloud computing provides solutions for diverse commercial and academic applications which is the primary goal. Scientific workflows are used in the cloud-computing environment to analyses large-scale scientific applications. For scientific workflows, many data is required, and a single scientific workflow that includes hundreds of stages, depending on the application's time restrictions, task failures, money limits, incorrect task organization, and task management issues can all hinder the implementation of scientific methods. In light of this, a cloud-based scientific workflow management and scheduling system that is fault-tolerant and data-oriented method are proposed. This research designs a novel hybrid cost-aware fault tolerant (HCFT) mechanism for minimizing the cost. Moreover, HCFT integrates optimal clustering and efficient resource utilization through parallel and distributed execution. Novelty of HCFT lies in novel clustering of the similar task for improvisation, CyberShake, laser interferometer gravitational wave observatory (LIGO), Montage, and sRNA identification protocol using high throughput technology (SIPHT) processes are used in the simulations to evaluate the performance of the proposed approach.