Analytic Process Research Articles

A New Brittleness Evaluation Index for Reservoir Rocks Based on Fuzzy Analytic Hierarchy Process and Energy Dissipation

Summary Accurate and quantitative evaluation of rock brittleness is essential for optimizing hydraulic fracturing parameter design. Specific aspects of rock brittleness can be characterized based on mineral composition, strength parameter, stress-strain, and elasticity parameter. However, in characterizing the effect of high confining pressure on rock brittleness, these advantages have not been shown for various reasons. The essence of rock fracture resides in the accrual and dissipation of energy, concomitant with the transformation of three distinct energy modalities: elastic energy, dissipated energy, and post-peak release energy. In this study, we proposed a new brittle index for reservoir rocks considering the interactions of the energy evolution process in different stages. The associated Bii values are computed through the analysis of energy evolution based on the fuzzy analytic hierarchy analysis process (FAHP) method. Tri-axial compression tests were conducted on granite, shale, and sandstone specimens under different confining pressures for the validation of the proposed brittleness evaluation (BE) index. The results indicate a decreasing trend in the brittleness index with increasing confining pressure. Comparative analysis with other existing brittleness evaluation indexes demonstrates that the new brittleness evaluation index offers a more precise evaluation of the brittleness of various rock types.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation.

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Application of a novel deep eutectic solvent as an eco-friendly medium for green extraction of cadmium from herbal medicines

In this research, hydrophobic DESs were successfully prepared using components with hydrophobic and sulfydryl group, which were applied to develop a green and eco-friendly method to extract and detect the cadmium (Cd) in herbal medicine (HM). The basic properties of the DESs were studied, which showed good hydrophobicity, low density and sufficient absorption capacity. In extraction process, the extraction efficiency (EE) of Cd employing proposed process ranged from 89.12 %∼94.59 % and the maximal enrichment factor (EF) was 46.85. The DES based method demonstrated a good linear response in the range of 1 ∼ 50 µg·L-1 with a detection limit (LOD) of 0.29 μg L-1, which was detected by Inductively coupled plasma mass spectrometry (ICP-MS). Additionally, the accuracy of method (as recoveries %) was obtained with the recoveries ranging from 98.38 %∼105.48 %. The extraction process was also compared with microwave digestion (MD) method and showed basically equivalent effect with good propensity to enrichment. The mechanism was explored by DFT calculation to reveal the interaction of extraction process. The greenness of the proposed method was also evaluated using the Green Analytical Procedure Index (GAPI) and Analytical Eco-Scale (AES), which indicated excellent environmental protection. This study successfully prepared novel DESs and established an eco-friendly and effective analytical process integrating adsorption, collection, separation and detection for the detection of heavy metals in HM, and the DESs were contributed to the first three parts as a multifunctional role. The absorption mechanism related to the formation of molecular complexes in quantum chemistry perspective, which were attribute to the electrostatic attraction and H+ exchange between Cd and adsorbent. This method showed vast potential in heavy metal research involving complex matrices such as HM and its processed products, and the proposed process provides the way for a greener alternative for Cd extraction rather than conventional polluting processes.

Decision making in human resource management: a systematic review of the applications of analytic hierarchy process.

The Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), and Analytic Network Process (ANP) methods are widely used for decision-making across various fields, and have shown success in numerous applications within human resource management (HRM). The purpose of this study is to present a systematic literature review on the applications of AHP, FAHP, and ANP in human resource management. The search process encompassed two main online databases, Scopus and Web of Science. This research covers a total of 180 application papers. To help readers extract quick and meaningful information, references are summarized in tabular format. The results showed that these methods have been applied in various domains of HRM such as performance management and appraisal, selecting human resources, talent attraction and retention, intellectual capital, workplace safety, reward management, e-HRM, green HRM, etc. To the best of our knowledge, no comprehensive research review has yet been conducted on the applications of AHP in HRM.

Evaluation of an automatic sample preparer in the development and validation of an analytical method for vial wall sorptive extraction

Automation has transformed the field of analytical chemistry by enhancing efficiency and precision in laboratory processes. A crucial step in analytical workflows, sample preparation, has traditionally relied on manual techniques, leading to variability and inefficiency. Vial wall sorptive extraction (VWSE) is a promising alternative for automating and enhancing sample preparation in analytical processes. This study aims to evaluate the performance of automated VWSE in the analysis of alkanes (C10-C18) in aqueous phases and to explore its potential use in routine laboratory settings for analyzing contaminants in environmental analysis, quality control, and forensics. VWSE devices were developed and characterized, a chromatographic method was established, and the sample preparation process was automated using an automated liquid handling system. The devices manufactured exhibit the best overall response and low standard deviation. Statistical tests confirm the equivalence of the vials prepared. The developed method achieved good results in analytical validation, a wide linear range with a high coefficient of determination (R2 > 0.90), low limits of detection (0.01–1.02 ng mL−1), and low limits of quantification (0.05–1.05 ng mL−1) were achieved. The parameters repeatability (<26.91 %), recovery (91 to 116 %), and intermediate precision (<22.29 %) also achieved a good analytical performance. The developed automated VWSE method proves effective in quantifying alkanes and meets analytical method validation criteria. This research highlights the potential of automated VWSE for sample preparation in various analytical applications.

Life cycle assessment and multi-criteria decision-making for sustainable building parts: criteria, methods, and application

Abstract Purpose Sustainable building design relies heavily on building parts, with crucial consideration for climate and environmental impact. Due to numerous criteria and diverse alternatives, employing multi-criteria decision-making (MCDM) to choose the best alternative is essential. Yet, relevant criteria and suitable MCDM methods for life cycle-based building planning still need to be determined. This study highlights prevalent environmental criteria and offers guidance on MCDM approaches for sustainable building parts. Methods This study introduces an innovative approach by integrating life cycle assessment and MCDM. This provides comprehensive decision support for planners. A systematic literature review identifies environmental criteria for building parts and is validated in expert workshops. Thus, the relevance of criteria across the building life cycle is established. Furthermore, the study analyzes MCDM approaches in the built environment. From this, the study employs and evaluates the Analytical Network Process (ANP) and Analytical Hierarchy Process (AHP) in a case study. Thereby, it offers insights into effective decision-making methodologies for sustainable building practices. Results This research categorizes environmental criteria for building parts and buildings into emissions, energy, resources, and circularity. Among 26 building part-related criteria, the global warming potential is highlighted. While the AHP is widely used in MCDM, a standardized method in planning processes is yet to emerge. Applying the ANP and AHP reveals similar rankings for the best and worst alternatives in a case study focused on selecting the optimal ceiling structure. Ribbed or box slab ceiling constructions are favored over reinforced concrete and composite timber-concrete constructions. Conclusions This study presents a novel method for life cycle-based MCDM challenges, identifying key environmental criteria. While material correlations exist, evaluating building parts demands simultaneous consideration of multiple criteria. Future research aims to compare further MCDM methods regarding their applicability, transparency, and ranking to enhance decision-making in sustainable construction. These investigations are essential for refining decision-making processes in the built environment, ensuring effective and transparent sustainability planning approaches.

Spectral intensity drift correction of Spark Mapping Analysis for large-size metal materials

BackgroundSpectral intensity drift is a frequent issue in analytical processes, especially in long time excitation scanning for large size metal materials, which can significantly adversely impact the accuracy and stability of analysis results. Spectral intensity drift correction is the process of preprocessing spectral data using mathematical algorithms in order to facilitate the subsequent qualitative and quantitative analysis of spectra, especially in combination with stoichiometric methods. Up to now, spectral intensity drift correction within prolonged excitation has not been reported yet. ResultsWe propose an intensity drift correction method for element content of large-size samples using the Spark Mapping Analysis for Large Samples (SMALS) technique. By considering the row-by-row and column-by-column mapping modes of the SMALS, this includes curve fitting baseline correction for in-row and in-column correction, as well as total average value correction for inter-row and inter-column correction. The final measurement values are derived by coupling rows with columns. The careful implementation of correction steps can enhance baseline correction performance, effectively reducing measurement errors a drift errors. Application of this method to characterize the cross and longitudinal sections of an oversized steel billet indicates high agreement with composition distribution obtained by micro-beam X-ray fluorescence (μ-XRF). The corrected longitudinal and cross-sectional data also exhibit strong alignment. Comparison of statistical analysis results pre- and post-correction demonstrates significant improvements in the clarity of elements segregation pattern. SignificanceThis intensity drift correction method not only enhances the spectral quality but also improves the accuracy and robustness of quantitative and qualitative spectral analysis. This study contributes to establishing a robust foundation for component characterization of large-size metal materials using the SMALS technique. The novel spectral intensity correction method shows theoretical significance and practical value for large-scale, long-duration excitation scanning analysis.

Profiling adolescents' vulnerability to racial misinformation: An hybrid intervention aimed at promoting mediated intergroup contact

AbstractThis study addresses the issue of adolescents' susceptibility to racial misinformation, testing a socio‐analytical intervention within an educational community through the inducement of analytical processing of misleading news on one side, and mediated contact on the other. Rolling Minds web app has been designed to implement a hybrid schools intervention thanks to which classmates engage with conversational agents, guiding them in deconstructing racial stereotypes, reframing misleading narratives and empathizing with immigrants' point of view. All the intervention activities aim to enhance intergroup contact. For this purpose, in this study, which involved 208 young participants (mean age 14.65; SD = 0.74), we pursued two research objectives. Firstly, by employing a person‐centred approach, our first research question (RQ1) is to identify subgroups of adolescents based on their propensity to engage in analytical reasoning, stereotypical beliefs and self‐transcendence values, finding different vulnerability clusters to racial misinformation. In relation to the emerging profiles, their reactions to the misleading news were initially observed (ie, sharing and fact‐checking). Secondly, regarding the second research question (RQ2), through path analysis we aim to understand whether and how adolescents characterized by different profiles vary in performing activities of socio‐analytical intervention aimed at enhancing contact intentions. These results can guide the development of tailored hybrid educational strategies to engage young people in approaching racial online misinformation in a more reflective and unbiased manner, considering the adolescents' individual differences in vulnerability to racial hoaxes.

Genesis 3:16—Text and Context

Genesis 3:16 is arguably the most troubling biblical verse for issues of gender relations and women’s roles. It figures prominently in later Jewish and especially Christian sources, and discussions in those texts have influenced subsequent understandings of the verse and of the Eden narrative in which it is embedded. This article engages in a careful reading of the biblical text in order to elucidate its meaning apart from later traditions. Recognizing the poetic character of the four lines of this verse is an important part of the analytical process, as is situating it within the Eden tale. Also, because no text arises in a vacuum, considering the Iron Age context—the world of the Israelite populace, that is, the world behind the text, a world vastly different from our own—provides the requisite socio-historical sensitivity. An awareness of that ancient context means openness to a suggestion about what Gen 3:16 may have meant to its ancient audience.

Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system

Electric vehicles (EVs) are positioned as an alternate green energy technology, which potentially can enable the efficient transition to sustainable low-carbon emission transportation systems. However, different barriers hamper the public adoption of EVs. The degree to which these barriers prevent the adoption of EVs and the potential alternatives to these barriers are mostly unknown. To fill this research gap, the current study contributes by systematically identifying those barriers based on their importance. Initially, several barriers were identified using existing literature, and then by employing the modified Delphi method, a total of twenty-five barriers were classified into five significant dimensions. The Analytical Hierarchical Process (AHP) determined the rankings of these barriers based on weight allocation. As a next step, the Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS) ranked alternative solutions to these barriers. The findings indicate that 'technological barrier' is the top-ranked barrier among all major dimensions. The overall ranking indicated that 'battery technology' is most influential among sub-dimensions. It is recommended that "investment in research and development" is the best alternative to overcome these barriers. In order to improve the renewable and sustainable urban transportation system, all stakeholders should cooperate in a consistent and coherent manner.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Trust value evaluation of cloud service providers using fuzzy inference based analytical process

Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for IT and healthcare industries over traditional methods. However, a lack of trust between CSUs and CSPs is hindering the widespread adoption of cloud computing across industries. Since cloud computing offers a wide range of trust models and strategies, it is essential to analyze the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggests an accurate, fuzzy logic-based trust evaluation model for evaluating the trustworthiness of a cloud service provider. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using Quality of Service (QoS) characteristics like security, privacy, dynamicity, data integrity, and performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting.

Analisis Penerapan Metode Analytical Network Process (ANP) Pada Pemilihan Tempat Kost Terbaik di Daerah Kampus ITK

The Analytic Network Process (ANP) method is an approach that can be used to represent the level of importance from various parties by considering the interconnected relationships between criteria and sub-criteria. This model is a development of the AHP method, thus it has a higher level of complexity compared to the AHP method. In this paper, the issue addressed is the selection of the best boarding house location in the ITK campus area located at Km. 15 Karang Joang, North Balikpapan. This selection is done using the super decision software, aiming to determine the ranking order of boarding houses that have the best quality or alternatives according to the students of the Kalimantan Institute of Technology (ITK) through questionnaire filling and interviews with the students. There are three criteria used in this selection, first is the distance with sub-criteria of distance from the boarding house to the campus and distance from the boarding house to the city. Second, the price criterion with sub-criteria of monthly rent and electricity-water costs, and others. Third, the facilities criterion with sub-criteria of bathroom, parking, and kitchen. As for the alternatives, 10 boarding houses are used. Based on the research results, the alternative solutions obtained are ranked in the top 3 boarding houses. The first is Kos Maghfiroh, the second is Kos Hira, and the third is Kos Griya Mansur

PET digitization chain for Monte Carlo simulation in GATE

Objective. We introduce a versatile methodology for the accurate modelling of PET imaging systems via Monte Carlo simulations, using the Geant4 application for tomographic emission (GATE) platform. Accurate Monte Carlo modelling involves the incorporation of a complete analytical signal processing chain, called the digitizer in GATE, to emulate the different count rates encountered in actual positron emission tomography (PET) systems. Approach. The proposed approach consists of two steps: (1) modelling the digitizer to replicate the detection chain of real systems, covering all available parameters, whether publicly accessible or supplied by manufacturers; (2) estimating the remaining parameters, i.e. background noise level, detection efficiency, and pile-up, using optimisation techniques based on experimental single and prompt event rates. We show that this two-step optimisation reproduces the other experimental count rates (true, scatter, and random), without the need for additional adjustments. This method has been applied and validated with experimental data derived from the NEMA count losses test for three state-of-the-art SiPM-based time-of-flight (TOF)-PET systems: Philips Vereos, Siemens Biograph Vision 600 and GE Discovery MI 4-ring. Main results. The results show good agreement between experiments and simulations for the three PET systems, with absolute relative discrepancies below 3%, 6%, 6%, 7% and 12% for prompt, random, true, scatter and noise equivalent count rates, respectively, within the 0–10 kBq·ml−1 activity concentration range typically observed in whole-body 18F scans. Significance. Overall, the proposed digitizer optimisation method was shown to be effective in reproducing count rates and NECR for three of the latest generation SiPM-based TOF-PET imaging systems. The proposed methodology could be applied to other PET scanners.

Flood Monitoring and Mapping in the Ghatal Region of West Bengal, India

Abstract The Ghatal block of Paschim Medinipur district in West Bengal, India experiences frequent flooding due to the overflow of the rivers Dwarakeswar, Rupnarayan, Shilabati, and their tributaries. Although the Ghatal master plan was prepared and the foundation stone was placed in 1982 to reduce the flood vulnerability in this region, the plan is still not being implemented. To determine the flood vulnerability in the Ghatal block, very few research investigations have been carried out. The previous studies just applied a successful scientific methodology to evaluate flood risk; they did not engage in participatory research with local community perception. Hence, a thorough investigation is needed to manage floods in a way that increases community resilience. Using a comparative review and the geographic information system (GIS), the study seeks to identify the flood-prone locations in the Ghatal block, which is essential for flood management in any region. The article uses a multi-criteria analysis to pinpoint probable flood hazard regions. Seven parameters are used in the study to carry out the multi-criteria analytical hierarchy procedure. Based on their relative importance for the event, the criteria’s weights are determined using the Analytical Hierarchical Process (AHP) method, and a Suitability Index is prepared. The index indicates that the severe problem of river flooding in the Ghatal block of Paschim Medinipur needs to be looked after on an urgent basis and with path-breaking solutions, especially for the Ghatal municipality and the various gram panchayats such as Irpala, Mansuka-I, Monoharpur-I, Ajobnagar-I, Mohonpur, Dewanchak-I and the North-Eastern part of Sultanpur and the Southern part of Dewanchak-II.

The priority scale determination in sustainable maintenance strategy for university building based on the analytical network process (ANP)

The priority scale determination in sustainable maintenance strategy for university building based on the analytical network process (ANP)

Spatial distribution and equity of urban green space provision in Tehran Metropolis using hybrid Factor Analysis and Analytic Network Process (F’ANP) model

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AHP and geospatial technologies for identifying groundwater exploration target regions in Dindigul, southern India

Recent advances in space technology are being widely utilized in resource mapping and management as they provide a high level of accuracy over a short duration and at a lower cost, when compared to traditional methods. Remote sensing and the geographical information system (GIS) play a vital role in collecting and analyzing information used to map resources and earth surface features. For example, anthropogenic activities and climate change affect the natural interaction between the surface and ground water making the monitoring and management of groundwater resources mandatory for future sustainable development. In this study, we attempt to identify the groundwater potential zones in the Dindigul district of Tamil Nadu, India using these emerging technologies. To map regions with groundwater potential, the analytical hierarchical process (AHP) was used to assign weights to all classes in each thematic layer based on groundwater-influencing factors. The sub-features of every parameter were also ranked, using a pairwise comparison method, based on the groundwater-influencing factors and expert knowledge. The parameters selected for the analysis were geology, geomorphology, soil, land-use/land-cover, drainage density, lineament density, slope, rainfall, thickness of top-soil, thickness of fractured zone, thickness of weathered zone, topographic wetness index, roughness and curvature. The weighted overlay analysis method was adopted for all the thematic layers, with technical support from the AHP for assigning weightages. The results were classified into five categories viz., very high, high, moderate, low, and very low potential zones, and used to prepare a groundwater potential map. This map revealed a nuanced distribution of groundwater potential across the district, with varying degrees of potentiality in different regions. Very high potential zones occupy a minimal area (0.07%) but are strategically located in areas characterized by high weathered zone thickness and conducive soil characteristics. High potential zones cover a significant area (22.33%) and are dispersed throughout the district, with specific concentrations in regions featuring highly fractured zones and favorable geological attributes. Moderate potential zones (35.12%) are primarily located in the northeastern part of the study area, while low and very low potential regions cover 19.56% and 22.92% of the study area. The low and very low potential zones appear particularly in hilly terrains, with unfavorable top soil and geological conditions. The validation of the groundwater potential zone (GWPZ) map using the annual average water level data demonstrates a substantial match (60.53%) between the identified groundwater conditions and actual well depths, affirming the reliability and significance of the findings from this study.

Engineering a GPCR-based yeast biosensor for a highly sensitive melatonin detection from fermented beverages

Melatonin is a multifunctional molecule with diverse biological roles that holds great value as a health-promoting bioactive molecule in any food product and yeast’s ability to produce it has been extensively demonstrated in the last decade. However, its quantification presents costly analytical challenges due to the usual low concentrations found as the result of yeast metabolism. This study addresses these analytical challenges by optimizing a yeast biosensor based on G protein-coupled receptors (GPCR) for melatonin detection and quantitation. Strategic genetic modifications were employed to significantly enhance its sensitivity and fluorescent signal output, making it suitable for detection of yeast-produced melatonin. The optimized biosensor demonstrated significantly improved sensitivity and fluorescence, enabling the screening of 101 yeast strains and the detection of melatonin in various wine samples. This biosensor’s efficacy in quantifying melatonin in yeast growth media underscores its utility in exploring melatonin production dynamics and potential applications in functional food development. This study provides a new analytical approach that allows a rapid and cost-effective melatonin analysis to reach deeper insights into the bioactivity of melatonin in fermented products and its implications for human health. These findings highlight the broader potential of biosensor technology in streamlining analytical processes in fermentation science.

Interpretation and utility of the Animal Health Loss Envelope as part of the Global Burden of Animal Diseases analytical process.

The Global Burden of Animal Diseases (GBADs) programme follows a multistage process to produce disease burden estimates in livestock and farmed aquatic animal production systems. The GBADs programme has broad goals of inclusivity, transparency and rigour. Meeting those goals means providing users of all levels of technical expertise with a clear explanation of the programme's output. In this way, the meaning and limitations of those results are clearly communicated, minimising the risk of misinterpretation. The first published estimates of disease burden have been calculated at farm level using a new metric called the Animal Health Loss Envelope. This metric estimates the cost of lost productivity and expenditure on disease control for profit-maximising producers by comparing current system performance to a hypothetical ‘ideal health'scenario. This ideal is a farm-specific concept and is critically different from an ideal health state when physiologically defined. The metric and its key concepts are described in this article.