Multi-attribute Approach Research Articles

Evaluation of fractured carbonate reservoir and prediction of favorable areas in the eastern area of Amu Darya Right Bank

Fractured carbonate reservoirs are significantly developed in the eastern area of the Amu Darya Right Bank. However, their types, distributions, and fracture characteristics remain unclear. This uncertainty complicates reservoir prediction and hampers exploration and development processes. Given the strong correlation between fracture development and productivity, analyzing fractures is crucial. Comprehensive evaluation and prediction methods for fractured reservoirs are essential for advancing the oil and gas industry. Based on core and geological data analyses, it finds that these reservoirs exhibit low porosity and low to ultra-low permeability. By employing conventional logging alongside specialized methods, such as electrical imaging, nuclear magnetic resonance, and far detection logging, fractures and their effectiveness can be identified and evaluated, clarifying the characteristics of reservoir spaces. Constrained by the results from core and logging analyses, seismic single attribute analysis techniques is applied to predict fractures in the HX block of Amu Darya. To mitigate the limitations of single-attribute analysis, utilize a well-supervised BP neural network method for comprehensive fracture prediction. This multi-attribute approach increases the fracture prediction probability from less than 70%–72.7%. By integrating geological understanding and well logging, and considering the influence of lithology and structure on the reservoir, synthesize the fracture prediction results to optimally select favorable areas.

Multi-attribute approach for selection of polymeric materials for manufacturing gears: A case study in the automotive industry

Automotive industry is characterized by mass production, a large share in the gross national income and high employment of workers with different knowledge and skills. Improving the production process, as well as product quality, is one of the most important tasks of automotive enterprise as well as government management. This research promotes a new fuzzy hybrid model for determining the priority of polymeric materials for manufacturing gears in an exact manner, which leads to an incremental improvement in the quality of the considered product. The analysis of polymeric materials and their characteristic is based on data from the relevant literature and experiences of the best practice. The relative importance of material characteristics is stated as fuzzy group decision making problem. The weights vector is determined by using the fuzzy Analytic Hierarchical Process and fuzzy geometric mean. The rank of polymeric materials is obtained by employing the proposed Technique for Order Preference by Similarity to Ideal Solution with triangular fuzzy numbers. The fuzzy algebra rules have been used for determining: (i) distances from Fuzzy Positive Ideal Solutions and Fuzzy Negative Ideal Solutions and (ii) closeness coefficient values. The proposed fuzzy hybrid model testing and verification are performed on real data in an automotive enterprise.

Frost Damage Mitigation in Flowers and Fruitlets of Peach and Almond from the Application of a Multi-Attribute Approach Biostimulant.

To prevent frost damage in fruit trees, growers employ passive and active methods, and one of these second methods is the use of biostimulant compounds against abiotic stress. In this study, two trials were conducted to evaluate the effectiveness of a multi-attribute approach biostimulant-containing α-tocopherol, boron, and glycols, in peach ('UFO-4' cultivar) and almond ('Vairo' cultivar) trees. In a first trial, one-year-old shoots with flowers were collected after 24 h, 48 h, and 96 h of the biostimulant applications. Two different application rates of the product (1000 and 2000 cc ha-1) were tested and compared to an untreated control. In a second trial, one-year-old shoots with fruitlets were collected after 24 h of the biostimulant applications. In this case, only an application rate (2000 cc ha-1) was tested. In the two trials, the collected one-year-old shoots were subjected to different frost temperatures using a controlled environment chamber. The damage level was assessed by a morphological analysis of the flowers and fruitlets 96 h after each frost cycle simulation. The lethal temperatures (LT10, LT50, and LT90) of each treatment were calculated by probit analysis. The product applied 24 h and 48 h before the frost simulations significantly decreased the LT10 and LT50 in 1.5 °C in peach flowers, and 2.5 °C in almond flowers (a temperature reduction of 50% and 75%, respectively). These results were more consistent when the application volume was 2000 cc ha-1, instead of 1000 cc ha-1. Significant differences between treated and non-treated fruitlets were observed only in almond fruitlets, with LT10 and LT50 being 0.5 °C lower in treated fruitlets (20% reduction). In conclusion, the multi-attribute approach biostimulant applied 24 or 48 h before the frost reduced the mortality of peach and almond flowers, but its effectiveness to protect fruitlets after bloom was inconsistent.

Analisis Sikap dan Kepuasan Petani terhadap Benih Padi Hibrida Pendekatan Multiatribut Fishbein dan Customer Satisfaction Index (Studi Kasus Desa Wonokarto Kecamatan Sekampung Kabupaten Lampung Timur)

Understanding farmers' attitudes and satisfaction towards hybrid seeds is essential in agriculture. This research aims to determine farmers' attitudes and satisfaction with using hybrid seeds using the Fishbein Multiattribute approach and the Customer Satisfaction Index. This research was carried out in April 2022. The farmers studied already had good experience running hybrid rice farming in Wonokarto Village, Sekampung District. The number of farmer respondents interviewed was 23, who were selected using purposive sampling techniques, namely farmers who used hybrid rice seeds. The approaches used in this research are the Fishbein Multiattribute approach and the Customer Satisfaction Index (CSI) approach. The results of the study show that based on the results of data processing using the Fishbein Multiattribute approach, there are five attributes of hybrid rice seeds that are considered the most important by farmers (marketing of produce, seed price, selling price of grain, seed productivity and seed availability). The calculation results show that the analysis of farmers' attitudes towards hybrid rice seeds obtained a total score of 609.14, which means that the hybrid rice seeds can meet the farmers' expectations and needs. Meanwhile, based on the analysis of farmer satisfaction levels regarding using hybrid rice seeds using the Customer Satisfaction Index (CSI) approach, a farmer satisfaction index of 72.96% (0.7296) was obtained. This value is in the range of 0.66 – 0.80, which means farmers are satisfied with hybrid rice seeds. By paying attention to farmers' attitudes and satisfaction with mixed seeds, stakeholders can design more effective policies, programs, and strategies to support sustainable agriculture and improve farmer welfare.

Borehole Ground Penetrating Radar attribute analysis for urban engineering

A precise and detailed knowledge and characterization of the subsurface is crucial for urban engineering planning and implementation. Drillings/boreholes are most widely used to provide the spatial distribution of blocks and boulders, but it is difficult to determine with high spatial-resolution location and distribution of the blocks and boulders in the soil stratum. We evaluate the applicability and the effectiveness of borehole Ground Penetrating Radar (GPR) for urban engineering purpose and we test attribute analysis to help the subway construction in the city center of Hangzhou, China. A multi-attribute approach exploiting amplitude-based and textural attributes is used. The results demonstrate that attribute analysis can visualize and quantitatively characterize borehole GPR features in an automatic and objective manner, maximizing the information for urban engineering. The integrated results from drillings and borehole GPR can provide an effective distribution of localized heterogeneities (such as blocks/boulders, sand/gravel lenses, cavities) in the potential target area.

Abstract 13233: Disparities in Speed of Diagnosis of Hypertrophic Cardiomyopathy in the United States

Background: Hypertrophic cardiomyopathy (HCM) is a cardiac disease that can be genetic in cause. Early diagnosis, risk stratification, and pharmacological management can reduce the disease burden, yet several studies have highlighted disparities in early diagnosis by gender and race. Goals: This study compares age, severity at diagnosis, and time-to-diagnosis, by gender, race, and county-level poverty rate in a nationally representative population of over 65-year-olds. Methods: We used Medicare claims data from 2016-2020 to construct a sample who had at least two HCM claims and continuous enrollment for at least 2 years prior to their first HCM claim. We measured age and severity of heart disease at diagnosis, as well as time from the first observed indication of heart disease to HCM diagnosis. Results: We identified 48,857 patients with an HCM diagnosis in the study period, of which 60% were women and 81% were Non-Hispanic White. Comparing relative rates, Black and Hispanic patients at the time of HCM diagnosis had a 16% higher prior rate of heart failure (HF), and a 11% higher rate of prior major adverse cardiovascular events (MACE). In addition, in Black HCM patients, the share of atrial and non-atrial arrhythmias that were clinically diagnosed before HCM diagnosis was 28% lower than among White patients (p<0.001). Importantly our multi-attribute approach highlighted the composite roles of race and county-level poverty: at the time of HCM diagnosis, Black men in the poorest quartile of counties had a 26% higher rate of HF, 22% higher rate of MACE and a 53% lower rate of clinically diagnosed arrhythmia (all p<0.001) than White men in the wealthiest quartile of counties. Additionally, the median time from the first indication of heart disease until the diagnosis of HCM in Black and Hispanic patients from the poorest quartile of counties was 347 days longer than for Non-Hispanic White patients from the wealthiest quartile of counties (p<0.001). Conclusions: This study shows evidence of disparities in diagnosis of older patients with HCM in the United States, particularly in terms of race and county-level poverty. New approaches are needed to address these disparities and improve access to care for patients in underserved and vulnerable communities.

A multi-attribute approach to evaluating the impact of biostimulants on crop performance.

An ever-growing collection of commercial biostimulants is becoming available in a wide variety of forms and compositions to improve crop performance. Given the intricate nature of deciphering the underlying mechanisms of commercial products, which typically comprise various biological components, it is crucial for research in this area to have robust tools to demonstrate their effectiveness in field trials. Here, we took a multi-attribute approach to evaluating the impact of biostimulants on crop performance. First, we assessed the impact of a biostimulant on the soil and rhizosphere microbiomes associated to crops in eight reference farms, including corn (3 farms), soybean (2), cotton (2) and sugarcane (1), in different biomes and production contexts in Brazil and Paraguay. Second, we modeled a set of integrated indicators to measure crop responses to biostimulant application, including five analytical themes as follows: i) crop development and production (9 indicators), ii) soil chemistry (9), iii) soil physics (5), iv) soil biology (6) and v) plant health (10). Amplicon 16S rRNA and ITS sequencing revealed that the use of the biostimulant consistently changes the structure of bacterial and fungal communities associated with the production system for all evaluated crops. In the rhizosphere samples, the most responsive bacterial taxa to biostimulant application were Prevotella in cotton; Prauserella and Methylovirgula in corn; and Methylocapsa in sugar cane. The most responsive fungal taxa to biostimulant use were Arachnomyces in soybean and cotton; and Rhizophlyctis in corn. The proposed integrated indicators yielded highly favorable positive impact indices (averaging at 0.80), indicating that biostimulant-treated fields correlate with better plant development and crop performance. Prominent indices were observed for indicators in four themes: soil biology (average index 0.84), crop production (0.81), soil physics (compaction reduction 0.81), and chemical fertility (0.75). The multi-attribute approach employed in this study offers an effective strategy for assessing the efficacy of biostimulant products across a wide range of crops and production systems.

A Multi-Attribute Approach for Low-Carbon and Intensive Land Use of Jinan, China

This paper establishes an evaluation system based on the low-carbon intensive land use in Jinan city from 2010 to 2017 and uses a multi-attribute approach named grey fuzzy integral to build the evaluation model. In this model, based on the Mobius transformation coefficient of subjective and objective weights of index factors and the interaction degree between index factors, 2-additive fuzzy measures can be obtained; therefore, evaluation of low-carbon and intensive land use in Jinan city is processed by combining the grey correlation degree and Choquet fuzzy integral. The results show that in the study period, land input intensity, land use degree, land output benefit and land sustainability in Jinan city all show a good upward trend, but the low-carbon land use level of has been in a declining state. Although there is a good development trend of low-carbon and intensive land use in Jinan, the state is not stable. A Low-carbon and intensive land use pattern will not be achieved completely overnight, and it is bound to be a dynamic game process.

Fault characterization in a postsalt reservoir interval, Jubarte Field (Campos Basin), using seismic attributes and machine learning

Seismic attributes are routinely applied to interpretation tasks to improve the recognition of geologic features in the subsurface. Subtle changes in the amplitude and phase components of the seismic data can reveal details and provide information about the management of hydrocarbon reservoirs. To this end, we investigate how instantaneous and geometric attributes can help in fault and discontinuity recognition. Seismic data from Jubarte’s 4D/4C Permanent Reservoir Monitoring system, offshore Brazil, were used to test the impact of a variety of algorithms and parameters in improving fault visualization. The presence of seismic noise and geologic units with weak acoustic impedance contrasts motivated the application of additional methods such as data conditioning workflows and unsupervised machine learning methods. We found that spectral balancing and structure-oriented filtering increased the lateral continuity of some stratigraphic reflectors and attenuated the random noise, which improved fault surface visibility. Attributes were calculated on the full-stack data and azimuth-restricted volumes revealing different impacts of the seismic noise and changes in the lateral continuity of the features. In some cases, faults are better delineated when the acquisition direction is oriented perpendicular (or nearly) to the discontinuity. In addition, the most-positive and most-negative curvature components indicate more details of major features. A large number of seismic cubes and attributes motivated the use of principal component analysis and self-organizing maps, which complement the identification of fault segments with clusters composed of specific neurons aligned within structural discontinuities. The improvements obtained in terms of fault visualization demonstrate the importance of having a workflow that combines different fault and fracture identification methods. An integrated study is important as it identifies effects related to seismic noise and stratigraphic features. For the Jubarte Field, a multiattribute approach demonstrates advantages for delimiting the lateral extension of faults and a more precise discontinuity location.

Projecting multi-attribute flood regime changes for the Yangtze River basin

Climate change is altering flood risk globally. The nature of those alterations varies locally, creating the need for regional assessments to inform flood-risk management and planning. This study projects future changes in flood regime in the Yangtze River basin. To characterize flood regimes, we use a multi-attribute approach that measures the relative change in flood magnitude, timing, frequency, and duration between historical and future periods. We use climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to force the Soil and Water Assessment Tool (SWAT), and generate daily streamflow projections along the Yangtze River network for two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. We use seasonal maxima, annual maxima, and peaks-over-threshold to determine the different flood attributes. Results show that downscaled CMIP6 precipitation and near-surface temperature are both projected to increase substantially in the future, with a consequent surge in floods across the basin. In the far future (years 2061–2100), under SSP5-8.5, average annual flood magnitude, timing, frequency, and duration are projected to increase 66.6 %, arrive 1.9 days earlier, increase 27.5 %, and prolong 19.1 days, respectively. Projected changes in flood attributes at annual timescale are generally higher in the basin's northern tributaries than in the southern ones. Changes in flood magnitude and frequency tend to resemble each other, whereas flood timing and duration exhibit distinct change patterns. Our multi-attribute approach to flood regime characterization provides a systematic way for translating raw CMIP6 projections into informative future flood projections. This approach can help inform flood-risk planning and improve the reliability of climate change impact assessment.

Composite ecotourism potential index based on an integrated stochastic CRITIC-weighted sum method

ABSTRACT This work develops a computational platform to evaluate the ecotourism potential of tourist sites (alternatives) under multiple indicators (criteria). Such a platform is a generalized multi-attribute decision-making approach based on the integration of Criteria Importance Through Intercriteria Correlation (CRITIC) and the weighted sum method (WSM) under a stochastic environment. In this proposed approach, a randomly generated decision matrix is formed, derived from the initial judgments of decision-makers. Then, the priority weights of the criteria are obtained through CRITIC, while the WSM generates the global priority vector of alternatives. A stochastic process based on Monte Carlo simulation generates a large set of global priority vectors. The aggregation of these vectors yields the final priorities of the alternatives. Contextually, it generates a single-valued composite ecotourism potential index which provides the overall performance of the potential ecotourism sites. Evaluating 47 tourist sites under eight indicators demonstrates the proposed approach. Findings and insights are discussed.

A Multi-Attribute Approach for Cyber Threat Intelligence Product and Services Selection

A Multi-Attribute Approach for Cyber Threat Intelligence Product and Services Selection

A multi-attribute approach to ranking departments based on performance: a balanced scorecard pilot study

Departments are assessed using performance evaluation systems (PES) based on their important performance criteria. The sum of the weighted KPIs’ actualization scores creates the departments' performance ratings, which are used to rank departments from highest to lowest performing. However, this strategy does not account for departmental variances. We used multi-attribute decision-making (MADM) techniques in this study to ascertain the performance of several divisions inside a business. Our study's structure is divided into two distinct parts. In the first stage, pair-wise evaluation matrices are built and attribute weights are established using the Analytic Hierarchical Process (AHP) to determine attribute precedence. The second step employs the Elimination and Choice Expressing the Reality (ELECTRE) methodology to rank the departments using the attribute weights generated in the first stage using the AHP method. At the conclusion of the paper, a case study is presented to demonstrate how departments are organized in accordance with performance management principles within a corporation that use the balance-scorecard program for performance evaluation.

Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization

Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM is carried out by broker. The main objective is to maximize the resource utilization and minimize brokerage in cloud computing by using Multi-Objective Optimization (MOO). It considered a multi-attribute approach as it has more than two objectives. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources. The outcome of the experiment shows that CMRBC outperforms 60 percent of reduction in brokerage and 10 percent in response time.

A multi-attribute approach to select the best method for repairing river bridge columns

PurposePier scour is one of the main causes of damage to the columns of the river bridges. It is essential to select the best method among various repair methods based on different evaluation indices. However, there is no procedure for ranking these repair methods based on their attributes. The present study seeks to set an approach for this ranking.Design/methodology/approachIn this paper, a multi-attribute decision-making (MADM) model is presented for ranking the repair techniques, in which alternatives are examined using the most important evaluation criteria. In addition, a combination of entropy and eigenvector methods has been proposed for weighting these attributes. A case study is then used to demonstrate the applicability and the validity of the method.FindingsThe execution of the model using two multi-criteria methods yielded similar results, which confirms its accuracy and precision. Moreover, the research findings showed the consistency of the objective and subjective weighting methods and the conformity of the weights obtained for the attributes from the combination of these methods to the nature of the problem.Originality/valueThe selection of the proper method for repairing the bridge columns plays an essential role in success of the bridge restoration. The proposed model introduces an approach for ranking repair methods and selecting the best one that has not been presented so far. Also, the weighing method for attributes is an innovative method for ranking restoration methods that has been proven in a case study.

Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms

Crowdfunding platforms offer project initiators the opportunity to acquire funds from the Internet crowd and, therefore, have become a valuable alternative to traditional sources of funding. However, some processes on crowdfunding platforms cause undesirable external effects that influence the funding success of projects. In this context, we focus on the phenomenon of project overfunding. Massively overfunded projects have been discussed to overshadow other crowdfunding projects which in turn receive less funding. We propose a funding redistribution mechanism to internalize these overfunding externalities and to improve overall funding results. To evaluate this concept, we develop and deploy an agent-based model (ABM). This ABM is based on a multi-attribute decision-making approach and is suitable to simulate the dynamic funding processes on a crowdfunding platform. Our evaluation provides evidence that possible modifications of the crowdfunding mechanisms bear the chance to optimize funding results and to alleviate existing flaws.

Cluster analysis of China’s inbound tourism market: A new multi-attribute approach based on association rule mining of tourist preferences at scenic spots

ABSTRACT This study adopts association rules to identify the co-occurrence of attributes concerned by inbound tourists visiting scenic spots and further make a cluster analysis. In total, 72,890 online reviews of 218 scenic spots in China from inbound travelers from TripAdvisor used for analysis. It is found that the tourists could be clustered into three groups: view-sensitive travelers, crowding-sensitive travelers, and price-sensitive travelers. This study makes an early attempt to cluster travelers according to their conjoint preference patterns based on the co-occurrence of destination attributes, and provides a new multi-attribute approach to the investigation of tourist preferences and market segmentation.

COVID-19 vaccines and treatments: When speed is necessary and not enough.

COVID-19 vaccines and treatments: When speed is necessary and not enough.

Multidimensional Multi-Attribute Approach to Counter the Routing Attacks on MANET

The Dynamic on demand network formation feature of MANET tend to have its biggest the vulnerability that infect the process of route establishment using routing algorithms and exploits many attacks. Identifying the attack and isolating the malicious node to reform the secured routing environment is the major goal of all researchers working for securing the MANET. IDS and trust based collaborative approach (ITCA) is the our first proposed published method which provides the platform for hybrid approach to trap the malicious nodes through combined efforts from multiple dimensions.The proposed framework utilize the benefit of Reputation and Trust based evaluation process to secure route discovery by isolating malicious nodes at early stage of route formation. The more advanced approach, multi-dimensional multi-attribute system (MDMA), incorporates multi-attribute direct, indirect trust mechanism, and use of measured risk by credibility factor, while considering indirect trust. It empowers IDS and make the ITCA more efficient. The attributes considered for calculations of trust are adaptable to change in application environment make MDMA a flexible generalized solution. The MDMA is evaluated by implementing using AODV and compared with our previous proposed method ITCA along with many other solutions. The results are better and more adaptable to the changing environment.

An ANN-based identification of geological features using multi-attributes: a case study from Krishna-Godavari basin, India

Artificial neural network (ANN)–based workflow has been applied to the multi-channel seismic reflection data from the Krishna-Godavari (K-G) basin on the Eastern continental margin of India for delineation of prominent structural features such as chimneys and faults. To eliminate the noise and enhance the data quality, the seismic data is conditioned and filtered by data-conditioning techniques such as dip-steered and structural filters. A single attribute is not sufficient for the delineation of structures such as faults and chimneys; hence, an amalgamation of multiple attributes is necessary for the enhancement of laterally continuous seismic events. Hence, multi-attributes such as dip variance, curvature, coherency, energy, similarity, instantaneous frequency, and frequency washout ratio are extracted from the conditioned data which were combined and fed as inputs for the ANN for the chimney and fault detection. The neural network is trained at the identified chimney/non-chimney and fault/non-fault locations, which generates a final output, namely chimney probability attribute and a fault probability attribute, revealing an improved visibility of faults and chimneys in the seismic data. This multi-attribute approach shows more reliability in comparison with individual attribute responses, which helps in better structural interpretation. The study presents a workflow for better visualization of the subsurface structural features which thereby helps in detailed structural interpretation of the seismic data.