Three-level Hierarchical Model Research Articles

A fuzzy based MCDM–GIS framework to evaluate groundwater potential index for sustainable groundwater management - A case study in an urban-periurban ensemble, southern India

The study encompasses the application of fuzzy based multi-criteria decision making (MCDM)- geospatial framework (GIS-Fuzzy-AHP) for evaluating index based groundwater potential assessment in two differently urbanized clusters of Northern Kerala, Southern India. The framework presents a very powerful way of managing groundwater at a regional and global level due to its succinct performance. The study area is characterized by a coastal zone with a high rate of urban development and relying on groundwater supply to meet their basic needs. In the Fuzzy-analytic hierarchy process (Fuzzy-AHP), a three-level hierarchical model is built using 8 criteria and 34 subcriteria of groundwater controlling factors. Weights and ratios of these factors have been generated by pairwise comparisons of fuzzified values in Saaty's 9-point fundamental scale, taking relative importance into account. Finally, the integration of spatial and non-spatial data into the GIS platform created a groundwater potential zone map with a unique integer value known as the groundwater potential index (GWPI). The resulting groundwater potential map classified the study area into four zones using quantile method and has shown that the very good potential zone occupies 34% and 10% of the urban and peri-urban zones of the study area, respectively. Gentle slope gradient with coastal alluvium formation, sandy loam to sandy clay textured soil type with moderate to rapid permeability favours aquifer recharge in these zones, resulting in a very high potential zone. The consistency analysis using the Eigenvector method showed that the consistency was well within the tolerance limit and validation analysis using the average depth of water level data checked the statistical efficiency and reliability of results in the overall decision-making phase. A well-balanced fuzzy framework has allowed the rapid and reliable assessment of hydrological behaviour of two differently urbanized environments.

Riding a long green wave: interdisciplinary environmental sciences and studies in higher education

ABSTRACT What accounts for the remarkable growth of environmental sciences and studies (ESS) in US higher education over the past 50 years? This paper focuses on institutional characteristics to explain this ‘long green wave’ of expansion. Drawing on data from 1345 US higher-education institutions from 1980–2010, we employ three-level hierarchical models to assess institutional and state-level factors associated with the presence of environmental studies and sciences. Findings indicate that environmental studies majors are most likely to be present at liberal arts schools and in states more inclined to adopting environmentally friendly policies, and less likely to exist at schools with large minority enrollments. Environmental sciences majors are less likely to be present at schools with large female enrollments. Two case studies of early adopters highlight the role of faculty, rather than student activists, as change-agents pushing for the development of ESS on college campuses in the 1960s and 70s.

How a choice between emission trading and tax schemes affects an environment-biased Environmental Protection Bureau's discretion?

In choosing environmental regulatory instrument and setting standards, a government relies on an Environmental Protection Bureau's (EPB) informational advantage, even though these two entities may have different preferences for internalizing environmental damage. A three-level hierarchical model between the government, the EPB and polluting firms is constructed to derive the government's optimal delegation policy for the EPB. Given the uncertainty in firms' costs and asymmetric information on transaction costs, this paper presents a demonstration of the communication process between the government and the EPB in formulating the standards of environmental regulation and choosing between the instruments of emission trading and tax schemes. This paper seeks to determine how the decisions in the environmental regulation should be delegated for the government to trade-off the cost to it of EPB's discretion versus the benefits of taking advantage of the EFB's expertise. Results suggest the bureaucratic political issue leads to a tendency towards either an emission trading or a tax scheme, depending on the direction of the EPB's environmental preferences. Compared with the case where only emission trading can be chosen, the choice of instrument results in less discretion left to the EPB in setting up the trading emission allowance and a higher tax, irrespective of whether the EPB is more or less concerned about the environmental damage than the government.

Jedan pristup za ocenjivanje i izbor ključnih poslovnih procesa

The traditional management is hierarchically organized. The transition from traditional management to process management requires a radical shift in thinking from a command-and-control, personality based, top-down hierarchy to horizontal, process-oriented, team-oriented structure. Business model of an organization, implementing business process management, is realized through a network of processes. The goal of business process management is to systematically improve all processes, espesially core processes. Core processes are work processes that cross functional boundaries, produce an output that is strategically important to the organization's success and are essential for organization survival at market. Very often an organization has a set of busness processes which are improvement candidates. The problem is how to choose the set of processes whose performance improvement will do the best for organization at all. In this paper an approach for evaluation and selection of core processes is presented. The proposed approach is based on process' objects and the three-level hierarchical quality evaluation model.

A Bayesian Hierarchical Model for Criminal Investigations

Potential violent criminals will often need to go through a sequence of preparatory steps before they can execute their plans. During this escalation process police have the opportunity to evaluate the threat posed by such people through what they know, observe and learn from intelligence reports about their activities. In this paper we customise a three-level Bayesian hierarchical model to describe this process. This is able to propagate both routine and unexpected evidence in real time. We discuss how to set up such a model so that it calibrates to domain expert judgments. The model illustrations include a hypothetical example based on a potential vehicle based terrorist attack.

Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity.

Health-related quality of life (HRQoL) is a key outcome in cost-utility analyses, which are commonly used to inform healthcare decisions. Different instruments exist to evaluate HRQoL, however while some jurisdictions have a preferred system, no gold standard exists. Standard meta-analysis struggles with the variety of outcome measures, which may result in the exclusion of potentially relevant evidence. Using a case study in multimorbidity, the objective of this analysis is to illustrate how a Bayesian hierarchical model can be used to combine data across different instruments. The outcome of interest is the slope relating HRQoL to the number of coexisting conditions. We propose a three-level Bayesian hierarchical model to systematically include a large number of studies evaluating HRQoL using multiple instruments. Random effects assumptions yield instrument-level estimates benefitting from borrowing strength across the evidence base. This is particularly useful where little evidence is available for the outcome of choice for further evaluation. Our analysis estimated a reduction in quality of life of 3.8-4.1% per additional condition depending on HRQoL instrument. Uncertainty was reduced by approximately 80% for the instrument with the least evidence. Bayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.

Educational inequalities in general health: Does the curricular tracking system matter?

Educational tracking amplifies social inequalities in a wide range of outcomes. From an institutional perspective, the current study examines whether this holds for educational disparities in general health. To investigate this question, we use information from the European Social Survey (Rounds 1–8: 2002–2016) for individuals between the ages of 18 and 45 years ( N = 99,771) in 22 European countries. The estimated three-level hierarchical models indicate that tracking is indeed associated with larger educational inequalities in overall health. Individuals who have attained vocational education fare worse in terms of general health than do individuals who have pursued academic qualifications. However, the strength of association is much higher in countries with highly tracked systems (e.g. Germany and Czech Republic) than it is in countries with more comprehensive systems (e.g. the United Kingdom and Scandinavian countries). This result suggests that health inequalities between educational groups can be reduced by reorganizing secondary educational systems.

Cv-CapsNet: Complex-Valued Capsule Network

Capsule network (CapsNet) can recognize the objects by encoding the part–whole relationships in a way similar to our human perceptual system and has already shown its great potential in image classification tasks. However, it is limited to the real domain while the complex numbers having much richer representational capacity and facilitating the noise-robust memory retrieval mechanisms. Therefore, we propose two architectures: Complex-valued Dense CapsNet (Cv-CapsNet) and Complex-valued Diverse CapsNet (Cv-CapsNet++), each of them consists of three stages. In the first stage, multi-scale complex-valued features are obtained by the restricted dense complex-valued subnetwork. Particularly, Cv-CapsNet++ utilizes a three-level Cv-CapsNet hierarchical model to extract the multi-scale high-level complex-valued features in order to adapt to the complicated datasets. In the second stage, these complex-valued features are encoded into the complex-valued primary capsules, Particularly, Cv-CapsNet++ encodes the complex-valued features from different hierarchies into the multi-dimensional complex-valued primary capsules. In the third stage, we generalize the dynamic routing algorithm to the complex-valued domain and employ it to fuse the real- and imaginary-valued information of complex-valued primary capsules. The experimental results show that the proposed architectures lead to fewer trainable parameters, better performance, and fewer iterations during training than Real-valued CapsNets (Rv-CapsNets) with similar structure and original CapsNet on FashionMNIST and CIFAR10 datasets.

Identifying the key source of deteriorating educational equity in Sweden between 1998 and 2014

Educational equity has deteriorated in Sweden from the late 1980s onwards. However, it is not clear what the main sources behind this trend are. The present study aims therefore to investigate the development of educational equity at different levels of the Swedish educational system in order to identify possible sources of the change. Students’ school grades and family educational background for the cohorts leaving compulsory school between 1998 and 2014 were analysed in three-level hierarchical models. Increased segregation with respect to student composition and academic outcomes across different schools was found to be the main source of the declining educational equity. The findings are discussed in the light of differentiated learning opportunities, as a result of recent school reforms in Sweden.

Application of Fuzzy Multiple Attribute Decision-Making for Selecting the Best Sustainable Orientation

Sustainability is an idea encompassed with various aspects still under exploration in addition to its vibrant and comprehensive nature. The Brundtland commission articulated that sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. A plunge for supporting the ideology of sustainability orientations with respect to manufacturing and their implementation are still in need, but the quantum of factors increases in the process of sustainability orientation selection during the implementation of sustainable manufacturing. Several undecided factors are intrinsic in the implementation of new sustainable manufacturing. Decision-making methods using probabilities to represent the three orientations such as alternate material change, manufacturing process change and design change of achieving sustainability cannot be used to prioritize them. Therefore, a fuzzy multiple attribute decision-making (FMADM) method was developed with a three-level hierarchical decision-making models based on the economic, environment and social aspects of sustainable development, to evaluate the three orientations involved in sustainable manufacturing. A case study was conducted in an Indian automotive component manufacturing organization with the FMADM model developed and the results effectively reflected the characteristics of the three orientations of sustainable manufacturing.

Strength loss of carbon nanotube fibers explained in a three-level hierarchical model

Although the tensile strength of carbon nanotubes inherited from the sp2 hexagonal carbon lattice is as high as 120 GPa, the state-of-the-art mechanical resistance of carbon nanotube fibers is below 10 GPa. Material imperfections embedded in the complex microstructures are responsible for this remarkable reduction across multiple length scales. In this study, we rationalize this multi-scale degradation of mechanical performance through theoretical analysis of the processing-microstructure-performance relationship for carbon nanotube fibers based on the experiment data, offering a simplified model that not only quantifies the breakdown of material strength at the nanotube, bundle, and fiber levels, respectively, but also provides practical advices to optimize the manufacturing processes for elevated mechanical performance.

Impulsivity interacts with momentary PTSD symptom worsening to predict alcohol use in male veterans

ABSTRACTBackground: Posttraumatic stress disorder (PTSD) is prevalent among veterans who served post-9/11, and co-occurs with problem alcohol and substance use. Studies using ecological momentary assessment have examined the temporal association between time-varying PTSD symptoms and alcohol use. Results suggest individual differences in these associations. Objectives: We tested hypotheses that alcohol use measured by momentary assessment would be explained by acute increases in PTSD symptoms, and the PTSD–alcohol association would be moderated by trait impulsivity. Methods: A sample of 28 male post-9/11-era veterans who reported past-month PTSD symptoms and risky alcohol use were enrolled. On a quasi-random schedule, participants completed three electronic assessments daily for 28 days measuring past 2-h PTSD symptoms, alcohol, and substance use. At baseline, trait impulsivity was measured by the Barratt Impulsiveness Scale. Past-month PTSD symptoms and alcohol use were measured. Using three-level hierarchical models, number of drinks recorded by momentary assessment was modeled as a function of change in PTSD symptoms since last assessment, controlling for lag-1 alcohol and substance use and other covariates. A cross-level interaction tested moderation of the within-time PTSD–alcohol association by impulsivity. Results: A total of 1,522 assessments were completed. A positive within-time association between PTSD symptom change and number of drinks was demonstrated. The association was significantly moderated by impulsivity. Conclusion: Results provide preliminary support for a unique temporal relationship between acute PTSD symptom change and alcohol use among veterans with trait impulsiveness. If replicated in a clinical sample, results may have implications for a targeted momentary intervention.

Variational Bayesian Inference-Based Counting and Localization for Off-Grid Targets With Faulty Prior Information in Wireless Sensor Networks

The benefits of compressive sensing (CS) on counting and localization in wireless sensor networks have already been demonstrated in previous works. However, most existing solutions usually become infeasible in the presence of off-grid targets, which is inevitable for the traditional CS-based localization methods. To mitigate the error caused by off-grid targets, in this paper, a more accurate sparse approximation model is applied where the true and unknown sparsifying dictionary is approximated with its first-order Taylor expansion around a known dictionary. Based on the model, the counting and localization problem is formulated as a sparse reconstruction problem that recovers two sparse signals with related supports. To solve the problem, we develop an iterative algorithm under a variational Bayesian inference framework. Moreover, in practice, some faulty prior information (e.g., coarse positions) is usually available. To incorporate the information, a three-level hierarchical prior model is introduced and imposed on the sparse signals to be recovered, where the supports of sparse signals are learned in the third level. Additionally, a grid pruning mechanism and matrix inversion lemma are leveraged to accelerate the inference process. Extensive simulation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm can achieve counting and localization with higher accuracy as well as lower cost.

Flow on the Internet: a longitudinal study of Internet addiction symptoms during adolescence

ABSTRACTInternet Addiction (IA) constitutes an excessive Internet use behavior with a significant impact on the user’s well-being. Online flow describes the users’ level of being absorbed by their online activity. The present study investigated age-related, gender, and flow effects on IA in adolescence. The sample comprised 648 adolescents who were assessed twice at age 16 and 18 years. IA was assessed using the Internet Addiction Test and online flow was assessed using the Online Flow Questionnaire. A three-level hierarchical model estimated age-related, gender, and online flow effects on IA symptoms and controlled for clustered random effects. IA symptoms decreased over time (for both genders) with a slower rate in males. Online flow was associated with IA symptoms and this remained consistent over time. Findings expand upon the available literature suggesting that IA symptoms could function as a development-related manifestation at the age of 16 years, while IA-related gender differences gradually increase between 16 and 18 years. Finally, the association between online flow and IA symptoms remained stable independent of age-related effects. The study highlights individual differences and provides directions for more targeted prevention and intervention initiatives for IA.

Design of a multi-criteria decision making model using fuzzy-AHP for selection of appropriate underground metal mining method

The aim of the current study is to develop a multi-criteria decision making model using fuzzy-analytical hierarchical process (FAHP) for selection of the appropriate underground metal mining method. The model considered eight criteria for evaluating the seven mining methods. A three-level hierarchical structured model is proposed for decision-making analysis. The criteria, sub-criteria, and the mining methods are listed respectively in the first, second, and third level of the hierarchy. The local and global weights for each parameter were determined using the FAHP method. The global weights of the various mining methods were used to determine the ranking of various mining method for a typical ore deposit. The model validation was done using the ore deposit data of two mines in India. It is observed that the adopted mining methods and the corresponding predicted mining methods are same in both the cases.

Design of a multi-criteria decision making model using fuzzy-AHP for selection of appropriate underground metal mining method

The aim of the current study is to develop a multi-criteria decision making model using fuzzy-analytical hierarchical process (FAHP) for selection of the appropriate underground metal mining method. The model considered eight criteria for evaluating the seven mining methods. A three-level hierarchical structured model is proposed for decision-making analysis. The criteria, sub-criteria, and the mining methods are listed respectively in the first, second, and third level of the hierarchy. The local and global weights for each parameter were determined using the FAHP method. The global weights of the various mining methods were used to determine the ranking of various mining method for a typical ore deposit. The model validation was done using the ore deposit data of two mines in India. It is observed that the adopted mining methods and the corresponding predicted mining methods are same in both the cases.

Hierarchical statistical techniques are necessary to draw reliable conclusions from analysis of isolated cardiomyocyte studies.

AimsIt is generally accepted that post-MI heart failure (HF) changes a variety of aspects of sarcoplasmic reticular Ca2+ fluxes but for some aspects there is disagreement over whether there is an increase or decrease. The commonest statistical approach is to treat data collected from each cell as independent, even though they are really clustered with multiple likely similar cells from each heart. In this study, we test whether this statistical assumption of independence can lead the investigator to draw conclusions that would be considered erroneous if the analysis handled clustering with specific statistical techniques (hierarchical tests).Methods and resultsCa2+ transients were recorded in cells loaded with Fura-2AM and sparks were recorded in cells loaded with Fluo-4AM. Data were analysed twice, once with the common statistical approach (assumption of independence) and once with hierarchical statistical methodologies designed to allow for any clustering. The statistical tests found that there was significant hierarchical clustering. This caused the common statistical approach to underestimate the standard error and report artificially small P values. For example, this would have led to the erroneous conclusion that time to 50% peak transient amplitude was significantly prolonged in HF.Spark analysis showed clustering, both within each cell and also within each rat, for morphological variables. This means that a three-level hierarchical model is sometimes required for such measures. Standard statistical methodologies, if used instead, erroneously suggest that spark amplitude is significantly greater in HF and spark duration is reduced in HF.ConclusionCa2+ fluxes in isolated cardiomyocytes show so much clustering that the common statistical approach that assumes independence of each data point will frequently give the false appearance of statistically significant changes. Hierarchical statistical methodologies need a little more effort, but are necessary for reliable conclusions. We present cost-free simple tools for performing these analyses.

Harms of off-label erythropoiesis-stimulating agents for critically ill people.

Anaemia is a common problem experienced by critically-ill people. Treatment with erythropoiesis-stimulating agents (ESAs) has been used as a pharmacologic strategy when the blunted response of endogenous erythropoietin has been reported in critically-ill people. The use of ESAs becomes more important where adverse clinical outcomes of transfusing blood products is a limitation. However, this indication for ESAs is not licensed by regulatory authorities and is called off-label use. Recent studies concern the harm of ESAs in a critical care setting. To focus on harms in assessing the effects of erythropoiesis-stimulating agents (ESAs), alone or in combination, compared with placebo, no treatment or a different active treatment regimen when administered off-label to critically-ill people. We conducted a systematic search of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, PsycINFO via OvidSP, CINAHL, all evidence-based medicine (EBM) reviews including IPA and SCI-Expanded, Conference Proceedings Citation Index- Science, BIOSIS Previews and TOXLINE up to February 2017. We also searched trials registries, checked reference lists of relevant studies and tracked their citations by using SciVerse Scopus. We considered randomized controlled trials (RCTs) and controlled observational studies, which compared scheduled systemic administration of ESAs versus other effective interventions, placebo or no treatment in critically-ill people. Two review authors independently screened and evaluated the eligibility of retrieved records, extracted data and assessed the risks of bias and quality of the included studies. We resolved differences in opinion by consensus or by involving a third review author. We assessed the evidence using GRADE and created a 'Summary of findings' table. We used fixed-effect or random-effects models, depending on the heterogeneity between studies. We fitted three-level hierarchical Bayesian models to calculate overall treatment effect estimates. Of the 27,865 records identified, 39 clinical trials and 14 observational studies, including a total of 945,240 participants, were eligible for inclusion. Five studies are awaiting classification. Overall, we found 114 adverse events in 33 studies (30 RCTs and three observational studies), and mortality was reported in 41 studies (32 RCTs and nine observational studies). Most studies were at low to moderate risk of bias for harms outcomes. However, overall harm assessment and reporting were of moderate to low quality in the RCTs, and of low quality in the observational studies. We downgraded the GRADE quality of evidence for venous thromboembolism and mortality to very low and low, respectively, because of risk of bias, high inconsistency, imprecision and limitations of study design.It is unclear whether there is an increase in the risk of any adverse events (Bayesian risk ratio (RR) 1.05, 95% confidence interval (CI) 0.93 to 1.21; 3099 participants; 9 studies; low-quality evidence) or venous thromboembolism (Bayesian RR 1.04, 95% CI 0.70 to 1.41; 18,917 participants; 18 studies; very low-quality evidence).There was a decreased risk of mortality with off-label use of ESAs in critically-ill people (Bayesian RR 0.76, 95% CI 0.61 to 0.92; 930,470 participants; 34 studies; low-quality evidence). Low quality of evidence suggests that off-label use of ESAs may reduce mortality in a critical care setting. There was a lack of high-quality evidence about the harm of ESAs in critically-ill people. The information for biosimilar ESAs is less conclusive. Most studies neither evaluated ESAs' harm as a primary outcome nor predefined adverse events. Any further studies of ESA should address the quality of evaluating, recording and reporting of adverse events.

Research on Consistent Pattern of Development of the Educators’ Ability to Self-Improvement

The educators’ ability to self-improvement is considered in the article by means of a special system. It can be represented as a three-level hierarchical model with fuzzy structural relationships. The intermediate level of this model contains components that enable the formation and development of the object of the upper level (educators’ ability to self-improvement): 1) motivational-valuable component; 2) the emotional-volitional component; 3) reflective-evaluative component; 4) cognitive component; 5) organizing component. The lower level of the model contains indicators. They make it possible to evaluate the degree of the selected components’ formation and the stage of the development of the investigated ability as a whole. Each state of the educators' ability to self-improvement is characterized by a tuple. The stage reached by the relevant components is the element of this tuple. According to self-assessments received from 214 lecturers and teachers, the most common states were defined. It has allowed to form a sequence of such states. The transitions between them describe the patterns of development of the educators' ability to self-improvement. The described technique allows us to characterize the development of this ability not only among educators, but also among professional managers, doctors, engineers in the field of electrical engineering and electronics, and others.

What Factors are Associated With 90-day Episode-of-care Payments for Younger Patients With Total Joint Arthroplasty?

Total joint arthroplasty (TJA) has been identified as a procedure with substantial variations in inpatient and postacute care payments. Most studies in this area have focused primarily on the Medicare population and rarely have characterized the younger commercially insured populations. Understanding the inpatient and postdischarge care service-componentdifferences across 90-day episodes of care and factors associated with payments for younger patients is crucial for successful implementation of bundled payments in TJA in non-Medicare populations. (1) To assess the mean total payment for a 90-day primary TJA episode, including the proportion attributable to postdischarge care, and (2) to evaluate the role of procedure, patient, and hospital-level factors associated with 90-day episode-of-care payments in a non-Medicare patient population younger than 65 years. Claims data for 2008 to 2013 from Blue Cross Blue Shield of Texas were obtained for primary TJAs. A total of 11,131 procedures were examined by aggregating payments for the index hospital stay and any postacute care including rehabilitation services and unplanned readmissions during the 90-day postdischarge followup period. A three-level hierarchical model was developed to determine procedure-, patient-, and hospital-level factors associated with 90-day episode-of-care payments. The mean total payment for a 90-day episode for TJA was USD 47,700 adjusted to 2013 USD. Only 14% of 90-day episode payments in our population was attributable to postdischarge-care services, which is substantially lower than the percentage estimated in the Medicare population. A prolonged length of stay (rate ratio [RR], 1.19; 95% CI, 1.15-1.23; p ≤ 0.001), any 90-day unplanned readmission (RR, 1.64; 95% CI, 1.57-1.71; p ≤ 0.001), computer-assisted surgery (RR, 1.031; 95% CI, 1.004-1.059; p ≤ 0.05), initial home discharge with home health component (RR, 1.029; 95% CI, 1.013-1.046; p ≤ 0.001), and very high patient morbidity burden (RR, 1.105; 95% CI, 1.062-1.150; p ≤ 0.001) were associated with increased TJA payments. Hospital-level factors associated with higher payments included urban location (RR, 1.29; 95% CI, 1.17-1.42; p ≤ 0.001), lower hospital case mix based on average relative diagnosis related group weight (RR, 0.94; 95% CI, 0.89-0.95; p ≤ 0.001), and large hospital size as defined by total discharge volume (RR, 1.082; 95% CI, 1.009-1.161; p ≤ 0.05). All procedure, patient, and hospital characterizing factors together explained 11% of variation among hospitals and 49% of variation among patients. Inpatient care contributed to a much larger proportion of total payments for 90-day care episodes for primary TJA in our younger than 65-year-old commercially insured population. Thus, inpatient care will continue to be an essential target for cost-containment and delivery strategies. A high percentage of hospital-level variation in episode payments remained unexplained by hospital characteristics in our study, suggesting system inefficiencies that could be suitable for bundling. However, replication of this study among other commercial payers in other parts of the country will allow for conclusions that are more robust and generalizable. Level II, economic analysis.