Proposed Model Framework Research Articles

Conceptual model for the health technology assessment of current and novel interventions in rheumatoid arthritis.

The objective of this study was to evaluate current approaches to economic modeling in rheumatoid arthritis (RA) and propose a new conceptual model for evaluation of the cost-effectiveness of RA interventions. We followed recommendations from the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modeling Good Research Practices Task Force-2. The process involved scoping the decision problem by a working group and drafting a preliminary cost-effectiveness model framework. A systematic literature review (SLR) of existing decision-analytic models was performed and analysis of an RA registry was conducted to inform the structure of the draft conceptual model. Finally, an expert panel was convened to seek input on the draft conceptual model. The proposed conceptual model consists of three separate modules: 1) patient characteristic module, 2) treatment module, and 3) outcome module. Consistent with the scope, the conceptual model proposed six changes to current economic models in RA. These changes proposed are to: 1) use composite measures of disease activity to evaluate treatment response as well as disease progression (at least two measures should be considered, one as the base case and one as a sensitivity analysis); 2) conduct utility mapping based on disease activity measures; 3) incorporate subgroups based on guideline-recommended prognostic factors; 4) integrate realistic treatment patterns based on clinical practice/registry datasets; 5) assimilate outcomes that are not joint related (extra-articular outcomes); and 6) assess mortality based on disease activity. We proposed a conceptual model that incorporates the current understanding of clinical and real-world evidence in RA, as well as of existing modeling assumptions. The proposed model framework was reviewed with experts and could serve as a foundation for developing future cost-effectiveness models in RA.

Robust Face Recognition under Limited Training Sample Scenario using Linear Representation

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

An analysis of the delivery challenges influencing public-private partnership in housing projects

PurposeThe purpose of this paper is twofold: first, to identify and rank the challenges influencing the delivery of the housing public-private partnership (HPPP) in Tanzania; and second, to suggest solutions in the form of a conceptual public-private partnership (PPP) framework model that will address the identified challenges and boost the chances of success.Design/methodology/approachUsing a convergent parallel (concurrent) mixed method approach, data were collected from 28 stakeholders involved with HPPP projects in Tanzania using a hand-delivered and e-mail survey and 13 semi-structured interviews with public and private sector respondents. The quantitative data included subjecting the 19 challenges as identified from the literature to parametric tests such as one-sample t-tests and descriptive statistics tests such as measures of central tendencies and frequency analysis through Statistical Package for Social Sciences (SPSS 22.0). Qualitative data employed content analysis. The research was further underpinned by a number of theoretical perspectives such as Gidden’s structuration theory, contingency theory, relational and equity theory.FindingsThe top five ranked challenges influencing the delivery of HPPP were “inadequate PPP skills and knowledge”; “poor contracting and tendering documents”; “inadequate project management”; “inadequate legal framework”; and “misinformation on financial capacity of private partners”. The least six ranked and most significant challenges based on the one-sample (single) t-tests were as follows: “Poor risk allocation”; “inexperienced private partner”; “unequal qualification and contributions of expertise”; “poor enabling environment to attract competent partners”; “inadequate mechanisms for recovery of private investors’ capital”; and “high costs in procuring PPP projects”. The qualitative study further confirmed the challenges and cited the reason for the failure of joint venture projects as the lack of motivation for undertaking similar PPP projects. Despite the increased awareness of PPP projects and associated marginal benefits, the main impediment to the uptake and delivery of PPP housing projects remained the lack of skills and expertise.Research limitations/implicationsThe proposed framework model is not yet tested, but since this paper is part of the ongoing research, the next stage involves the testing and validation of the model. Future studies could test the applicability of the proposed framework in other HPPP projects in Tanzania, and in other similar developing countries. Second, the validated framework can contribute towards addressing similar challenges as well as providing guidance. The proposed framework model is not yet tested, but since this paper is part of the ongoing research, the next stage involves the testing and validation of the model. Furthermore, recommendation for future research is to test the alignment of the identified challenges to the proposed remedial solutions across the five phases within the proposed PPP framework with a number of case studies.Practical implicationsThe identified challenges were used to form the basis of the framework presented in this paper. Furthermore, these provide useful information, thus leading to increased awareness to enable successful delivery of HPPP in Tanzania. Similarly, both the government and policy makers could use the findings as the basis for re-examining the existing PPP policy and regulations, and reflecting on the existing situation with a view to improving the delivery of future HPPP projects.Originality/valueThe empirical study is among the first that identifies and ranks the challenges of PPP for housing projects delivery within the Tanzanian context. The identification of the challenges enabled their ranking, resulting in the mapping out of the most critical challenges. Furthermore, using the Gidden’s structuration theory, the study illustrates how institution mechanisms (structures) address these delivery challenges, thus influencing the implementation of HPPP in Tanzania, and how individual stakeholders (human agents or agency) are able to make choices (advocated solutions) in dealing with the challenges. More so, these constraints (challenges) as identified and viewed through the contingency and equity theoretical lenses form the foundation for developing the PPP conceptual framework. The proposed framework would thus serve as a mechanism for providing practical solutions as well as reducing the level of severity of the identified challenges.

Estimating future wood outtakes in the Norwegian forestry sector under the shared socioeconomic pathways

Abstract The forest sector plays a key role in achieving low temperature stabilization targets, as woody biomass represents a cost-efficient alternative to fossil fuels for energy and material production. Estimates of future woody biomass demands vary in the Shared Socioeconomic Pathways (SSPs), depending on societal development trends, climate model projections, socioeconomic conditions, and climate and energy policies. The SSPs are qualitatively and quantitatively defined at global and macro-regional level, and their implementation for individual sectors at a national basis is challenging. In this paper, we provide estimates for forest wood outtakes in Norway until 2100 using key drivers from the SSPs such as population and Gross Domestic Product (GDP) and specific aspects of developments in the land use sector. First, we analyze historical wood harvest trends from 1960 to 2016 for the main tree species and wood classes and construct a regression model based on population, GDP and time. The model framework is based on a statistical approach that does not explicitly include market adjustments, but it is adapted to an interpretation of the salient characteristics of the different SSP scenarios to estimate future outtake volumes for each combination of tree species and wood classes in Norway. The SSP characteristics implemented in the model framework are GDP and population, land-use change regulation, participation of the land-use sector to climate change mitigation, and starting year of international cooperation for climate change mitigation. The produced estimates span a range of possible harvest rates and resource use potentials. Results show that SSP1 achieves a maximum mean extraction rate of 17.7 million m3 (in 2090). Forest wood outtake volumes are the lowest in SSP3, reaching a maximum of about 11.9 million m3 in 2040 and then declining. SSP2 and SSP4 generally lie in between SSP1 and SSP3. SSP5 is the most resource intensive scenario, with harvest rates achieving 27.5 million m3 in 2100. Driven by high population and GDP, SSP5 far exceeds the forest maximum harvest potential in Norway. Variability in the estimates is larger when land use regulation is weak and market fluctuations are high, such as in SSP2, SSP3 and SSP5. The proposed model framework is an approach to interpret and translate the global qualitative SSP narratives into quantitative projections at a finer scale, and can favor the use of a consistent background setting such as the SSPs in interdisciplinary research activities across different spatial scales of analysis.

A Model Framework for Measuring and Managing Operational Risks in Treasury Operations in Financial Institutions

In this Ph.D thesis, we develop a model framework for assessment of operational risks, in four stages. We start with a process flow chart detailing sequence of actions and an overview of the model framework at a glance. We show in this process flow chart key steps for identification and specification of important treasury activities, sub activities, decisions making points etc. Then we develop a basic framework for identifying KRIs with respect to risk sensitivity and severity. After this KRI framework, in the third step of the model, we propose a comprehensive framework showing relationship among various operational risk contributing factors- people, process, systems and external events, their strategy, structure and execution for controls, checks and balances, internal and external changes. Since our research results show people as the most important risk-contributing factor, we develop, in the fourth and final step of the model a framework for identifying and mitigating people related operational risk factor. Thus, we complete our model framework in four stages. The proposed model framework is based on KRIs/RCSA approach, which provides a methodological advantage over the traditional statistical techniques, since in this framework, the qualitative aspects of ORCs viz importance of risk severity, their frequency and relationship among various operational risk contributing factors- people, process, systems and external events can be reviewed frequently and consistently. With this cyclical and concurrent review of all the four stages of the model framework, this may help in understanding the existing risks, known risks, unknown risk, misses, near misses, present threats and future risks and the like and based on these review, risk mitigation techniques can be implemented in time.

Too conservative to hedge: How much does a corn ethanol facility lose?

While the process of producing ethanol from corn has been well established for over a decade, the process profitability still eludes the industry investors. In particular, uncertainties exist in both the feedstock and the product spot prices. To manage the sophisticated price uncertainty, current practice in industry is to negotiate long-term fixed rate prices with both the suppliers of the feedstock and the purchasers of the product. Although this practice completely eliminates the price uncertainty and simplifies production decisions, it entails the potential loss of profit. Stochastic programming methods provide a framework which takes into account the uncertainties and determines the decisions that are optimal under this uncertain future. Therefore, in this study, a stochastic optimization model is presented to determine the weekly production level and swap hedging portfolios over a year's planning horizon with the objective of maximizing the process profit. The approach is also used in a back-testing perspective to compare the profit obtained from applying the optimal decisions with the negotiated profit derived from using the current practice. This comparison in turn validates the effectiveness of the proposed model framework, in particular during times of high price uncertainty.

Pathfinder: Application-Aware Distributed Path Computation in Clouds

Path computation in a network is dependent on the network's processes and resource usage pattern. While distributed traffic control methods improve the scalability of a system, their topology and link state conditions may influence the sub-optimal path computation. Herein, we present Pathfinder, an application-aware distributed path computation model. The proposed model framework can improve path computation functions through software-defined network controls. In the paper, we first analyse the key issues in distributed path computation functions and then present Pathfinder's system architecture, followed by its design principles and orchestration environment. Furthermore, we evaluate our system's performance by comparing it with FreeFlow and Prune-Dijk techniques. Our results demonstrate that Pathfinder outperforms these two techniques and delivers significant improvement in the system's resource utilisation behaviour.

Design a Cloud Security Model in VANET Communication: Implementation, Performance and Security Analysis

first paper of this work, the design and the architecture of our proposed model framework, VANET Security as a Service (VSaaS), was discussed. In this second paper, the performance metrics measurements will be investigated through the NS2, SUMO and Trans simulations, to evaluate the security overhead of the secure Vehicle Information Messages (VIMs), which are sent by the vehicles to the cloud as a coarse-grained information. Moreover, our proposed model framework (VSaaS) will be discussed against the security requirements in the VANET.

Selected indicators for evaluation of eco-innovation projects

This paper addresses a particular innovation projects category, that is, eco-innovation, in line with the fact that a lot of research has been recently carried out in this area, due to increasing attention. Eco-innovation projects are related to development and introducing the new products, processes and services that reduce the overall negative impact on the environment, putting the business and innovation together to create sustainable solutions. The aim of our research is developing a model for prioritization of the most frequent eco-innovation projects in the recycling field. It should be one of the key challenges for national economies as it happens to be one of the current priorities and burning issues for the European Commission (Horizon 2020, CIP, COSME, etc.), giving the proposed model framework. The ranking has been established with regard to common national economy benefits defined through identified criteria, by applying the well-known analytic hierarchy process method of multi-criteria decision-making.

Modeling and simulation of trabecular pattern formation in a cancellous bone defect under mechanical forces.

Understanding regeneration of the trabecular structure in cancellous bone defects is an important issue in bone tissue engineering and regenerative medicine. Biochemical and biomechanical viewpoints are indispensable for understanding the fundamental mechanism that underlies the regeneration of the trabecular structure. In vitro observations of the Turing pattern-like bone differentiation into osteoblasts from human mesenchymal stem cells suggest that mathematical modeling and simulation based on a reaction-diffusion system model would help us to understand the mechanism of trabecular pattern formation during cancellous bone regeneration. In this article, we propose a mathematical model of trabecular morphogenesis based on the reaction-diffusion system in 3D, which comprises activators and inhibitors of bone formation by combining with mechanical factors. Based on the proposed model, we conduct computational simulation of trabecular regeneration in a cancellous bone defect using a voxel-based finite element method for stress analysis and a finite difference method for reaction-diffusion analysis. The proposed model could express the regeneration of the three-dimensional trabecular structure with mechanically adapted functions as a load-bearing structure. Based on these results, the proposed model and simulation framework are expected to facilitate the analysis of regeneration of the cancellous bone;this will help us to examine bone regeneration that involve complex biological factors.

The interaction of protein-coated bionanoparticles and surface receptors reevaluated: how important is the number of bonds?

Specifically designed bionanoparticles with a function-oriented protein-coating layer interact with self-prepared receptor surfaces as the counterpart. Based on surface plasmon resonance biosensing experiments, a model framework is validated to estimate the number of bonds formed between these bionanoparticles and the receptor surface based on multivalent interactions. Our multi-site kinetic model is able to analyze the adsorption rate constants and the number of bonds from experimental data of natural and synthetic bionanoparticles. The influence of the mass transport on the adsorption kinetics is modeled including a diffusional boundary layer where a helpful analytical solution has been derived. Our model framework extends previous studies to include a higher number of bonds, ranging from 1 up to 1000. An almost linear relationship between the number of bonds and the adsorption amount of bionanoparticles makes the model framework suitable to predict, for example, ligand density and to further assess coating performance. The proposed model framework can serve as a design tool for multivalent interaction experiments under variable process conditions.

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

In the petroleum industry, oil and gas pipeline operators routinely employ non-destructive in-line inspection (ILI) tools to perform integrity assessment, in which corrosion defects are detected, located and sized. However, the inspection technology is not perfect, and its accuracy is influenced by intrinsic measurement error of the ILI device as well as measurement noise. The quality of the inspection result should be assessed, and the inspection device should be calibrated before further assessment is performed. In the present work, a calibration model for an ultrasonic ILI device is proposed based on physical principles, and both systematic error and random error are able to be estimated. In addition to the errors from inspection devices, the soil environment introduces various uncertainties into the corrosion propagation. This paper presents a methodology to infer the actual corrosion defect depth based on detection theory and to account for the effect of soil property variation by combining cluster analysis with a Bayesian inferential framework. A numerical study on calibration uncertainty shows the influence of the number of field verifications. The proposed model framework is applied to a 110-km pipeline system to illustrate its application.

Key Performance Indicators Evaluation and Performance Measurement in Dry Port-Seaport System: A Multi Criteria Approach

Dry port-seaport services are key elements of a multimodal transport. They represent the needed infrastructure for its development. In this case, evaluating and improving their performance is necessary to achieve international competitiveness. The purpose of this paper is to develop a new model of performance measurement for the dry port-seaport system. To this aim, we have consolidated available researches and existing studies in order to identify and develop our proposed model framework. In this paper, a multi-criteria hierarchical model framework using MACBETH for dry port-seaport system has been developed. This framework can be used by managers at different levels of the system. The proposed model has been developed by exploring measurement gaps in multimodal transport field and by discovering prospective options from this area. The results and the methodology are practical. We obtain the global performance level of our involved system using our methodology, which can help managers in taken decisions and increase global performance of this system.

A model framework to describe growth-linked biodegradation of trace-level pollutants in the presence of coincidental carbon substrates and microbes.

Pollutants such as pesticides and their degradation products occur ubiquitously in natural aquatic environments at trace concentrations (μg L(-1) and lower). Microbial biodegradation processes have long been known to contribute to the attenuation of pesticides in contaminated environments. However, challenges remain in developing engineered remediation strategies for pesticide-contaminated environments because the fundamental processes that regulate growth-linked biodegradation of pesticides in natural environments remain poorly understood. In this research, we developed a model framework to describe growth-linked biodegradation of pesticides at trace concentrations. We used experimental data reported in the literature or novel simulations to explore three fundamental kinetic processes in isolation. We then combine these kinetic processes into a unified model framework. The three kinetic processes described were: the growth-linked biodegradation of micropollutant at environmentally relevant concentrations; the effect of coincidental assimilable organic carbon substrates; and the effect of coincidental microbes that compete for assimilable organic carbon substrates. We used Monod kinetic models to describe substrate utilization and microbial growth rates for specific pesticide and degrader pairs. We then extended the model to include terms for utilization of assimilable organic carbon substrates by the specific degrader and coincidental microbes, growth on assimilable organic carbon substrates by the specific degrader and coincidental microbes, and endogenous metabolism. The proposed model framework enables interpretation and description of a range of experimental observations on micropollutant biodegradation. The model provides a useful tool to identify environmental conditions with respect to the occurrence of assimilable organic carbon and coincidental microbes that may result in enhanced or reduced micropollutant biodegradation.

Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach

Abstract Automotive industries have attracted attention from international sectors recently. This attention to the industry results in many innovative technologies being integrated in these manufacturing arenas. In developing countries such as the BRIC (Brazil, Russia, India, and China) countries, especially China who is a global leader in automotive sales and production, researchers see substantial attempts to increase economic and societal values in automotive sectors through the use of many sustainable strategies. Among these sustainable strategies, remanufacturing has witnessed much debate in recent years. In China, automotive parts remanufacturing is still in a nascent stage, but the country is striving hard to implement a strategy in industries to result in cleaner production. Additionally, remanufacturing yields high financial, societal, and environmental returns, and it provides advantages such as reducing costs (compared to virgin products), an increase in jobs, and the conservation of scare resources. Some earlier studies examined Chinese remanufacturing issues but they only focused on modeling concepts, without considering significant barriers. To bridge this gap, this paper analyzes internal barriers met by automotive parts remanufacturers and evaluates causal barriers by a proposed model framework. This objective is illustrated by employing the Grey Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. By virtue of these findings, remanufacturers can eradicate major internal barriers and can increase the chances of practicing more productive remanufacturing in the Chinese automotive environments. This study extends the scope of this problem through varied applications of different techniques such as a fuzzy approach, matrix laboratory, and operations research tools by diversifying and including additional barriers.

Optimizing Headways for Mass Rapid Transit Services

Mass rapid transit (MRT) operators need to balance the total system costs with the level of service. The primary service of MRT systems is to provide cost-efficient mobility. Governments are responsible for establishing and regulating minimum standards to ensure that the MRT service indicators meet a specific level. Thus, approaches to optimizing MRT system service frequencies are critical concerns for MRT system operators. Previous studies on MRT operations have focused primarily on delays, energy conservation, route design, and general system operations. Conversely, few studies have focused on optimizing headway problems. This study adopts a mathematical programming method to develop a headway-oriented model for the Kaohsiung MRT (KMRT) system aiming to minimize system costs while maintaining an acceptable level of train services. The developed model systematically adjusts train headways based on time-series passengers’ spatio-temporal distribution data. A numerical case study and sensitivity analysis were conducted to test the feasibility and effectiveness of the proposed models and solution algorithms. The proposed model framework provided the KMRT operator with a flexible tool to evaluate the effects of service frequency on both operating cost and passengers’ waiting cost, which facilitates the operator to prepare a cost-efficient train service plan.

Stress Measures Affecting Deformation of Granular Materials

For the saturated case with only one pore fluid, either water or air, the roles of both the intergranular stress tensor and the pore fluid stress can be distinguished easily. In the unsaturated case, the capillary water is recognized to induce capillary suction in the pores and capillary‐suction‐induced interparticle forces. At the macroscale, volume averaging of these forces would lead to the capillary‐suction‐induced intergranular stress tensor. In its approximate formulation, the concept of the fabric stress tensor is applied, enabling the effect of the spatial distribution of the intergranular fabric on the capillary water bridges as occurring in the drier pendular saturation phase to be accounted for. Subsequently, the combined intergranular stress tensor and the combined pore fluid stress tensor can be derived directly. The constitutive relation of a granular skeleton, composed of elastic particles with mainly frictional interaction, like quartz sands and silts, is considered to remain independent of the degree of saturation. Under such restrictive conditions, only the additional physical parameters of the capillary‐suction‐induced intergranular stress tensor need to be determined, which can be achieved by means of inverse modeling, taking advantage of all macroscale experimental data and physical modeling for the whole unsaturated range. For clays and peats, with potential physicochemical and biochemical actions and double porosity and/or fibrous microstructures, the constitutive models can be expected to be physically more complicated, thus involving more physically relevant parameters. Hence, clays and peats must be considered to fall outside the scope of the proposed model framework.

The need for a behavioural science focus in research on mental health and mental disorders.

Psychology as a science offers an enormous diversity of theories, principles, and methodological approaches to understand mental health, abnormal functions and behaviours and mental disorders. A selected overview of the scope, current topics as well as strength and gaps in Psychological Science may help to depict the advances needed to inform future research agendas specifically on mental health and mental disorders. From an integrative psychological perspective, most maladaptive health behaviours and mental disorders can be conceptualized as the result of developmental dysfunctions of psychological functions and processes as well as neurobiological and genetic processes that interact with the environment. The paper presents and discusses an integrative translational model, linking basic and experimental research with clinical research as well as population-based prospective-longitudinal studies. This model provides a conceptual framework to identify how individual vulnerabilities interact with environment over time, and promote critical behaviours that might act as proximal risk factors for ill-health and mental disorders. Within the models framework, such improved knowledge is also expected to better delineate targeted preventive and therapeutic interventions that prevent further escalation in early stages before the full disorder and further complications thereof develop. In contrast to conventional "personalized medicine" that typically targets individual (genetic) variation of patients who already have developed a disease to improve medical treatment, the proposed framework model, linked to a concerted funding programme of the "Science of Behaviour Change", carries the promise of improved diagnosis, treatment and prevention of health-risk behaviour constellations as well as mental disorders.

A groundwater–soil–plant–atmosphere continuum approach for modelling water stress, uptake, and hydraulic redistribution in phreatophytic vegetation

ABSTRACTModelling groundwater uptake provides a powerful tool for examining the tight linkage of phreatophytic vegetation with spatial and temporal variations in groundwater and soil moisture. Here, a physically based modelling framework was developed to simulate groundwater uptake and hydraulic redistribution (HR), as driven by the potential gradients along the groundwater–soil–plant–atmosphere continuum. A new water stress function, based on the ‘vulnerability curve’ theory, was introduced; it integrates the influence of both soil water and groundwater on transpiration. The model was then implemented using a system dynamics approach and applied to simulate groundwater uptake of Quercus douglasii (blue oak) in a California savanna. It showed good agreement with the measured evapotranspiration, soil moisture, and leaf water potential data. The model results indicated that the dominant water source for blue oaks switched from soil water in the wet season to groundwater in the dry season. During the dry period, cavitation led to a hydraulic conductivity loss of approximately 85% in the shallow roots, and groundwater uptake contributed over 80% of transpiration. The dry, shallow soil layers received water from groundwater and deep soil layers at night through HR. The new water stress function performed well when simulating daily transpiration (r2 = 0·69). The model indicated that blue oaks maintained only a small portion of deep roots (15% of the total) to uptake groundwater and mitigate the impacts of drought. The proposed model framework can be incorporated into coupled climate, hydrological, and ecological models to improve their performance when studying ecohydrological process and climate feedbacks. Copyright © 2013 John Wiley & Sons, Ltd.

Knowledge Discovery in Endangered Species Diversification

Classification of regional territories and countries related to endangered species has been investigated by data mining techniques and graphical modeling using an extensive data set of species. We developed the graphical models (hereafter referred to as ESDI) using cosine, jaccard similarity, K Mean clustering and cliques in graph modeling for a large number of countries. Environmental variables associated with species records were identified in context of their diversification to integration with our proposed prototype. We have shown that the problem of finding the most coherent clusters is reducible to finding maximum clique. Key findings include the urge to ameliorate communication about the loss and protection of endangered species and their concerned projects. The proposed framework is presented to serves a portal to knowledge discovery. We have concluded that the proposed framework model and its associated data mining similarity measures can be useful for investigating various scientific and management oriented questions related to protection of endangered species with emphasis on collaboration among regional countries. The rationale behind the proposed approach is that the countries which have been grouped into same clique inherit a lot of argues illustrating common reasons of their struggles towards ecological safety with minimization of perils for endangered species. The development and implementation of a regional approach based on this similar grouping address the actions that could offer significant benefits in achieving their goal for ecological policies. Other critical actions at this clique level include fortifying and elevating harmonization of legal frameworks with emphasis on prevention procedural issues; awareness realizations of endangered species issues and its priority. Such actions will eventually lead towards implementation of essential plans fulfilling co-operative expertise and common endeavors.