金融机构ESG投资的“漂绿”与“反漂绿”——基于DWSGroup的案例分析

深化绿色金融发展、助力经济绿色发展,是实现“双碳”目标的重要途径,特别是在ESG理念盛行的今天,金融机构的ESG投资行为因其深远影响而颇受投资者青睐,但层出不穷的“漂绿”现象也牢牢制约着绿色金融的发展,制约着ESG投资的发展。西方国家作为绿色金融发展先驱,从信息披露、制度监管、实践探索等方面对“漂绿”现象进行了研究和治理。本文在学习其先进经验的基础上,基于舞弊三角理论对DWSGroup案例进行分析,从压力、机会和自我合理化因素角度对金融机构ESG投资“漂绿”行为的动因进行分析,并据此提出我国“反漂绿”体系框架,以期为绿色金融的平稳发展和“双碳”目标的实现作出贡献。

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Enhancing Identity Protection in Metaverse-Based Psychological Counseling System

Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regard-ing client identity.However,these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions,such as facial cues,to the counselor.This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling.The proposed systemincorporates a voicemodulation function that instantlymodifies/masks the client’s voice to safeguard their identity.Additionally,it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s non-verbal expressions through their avatar in the metaverse environment.The system is adaptable for use on personal computers and smartphones,offering users the flexibility to access metaverse-based psychological counseling across diverse environments.The performance evaluation of the proposed system confirmed that the voice modulation and real-time facial expression replication consistently achieve an average speed of 48.32 frames per second or higher,even when tested on the least powerful smartphone configurations.Moreover,a total of 550 actual psychological counseling sessions were conducted,and the average satisfaction rating reached 4.46 on a 5-point scale.This indicates that clients experienced improved identity protection compared to conventional non-face-to-face metaverse counseling approaches.Additionally,the counselor successfully addressed the challenge of conveying non-verbal cues from clients who typically struggled with non-face-to-face psychological counseling.The proposed systemholds significant potential for applications in interactive discussions and educational activities in the metaverse.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Introducing hydroxyl groups to tailor the d-band center of Ir atom through side anchoring for boosted ORR and HER

Tuning the coordination atoms of central metal is an effective means to improve the electrocatalytic activity of atomic catalysts.Herein,iridium(Ir) is proposed to be asymmetrically anchored by sp-N and pyridinic N of hydrogen-substituted graphdiyne(HsGDY),and coordinated with OH as an Ir atomic catalyst(Ir_(1)-N-HsGDY).The electron structures,especially the d-band center of Ir atom,are optimized by these specific coordination atoms.Thus,the as-synthesized Ir_(1)-N-HsGDY exhibits excellent electrocatalytic performances for oxygen reduction and hydrogen evolution reactions in both acidic and alkaline media.Benefiting from the unique structure of HsGDY,IrN_(2)(OH)_(3) has been developed and demonstrated to act as the active site in these electrochemical reactions.All those indicate the fresh role of the sp-N in graphdiyne in producing a new anchor way and contributing to promote the electrocatalytic activity,showing a new strategy to design novel electrochemical catalysts.

Survey and clinical considerations of gender identity in lower primary school children

BACKGROUND Gender consciousness directly affects the development of gender identity,which is a continuous and lifelong process.Meanwhile,hospitalization is a part of many children's lives and has an impact on their gender development.AIM To investigate the current situation of gender identity in lower primary school children by conducting a survey of 202 hospitalized children in the lower grades and to provide a theoretical basis and foundation for the cultivation of gender identity and medical treatment of children based on the results.This study aims to inspire clinical medical staff to scientifically and reasonably arrange hospital wards for lower primary school children and pay attention to gender protection during the medical treatment process and to help children shape a unified and clear gender identity,which will enable them to better integrate into society and promote their personality development.METHODS The gender consciousness scale for elementary and middle school students was RESULTS Gender identity was already present in lower primary school children.The children's gender roles and gender equality consciousness were strong,exceeding the critical value,but their gender characteristics,gender identity,and gender ideal consciousness were weak.Children aged 6 had the weakest gender identity,and girls had significantly stronger gender identity than boys.CONCLUSION Gender identity is already present in lower primary school children,providing a basis and inspiration for the cultivation of gender identity and medical treatment of lower primary school children.Clinical medical staff should be aware of and understand these results and should scientifically and reasonably arrange hospital wards for lower primary school children.

SUPER WOMAN 2(SPW2)maintains organ identity in spikelets by inhibiting the expression of floral homeotic genes OsMADS3,OsMADS58,OsMADS13,and DROOPING LEAF

Flower organ identity in rice is mainly determined by the A-,B-,C-and E-class genes,with the majority encoding MADS-box transcription factors.However,few studies have investigated how the expression of these floral organ identity genes is regulated during flower development.In this study,we identified a gene named SUPER WOMAN 2(SPW2),which is necessary for spikelet/floret development in rice by participating in the regulation of the expression of pistil identity genes such as OsMADS3,OsMADS13,OsMADS58 and DL.In the spw2 mutant,ectopic stigma/ovary-like tissues were observed in the non-pistil organs,including sterile lemma,lemma,palea,lodicule,and stamen,suggesting that the identities of these organs were severely affected by mutations in SPW2.SPW2 was shown to encode a plant-specific EMF1-like protein that is involved in H3K27me3 modification as an important component of the PRC2 complex.Expression analysis showed that the SPW2 mutation led to the ectopic expression of OsMADS3,OsMADS13,OsMADS58,and DL in non-pistil organs of the spikelet.The ChIP-qPCR results showed significant reductions in the levels of H3K27me3 modification on the chromatin of these genes.Thus,we demonstrated that SPW2 can mediate the process of H3K27me3 modification of pistil-related genes to regulate their expression in non-pistil organs of spikelets in rice.The results of this study expand our understanding of the molecular mechanism by which SPW2 regulates floral organ identity genes through epigenetic regulation.

End-group modulation of phenazine based non-fullerene acceptors for efficient organic solar cells with high open-circuit voltage

Phenazine-based non-fullerene acceptors(NFAs)have demonstrated great potential in improving the power conversion efficiency(PCE)of organic solar cells(OSCs).Halogenation is known to be an effective strategy for increasing optical absorption,refining energy levels,and improving molecular packing in organic semiconductors.Herein,a series of NFAs(Pz IC-4H,Pz IC-4F,Pz IC-4Cl,Pz IC-2Br)with phenazine as the central core and with/without halogen-substituted(dicyanomethylidene)-indan-1-one(IC)as the electron-accepting end group were synthesized,and the effect of end group matched phenazine central unit on the photovoltaic performance was systematically studied.Synergetic photophysical and morphological analyses revealed that the PM6:Pz IC-4F blend involves efficient exciton dissociation,higher charge collection and transfer rates,better crystallinity,and optimal phase separation.Therefore,OSCs based on PM6:Pz IC-4F as the active layer exhibited a PCE of 16.48%with an open circuit voltage(Voc)and energy loss of 0.880 V and 0.53 e V,respectively.Accordingly,this work demonstrated a promising approach by designing phenazine-based NFAs for achieving high-performance OSCs.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Molecular packing tuning via chlorinated end group enables efficient binary organic solar cells over 18.5%

Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.

Spherical Functions on Fuzzy Lie Group

Let G be a locally compact Lie group and its Lie algebra. We consider a fuzzy analogue of G, denoted by called a fuzzy Lie group. Spherical functions on are constructed and a version of the existence result of the Helgason-spherical function on G is then established on .

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

How Group Leaders Build Stable Community Buying Groups:A Perspective Based on the Differential Mode of Association

Since 2016,community group buying has grown significantly in China,largely driven by its efficient logistics,supply chains,low prices,and convenience.This model has been further popularized during the COVID-19 pandemic due to its effectiveness in meeting daily needs while minimizing human-to-human contact.A key component of this business model is the“group leaders”-influential individuals within a community responsible for managing group buying activities,which include order collection,supplier liaison,and goods distribution.Their primary task is to form and sustain a reliable community group buying consortium,a task that demands excellent organizational and interpersonal skills.This paper examines this phenomenon using the lens of the differential mode of association,a theoretical model explaining interpersonal relationships in traditional Chinese society.The research indicates that group leaders,through regular interaction with consumers,are able to alter their social network position,increase their influence,understand consumer needs,provide satisfying services,and enhance trust,thereby transforming consumers into loyal group buying participants.This transformation not only brings stability to group buying activities but also reinforces the community influence of group leaders,thus fostering the growth of community group buying.

Combined Promoting Effects of Specific Organic Functional Groups and Alumina Surface Characteristics for the Design of a Highly Efficient NiMo/Al_(2)O_(3) Hydrodesulfurization Catalyst

To prepare a highly efficient NiMo/Al_(2)O_(3) hydrodesulfurization catalyst,the combined effects of specific organic functional groups and alumina surface characteristics were investigated.First,the correlation between the surface characteristics of four different alumina and the existing Mo species states was established.It was found that the Mo equilibrium adsorption capacity can be used as a specific descriptor to quantitatively evaluate the changes in surface characteristics of different alumina.A lower Mo equilibrium adsorption capacity for alumina means weaker metal-support interaction and the loaded Mo species are easier to transform into MoS2.However,the Mo-O-Al bonds still exist at the metal-support interface.The introduction of cationic surfactant hecadecyl trimethyl ammonium bromide(CTAB)can further improve Mo species dispersion through electrostatic attraction with Mo anions and interaction of its alkyl chain with the alumina surface;meanwhile,the introduction of ethylenediamine tetraacetic acid(EDTA)can complex with Ni ions to enhance the Ni-promoting effect on Mo.Therefore,the NiMo catalyst designed using alumina with lower Mo equilibrium adsorption capacity and the simultaneous addition of EDTA and CTAB exhibits the highest hydrodesulfurization activity for 4,6-dimethyl dibenzothiophene because of its proper metal-support interaction and more well-dispersed Ni-Mo-S active phases.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Metamorphic P-T conditions and ages of garnet-biotite schists in the Dahongshan Group from the southwestern Yangtze Block

The Dahongshan Group,situated at the southwestern margin of the Yangtze Block,represents a geological unit characterized by relatively high-grade metamorphism in the region.This paper investigates the garnet-biotite schist from the Laochanghe Formation of the Dahongshan Group,employing an integrated approach that includes petrological analysis,phase equilibrium modeling,and zircon U-Pb dating.The schist is mainly composed of garnet,biotite,plagioclase,quartz,rutile,and ilmenite.Phase equilibrium modeling revealed the peak metamorphic conditions of 8-9 kbar and 635-675°C.By further integrating the prograde metamorphic profile of garnet and geothermobarometric results,a clockwise P-T metamorphic evolution path is constructed,which includes an increase in temperature and pressure during the prograde stage.LA-ICP-MS zircon U-Pb dating and zircon Ti thermometry constrains the post-peak metamorphic age of 831.2±7.2 Ma.Integrated with previously reported results,it is revealed that the southwestern margin of the Yangtze Block experienced a large-scale regional metamorphism during the Neoproterozoic(890-750 Ma),which is related to the collisional orogenic process.This may be associated with the late-stage assembly of the Rodinia supercontinent or with local compression and subduction processes during the breakup of the Rodinia supercontinent.

Utilizing Certificateless Cryptography for IoT Device Identity Authentication Protocols in Web3

Traditional methods of identity authentication often rely on centralized architectures,which poses risks of computational overload and single points of failure.We propose a protocol that offers a decentralized approach by distributing authentication services to edge authentication gateways and servers,facilitated by blockchain technology,thus aligning with the decentralized ethos of Web3 infrastructure.Additionally,we enhance device security against physical and cloning attacks by integrating physical unclonable functions with certificateless cryptography,bolstering the integrity of Internet of Thins(IoT)devices within the evolving landscape of the metaverse.To achieve dynamic anonymity and ensure privacy within Web3 environments,we employ fuzzy extractor technology,allowing for updates to pseudonymous identity identifiers while maintaining key consistency.The proposed protocol ensures continuous and secure identity authentication for IoT devices in practical applications,effectively addressing the pressing security concerns inherent in IoT network environments and contributing to the development of robust security infrastructure essential for the proliferation of IoT devices across diverse settings.

Identity(ies)and Transnationalization of Brazilian Evangelical Churches in Portugal:Case study of World Cathedral of Hope and Victory in Christ Churches

The presence of evangelicals in Brazil is a social and religious phenomenon that has aroused interest of many scholars due to its rapid and significant expansion in recent decades and its projection beyond national borders.Among evangelical denominations that stand out in this scenario are Pentecostal churches,which have developed intense missionary activity in several countries,including Europe.In this article,we intend to analyze features and challenges of Brazilian evangelical churches:World Cathedral of Hope(CIME)(Catedral Mundial da Esperança-ICME)and Assembly of God Victory in Christ(ADVEC)(Assembleia de Deus Vitória em Cristo-ADVEC)that operate in Portugal,seeking to understand issues of identity and belonging of these communities,their models of transnationalization,its evangelization strategies and its adaptations to Portuguese context,in the period from 2000 to 2020.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.