Numerical Criteria Research Articles

Selection of Health Insurance Policy: Using Analytic Hierarchy Process and Combined Compromised Solution Approach Under Spherical Fuzzy Environment

The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.

Comprehensive vulnerability assessment for environmental facility depending on spatial characteristics in South Korea

Among environmental facilities, wastewater treatment facilities have a crucial role in sustaining human life, and any occurrence of an earthquake or flood within these facilities can result in various social, economic, and environmental issues, either directly or indirectly. Therefore, a quantitative vulnerability assessment of wastewater treatment facilities is necessary to minimize and prevent damage from earthquakes and flood disasters. For this reason, this study introduces a novel indicator to assess the susceptibility of disasters, considering aspects of exposure, sensitivity, and adaptive capacity. The newly proposed indicator encompasses numerous evaluation criteria, topography, natural surroundings, hydraulic systems, structural composition, and non-structural features. Also, Weights derived using the combined weight calculation (CWC) method, which combined the analytic hierarchy process (AHP) and entropy weight method were applied to the indicator. It was tested across 23 cities to validate its efficacy, revealing a substantial correlation between the vulnerability index and the specific attributes of the city's wastewater treatment facilities. Therefore, this study analyzed wastewater treatment facilities by comparing the attributes of the urban areas under investigation, such as topological characteristics, urbanization levels, population density, infrastructure quality, and disaster preparedness resources available. The suggested methodology can facilitate the development of strategies aimed at averting damage caused by earthquakes or floods and reducing the adverse impact on wastewater treatment facilities while considering the unique characteristics of the urban setting in question.

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

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

Implications of changing the base raw material – the case of license plate manufacturing

ABSTRACT License plates to uniquely identify vehicles mainly use aluminum as the base material. Currently, there is no distinction of different use cases of license plates, such as short-time usage for test drive and transportation purposes that do not need such long-lasting materials not only from cost but also from sustainability perspectives. This paper presents a methodology to select the best material for different use cases under the holistic consideration of specifically defined criteria as to material properties, sustainability aspects, and supply chain implications. We show that there are several candidate materials for different use cases that stick out by changing the importance of these numerous criteria. In addition, the paper delves deeper into the sustainability aspect by means of a comprehensive System Dynamics model. We show that a scenario in which the company picks up used license plates by relying on a logistics service provider to get them delivered to an external recycling service provider yields the best results.

Typology of Traditional Domestic Architecture based on Social Stratifications: The Case of Mamasa Tribe, Indonesia

Typologies of traditional domestic architecture across the world are based on numerous criteria. Certainly, social stratification is one among them. Mamasa tribe located in the West Sulawesi Province, which is one of the Indonesian ethnic groups with traditional architecture do so based on the social and economic levels of the community and the caste divisions. Their traditional architectural heritage still maintained to this day is the Mamasa traditional house called banua. The Mamasa community introduced caste divisions based on the social and economic levels of the community which physically influenced the form and typology of the Mamasa traditional house. This study examines the architectural aspects influenced by the social stratification of the Mamasa community: the function, form, space, material, appearance and decorative variety of the Mamasa traditional house. The research employed exploratory qualitative methods. Data collection techniques included direct observations, interviews and literature studies. In-depth interviews were conducted with several traditional leaders related to the Mamasa traditional house. The field data was correlated with data from the literature studies: journals, books, or website sources related to the research study. The findings indicate a strong correlation between the architecture of the traditional Mamasa house and the social stratification that exists in the Mamasa community. The typology based on the social stratification of the Mamasa society is: the first type, Banua Sura' is intended for the tana' bulawan or high nobility class. The second type, Banua Bolong is intended for the tana' bassi or the ordinary nobility class. The third type, Banua Rapa is intended for the tana' karurung or free-people class. The fourth type, Banua Longkarrin is intended for the tana' kua-kua or the slave class. These four types of banua each reflect the social and economic status of the homeowners through their architecture.

On the Generalized Numerical Criterion

Abstract In this note, we give examples that demonstrate a negative answer to the generalized numerical criterion problem for pairs.

Numerical analysis of the axisymmetric lattice Boltzmann method for steady and oscillatory flows in periodic geometries

Compared to more typical computational fluid dynamics techniques, the lattice Boltzmann method (LBM) is relatively new and unexplored. In recent years, axisymmetric LBM formulations, which can simulate flow in rotationally symmetric 3D geometries, have been published. Here we verify a novel axisymmetric LBM implementation using numerical criteria. Hagen–Poiseuille and Womersley flow are considered within a straight tube where analytic solutions are available. With this, we establish sufficient accuracy of the approximated flow and study the effects of changing simulation parameters (e.g. Reynolds number, Womersley number) and spatial/temporal parameters (e.g. relaxation time, mesh nodes, time steps). Furthermore, steady and oscillatory flows within a periodically-varying, longitudinally asymmetric geometry are considered. Analytic solutions are not available in these cases; however, the validity of the axisymmetric LBM for curved boundaries is ensured through convergence, mesh independence and qualitative observations. Guaranteeing reasonable flow field determination for the aformentioned geometry is relevant to a larger problem where particulate suspension is pumped back and forth through a membrane of axisymmetric micropores. In these circumstances, experiments have induced directed particle transport even though there is no net flow of the carrier fluid. Hence, our work aims to improve current numerical simulations of these flow problems to better understand the factors that facilitate particle transport.

Evaluation of Explainable Artificial Intelligence using TOPSIS Method

Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-world Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank.

Limonene-enriched ultra-structural cubosomes to augment ocular delivery of a poorly water soluble anti-fungal drug: Fabrication, characterization, statistical optimization, in vivo corneal uptake and histopathological evaluation in rabbits

The target of this investigation was to obtain the composition of the optimized Fenticonazole nitrate (FCN)-loaded cubosomal formula that can prolong precorneal retention, increase corneal absorption, enhance corneal permeation, and anti-fungal efficacy of FCN. Cubosomes are bi-continuous structural vesicles fabricated using the hot emulsification technique in accordance with a 23-full factorial design. The selected fabrication factors were the concentration of the Lipid phase (X1), glyceryl monooleate: lipid phase stabilizer ratio (X2), and aqueous phase stabilizer types (X3). Whereas, the encapsulation efficiency percent (EE%), Mean particle size (PS), Polydispersity index (PDI), and Zeta potential (ZP) were chosen as measured responses. The numerical optimization criterion of the design expert software was utilized to select the optimized formula (Op) for extra investigations. The Op formula had EE% of 87.94 ± 0.7 %, PS of 249.75 ± 1.48 nm, and ZP of −34.25 ± 0.64 mV. The in vitro release study of the Op formula in comparison with FCN suspension showed a zero-order and controlled kinetic release behavior. Moreover, the results of the pH, refractive index, surface tension, and mucoadhesion study confirmed the superiority and the safety of the Op formula. Further, the ex vivo permeation study demonstrated a significant enhancement in the transcorneal permeation of the Op formula than FCN suspension by 2.69-fold. Besides, the in vivo corneal uptake confirmed the higher permeation of the Op formula in comparison with FCN suspension. Draize test and histopathological examination ensured the in vivo safety and tolerability of the Op formula. Overall, the previous findings ensured the efficacy of cubosomes in enhancing ocular retention, absorption, permeation, and bioavailability of FCN.

THE COMPARISON SOME MODELS DAILY WIND SPEED DATA IN PEKANBARU CITY USING TWO, THREE AND FOUR PARAMETERS GAMMA DISTRIBUTIONS

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. One of the bases for assessment of wind energy potential for a specified region is the probability distribution of wind speed, therefore wind speed data is needed to produce statistical modeling, especially in determining the best probability distribution. For this purpose, several modified distributions will be used and tested to determine the best model to describe wind speed in Pekanbaru. Six continuous probability density function such as Two Parameters Gamma (GM), Three Parameters Slashed Quasi-Gamma (SQG), Three Parameters Quasi Gamma (QG), Three Parameters Generalized Gamma (GG), Four Parameters Poly Weighted Exponentiated Gamma (PWEG) and Four Parameters Exponentiated Gamma Exponential (EGE) distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the daily wind speed data, several goodness-of-fit tests were applied, including the graphical methos test (pdf plot) and numerical criteria method test (AIC). The QG and GG distributions are found to be the best-fitted probability distribution based on goodness-of-fit tests for the daily wind speed data at the Pekanbaru station.

Development of forecasting of monthly SAR time series in river systems: A multivariate data decomposition-based hybrid approach

The sodium adsorption ratio (SAR) is a critical variable in assessing the quality of water resources, and accurately forecasting its time series is operationally valuable. This study developed a hybrid approach using the multivariate variational mode decomposition (MVMD) model for signal analysis, Boruta model for feature selection, pure linear neural network (PLNN), support vector regression (SVR), Lasso regression, and Elman neural network (ENN) models to forecast monthly SAR time series for rivers. Data from two rivers were used to enhance result reliability, and the developed models were compared with corresponding basic models to evaluate their impact. Numerical and graphical criteria demonstrated the significant superiority of the developed models over the basic ones. Among the basic models, the ENN model exhibited the highest accuracy, while the MVMD-Boruta-ENN model surpasses all investigated models. This finding suggested that the ENN model's structure is more suitable for SAR time series forecasting than other basic models. Analyzing the models' residuals revealed lower mean, standard deviation, skewness, and error range in the developed models, indicating their robust behavior in forecasting the SAR time series. Notably, forecasting extreme SAR values holds greater importance than other values. Anderson-Darling and Kolmogorov-Smirnov tests identified the dominance of the generalized logistic and log-logistic (3-parameters) functions in SAR time series. Probability distribution functions were used to estimate extreme values, and the studied models exhibited more accurate estimations compared to the basic models, indicating their enhanced resilience. The consistent patterns observed in comparing developed and basic models for the entire series, as well as extreme values and residuals across the two investigated rivers, emphasized the reliability of the results.

Population Pharmacokinetics and Absolute Oral Bioavailability of Lasalocid after Single Intravenous and Intracrop Administration in Laying Hens.

Lasalocid sodium is a polyether carboxylic ionophore agent authorized by the EU for use as a coccidiostat in broilers, turkeys, and pullets up to 16 weeks of age, except for laying hens. However, laying hens are the most common nontarget species exposed to lasalocid sodium, mainly due to cross-contamination from feed mills. This exposure may result in potential drug residue deposition in eggs, which could potentially expose consumers to the drug. The breeds commonly used for commercial egg production in Poland are Isa Brown and Green-legged Partridge hens, which have been found to significantly differ in egg-laying performance. This variability may also affect the pharmacokinetics of lasalocid. Data on lasalocid plasma pharmacokinetics in laying hens are lacking. In this study, we aimed to determine typical population pharmacokinetic parameters, absolute oral bioavailability, and how breed may influence the pharmacokinetics of lasalocid. Twenty-layer hens of the two breeds were used in this study. Lasalocid was administered orally at a single dose of either 1 mg or 5 mg/kg body weight or intravenously at a dose of 1 mg/kg body weight, in a crossover design with a three-week washout period between study periods. Blood samples were collected for 72 h, and lasalocid concentrations were measured using high-performance liquid chromatography with fluorescence detection. A population pharmacokinetic analysis was conducted using nonlinear mixed effects modeling. Standard numerical and graphical criteria were used to select the best model, and a stepwise covariate modeling approach was used to determine any influencing factors. The best model was a three-compartment mammillary model with first-order absorption, transit compartments, and linear elimination. The estimated absolute oral bioavailability was low (36%). It was found that breed significantly influenced not only absorption but also the elimination of lasalocid. This study revealed that lasalocid absorption and elimination varied between the two breeds. This variability in pharmacokinetics may result in breed-related differences in drug residue accumulation in eggs, and ultimately, the risk associated with consumer exposure to drug residues may also vary.

Applications of advanced technology for Prioritization of Watershed

Prioritization of sub-watersheds is critical in strategic planning for incorporating management practices in delicate semi-arid regions. Geomorphometric as well as land use/cover data sets are essential for determining subwatershed priorities for integrated watershed management. Prioritizing watersheds entails ranking of sub-watersheds according to their susceptibility based on several variables, including the average yearly soil loss, the depletion of water resources, and ecological deterioration. The sub-watershed prioritization for the study area Muzaffarpur district was done by combining Principal Component Analysis (PCA) and Weighted Sum Approach (WSA). PCA was used to differentiate important parameters but WSA was used to measure compound values for priority ranking and determining weights for significant parameters. The PCA was effective in obtaining the most crucial values (i.e., WB, Dt, Re, and Rb). A load of each significant parameter was successfully defined by the WSA application. Traditional prioritizing procedures uses numerous criteria in a complex manner and presumptively contribute equally but PCA-WSA integration results in more dynamic, effective, and efficient solutions.

Water Scarcity Zonation Mapping using MCDM Method in District, NAMCHI, South Sikkim using QGIS

Abstract: A major worldwide issue, water scarcity has especially serious repercussion like India that have an abundance of water. There are only so many fresh water sources available to meet the growing demand for water brought on by urbanisation, industrialisation, and population expansion, Since the limited water supplies cannot satisfy the population’s needs, a state of water scarcity has resulted. The present research is conducted in CCCT Chisopani, Sikkim, India. Water scarcity is a major issue in the area we selected, the multiple criteria decision making (MCDM) approach will be used in this case study to solve the ranking water scarcity problem, and the next upcoming major problem for water scarcity is the growing population in the study area. Along with the households the area consists of 2 study institutes due to which the population may increase in the coming years effecting the water management. The first step was to conduct a survey in and around the study area by preparing questionnaire including total population of household, highest qualification in the house, storage capacity, tenants, local source, rain water harvesting, hours water received, water fetched manually or not? Type of roofs, distance to perennial source. After collection, the data was evaluated and standardised to guarantee comparability across numerous criteria. Further the study was continued with water scarcity index calculation making it easier to prepare the zonation map for the study area. The MCDM method exhibits how the method can be used against the water scarcity problem. The main focus of the study was to create a scarcity map for the study zone in QGIS using RS&GIS method.

AHP TOPSIS Analysis in Selecting Waste Processing Technology Based on Energy Justice

The amount of waste in Bantul Regency increased by 4.96% from 2020 to 2021, indicating that the capacity of final disposal sites (FDS) in Piyungan District, Bantul Regency, was decreasing. The peak occurred from 23 July 2023 to 5 September 2023, during which the Piyungan FDS could not provide waste disposal services. The high poverty rate in Bantul Regency forces the government to process waste into energy as a sustainable waste management effort. However, numerous criteria make it difficult to determine which technology is most suitable for this purpose. Energy justice criteria need to be considered when choosing technology and efforts to improve the welfare of Bantul Regency's residents. This research aimed to present an assessment of each alternative technology for processing waste into energy and decide one suitable alternative for sustainable waste management in Bantul Regency using a combination of AHP and TOPSIS methods based on energy justice. AHP was used to assess the level of importance of each criterion, while TOPSIS was used to determine the optimal alternative based on the criteria by considering costs and benefits. The findings showed that the preference value for three alternatives was 0.579, 0.414, and 0.341 for incineration, pyrolysis, and gasification, respectively. According to these preference value, incineration was identified as the foremost viable alternative technology for implementation in Bantul Regency. Gasification and pyrolysis ranked as the subsequent and third alternatives, respectively.

Influence of W-cut twisted tape inserts on heat transfer characteristics of a counter flow heat exchanger using CeO2 nanofluid – an experimental and numerical investigations

The motive of the study is to determine the influence of W-cut twisted tape (TT) inserts on heat transfer rate (HTR) and the pressure drop of a TTCFHEx. The TT inserts involved a constant twist ratio (TR = 3) with variations in width ratio (WR) and the depth ratio (DR), respectively. Cold fluid (CeO2 nanofluid) Reynolds number (Re) varied from 2,436 to 11,626 while the hot fluid Re (Re = 8,322) is kept constant. From the study, insertion of TT insets in a TTCFHEx creates a swirl flow along with secondary vertex flow in the flow direction, leads to disruption of boundary layer thickness and which helps in improve the heat dissipation rate. Further, important findings from the study is noted that the Nusselt number (Nu) increases significantly at higher WR and DR with considerable increase in the pressure drop. However, at ϕ = 0.3% with insertion of TT inserts in a TTCFHEx, Nu number advanced by 71.52%, 80.40%, 127.65% than the base fluid without inserts. Further, at higher WR and DR the augmentation of HTR is found to be 1.5-times higher than the plain tube (PT) with considerable increase in the pressure drop. In the presence of nanofluid with W-cut TT inserts, the overall heat transfer coefficient (HTC) is enhanced by 34.68%, 41.82%, 52.65% and 36.26%, 44.25%, 50.14% for WR (4/15, 8/15, 12/15) and DR (2/15, 4/15, 6/15) compared to base fluid water with PT for both numerical and experimental investigations are considered in range of Re studied. Also, the computed experimental and numerical performance evaluation criteria (PEC) is found to be 2.16 and 2.09 for WR = 12/15 and DR = 6/15, respectively, at higher values of Re number.

Subjectivity in Unsupervised Machine Learning Model Selection

Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and metrics, model selection remains subjective. A high degree of subjectivity may lead to questions about repeatability and reproducibility of various machine learning studies and doubts about the robustness of models deployed in the real world. Yet, the impact of modelers' preferences on model selection outcomes remains largely unexplored. This study uses the Hidden Markov Model as an example to investigate the subjectivity involved in model selection. We asked 33 participants and three Large Language Models (LLMs) to make model selections in three scenarios. Results revealed variability and inconsistencies in both the participants’ and the LLMs' choices, especially when different criteria and metrics disagree. Sources of subjectivity include varying opinions on the importance of different criteria and metrics, differing views on how parsimonious a model should be, and how the size of a dataset should influence model selection. The results underscore the importance of developing a more standardized way to document subjective choices made in model selection processes.

A novel coordinate transformation stability criterion and parameter selection for grid-connected inverter

The negative resistance of grid-connected inverter (GCI) and the increasing number of GCI in power grid pose great challenges to the stability of GCI. This paper proposes a coordinate transformation stability criterion (CTSC) based on the single-input single-output (SISO) impedance model for GCI system. A low complexity fractional polynomial model of GCI is derived by the Pade approximation and the SISO impedance model in the αβ domain. CTSC is proposed by applying the Nyquist stability criterion, which simplifies the graph trajectory criterion to a numerical criterion. Since the effect of parameter on stability is usually limited to the neighborhood, average parameter assessment metric (APAM) is proposed based on CTSC to evaluate the impact of each parameter over a wide range. Additionally, joint parameter assessment metric (JPAM) is proposed to simultaneously adjust multiple parameters on stability. Finally, compared with the three criteria for conservatism analysis, CTSC possesses the lowest conservatism. The theory proposed in this paper is verified by experiment.

Moduli of representations of one-point extensions

We study moduli spaces of (semi-)stable representations of one-point extensions of quivers by rigid representations. This class of moduli spaces unifies Grassmannians of subrepresentations of rigid representations and moduli spaces of representations of generalized Kronecker quivers. With homological methods, we find numerical criteria for non-emptiness and results on basic geometric properties, construct generating semi-invariants, expand the Gel’fand MacPherson correspondence, and derive a formula for the Poincaré polynomial in singular cohomology of these moduli spaces.