Level Of Credibility Research Articles

Planning of best management practices for watershed-scale pollution control: A simulation-based inexact fractional programming approach

The implementation of best management practices (BMPs) is crucial for protecting and restoring watersheds from nonpoint source (NPS) pollution. BMP placement plans with a high pollution reduction to cost ratio are highly desired by decision makers. Nevertheless, few studies tackled such a ratio optimization problem in their efforts to identify BMP placement plans. BMP planning also faces challenges in handling inherent uncertainties and estimating NPS pollution loads. To address these gaps, a simulation-based inexact credibility-constrained mixed-integer fractional programming (SICMFP) model was proposed for supporting the placement of BMPs. This model was developed by integrating the export coefficient model (ECM), compound topographic index (CTI), inexact programming, and mixed-integer programming within a fractional programming framework. SICMFP extends the capability of previous BMP placement models by optimizing the ratio of conflicting objectives and handling uncertain parameters based on interval theory and credibility measure. It can also conveniently simulate the spatiotemporal distribution of pollution loads through the ECM, and efficiently identify potential wetland sites using CTI. SICMFP was applied to plan BMP placement in the Luan River Basin, located in northern China. The results showed that SICMFP could provide BMP placement plans with the optimal system efficiency between pollution reductions and cost savings under different credibility levels. These plans could help decision makers gain in-depth insights into the tradeoffs between expected system efficiency and the risk of constraint violation. As the credibility level rises from 0.6 to 0.9, the optimal ratio between pollution reduction and cost declined from [0.88, 1.66] kg/103 yuan to [0.70, 1.15] kg/103yuan, whereas the costs and pollution reductions increased. Compared with the least-cost single-objective model, SICMFP could lead to more pollution reductions and higher system efficiency. The developed SICMFP approach can also be extended to address the ratio optimization problems of water pollution management in other watersheds.

Linking agricultural water-food-environment nexus with crop area planning: A fuzzy credibility-based multi-objective linear fractional programming approach

To readily addressed three key issues including fuzzy parametric information, multiple objectives and fractional ratios in planning problems, this study presents a fuzzy credibility-based multi-objective linear fractional programming approach for building the linkage between agricultural water-food-environment nexus and crop area planning. This approach is developed by incorporating fuzzy credibility-constrained programming into multi-objective linear fractional programming within the optimization model. To demonstrate its applicability, the approach is then applied to the Hetao Irrigation District in the northwest China to unite the multiple objective problems in agricultural irrigation with crop area planning. Three ratio optimization problems associated with agricultural, economic and environmental objectives are concurrently considered including maximum economic benefit per unit of irrigation water, maximum crop yield per unit of irrigated area and minimal grey water footprint per unit of crop production. Therefore, this study has the following advantages. (1) Fuzzy parameters existing in the objective and violated constraints can be effectively tackled. (2) The multiple ratio optimization problems can be efficiently solved through a linearization procedure in a straightforward manner, thereby reflecting desired system efficiency and reducing computational difficulties. (3) The mathematical and practical interactions of agricultural water-food-environment nexus can be investigated based on intermediate variables, i.e., irrigated area, irrigation water, crop yield, economic benefits and greywater footprint. (4) Optimal solutions can be flexibly generated to facilitate crop area planning through given aspiration levels of objective goals and credibility levels of constraints. The results indicate that optimal objective values have slight changes with the form of each objective (e.g., maximization or minimization). The results are useful for facilitating insightful interpretation of inter-relationships among sustainable agricultural production, efficient irrigation water use, and favorable agro-ecological conditions.

FEATURES OF FEMALE HEROIC IMAGES IN CHINESE FOLK OPERA

The article discusses the features of the embodiment of female heroic images in Chinese folk operas on a military-patriotic theme. The material for the analysis was the images of Sister Jiang (opera “Sister Jiang”, 1964), Tian Yumei (opera “Daughters of the Party”, 1991) and Haitang (opera “The Yimenshan Mountains”, 2018). One of the objectives of the study is to identify the dynamics of development of this type of hero in the genre of Chinese folk opera. The article emphasizes that the heroic image of a woman is important for Chinese culture and is associated with the feminist movement in the first half of the 20th century. The analysis of the operas showed similarities and differences in the dramatic role and in the musical characteristics of Sister Jiang, Tian Yumei and Haitan. They have a similar experience of fighting the enemy, they are ready to sacrifice themselves for the common good. In their vocal parts, “central songs” are carried out, the most important part of their musical characteristics are large monologue arias. The differences are related to the level of psychological credibility of these characters. The example of Sister Jiang, Tian Yumei and Haitan clearly shows the trend towards a gradual increase in Chinese folk operas of elements of femininity and psychologism. This trend is manifested in an increase in the number of vocal numbers with episodes of a subjective-lyrical nature that reflect the feelings of the heroines and their state of mind.

Credibility of Information Sources and Channels as Perceived by Paddy Farmers in Telangana State

This paper investigates perceived credibility of paddy farmers’ information sources and channels. The dissemination of agricultural information to farmers has been one of the crucial factors for the transmission of newer and relevant technologies. Farmers’ information needs are transforming to more need-based than demand-driven. A sum of 120 respondents was selected based on proportionate random sampling from two villages selected from each of the two blocks of Khammam district. Mean per cent score was used for obtaining the degree of credibility of sources and channels. Opinion leaders in personal localite sources, agricultural scientists in personal cosmopolite sources, result demonstration in personal cosmopolite channels and television in impersonal cosmopolite channels were perceived as highly credible by paddy farmers. One-third of respondents had high level of overall credibility.

Colonialism, Environmental Policy, and Epistemic Injustice

This paper explores environmental protection policies and initiatives, such as conservation, through the lens of an orientalist epistemic injustice. This is a form of epistemic injustice that occurs when the orientalizing of space and access to sovereign systems of knowledge causes the assigning of an unjust deflated or elevated level of credibility to a knower. Under this framework of orientalist epistemic injustice, the author criticizes the credibility excess assigned to Western subjects that perform conservation efforts in third-world countries and the related credibility deficit assigned to indigenous and local knowledge and conservation practices.

Autonomous and sustainable machine learning: pursuing new horizons of intelligent systems

The paradigm of Artificial Intelligence and Machine Learning has resulted in an amazingly diverse plethora of models operating in various environments and quite often exhibiting numerous successes. There is a growing spectrum of challenging application areas of high criticality where one has to meet a number of fundamental requirements. Those manifest evidently when Machine Learning constructs have to function autonomously and any decisions being rendered entail far reaching implications. The carefully crafted learning process has to result with advanced models. Along with the developed models, they have to come hand-in-hand with credibility measures that are crucial to assess an extent to which the results generated by such measures are meaningful, trustworthy and credible. The credibility of the Machine Learning models becomes of paramount importance given the nature of application domains. Autonomous systems including autonomous vehicles, user identification (both using audio and video channels), financial systems (calling for sound mechanisms to quantify risk levels) require the ML system making classification or prediction decisions some level of self-awareness. Among others, this translates to forming sound answers to the following crucial questions emerging within the design process: How much confidence could be associated with the result? Could any action /decision be taken on a basis of obtained result? Given the reported level of credibility, is there any other experimental evidence one could acquire to validate the decision? In this study, we advocate that a general way to achieve such goals is to engage the mechanism of Granular Computing; subsequently, the granularity endowing the results are sought as a vehicle use to quantify the credibility level. Sustainable (or green) Machine Learning gives rise to the agenda of knowledge reuse, namely exploring possibilities of potential reuse of the already designed models in a spectrum of current environments where computing overhead as one of the ways to contribute to the agenda of sustainable Machine Learning and discuss a crucial role of information granularity in this context.

АЛГОРИТМ ОЦЕНКИ РИСКА РАЗВИТИЯ МИКРОСОСУДИСТЫХ ОСЛОЖНЕНИЙ У ПАЦИЕНТОВ С САХАРНЫМ ДИАБЕТОМ 2 ТИПА ДЛЯ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ ВРАЧА

Relevance: most experts agree that regular and high-quality dispensary monitoring of patients with diabetes mellitus at the outpatient stage is a necessary condition for preventing the development of vascular complications. The solution of this problem is closely connected with the development and widespread clinical implementation of digital healthcare tools, automated workplaces of doctors, decision support algorithms. Objective: to improve the quality and effectiveness of outpatient care for patients with type 2 diabetes mellitus by digitalizing outpatient admission, using the software of the automated workplace of an endocrinologist (AWPE DM), developing an algorithm for assessing the risk of microvascular complications as an element of a decision support system. Materials and methods: based on the analysis of the results of clinical experience in the use of ARME DM, an algorithm for assessing the risk of microvascular complications has been developed, which allows optimizing the complex of measures for dispensary observation of patients with type 2 diabetes mellitus. Results: demonstrated an increase in the efficiency and quality of medical care for patients with type 2 diabetes mellitus when using AWPE DM in clinical practice; developed a digital algorithm for assessing the risk of microvascular complications, providing stratification of the risks of their development according to the selected criteria for each of the classes; determination of a sufficient minimum of dispensary laboratory and instrumental studies, taking into account the level of credibility of recommendations; personalized correction of therapeutic goals of treatment taking into account the individual characteristics of the patient, including the stage of chronic kidney disease and diabetic retinopathy. Conclusions: the clinical application of the algorithm for assessing the risk of vascular complications, developed taking into account current domestic and foreign clinical recommendations, allows objectively in numerical values to assess the probability of progression of certain classes of microvascular complications of diabetes mellitus in a particular patient, to determine the activities of a personalized program of dispensary observation and rehabilitation. For laboratory and instrumental verification, examinations with the greatest diagnostic significance and suitable for storage and processing in digital format are proposed. Based on the results of the risk assessment directly during the outpatient appointment, the algorithm forms recommendations for the differentiated choice of therapeutic treatment goals, which makes it possible to optimize the personalized program of dispensary observation of the patient.

MODERN APPROACHES TO DETECTING AND COMBATING DISINFORMATION IN INFORMATION SYSTEMS: ANALYSIS AND IMPROVEMENT

This scientific article analyzes and characterizes various types of false information disseminated in modern information systems. The main focus is on detecting and identifying the dangers associated with the spread of unreliable information in society. The serious consequences of this phenomenon can reach a global scale, making effective countermeasures critically important. The study examines and compares various software methods to counter the dissemination of false information. In particular, different methods of analyzing and filtering information aimed at detecting and localizing unreliable messages were studied and compared. This helps identify the most effective approaches to data analysis in the field of information systems and determine optimal methods to combat the spread of fake news. The conclusions of this research have practical applications and can be used to improve the analysis of information from various sources in information systems. The implementation of the solutions developed in this research will contribute to increasing the level of credibility and objectivity in information processing, thereby enhancing the quality of information analysis and its utilization in various sectors of society.

Profiling the vendors of COVID‐19 related product on the Darknet: An observational study

BackgroundIn a time of unprecedented global change, the COVID-19 pandemic has led to a surge in demand of COVID-19 vaccines and related certifications. Mainly due to supply shortages, counterfeit vaccines, fake documentation, and alleged cures to illegal portfolios, have been offered on darkweb marketplaces (DWMs) with important public health consequences. We aimed to profile key DWMs and vendors by presenting some in-depth case studies. MethodsA non-systematic search for COVID-19 products was performed across 118 DWMs. Levels of activity, credibility, content, COVID-19 product listings, privacy protocols were among the features retrieved. Open web fora and other open web sources were also considered for further analysis of both functional and non functional DWMs. Collected data refers to the period between January 2020 and October 2021. ResultsA total of 42 relevant listings sold by 24 vendors across eight DWMs were identified. Four of these markets were active and well-established at the time of the study with good levels of credibility. COVID-19 products were listed alongside other marketplace content. Vendors had a trusted profile, communicated in English language and accepted payments in cryptocurrencies (Monero or Bitcoin). Their geographical location included the USA, Asia and Europe. While COVID-19 related goods were mostly available for regional supply, other listings were also shipped worldwide. InterpretationFindings emerging from this study rise important questions about the health safety of certain DWMs activities and encourage the development of targeted interventions to overcome such new and rapidly expanding public health threats. FundingCovSaf, National Research centre on Privacy, Harm Reduction and Adversarial Influence Online (REPHRAIN), Commonwealth Fund.

Intuitionistic fuzzy credibility Dombi aggregation operators and their application of railway train selection in Pakistan

<abstract><p>The degree of credibility of the fuzzy assessment value demonstrates its significance and necessity in the fuzzy decision making problem. The fuzzy assessment values should be closely related to their credibility measures in order to increase the credibility levels and degrees of fuzzy assessment values. This will increase the abundance and the credibility of the assessment information. As a new extension of the intuitionistic fuzzy concept, this study suggests the idea of an intuitionistic fuzzy credibility number (IFCN). So, based on Dombi norms, we proposed some new operational laws for intuitionistic fuzzy credibility numbers. Different intuitionistic fuzzy credibility aggregation operators are defined using Dombi t-norm and t-conorm operations. i.e., intuitionistic fuzzy credibility Dombi weighted averaging (IFCDWA), intuitionistic fuzzy credibility Dombi ordered weighted averaging (IFCDOWA), intuitionistic fuzzy credibility Dombi hybrid weighted averaging (IFCDHWA) operators. Next, we defined multiple criteria group decisions (MCGDM) approach. To ensure that their results are reliable and applicable, we also gave an example of railway train selection and discussed comparative result analysis.</p></abstract>

Learning‐based adaptive‐scenario‐tree model predictive control with improved probabilistic safety using robust Bayesian neural networks

SummaryScenario‐based model predictive control (MPC) methods can mitigate the conservativeness inherent to open‐loop robust MPC. Yet, the scenarios are often generated offline based on worst‐case uncertainty descriptions obtained a priori, which can in turn limit the improvements in the robust control performance. To this end, this paper presents a learning‐based, adaptive‐scenario‐tree model predictive control approach for uncertain nonlinear systems with time‐varying and/or hard‐to‐model dynamics. Bayesian neural networks (BNNs) are used to learn a state‐ and input‐dependent description of model uncertainty, namely the mismatch between a nominal (physics‐based or data‐driven) model of a system and its actual dynamics. We first present a new approach for training robust BNNs (RBNNs) using probabilistic Lipschitz bounds to provide a less conservative uncertainty quantification. Then, we present an approach to evaluate the credible intervals of RBNN predictions and determine the number of samples required for estimating the credible intervals given a credible level. The performance of RBNNs is evaluated with respect to that of standard BNNs and Gaussian process (GP) as a basis of comparison. The RBNN description of plant‐model mismatch with verified accurate credible intervals is employed to generate adaptive scenarios online for scenario‐based MPC (sMPC). The proposed sMPC approach with adaptive scenario tree can improve the robust control performance with respect to sMPC with a fixed, worst‐case scenario tree and with respect to an adaptive‐scenario‐based MPC (asMPC) using GP regression on a cold atmospheric plasma system. Furthermore, closed‐loop simulation results illustrate that robust model uncertainty learning via RBNNs can enhance the probability of constraint satisfaction of asMPC.

Human Capital Development: A Good Leadership and Economic Growth Based Approach

This study purports to discuss the impact of Human Capital Development in fostering good leadership and growth in Nigeria economy. How to keep most of graduates of higher institution of learning in Nigeria to embrace the expected entrepreneurial and technical skills, communication and human relations needed for self-employment and self-sustenance rather than pragmatic or functional education that has resulted to the production of bad leadership and impracticable graduates unfitting for the labour market becomes the problem. The literature was reviewed. Methodology adopted survey design. However, it was found that misapplication of human capital, inequitable distribution of qualified workforce, in Nigeria, impoverished remuneration is hamstringing the laying claim and establishment of human capital. This study concludes that acquiring adequate training and development of Humans brings about better society. Recommendation were made that government should ensure that the right personnel are recruited so as to maintain high level of credibility in an organization, which will yield growth in economy for a better society.

Parental Acceptance of COVID-19 Vaccination for Children and Its Association With Information Sufficiency and Credibility in South Korea

Vaccination against COVID-19 is an effective method for individuals to reduce negative health outcomes. However, widespread COVID-19 vaccination among children has been challenging owing to parental hesitancy. To examine parental decision-making in favor of the COVID-19 vaccine for their children and its association with the sufficiency and credibility of the information about the vaccine. This cross-sectional survey study was conducted in South Korea from February 7 to 10, 2022, 7 weeks before initiation of the COVID-19 vaccine for children aged 5 to 11 years. Parents were included if they spoke Korean and had at least 1 child in elementary school (grades 1-6). Parents and children were included in a 1:1 ratio; a total of 113 450 parents and 113 450 children were included in the analysis. Statistical analysis was performed between March and April 2022. The main outcomes of interest were (1) parental acceptance of COVID-19 vaccination for their children and (2) its association with self-reported sufficiency and credibility of information about the vaccine. A multivariable logistic regression was used to evaluate factors associated with parental decision-making in favor of COVID-19 vaccination; path analysis was used to examine indirect effects of information sufficiency and credibility. Of the 113 450 children, 58 342 (51.4%) were boys, and the mean (SD) age was 10.1 (1.5) years. Of the 113 450 parents who responded, 7379 (6.5%) were accepting vaccination for their children; 15 731 (13.9%) reported the vaccine-related information they received was sufficient, and 23 021 (20.3%) reported the information was credible. Parents who reported that the information was sufficient were 3.08 times (95% CI, 2.85-3.33; P < .001) more likely to report being willing to vaccinate their children than those who believed the information was insufficient, and those who reported that the information was credible were 7.55 times (95% CI, 6.46-8.87; P < .001) more likely to report being willing to vaccinate their children than those who believed the information was not credible. Higher levels of information sufficiency and credibility were associated with perceptions of increased vaccine safety (sufficiency: β = 0.08; P < .001; credibility: β = 0.59; P < .001) and effectiveness (sufficiency: β = 0.05; P < .001; credibility: β = 0.60; P < .001). In this study, a significant association was found between self-reported sufficiency and credibility of vaccine-related information and parental decision-making regarding COVID-19 vaccination for their children, suggesting that communications and policies that provide sound information are essential to improve vaccination rates.

PSR-FCCLP model based total maximum allocated loads optimization of TN and TP in Bohai Bay.

Eutrophication is a global problem for coastal ecosystems, one that Bohai Bay, China, has been severely afflicted due to rapid economic and social development in the past decades. It was urgent to manage the total maximum allocated loads (TMALs) of the total nitrogen (TN) and total phosphorus (TP) on land-based, which was closely related to coastal eutrophication. In this study, from the perspective of economy-resources-environment (ERE) system, we presented the fuzzy credibility constrained linear programming based on pressure-state-response (PSR-FCCLP) model, which calculated the TMALs of TN and TP at three credibility levels (λ=0.55, 0.75, and 0.95) in Bohai Bay. The results showed that when the credibility level increased from 0.55 to 0.95, the TMALs of TN and TP would decrease from 13,573.38t/a to 12,820.00t/a and from 2665.58t/a to 2514.90t/a, respectively. This study will provide support for environmental management in coastal regions.

Searching for Candidates of Coalescing Binary Black Holes Formed through Chemically Homogeneous Evolution in GWTC-3

The LIGO, Virgo, and KAGRA (LVK) Collaboration has announced 90 coalescing binary black holes (BBHs) with p astro > 50% to date; however, the origin of their formation channels is still an open scientific question. Given various properties of BBHs (BH component masses and individual spins) inferred using the default priors by the LVK, independent groups have been trying to explain the formation of the BBHs with different formation channels. Of all formation scenarios, the chemically homogeneous evolution (CHE) channel has stood out with distinguishing features, namely, nearly equal component masses and preferentially high individual spins aligned with the orbital angular momentum. We perform Bayesian inference on the BBH events officially reported in GWTC-3 with astrophysically predicted priors representing different formation channels of the isolated binary evolution (common-envelope evolution channel, CEE; CHE; stable mass transfer, SMT). Given assumed models, we report strong evidence for GW190517_055101 being most likely to have formed through the CHE channel. Assuming the BBH events in the subsample are all formed through one of the isolated binary evolution channels, we obtain the lower limits on the local merger rate density of these channels at 11.45 Gpc−3 yr−1 (CEE), 0.18 Gpc−3 yr−1 (CHE), and 0.63 Gpc−3 yr−1 (SMT) at 90% credible level.

Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand

Pore pressure is an essential parameter for establishing reservoir conditions, geological interpretation and drilling programs. Pore pressure prediction depends on information from various geophysical logs, seismic, and direct down-hole pressure measurements. However, a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells. Applying machine learning (ML) algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited. In this research, several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field, New Zealand. Their predictions substantially outperform, in terms of prediction performance, those generated using a multiple linear regression (MLR) model. The geophysical logs used as input variables are sonic, temperature and density logs, and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions. A total of 25,935 data records involving six well-log input variables were evaluated across the four wells. All ML methods achieved credible levels of pore pressure prediction performance. The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree (DT), adaboost (ADA), random forest (RF) and transparent open box (TOB). The DT achieved root mean square error (RMSE) ranging from 0.25 psi to 14.71 psi for the four wells. The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores. For two wells (Mangahewa-03 and Mangahewa-06), semi-supervised prediction achieved acceptable prediction performance of RMSE of 130–140 psi; while for the other wells, semi-supervised prediction performance was reduced to RMSE > 300 psi. The results suggest that these models can be used to predict pore pressure in nearby locations, i.e. similar geology at corresponding depths within a field, but they become less reliable as the step-out distance increases and geological conditions change significantly. In comparison to other approaches to predict pore pressures, this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.

ІНСТИТУЦІЙНІ ДЕТЕРМІНАНТИ КОНКУРЕНТНОЇ ПОЛІТИКИ НА РИНКУ ФІНАНСОВИХ ПОСЛУГ

Проблематика інституційних детермінант конкурентної політики на ринку фінансових послуг являє собою актуальний і перспективний дослідницький напрям, важливий як у теоретичному контексті, так і в практичному вимірі. Обґрунтування напрямів реалізації конкурентної політики на ринку фінансових послуг є особливо актуальним у країнах з ринками, що формуються, до яких належить і Україна. Зроблено висновок, що протягом останніх років застосування регуляторних механізмів на ринку фінансових послуг стало основою цифрової трансформації підприємницької моделі його суб’єктів. Активізацію розвитку таких технологій обумовлено діями регуляторів, спрямованими на підвищення дієвості інструментів нагляду після фінансових криз і зростання витрат на дотримання нових нормативних вимог. Засадничими детермінантами досягнення належного рівня конкурентних відносин між суб’єктами ринку фінансових послуг є розроблення і реалізація адекватної та збалансованої конкурентної політики держави. В Україні створено законодавчу базу й сформовано організаційні засади для реалізації ефективної державної конкурентної політики, проте поглиблення ринкових перетворень постійно породжує нові проблеми у сфері конкурентної політики, які потребують удосконалених підходів до їх вирішення. Дослідження інституційних детермінант конкурентної політики на ринку фінансових послуг дозволило визначити основні, серед яких зростання залежності від екзогенних чинників, недосконалість стандартів поширення інформації та її асиметрія, порушення прав споживачів, деформація норм і практик провадження підприємницької діяльності, низький рівень довіри до суб’єктів ринку, динамічний розвиток фінансових інновацій. Інституційне середовище детермінує особливості функціонування ринку фінансових послуг, при цьому розвиток національної економіки неможливий за умов його розбалансованості. Завдання вирішення ідентифікованих проблем покладається в першу чергу на державні інститути, водночас і споживачі фінансових послуг мають брати активну участь у здійсненні постійного моніторингу діяльності суб’єктів ринку.

Parameter estimation of lifetime distribution for the meta-action unit with uncertainty failure modes under type-I censored data

This paper presents a parameter estimation method for the lifetime distribution of the Meta-Action Unit (MAU) with uncertainty failure modes under type-I censored data. The MAU is regarded as the basic functional unit to accomplish the function of mechanical equipment, and its failure modes are classified according to the abnormal kinematic parameters in Meta-Action (MA), which are more succinct than the traditional mechanical failure modes on parts. However, there is some uncertain information about the failure data and censored data of MAU because of the technology limitations and the space accessibility constraints for monitoring the kinematic parameters of MA, which uncertainty information can impact the parameter estimates of MAU lifetime distribution. In order to avoid the impacts on the estimating accuracy of distribution parameters, the evidential likelihood function based on the belief function theory is constructed in view of the credibility level of the failure data and censored data. In addition, the Evidential Expectation Maximization (E2M) algorithm is proposed to estimate the parameters of the mixed exponential distribution of MAU lifetime under type-I censored data. Finally, an application of an Automatic Pallet Changer (APC) is used to illustrate the validity of the MAU failure modes classification. The simulations of the E2M algorithm are conducted to show that the proposed parameters estimation method can integrate uncertain information in the failure data and the censored data, and obtain more stable results than those based on the conventional Expectation-Maximization (EM).

Do consumers care about credibility? Examining corporate credibility and price on sport ticket purchase decision making

ABSTRACT The ticket market for sport events has undergone substantial changes following the emergence of secondary markets (i.e., ticket resale platforms) and changes in consumer preferences. Therefore, the concept of corporate credibility warrants attention in the context of the secondary ticket market, which comprises ticket sellers with varying levels of credibility. Additionally, since secondary ticket companies engage in demand-based pricing, it is critical to measure the effect of ticket prices on the relationship between corporate credibility and purchase intention. In this research we utilize a multi-study design to examine the relationship between corporate credibility and consumers’ purchase intention in the secondary ticket market, as well as the moderating effect of ticket prices. The findings, which run counter to previous literature, suggest that the impact of corporate credibility on purchase intention in the secondary ticket market is negligible.

A mixed integer fuzzy-interval credibility-constrained programming for booster optimization in water distribution system under uncertainty

Generally, boosters should be placed in water distribution system (WDS) to preserve the required residual disinfectant concentration at the far-ends. To cope with the fuzzy and interval uncertainties in the optimization of boosters, a mixed integer fuzzy-interval credibility-constrained programming (MIFICCP) model was formulated for disinfection booster design, location, and operation, which is solved by transforming the model into two linear sub-models. The results indicated that: The booster injection rates, operational injection costs (BCI), and booster capital costs (BCD) increased with the credibility levels; The lower and upper booster costs under fuzzy-interval set (FIS) with trapezoidal distribution are higher than FIS with triangular distribution for various credibility levels except for the credibility level of 0.5; With the increase of maximum booster number, the values of BCI decreased, the value of BCD increased, and the total costs may increase or decrease, which depend on the relative magnitude of BCI to the total costs. The results obtained can give more information for managers to make decisions on the booster strategies to uniform chlorine distribution along WDS scientifically and reasonably, especially under dual uncertainties of fuzzy and interval.