DSpace collection: 期刊論文

Bayesian inference and optimal plan for the family of inverted exponentiated distributions under doubly censored data

title: Bayesian inference and optimal plan for the family of inverted exponentiated distributions under doubly censored data abstract: In this paper, we consider inference upon unknown parameters of the family of inverted exponentiated distributions when it is known that data are doubly censored. Maximum likelihood and Bayes estimates under different loss functions are derived for estimating the parameters. We use Metropolis-Hastings algorithm to draw Markov chain Monte Carlo samples, which are used to compute the Bayes estimates and construct the Bayesian credible intervals. Further, we present point and interval predictions of the censored data using the Bayesian approach. The performance of proposed methods of estimation and prediction are investigated using simulation studies, and two illustrative examples are discussed in support of the suggested methods. Finally, we propose the optimal plans under double censoring scheme. Keywords

Analysis of stress-strength reliability for multicomponent system from Rayleigh model with a hybrid censored data

title: Analysis of stress-strength reliability for multicomponent system from Rayleigh model with a hybrid censored data abstract: This study evaluates multicomponent stress-strength system reliability (MSR) using data from adaptive Type-II hybrid progressive censoring. For strength and stress variables following two-parameter Rayleigh models with shared parameters, maximum likelihood estimation is derived for the MSR quantity, where the existence and uniqueness of the likelihood estimators are also established for model parameters. Approximate confidence interval for MSR is developed based on asymptotic theory and delta method. For comparative analysis, generalised estimation methods are proposed based on proposed pivotal quantities, and alternative point estimates and confidence intervals are constructed in consequence. The methodology is further extended to case with fully unequal parameters, both classical and generalised estimations are conducted as well. Additionally, a likelihood ratio testing framework is provided to verify parameter equivalence between strength and stress variables. The performance of different estimation methods is evaluated via Monte Carlo simulations, and a real-life example is also conducted for applications.

Optimization for the Zero-Inflated Binary Classification Model with Regulation Rules Using Evolutionary Algorithms

title: Optimization for the Zero-Inflated Binary Classification Model with Regulation Rules Using Evolutionary Algorithms abstract: To improve the classification quality of using the regulation rule in a zeroinflated binary (ZIB) model, the differential evolution (DE) and particle swarm optimization (PSO) algorithms are used in this study for optimization. The performance of the two algorithms is compared with the maximum likelihood estimation method. The elastic net regularization rule (ENR) is used to construct the loss function for the ZIB model, named the ENR-ZIB model, to prevent overfitting. The estimates of the model parameters of the ENR-ZIB model are obtained to minimize a specified loss function. Moreover, the classification performance of the obtained model is studied. Monte Carlo simulations are conducted to compare the performance of the ENR-ZIB model using two proposed optimization procedures with the maximum likelihood estimation method. Simulation results show that the proposed optimization procedures can have a good classification quality for the ENR-ZIB model.

Inference for simple step stress accelerated life test model under progressively censored Gompertz data

title: Inference for simple step stress accelerated life test model under progressively censored Gompertz data abstract: In this article analysis of a simple step-stress accelerated life test is considered under progressive type-II censoring. A cumulative exposure model is considered when the latent lifetimes of test units follow the Gompertz distribution with different shape parameters and a common scale parameter. We explore the study by estimating all unknown parameters using classical and Bayesian techniques. The model parameters are estimated using maximum likelihood and Bayesian methods. Subsequently, interval estimates are derived based on the observed Fisher information matrix. Bayesian estimates are obtained using squared error and linear exponential loss functions. Subsequently highest posterior density intervals are also constructed. We examine the efficiency of all estimators through simulation studies. Finally, we provide a real-life example in support of the considered model.

Computational Testing Procedure for the Overall Lifetime Performance Index of Multi-Component Exponentially Distributed Products

title: Computational Testing Procedure for the Overall Lifetime Performance Index of Multi-Component Exponentially Distributed Products abstract: Download PDFsettingsOrder Article Reprints Open AccessArticle Computational Testing Procedure for the Overall Lifetime Performance Index of Multi-Component Exponentially Distributed Products by Shu-Fei Wu *ORCID andChia-Chi Hsu Department of Statistics and Data Science, Tamkang University, New Taipei City 251301, Taiwan * Author to whom correspondence should be addressed. Stats 2025, 8(4), 104; https://doi.org/10.3390/stats8040104 Submission received: 7 September 2025 / Revised: 15 October 2025 / Accepted: 23 October 2025 / Published: 2 November 2025 Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract In addition to products with a single component, this study examines products composed of multiple components whose lifetimes follow a one-parameter exponential distribution. An overall lifetime performance index is developed to assess products under the progressive type I interval censoring scheme. This study establishes the relationship between the overall and individual lifetime performance indices and derives the corresponding maximum likelihood estimators along with their asymptotic distributions. Based on the asymptotic distributions, the lower confidence bounds for all indices are also established. Furthermore, a hypothesis testing procedure is formulated to evaluate whether the overall lifetime performance index achieves the specified target level, utilizing the maximum likelihood estimator as the test statistic under a progressive type I interval censored sample. Moreover, a power analysis is carried out, and two numerical examples are presented to demonstrate the practical implementation for the overall lifetime performance index. This research can be applied to the fields of life testing and reliability analysis.

Measuring and managing urban quietness: refining the quietness suitability index (QSI) model for Asia's densely populated cities

title: Measuring and managing urban quietness: refining the quietness suitability index (QSI) model for Asia's densely populated cities abstract: Noise pollution is a growing threat to public health in dense cities yet widely used quietness indices were calibrated in European contexts and transfer poorly to Asian megacities. This study proposed the Adaptive Quietness Suitability Index (AQSI), a refined, validated framework that enhances sensitivity, accuracy, and transferability in mixed-use urban environments. The AQSI advances three elements: (i) dynamic impact-zone delineation via physics-based sound propagation rather than fixed buffers, (ii) alignment with locally regulated noise limits and control zones, and (iii) explicit temporal stratification (daytime, evening, nighttime, all-day) to capture diurnal variability. We validated the AQSI using monitoring data from 1997 to 2024 in New Taipei City, Taiwan, and compared it to the original QSI through regression and spatial analyses. The AQSI showed stronger agreement with measured sound levels than the original QSI (Pearson’s r = −0.238 vs. −0.202), representing a 39 % improvement in explained variance (R 2 = 0.057 vs. 0.041). It eliminated extreme-value saturation (0 or 1 in 86.2 % of cells) and reduced it to 0 %, yielding continuous gradients that better resolved intermediate environments. Nighttime emerged as the critical period, with the steepest negative coefficient (−0.0142, p < 0.001) and the highest adjusted R 2 (0.16), highlighting the need for nocturnal noise management. By integrating operational mapping with soundscape-aware considerations at a 50 m resolution, the AQSI provides a transparent, context-sensitive tool for urban planning, regulatory compliance, and targeted mitigation in diverse metropolitan settings.

Pricing optimization for inventory with integrated storage and credit constraints

title: Pricing optimization for inventory with integrated storage and credit constraints abstract: Price is a pivotal determinant of market demand, as higher prices typically reduce sales while lower prices stimulate them. Thus, incorporating price-dependent demand into inventory models is both realistic and necessary. In practice, limited storage capacity often forces retailers to rent additional space, motivating the adoption of two-warehouse systems. Trade credit also plays a critical role in supply chain management: suppliers may offer cash discounts or deferred payments to encourage larger orders, while retailers extend credit to customers to boost sales. To reduce default risk, however, retailers usually provide only partial credit. Considering the time value of money, costs and profits are assessed using discounted cash-flow analysis to account for payment delays and inflation. This study develops an integrated supplier–retailer–customer chain model that (1) incorporates price-dependent demand, (2) includes a rented warehouse for limited storage, (3) considers partial trade credit, (4) links two-level trade credit terms to order quantity, and (5) evaluates financial performance on a present-value basis. The model aims to maximize total profit by determining optimal price, replenishment cycle, and order quantity. Numerical and sensitivity analyses confirm that extending supplier credit can lower prices and improve overall profitability, offering useful insights for strategic inventory management.

Approaching precision public health by automated syndromic surveillance in communities

title: Approaching precision public health by automated syndromic surveillance in communities abstract: Background: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.

The metabolic syndrome is associated with the risk of urothelial carcinoma from a health examination database

title: The metabolic syndrome is associated with the risk of urothelial carcinoma from a health examination database abstract: Purpose The metabolic syndrome was associated with bladder cancer in the previous studies. However, there have no large-scale cohort studies to elucidate the relationship between metabolic syndromes and urothelial carcinoma including urinary bladder urothelial carcinoma (UBUC) and upper tract urothelial carcinoma (UTUC). Methods We analyze a population-based cohort study by using physical examination data and diagnosis of UC from the Taiwan Cancer Registry Database. Differences in demographic and clinical characteristics among UTUC and non-UTUC groups, UBUC and non-UBUC groups were compared. Odds ratios (ORs) for determining risk factors were estimated through the multiple logistic regression model. Results A total of 557,063 records for 211,319 participants which consisted of 31 UTUC and 309 UBUC met the eligibility criteria in this study. Our results showed that female are more likely to develop UTUC than male. As opposed to UTUC, male are more likely to develop UBUC than female. It also showed that participants smoked or chewed betel quid daily are more likely to develop UBUC. Age and estimated glomerular filtration rate (eGFR) are significantly increased the risk of developing UTUC. The association between the eGFR and risk of UTUC is stronger (P < 0.001) for eGFR < 45 (vs. eGFR ≥ 75, OR = 6.795; 95% CI 2.901–15.917). Metabolic syndrome is related to higher risk of UBUC incidence [OR was 1.373 (95% CI 1.104–1.707)]. Conclusions There was a significant relationship between the incidence of UBUC and metabolic syndrome. Renal function impairment presents higher risk in both UBUC and UTUC development.

Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles

title: Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles abstract: Identifying realistic pollution source profiles and quantifying the contributions of atmospheric particulate matter are crucial for the development of pollution mitigation strategies to protect public health. In this paper, we proposed a multivariate source apportionment model by using a Bayesian framework for latent source profiles to incorporate expert knowledge regarding emissions that can facilitate source profile estimation, and atmospheric effects, such as meteorological conditions, can improve source concentration estimations. This approach can maintain positivity and summation constraints for source contributions and profiles. Furthermore, available expert knowledge regarding source profiles is incorporated as prior knowledge to avoid restrictive assumptions regarding the presence or absence of chemical constituent tracers in source profile modeling. We used long-term PM2.5 measurements collected from two locations with different environmental characteristics in northern Taiwan to demonstrate the feasibility of the proposed model and evaluated its performance by using simulated data.

Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic

title: Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic abstract: A resurgence of scarlet fever has caused many pediatric infections in East Asia and the United Kingdom. Although scarlet fever in Taiwan has not been a notifiable infectious disease since 2007, the comprehensive national health insurance data can still track its trend. Here, we used data from the open data portal of the Taiwan Centers for Disease Control. The scarlet fever trend was measured by outpatient and hospitalization rates from 2009 to 2017. In order to elucidate the spatio-temporal hotspots, we developed a new method named the spatio-temporal Gi* statistic, and applied Joinpoint regression to compute the annual percentage change (APC). The overall APCs in outpatient and hospitalization were 15.1% (95% CI: 10.3%-20.2%) and 7.7% (95%CI: 4.5% -10.9%). The major two infected groups were children aged 5–9 (outpatient: 0.138 scarlet fever diagnoses per 1,000 visits; inpatient: 2.579 per 1,000 visits) and aged 3–4 (outpatient: 0.084 per 1,000 visits; inpatient: 1.469 per 1,000 visits). We found the counties in eastern Taiwan and offshore counties had the most hotspots in the outpatient setting. In terms of hospitalization, the hotspots mostly occurred in offshore counties close to China. With the help of the spatio-temporal statistic, health workers can set up enhanced laboratory surveillance in those hotspots.

Association between health behaviors and mood disorders among the elderly: a community-based cohort study

title: Association between health behaviors and mood disorders among the elderly: a community-based cohort study abstract: Background: According to a WHO report, nearly 15% of adults aged 60 and over suffer from a mental disorder, constituting 6.6% of the total disability for this age group. Taipei City faces rapid transformation towards an aging society, with the proportion of elderly in the total population rising from 12% in 2008 to 16% in 2016. The aim of this study is to identify the prevalence of mental disorders among the elderly in Taipei City and to elucidate risk factors contributing to mental disorders. Methods: The elderly health examination database was obtained from the Department of Health, Taipei City government, from 2005 to 2012. A total of 86,061 people underwent publicly funded health examinations, with 348,067 visits. Each year, there are around 43,000 elderly persons in Taipei City using this service. We used a mental health questionnaire including five questions to estimated relative risks among potential risk factors with the generalized estimating equations (GEE) model to measure the mental health status of the elderly. Mood disorders were measured with the Brief Symptom Rating Scale (BSRS-5) questionnaire. Age, education level, gender, marital status, living alone, drinking milk, eating vegetables and fruits, long-term medication, smoking status, frequency of alcohol consumption, frequency of physical activity, BMI, and number of chronic diseases were included as covariates. Results: The results show that being male (odds ratio (OR) 0.57; 95% CI = 0.56, 0.59), higher education (OR 0.88; 95% CI = 0.82, 0.95), no long-term medication (OR 0.57; 95% CI = 0.56, 0.58), and exercising three or more times per week (OR 0.94; 95% CI = 0.91, 0.98) were all positively correlated with better emotional status. However, being divorced (OR = 1.22, 95% CI = 1.09, 1.36), not drinking milk (OR = 1.12, 95% CI = 1.09, 1.14), not eating enough vegetables and fruits every day (OR = 1.78, 95% CI = 1.73, 1.83), daily smoking (OR = 1.15, 95% CI = 1.01, 1.32), and having more chronic diseases (OR = 1.02, 95% CI = 1.01, 1.03) were all correlated with poor mental status among the elderly. Conclusions: The findings of this research can both estimate the prevalence of mood disorders at the community level, and identify risk factors of mood disorders at the personal level.

A flow-based statistical model integrating spatial and nonspatial dimensions to measure healthcare access

title: A flow-based statistical model integrating spatial and nonspatial dimensions to measure healthcare access abstract: Assessing access to healthcare for an entire healthcare system involves accounting for demand, supply, and geographic variation. In order to capture the interaction between healthcare services and populations, various measures of healthcare access have been utilized, including the popular two-step floating catchment area (2SFCA) method. However, despite the many advantages of 2SFCA, the problems, such as inappropriate assumption of healthcare demand and failure to capture cascading effects across the system have not been satisfactorily addressed. In this paper, a statistical model for evaluating flows of individuals was added to the 2SFCA method (hereafter we refer to it as F2SFCA) in order to overcome limitations associated with its current restriction. The proposed F2SFCA model can incorporate both spatial and nonspatial dimensions and thus synthesizes them into one framework. Moreover, the proposed F2SFCA model can be easily adapted to measure access for different types of individuals, over different service provider types, or with capacity constraints in a healthcare system. We implemented the proposed model in a case study assessing access to healthcare for the elderly in Taipei City, Taiwan, and compared the weaknesses and strengths to the 2SFCA method and its variations.

Analyzing Personal Happiness from Global Survey and Weather Data: A Geospatial Approach

title: Analyzing Personal Happiness from Global Survey and Weather Data: A Geospatial Approach abstract: Past studies have shown that personal subjective happiness is associated with various macro- and micro-level background factors, including environmental conditions, such as weather and the economic situation, and personal health behaviors, such as smoking and exercise. We contribute to this literature of happiness studies by using a geospatial approach to examine both macro and micro links to personal happiness. Our geospatial approach incorporates two major global datasets: representative national survey data from the International Social Survey Program (ISSP) and corresponding world weather data from the National Oceanic and Atmospheric Administration (NOAA). After processing and filtering 55,081 records of ISSP 2011 survey data from 32 countries, we extracted 5,420 records from China and 25,441 records from 28 other countries. Sensitivity analyses of different intervals for average weather variables showed that macro-level conditions, including temperature, wind speed, elevation, and GDP, are positively correlated with happiness. To distinguish the effects of weather conditions on happiness in different seasons, we also adopted climate zone and seasonal variables. The micro-level analysis indicated that better health status and eating more vegetables or fruits are highly associated with happiness. Never engaging in physical activity appears to make people less happy. The findings suggest that weather conditions, economic situations, and personal health behaviors are all correlated with levels of happiness.

Latitude-based approach for detecting aberrations of hand, foot, and mouth disease epidemics

title: Latitude-based approach for detecting aberrations of hand, foot, and mouth disease epidemics abstract: Background Epidemics of hand, foot and mouth disease (HFMD) among children in East Asia have been a serious annual public health problem. Previous studies in China and island-type territories in East Asia showed that the onset of HFMD epidemics evolved with increased latitude. Based on the natural characteristics of the epidemics, we developed regression models for issuing aberration alerts and predictions. Methods HFMD sentinel surveillance data from 2008 to 2014 in Japan are used in this study, covering 365 weeks and 47 prefectures between 24 and 46° of north latitude. Average HFMD cases per sentinel are standardized as Z rates. We fit weekly Z rate differences between prefectures located in the south and north of a designated prefecture with linear regression models to detect the surging trend of the epidemic for the prefecture. We propose a rule for issuing an aberration alert determined by the strength of the upward trend of south–north Z rate differences in the previous few weeks. In addition to the warning, we predict a Z rate for the next week with a 95 % confidence interval. Results We selected Tokyo and Kyoto for evaluating the proposed approach to aberration detection. Overall, the peaks of epidemics in Tokyo mostly occurred in weeks 28–31, later than in Kyoto, where the disease peaked in weeks 26–31. Positive south–north Z rate differences in both prefectures were clearly observed ahead of the HFMD epidemic cycles. Aberrations in the major epidemics of 2011 and 2013 were successfully detected weeks earlier. The prediction also provided accurate estimates of the epidemic’s trends. Conclusions We have used only the latitude, one geographical feature affecting the spatiotemporal distribution of HFMD, to develop rules for early aberration detection and prediction. We have also demonstrated that the proposed rules performed well using real data in terms of accuracy and timeliness. Although our approach may provide helpful information for controlling epidemics and minimizing the impact of diseases, the performance could be further improved by including other influential meteorological factors in the proposed latitude-based approach, which is worth further investigation.

Closed-form solutions for the Weibull distribution parameters and performance lifetime index with interval-censored data

title: Closed-form solutions for the Weibull distribution parameters and performance lifetime index with interval-censored data abstract: In lifetime testing, reliably assessing the life performance index of the Weibull distribution under Type I interval-censored data is a critical task. Although maximum likelihood estimation (MLE) is a conventional approach for parameter estimation, closed-form solutions are unavailable for this data type. To address this limitation, four least-squares estimation methods based on data transformation are developed. The proposed estimations can provide closed-form solutions for the Weibull distribution and life performance index. The asymptotic unbiasedness and normality of the proposed estimators are rigorously established. Their effectiveness is further supported by simulation studies. Moreover, the practical relevance of the methods is illustrated with two real-data applications.

The Pearson residual-based control charts for monitoring overdispersed count sequences

title: The Pearson residual-based control charts for monitoring overdispersed count sequences abstract: Most existing control charts are designed for positively autocorrelated count data and seldom address the issue of overdispersion. The log-linear Poisson autoregression model (LLPAM) can capture overdispersion in count data, accommodate both positive and negative autocorrelations, and incorporate real-valued covariates. This makes it a more flexible alternative to the standard Poisson model. However, Shewhart-type charts applied to LLPAM often exhibit inflated false alarm rates and reduced sensitivity to parameter shifts under moderate temporal dependence. To address these limitations, we propose two monitoring schemes based on Pearson residuals (PRs): a Shewhart-type chart and an exponentially weighted moving average (EWMA) chart. Both methods allow simultaneous monitoring of LLPAM parameters under positive or negative autocorrelation. Simulation studies show that the proposed PR-based charts consistently outperform the observation-based Shewhart chart in terms of average run length (ARL), standard deviation of run length (SDRL), median run length (MDRL), and relative mean index (RMI), while maintaining false alarm rates close to nominal levels. An application to Escherichia coli infection data from North Rhine–Westphalia further demonstrates the practical utility of the proposed control charts.

Pricing Optimization for Inventory with Integrated Storage and Credit Constraints

title: Pricing Optimization for Inventory with Integrated Storage and Credit Constraints abstract: Price is a pivotal determinant of market demand, as higher prices typically reduce sales while lower prices stimulate them. Thus, incorporating price-dependent demand into inventory models is both realistic and necessary. In practice, limited storage capacity often forces retailers to rent additional space, motivating the adoption of two-warehouse systems. Trade credit also plays a critical role in supply chain management: suppliers may offer cash discounts or deferred payments to encourage larger orders, while retailers extend credit to customers to boost sales. To reduce default risk, however, retailers usually provide only partial credit. Considering the time value of money, costs and profits are assessed using discounted cash-flow analysis to account for payment delays and inflation. This study develops an integrated supplier–retailer–customer chain model that (1) incorporates price-dependent demand, (2) includes a rented warehouse for limited storage, (3) considers partial trade credit, (4) links two-level trade credit terms to order quantity, and (5) evaluates financial performance on a present-value basis. The model aims to maximize total profit by determining optimal price, replenishment cycle, and order quantity. Numerical and sensitivity analyses confirm that extending supplier credit can lower prices and improve overall profitability, offering useful insights for strategic inventory management.

Spatiotemporal impact of urban development on nighttime light intensity and its hotspot distribution

title: Spatiotemporal impact of urban development on nighttime light intensity and its hotspot distribution abstract: Nighttime light (NTL) data serve as a valuable proxy for accessing urbanization and socio-economic activities at various scales. This study investigated the spatiotemporal evolution of NTL intensity in Taipei City from January 2018 to June 2023 using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) via the Google Earth Engine (GEE) platform. A grid system comprising 1,211 cells (500-m resolution) was established to integrate land use, road networks, population, electricity consumption, and business prosperity into temporal, spatial, and spatiotemporal models using Integrated Nested Laplace Approximations (INLA). Additionally, spatiotemporal patterns were analyzed through the space-time cube in ArcGIS Pro. This finding highlights the strong influence of commercial activities and electricity consumption on NTL intensity, with persistent hotspots in commercial and industrial areas and cold spots in forested and agricultural zones. This study underscores the potential of NTL data to capture the interplay between urbanization, land use, and socioeconomic factors. Emphasizing land use as a central analytical focus provides a scalable framework for future urban studies and policy development that can be applied to diverse urban contexts.

Exploring the spatial association between the distribution of temperature and urban morphology with green view index

title: Exploring the spatial association between the distribution of temperature and urban morphology with green view index abstract: Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators.

The impact of co‑exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

title: The impact of co‑exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort abstract: Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

Associations between community green view index and fine particulate matter from Airboxes

title: Associations between community green view index and fine particulate matter from Airboxes abstract: Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Dynamic modeling for noise mapping in urban areas

title: Dynamic modeling for noise mapping in urban areas abstract: Environmental noise has been a major environmental nuisance in metropolitan cities. To achieve the goal of sustainable community, noise reduction is an important approach. Without systematic noise mapping, the spatio-temporal distribution of noise variations is hard to capture. This study proposes a new methodology framework to combine statistical models and acoustic propagation for dynamic updates of 2D and 3D traffic noise maps by using a limited number of noise sensors in Taipei City based on multisource data including noise monitoring, vehicle detectors, meteorological data, road characteristics, and socio-demographic data. The hourly mean difference between the predicted and measured noise level is within the range of −6.25 dBA to −4.46 dBA in the 2D noise model. For the 3D noise model, the hourly mean prediction error is within the range of 0.02 dBA to 1.93 dBA. Based on the WHO benchmark for excessive road traffic noise, we found at least 30% of inhabitants in Taipei City are exposed to levels exceeding 53 dBA Lden, and >25% are exposed to noise levels exceeding 45 dBA Lnight. The noise maps not only can help identify vulnerable communities to adopt proper approaches for noise reduction but also can remind the residents to take action to improve their quality of life.

Effectiveness of controlling COVID‑19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan

title: Effectiveness of controlling COVID‑19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan abstract: Strict and repeated lockdowns have caused public fatigue regarding policy compliance and had a large impact on several countries’ economies. We aimed to evaluate the effectiveness of a soft lockdown policy and the strategy of active community screening for controlling COVID-19 in Taiwan. We used village-based daily confirmed COVID-19 statistics in Taipei City and New Taipei City, between May 2, 2021, and July 17, 2021. The temporal Gi* statistic was used to compute the spatiotemporal hotspots. Simple linear regression was used to evaluate the trend of the epidemic, positivity rate from community screening, and mobility changes in COVID-19 cases and incidence before and after a level three alert in both cities. We used a Bayesian hierarchical zero-inflated Poisson model to estimate the daily infection risk. The cities accounted for 11,403 (81.17%) of 14,048 locally confirmed cases. The mean effective reproduction number (Re) surged before the level three alert and peaked on May 16, 2021, the day after the level three alert in Taipei City (Re = 3.66) and New Taipei City (Re = 3.37). Mobility reduction and a lower positive rate were positively associated with a lower number of cases and incidence. In the spatiotemporal view, seven major districts were identified with a radial spreading pattern from one hard-hit district. Villages with a higher inflow degree centrality among people aged ≥ 60 years, having confirmed cases, specific land-use types, and with a higher aging index had higher infection risks than other villages. Early soft lockdown policy and detection of infected patients showed an effective strategy to control COVID-19 in Taiwan.

Effect of Meteorological and Geographical Factors on the Epidemics of Hand, Foot, and Mouth Disease in Island-Type Territory, East Asia

title: Effect of Meteorological and Geographical Factors on the Epidemics of Hand, Foot, and Mouth Disease in Island-Type Territory, East Asia abstract: Hand, foot, and mouth disease (HFMD) has threatened East Asia for more than three decades and has become an important public health issue owing to its severe sequelae and mortality among children. The lack of effective treatment and vaccine for HFMD highlights the urgent need for efficiently integrated early warning surveillance systems in the region. In this study, we try to integrate the available surveillance and weather data in East Asia to elucidate possible spatiotemporal correlations and weather conditions among different areas from low to high latitude. The general additive model (GAM) was applied to understand the association between HFMD and latitude, as well as meteorological factors for islands in East Asia, namely, Japan, Taiwan, Hong Kong, and Singapore, from 2012 to 2014. The results revealed that latitude was the most important explanatory factor associated with the timing and amplitude of HFMD epidemics (P < 0.0001). Meteorological factors including higher dew point, lower visibility, and lower wind speed were significantly associated with the rise of epidemics (P < 0.01). In summary, weather conditions and geographic location could play some role in affecting HFMD epidemics. Regional integrated surveillance of HFMD in East Asia is needed for mitigating the disease risk.

國際綠色城市淨零轉型推動與借鏡

title: 國際綠色城市淨零轉型推動與借鏡

出口﹑進口與經濟成長的因果關係-以台灣和日本為例

title: 出口﹑進口與經濟成長的因果關係-以台灣和日本為例

The computational assessment on the performance of products with multi-parts using the Gompertz distribution

title: The computational assessment on the performance of products with multi-parts using the Gompertz distribution abstract: The lifetime performance index is widely used in the manufacturing industry to assess the capability and effectiveness of production processes. A new overall lifetime performance index is proposed when multiple parts of products are produced in multiple dependent production lines. Each individual lifetime performance index for a single production line is connected to the overall lifetime performance index for multiple independent or dependent production lines. The overall lifetime performance index increases with the overall process yield. We analyze the maximum likelihood estimators for the individual lifetime performance indices using progressively type I interval-censored samples while the lifetime of the ith part of products follows a Gompertz distribution for either independent or dependent cases. To determine whether the overall lifetime performance index meets the desired target value, the maximum likelihood estimator for the individual index is utilized separately to conduct the testing procedures about the overall lifetime performance index for either independent or dependent cases. Power analysis of the multiple testing procedure is illustrated with figures, and key findings are summarized. A simulation study is conducted for the test powers. Lastly, a practical example involving products with two parts is presented to demonstrate the application of the proposed testing algorithm. Given the asymmetry of the lifetime distribution, this research aligns with the study of asymmetric probability distributions and their diverse applications across various fields.

Progressive

title: Progressive

Optimizing carbon tax compliance in steel manufacturing through green finance-driven internal supply chain management

title: Optimizing carbon tax compliance in steel manufacturing through green finance-driven internal supply chain management abstract: This paper models an internal supply chain for a steel manufacturer, involving multiple inputs purchased and transported to produce, transport, and sell carbon-intensive steel in the domestic and foreign markets. A life insurer provides financing to the manufacturer, and the insurer's equity is assessed using a capped call option framework that accounts for the borrower's operational risks—an approach aligned with green finance principles. The model evaluates how carbon taxes and steel export levels influence the equity of both the manufacturer and the insurer, providing insight into sustainable production and financing strategies. Key findings indicate that reduced steel exports increase the manufacturer's equity and lower the insurer's equity risk, contributing to the mitigation of carbon leakage. Domestic carbon taxes primarily reduce emissions in local markets, while export carbon taxes are more effective in curbing cross-border carbon leakage. These effects are more pronounced within internal supply chains compared to external supply chains, regardless of transportation-related carbon footprints considered in the model. Lower carbon tax burdens also incentivize the adoption of green finance mechanisms. Overall, the results highlight the role of carbon taxation and sustainable finance in promoting low-carbon steel production, s supporting the achievement of the United Nations Sustainable Development Goals, specifically Goals 3 (Good health and well-being) and 7 (Affordable and clean energy).

The role of misclassification and carbon tax policies in determining payment time and replenishment strategies for imperfect product shipments

title: The role of misclassification and carbon tax policies in determining payment time and replenishment strategies for imperfect product shipments abstract: The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations.

Deteriorating inventory model with advance-cash-credit payment schemes and partial backlogging

title: Deteriorating inventory model with advance-cash-credit payment schemes and partial backlogging abstract: In business transactions, suppliers often ask retailers for advance-cash-credit (ACC) payments, and retailers offer customers a cash-credit (CC) payment plan. An advance payment is generally requested to avoid order cancellation, while a credit payment serves as an efficient approach to stimulate sales. With supply chains being usually subject to inventory shortages in view of various uncertainties, this study explores an optimal inventory policy for perishable goods with partial backlogging considerations when suppliers adopt an ACC payment plan for retailers and retailers offer customers a CC payment plan. For this purpose, we establish a model based on two theorems and provide an easy-to-use method to derive the optimal ordering policy to maximize retailers’ total profits. This solution is illustrated using numerical examples. Finally, we conduct a sensitivity analysis to examine the influence of changes in the values of key parameters on the optimal solution.

An optimal policy for Weibull distribution of deteriorating items with backlogging and ramp-type demand under inflation

title: An optimal policy for Weibull distribution of deteriorating items with backlogging and ramp-type demand under inflation abstract: Generally, a high-tech product's demand rate during the growth stages increases significantly with linear or exponential growth, and then, in the maturity stage, it remains almost the same. This is a so-called ramp-type demand rate. Additionally, a specific product may deteriorate over time. The more deterioration there is, the higher the order quantity. Based on this consideration, the deterioration rate could not be ignored. Therefore, this paper established a two-warehouse partial backlogging inventory model incorporating a ramp-type demand for three-parameter Weibull distribution deteriorating items. The main task is to derive an optimal replenishment strategy that minimizes the net present value of the total relevant cost per unit of time. The results of the proposed inventory system are verified through numerical examples and sensitivity analysis. The numerical result offers a reference for inventory managers to reasonably order quantities when facing a ramp-type demand rate with Weibull distributed deterioration.

Reliability assessment and remaining useful prediction based on the inverse Gaussian step-stress accelerated degradation data.

title: Reliability assessment and remaining useful prediction based on the inverse Gaussian step-stress accelerated degradation data. abstract: An inverse Gaussian step-stress accelerated degradation test model was put forward, in which the drift and shape parameters are functions of the stress levels. The confidence intervals of the model parameters and some reliability measures, such as the mean lifetime, the reliability function, and the pth percentile under the rated usage stress, are presented. The online and offline remaining useful life prediction intervals under the rated usage stress level are also acquired. Simulation technologies are used to examine the effect of the presented interval estimation approaches. Simulation results manifest that the presented interval estimation method performs well in all cases. Finally, a case study is provided to illustrate our inference approaches.

A hybrid algorithm with a data augmentation method to enhance the performance of the zero-inflated Bernoulli model

title: A hybrid algorithm with a data augmentation method to enhance the performance of the zero-inflated Bernoulli model abstract: The zero-inflated Bernoulli model, enhanced with elastic net regularization, effectively handles binary classification for zero-inflated datasets. This zero-inflated structure significantly contributes to data imbalance. To improve the ZIBer model’s ability to accurately identify minority classes, we explore the use of momentum and Nesterov’s gradient descent methods, particle swarm optimization, and a novel hybrid algorithm combining particle swarm optimization with Nesterov’s accelerated gradient techniques. Additionally, the synthesized minority oversampling technique is employed for data augmentation and training the model. Extensive simulations using holdout cross-validation reveal that the proposed hybrid algorithm with data augmentation excels in identifying true positive cases. Conversely, the hybrid algorithm without data augmentation is preferable when aiming for a balance between the metrics of recall and precision. Two case studies about diabetes and biopsy are provided to demonstrate the model’s effectiveness, with performance assessed through K-fold cross-validation.

Support vector machines and model selection for control chart pattern recognition

title: Support vector machines and model selection for control chart pattern recognition abstract: Resource-intensiveness often occurs in modern industrial settings; meanwhile, common issues and irregular patterns in production can lead to defects and variations in work-piece dimensions, negatively impacting products and increasing costs. Utilizing traditional process control charts to monitor the process and identify potential anomalies is expensive when intensive resources are needed. To conquer these downsides, algorithms for control chart pattern recognition (CCPR) leverage machine learning models to detect non-normality or normality and ensure product quality is established, and novel approaches that integrate the support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) methods with the model selection criterion, named SVM-, RF-, and KNN-CCPR, respectively, are proposed. The three CCPR approaches can save sample resources in the initial process monitoring, improve the weak learner’s ability to recognize non-normal data, and include normality as a special case. Simulation results and case studies show that the proposed SVM-CCPR method outperforms the other two competitors with the highest recognition rate and yields favorable performance for quality control.

An Optimal Policy for Weibull Distribution of Deteriorating Items with Backlogging and Ramp-type Demand Under Inflation

title: An Optimal Policy for Weibull Distribution of Deteriorating Items with Backlogging and Ramp-type Demand Under Inflation abstract: Generally, a high-tech product's demand rate during the growth stages increases significantly with linear or exponential growth, and then, in the maturity stage, it remains almost the same. This is a so-called ramp-type demand rate. Additionally, a specific product may deteriorate over time. The more deterioration there is, the higher the order quantity. Based on this consideration, the deterioration rate could not be ignored. Therefore, this paper established a two-warehouse partial backlogging inventory model incorporating a ramp-type demand for three-parameter Weibull distribution deteriorating items. The main task is to derive an optimal replenishment strategy that minimizes the net present value of the total relevant cost per unit of time. The results of the proposed inventory system are verified through numerical examples and sensitivity analysis. The numerical result offers a reference for inventory managers to reasonably order quantities when facing a ramp-type demand rate with Weibull distributed deterioration.

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

title: Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis abstract: This study investigated non-instantaneous deteriorating items because not all products deteriorate immediately. In the high-tech product life cycle, the product demand increases linearly substantially in the growth stage and maintains a near-constant level in the maturity stage. This is a ramp-type demand rate. To satisfy the demand as shortages occur, partial backlogging is necessary. The advance-cash-credit payment scheme, comprising advance, cash, and credit payments, has gained popularity in business transactions to improve cash flow flexibility among supply chain participants. This study explored a partial backlogging inventory model with ramp-type demand for non-instantaneous deteriorating items under generalized payment. The proposed model also incorporated discounted cash flow analysis to account for the time value of the profit function. This study attempted to determine the optimal replenishment strategy to maximize the present value of the total profit. Finally, we conducted a sensitivity analysis to examine the efficacy of the proposed model and gain managerial insights. The optimal total profit rises with an increase in the permissible delay period and sale price but decreases with an increase in ordering and purchase costs. Then, the decision-maker can refer to the managerial insights to choose the appropriate parameter value for the operation.

Domestic and foreign cap-and-trade regulations, carbon tariffs, and product tariffs during international trade conflicts: A multiproduct cost-efficiency analysis

title: Domestic and foreign cap-and-trade regulations, carbon tariffs, and product tariffs during international trade conflicts: A multiproduct cost-efficiency analysis abstract: This paper develops a down-and-out call option model with structural breaks to examine the effects of domestic environmental policies on carbon-intensive firms amid international trade conflicts. The findings reveal that stricter cap-and-trade regulations, carbon tariffs, and product tariffs exacerbate pollutant-specific diseconomies of scale, limit economies of scope, and reduce firm equity. The positive impact on pollutant-specific diseconomies of scale leads to higher pollution, hindering progress toward Sustainable Development Goal 7 (SDG 7: Affordable and Clean Energy) from a multiproduct cost-efficiency perspective. Meanwhile, the negative impact on economies of scope results in fewer products and pollutants, aligning with SDG 7 but conflicting with Sustainable Development Goal 8 (SDG 8: Decent Work and Economic Growth), as the scope measure accounts for efficiency in both products and pollutants. Additionally, the negative impact on firm equity discourages progress toward both SDGs, especially during trade conflicts. In summary, environmental policies distinctly affect firm multiproduct cost efficiency and equity, particularly under varying trade conflict conditions.

Likelihood and pivotal inference for Kumaraswamy parameters under progressive type-II censoring

title: Likelihood and pivotal inference for Kumaraswamy parameters under progressive type-II censoring abstract: In this paper, we investigate parameter inference for the Kumaraswamy distribution based on progressively type-II censored data. Our approach involves employing the method of maximum likelihood to derive point estimates for the model parameters. We establish the existence and uniqueness of these maximum likelihood estimators. Additionally, we present pivotal quantities that enable the construction of exact confidence intervals and joint confidence regions for the model parameters. To assess the performance of our proposed estimation techniques, we conduct comprehensive simulation studies. In conclusion, we apply the introduced estimation methods to analyze and discuss the results obtained from three real datasets, providing practical insights into their performance. These exact joint confidence regions can be directly utilized to construct confidence bounds for various reliability indices and quality control measures, enhancing their applicability in industrial settings.