AI based UPQC control technique for power quality optimization of railway transportation systems.

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together.

Flood resilience assessment of metro station entrances based on the PSR model framework: A case study of the Donghaochong Basin, Guangzhou.

Increasingly frequent intense rainfall events have caused flood disasters of metro systems worldwide. Flood management based on flood resilience is a novel strategy for dealing with floods. Nevertheless, limited research has been conducted on the correlation between metro systems and flood resilience. In this study, an index system-based framework is proposed to evaluate the flood resilience of metro station entrances, the selected indices are multidimensional, emcompassing external environment, entrance structure, socio-economic, and post-disaster response factors. The flood resilience assessment of metro station entrances is carried out via a case study of the Donghaochong Basin, Guangzhou, China. The pressure-state-response (PSR) model is used to establish the index system for the assessment of the flood resilience. Indices of inundation at the entrance sites are simulated and extracted using the InfoWorks ICM model. Two rainfall scenarios are chosen to simulate inundation, and the resilience results of metro station entrances in each scenario are compared. The results suggest that in the 200-year (200a) return period scenario, 70% of the entrances are at the high and highest resilience levels, whereas in the Zhengzhou "720" scenario, the proportion of that is 43.33%. The resilience of metro station entrances is significantly reduced under the higher rainfall scenario. However, factors unrelated to rainfall are found to mitigate the extent to which total resilience is reduced. Before and during a flood disaster, the indices indicate the resistance and adaptability of metro entrances to the disaster. After a flood disaster, they indicate the ability of entrances to recover to regular functioning. Specific steps should be implemented before and after a disaster to effectively enhance the flood resilience of metro entrances. This study provides valuable insights into enhancing the pertinence and effectiveness of flood disaster management at metro station entrances, and reducing flood damage to metro systems.

Effect of an accelerating metro cabin on the diffusion of cough droplets.

Coronaviruses being capable of spreading through droplet contamination have raised significant concerns regarding high-capacity public rail transport, such as the metro. Within a rapidly moving railcar cabin, the internal airflow lags behind the bulkhead, generating internally induced airflow that accelerates droplet dispersion within a non-inertial reference system. This study investigates the impact of acceleration on the diffusion of cough droplets of varying sizes using computational fluid dynamics. The modified k-ε equation in ANSYS® Fluent was utilized to simulate droplet diffusion under different body orientations by adjusting the inertial force correction source term. Results indicate that droplets in the middle size range (50-175 µm) are primarily influenced by inertial forces, whereas smaller droplets (3.5-20 µm) are predominantly controlled by air drag forces. Regardless of facial orientation, the outlet of high-capacity public rail transport poses the highest risk of infection.

Effective small crack detection based on tunnel crack characteristics and an anchor-free convolutional neural network.

Tunnel cracks are thin and narrow linear targets, and their pixel proportions in images are usually very low, less than 6%; therefore, a method is needed to better detect small crack targets. In this study, a crack detection method based on crack characteristics and an anchor-free framework is investigated. First, the characteristics of cracks are analyzed to obtain the real crack texture, interference noise texture, and targets appearing near each crack as the context information for the model to filter and remove noise. We discuss the crack detection performance of anchor-based and anchor-free algorithms. Then, an optimized anchor-free algorithm is proposed in this paper for crack detection. Based on the advantages of YOLOX-x, we add a semantic enhancement module to better use contextual information. The experimental results show that the anchor-free algorithm performs slightly better than other algorithms in crack detection situations. In addition, the proposed method displays better detection performance for slender and inconspicuous cracks, with an average precision of 0.858.

Study on dynamic response and long-term settlement of silty soil around Shanghai metro tunnel.

At present, the method for calculating long-term tunnel settlement predictions under metro loading considers only one working condition of passenger loading, which is inconsistent with actual working conditions. To establish a tunnel settlement model that accounts for variations in passenger flow, this study uses data mining methods to categorize metro operation into three working conditions: "peak period, secondary period, and low period." The impact of these passenger flow conditions on the dynamic response of the soil around the tunnel is analyzed. Then, based on the principles of calculus, a calculus-based prediction model is established to consider the changing patterns of metro passenger flow. The model is applied to analyze the long-term settlement characteristics of Shanghai Metro Line 10. The results indicate that, under identical conditions, soil displacement and dynamic deviatoric stress around the tunnel increase with passenger capacity. The calculus prediction model aligns more closely with actual working conditions than the conventional model. The predicted tunnel settlement of Shanghai Metro Line 10 after 20 years of operation is approximately 37.07 mm, with most settlement occurring in the early stages, primarily due to cumulative plastic deformation of the soil.

A High-Capacity Optical Metro Access Network: Efficiently Recovering Fiber Failures with Robust Switching and Centralized Optical Line Terminal.

This study proposes and presents a new central office (CO) for the optical metro access network (OMAN) with an affordable and distinctive switching system. The CO's foundation is built upon a novel optical multicarrier (OMC) generation technique. This technique provides numerous frequency carriers that are characterized by a high tone-to-noise ratio (TNR) of 40 dB and minimal amplitude excursions. The purpose is to accommodate multiple users at the optical network unit side in the optical metropolitan area network (OMAN). The OMC generation is achieved through a cascaded configuration involving a single phase and two Mach Zehnder modulators without incorporating optical or electrical amplifiers or filters. The proposed OMC is installed in the CO of the OMAN to support the 1.2 Tbps downlink and 600 Gbps uplink transmission, with practical bit error rate (BER) ranges from 10-3 to 10-13 for the downlink and 10-6 to 10-14 for the uplink transmission. Furthermore, in the OMAN's context, optical fiber failure is a main issue. Therefore, we have proposed a possible solution for ensuring uninterrupted communication without any disturbance in various scenarios of main optical fiber failures. This demonstrates how this novel CO can rapidly recover transmission failures through robust switching a and centralized OLT. The proposed system is intended to provide users with a reliable and affordable service while maintaining high-quality transmission rates.

Incipient fault diagnosis of metro train bearing under strong wheel-rail impact interferences using improved complementary CELMDAN and mixture correntropy-based adaptive feature enhancement.

Diagnosis of incipient faults of metro train bearings is a difficult problem under the double masking of strong wheel-rail impact interference and background noise. A novel feature extraction method using improved complementary complete local mean decomposition with adaptive noise (ICCELMDAN) and mixture correntropy-based adaptive feature enhancement (AFE) is proposed in this paper. The ICCELMDAN method uses a proposed complementary adaptive noise-assisted iterative sifting method to improve its anti-mixing and anti-splitting performance, and then can extract the complete feature from faulty bearing signals under strong background noise. The AFE method adaptively obtains the optimal parameters of mixture correntropy (MC) by employing a newly developed fault energy of mixture correntropy as the objective function in the marine predators algorithm (MPA), and can enhance the weak fault characteristic signal under strong wheel-rail impact interferences. The proposed method effectively combines the complete feature extraction capability of ICCELMDAN and the powerful feature enhancement capability of AFE, which can accurately diagnose the weak faults of metro train bearings under strong wheel-rail impact interferences in simulated and practical scenarios. Furthermore, it outperforms the existing methods in completeness of feature extraction, diagnosis accuracy and robustness from the comparative studies.

Risk analysis in underground tunnel construction with tunnel boring machines using the Best-Worst method and data envelopment analysis.

Background: The use of tunnel boring machines is continuously increasing; however, this activity faces many risks, resulting in negative events. Methods: A novel approach using the best-worst method and a data envelopment analysis model was proposed to analyze the risks of underground tunnel construction with tunnel boring machines. The proposed approach was validated using a realistic case study of metro construction in Thailand. Results: The proposed approach efficiently analyzed the risks and produced more solid findings. The most critical and least affected risks can be identified according to risk scores in descending order. Implication: This study contributes a new method of risk analysis that benefits project managers and stakeholders who design risk management plans to reduce the occurrence and mitigate the severity of metro works. Originality: The new risk analysis can obtain the best compromise ranking of risks based on decision-makers' preferences, probabilities, and various consequences under different circumstances.

Integrating PV-based energy production utilizing the existing infrastructure of MRT-6 at Dhaka, Bangladesh.

In a very dense urban landscape, incorporating renewables becomes challenging due to a lack of space, planning, and mindset. Utilization of already existing large infrastructures in combination with existing technology and necessary adaptation can create the right synergy for harnessing renewables like solar. This paper proposes the installation of a solar power plant in Dhaka, Bangladesh, using available space on Metro Rail Line 6 to meet the increasing demand for clean and renewable energy. The proposed system involves the selection of suitable space, and PV panels, the calculation of annual electricity generation, and performing financial and environmental analyses. The proposed on-grid/grid-tied system offers the advantage of reducing dependence on power supplied to the grid, resulting in lower energy costs, and promoting the use of green energy. The system has a payback period of 7.7 years and a return on investment of 45.7 %. It is estimated that the system saves 14,053.203 tons of CO2 emissions per year and 281,064.06 tons of CO2 emissions over its lifetime. Also, the grid life cycle emission is 584 gCO2/kWh, and the system life cycle emission is 39,119.4 tCO2, which further proves that it is a feasible solution to meeting energy demands while reducing the dependency on fossil fuels and promoting sustainable energy utilization. The results of simulations run using PVsyst and HOMER confirm the economic viability of the proposed solar power station, supporting its viability. The levelized energy cost (LCOE), as projected by PVsyst, is $0.09 per kWh, nearly matching HOMER's prediction of $0.0835. This convergence of results from several simulation tools supports the solar power plant's predicted cost-effectiveness, demonstrating its potential as a key player in the effort to create a greener and more affordable energy landscape.

Long-term exposure to PM10 and respiratory health among Parisian subway workers.

Exposure to ambient PM10 may increase the risk of chronic obstructive pulmonary disease (COPD) and lung function decline. We evaluated the long-term exposure to PM10 and its relationship with COPD prevalence and lung function in Parisian subway workers. Participants were randomly selected from a 15,000-subway worker cohort. Individual annual external exposure to PM10 (ePM10) was estimated using a company-specific job-exposure-matrix based on PM10 measurements conducted between 2004 and 2019 in the Parisian subway network. Mean annual inhaled PM10 exposure (iPM10) was modeled as function of ePM10 exposure, inhalation rate, and filtration efficiency of the respiratory protection used. COPD diagnosis was performed in March-May 2021 based on post-bronchodilator spirometry. The relationship between iPM10 and outcomes was assessed using logistic and linear regression models, adjusted for exposure duration and potential confounders. Amongst 254 participants with complete data, 17 were diagnosed as COPD. The mean employment duration was 23.2 ± 7.3years, with annual mean ePM10 of 71.8 ± 33.7 µg/m3 and iPM10 of 0.59 ± 0.27 µg/shift, respectively. A positive but statistically non-significant association was found for COPD prevalence with iPM10 (OR = 1.034, 95%-CI = 0.781; 1.369, per 100 ng/shift) and ePM10 (OR = 1.029, 95%-CI = 0.879; 1.207, per 10 µg/m3). No decline in lung function was associated with PM10 exposure. However, forced expiratory volume during the first second and forced vital capacity lower than normal were positively associated with exposure duration (OR = 1.125, 95%-CI = 1.004; 1.260 and OR = 1.171, 95%-CI = 0.989; 1.386 per year, respectively). Current smoking was strongly associated with COPD prevalence (OR = 6.85, 95%-CI = 1.87; 25.10) and most lung function parameters. This is the first study assessing the relationship between long-term exposure to subway PM10 and respiratory health in subway workers. The risk estimates related with subway PM10 exposure are compatible with those related to outdoor PM10 exposure in the large recent studies. Large cohorts of subway workers are necessary to confirm these findings.

A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation.

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.

Risk Assessment of Passenger Behaviors That Influence Accident Type and Severity in Metro Operation.

Background: The unsafe behavior of passengers frequently causes metro operation accidents. This research aims to establish a model for evaluating the risk of unsafe behavior among subway passengers and for assessing the severity of different types of accidents caused by passenger unsafe behavior. Methods: A risk assessment model that combines the Interaction Matrix (IM) model with a Monte Carlo algorithm was established to quantitatively test the risk of unsafe behavior among passengers. Based on the initial data of 234 cases, the behavioral risks in accidents were simulated, and the resulting risks follow a normal distribution. After analyzing the differences in behavioral risk distribution characteristics, the targeted risk mitigation countermeasures were obtained. Results: Results showed that there are 12 kinds of unsafe behaviors related to 4 metro operation accident types. Among them, crowded stampede caused by four kinds of passengers' unsafe behavior has the highest risk mean (µ) of 5.14, followed by escalator injury (4.72), pinched by a shielding barrier (4.42) and fall injury (4.14). Conclusion: The severity of different types of accidents caused by different unsafe behaviors of passengers was obtained, which can provide a basis for targeted risk mitigation strategies and measures.

Detection of SARS-CoV-2 in the indoor air and surfaces of subway trains in Mashhad, Iran.

INTRODUCTION: Millions of passengers around the world are concerned with the possibility of SARS-CoV-2 contamination on public transportation. Therefore, this study aimed to investigate the presence of SARS-CoV-2 virus in indoor air and subway surfaces in Mashhad. METHODS: In this study, air and surface sampling were done at two times in the morning (7-8:30 a.m.) and evening (3:30-5 p.m.), simultaneously in two wagons for men and women in line 1 of Mashhad Metro in March 2021 to detect the virus and measure the concentration of particulate matter. Totally, 30 air and 30 metro samples were collected and examined by reverse transcriptase-polymerase chain reaction (RT-PCR). RESULTS: The results showed that three and two cases in the air and surface samples were infected with the SARS-CoV-2 virus, respectively. There was a significant relationship between the mean concentration of suspended particles PM1 (particulate matter smaller than 1 µm) with PM2.5 (particulate matter smaller than 2.5 µm) and PM10 (particulate matter smaller than 10 µm) (p < 0. 05). There was also a significant relationship between the mean concentration of suspended particles PM2.5 and PM10. The results showed that the mean PM2.5 measured in the indoor air of the Mashhad metro wagon had a significant relationship with WHO and US EPA and national standards, and its value was higher than the standards (p < 0.05). The average particle concentrations of PM1, PM2.5, and PM10 were equal to 40.46, 42.61, and 48.31 µg/m3. CONCLUSION: According to the results of the pollution detected in this study, COVID-19 may be transmitted by air and environmental surfaces. Our study emphasizes the need for continuous assessment of the presence of the virus in public transportation. Detection of viral RNA in subways indicates the necessity of adequate disinfection in public settings, strictness in disinfection methods, strengthening of educational activities for sanitary measures, physical spacing plan, and increasing ventilation of wagons.

Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet.

Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model's ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.

Methods of Passenger Ride Comfort Evaluation-Tests for Metro Cars.

Ride comfort is one of the key issues in passenger transport. Its level depends on many factors related to both environmental factors and individual human characteristics. Ensuring good travel conditions translates into higher quality transport services. This article presents a literature review, which shows that ride comfort is most often considered in the context of the impact of mechanical vibrations on the human body, while other factors are usually neglected. The aim of this study was to conduct experimental studies that take into account more than one type of ride comfort. These studies concerned metro cars in the Warsaw metro system. Three types of comfort were evaluated: vibrational, thermal, and visual, based on vibration acceleration measurements, air temperature, relative air humidity, and illuminance. The ride comfort in the vehicle bodies' front, middle, and rear parts was tested under typical running conditions. The criteria for assessing the effect of individual physical factors on ride comfort were selected based on applicable European and international standards. The test results indicate good thermal and light environment conditions in every measuring point. The slight decrease in passenger comfort is undoubtedly due to the effects of vibrations occurring while mid journey. In tested metro cars, horizontal components have a more significant impact on reducing vibration comfort than other components.

Physical health caregiver, mental wellness supporter, and overall well-being advocate: Women's roles towards animal welfare during the COVID-19 emergency response.

Women's health-specific contributions in emergency response stages pertain primarily to family and community-based rescue and support-focused roles. As disasters affect both human beings and their animal co-inhabitants, comprehensive literature exploring women's contributions towards companion animal welfare in emergency response settings remains sparse. COVID-19-triggered public health mitigation strategies caused diverse challenges relating to veterinary medical service access, thus establishing a platform for a nuanced exploration of gendered roles vis-a-vis animal health and well-being during the initial COVID-19 emergency response period. This project employs a semi-structured interview approach to qualitatively investigate the roles, responsibilities, and experiences of twelve people, eleven of whom self-identify as women, who cared for animal co-inhabitants while seeking veterinary medical services during the COVID-19 emergency response in Metro Vancouver, British Columbia, Canada. This research identifies three primary animal welfare-related roles that woman companion animal guardians (WCAGs) assumed during the COVID-19 emergency response period: 1) Companion animal physical health caregiver, spanning from nuclear to extended families and into the community; 2) Companion animal mental wellness supporter, associated with human-animal interactions in family/household, community, and veterinary clinic settings; 3) Companion animal holistic well-being advocate, utilizing various strategies at family, community, and societal levels. Understanding gender-specific animal welfare contributions in an emergency response setting narrows knowledge gaps and provides WCAGs and animal welfare-related public, private, and not-for-profit sectors with evidence-based strategies for emergency response planning improvements, supporting healthy and sustainable human-animal bonds in the current COVID-19 pandemic and future extreme events.

The Struggle to Balance Work and Family Life During the COVID-19 Pandemic: Insights Based on the Situations of Working Women in Delhi.

The COVID-19 pandemic generated economic contraction across the world. In India, the stringent lockdown led to extreme distress. The unprecedented situation adversely affected the women's efforts to balance professional life with family life because of a disproportionate increase in their domestic work burden and a shift in their workstation to home. Since every job cannot be performed remotely, women employed in healthcare services, banks and media witnessed additional risks of commuting and physical interaction at the workplace. Based on personal interviews of women in the Delhi-NCR region, the study aims to explore the commonalities and variances in the challenges experienced by the women engaged in diverse occupations. Using the qualitative methodology of flexible coding, the study finds that a relatively larger section of women travelling to their office during the pandemic, rather than those working from home, had an effective familial support system that helped them navigate this tough time.

Social and spatial heterogeneities in COVID-19 impacts on individual's metro use: A big-data driven causality inference.

While mobility intervention policies implemented during the early stages of the COVID-19 outbreak had a significant impact on public transit use, few studies have investigated the individual-level responses in metro transit riding behaviors. Using long time-series cellphone big data from frequent metro users in Shenzhen, China, we developed a quasi-experimental interrupted time series (ITS) design to estimate the treatment effects of mobility intervention policies on people's daily shares of metro transit use (SMU). The results indicate that the first-level emergency response (FLR) and the public transit restriction (PTR) policy yielded abrupt drops in SMU of 8.0% and 17.6%, respectively, whereas the return-to-work (RTW) order had an immediate recovery effect of 14.5%. The effect of the FLR is time-decreasing while those effects of the PTR and the RTW are time-increasing. Females and elderly people living in neighborhoods near the city center with low population density and fewer transit stations are more adaptable to policy interventions for reducing SMUs, while the recovery effect of RTW is relatively low for the elderly living in less mixed-use neighborhoods with reduced transit service. These findings can help policymakers design more socially- and spatially-precise and equity mobility intervention policies during a pandemic.

A Unique Metro Choice Behaviour of Suburban Rail Passengers in India.

This study aims to analyse the inter-rail modal shift behaviour of suburban rail passengers to examine ridership for the proposed metro extension corridor in Chennai, India. This investigation was conducted in 2019 as part of a feasibility study for the extension of the metro line spanning between Chennai Airport and Kilambakkam, a southern suburb of Chennai. The same origin-destination pair is also served by the suburban rail system. It is an extension of the operating line from Washermenpet to the airport of the Phase I metro project. For this inter-rail competition study, a sample of 272 suburban rail passengers covering work, education and other trip purposes were interviewed using a stated preference questionnaire. Six stated scenarios were considered for analyses which included travel time saving by using the metro along with the fare difference between metro and bus. The study revealed that suburban rail passengers were less concerned about travel time saving and gave priority to fare difference irrespective of trip purpose. This shows the unique metro choice behaviour of suburban rail travellers in the Indian context.

Dementia Caregiving Experiences and Health Across Geographic Contexts by Race and Ethnicity.

OBJECTIVES: Few studies have examined how the intersectionality of geographic context and race/ethnicity influences dementia caregiving. Our objectives were to determine whether caregiver experiences and health (a) differed across metro and nonmetro areas and (b) differed by caregiver race/ethnicity and geography. METHODS: We used data from the 2017 National Health and Aging Trends Study and National Study of Caregiving. The sample included caregivers (n = 808) of care recipients ages 65 and older with "probable" dementia (n = 482). The geographic context was defined as the care recipient's residence in metro or nonmetro counties. Outcomes included caregiving experiences (care situation, burden, and gains) and health (self-rated anxiety, depression symptoms, and chronic health conditions). RESULTS: Bivariate analyses indicated that nonmetro dementia caregivers were less racially/ethnically diverse (82.7% White, non-Hispanic) and more were spouses/partners (20.2%) than their metro counterparts (66.6% White, non-Hispanic; 13.3% spouses/partners). Among racial/ethnic minority dementia caregivers, nonmetro context was associated with more chronic conditions (p < .01), providing less care (p < .01), and not coresiding with care recipients (p < .001). Multivariate analyses demonstrated that nonmetro minority dementia caregivers had 3.11 times higher odds (95% confidence interval [CI] = 1.11-9.00) of reporting anxiety in comparison to metro minority dementia caregivers. DISCUSSION: Geographic context shapes dementia caregiving experiences and caregiver health differently across racial/ethnic groups. Findings are consistent with previous studies that have shown that feelings of uncertainty, helplessness, guilt, and distress are more prevalent among people providing caregiving from a distance. Despite higher rates of dementia and dementia-related mortality in nonmetro areas, findings suggest both positive and negative aspects of caregiving among White and racial/ethnic minority caregivers.