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Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Natural disasters are mostly seasonal and caused by anthropological, climatic, and geological factors that impact human life, economy, ecology, and natural resources. This paper focuses on increasingly widespread forest fires, causing greater destruction in recent years. Data obtained from sensors for predicting forest fires and assessing fire severity, i.e., area burned, are multivariate, seasonal, and highly imbalanced with a ratio of 100,000+ non-fire events to 1 fire event. This paper presents Spatio-Temporal Agnostic Sampling (STAS) to overcome the challenge of highly imbalanced data. This paper first presents a mathematical understanding of fire and non-fire events and then a thorough complexity analysis of the proposed STAS framework and two existing methods, NearMiss and SMOTE. Further, to investigate the applicability of STAS, binary classification models (to determine the probability of forest fire) and regression models (to assess the severity of forest fire) were built on the data generated from STAS. A total of 432 experiments were conducted to validate the robustness of the STAS parameters. Additional experiments with a temporal data split were conducted to further validate the results. The results show that 180 of the 216 binary classification models had an F1score>0.9 and 150 of the 216 regression models had an R2score>0.75. These results indicate the applicability of STAS for fire prediction with highly imbalanced multivariate seasonal time series data.

There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the data for a single individual, or dynamic multilevel modeling, which consists of using a time-series model at Level 1 to describe the within-person process while allowing for individual differences in the parameters of these processes at Level 2, has led to new insights in clinical psychology. In addition, we discuss several methodological and statistical challenges that researchers face when they are interested in studying the dynamics of psychological processes using intensive longitudinal data.

In this study, we used a comprehensive array of sampling techniques to examine the pollution caused by organic micropollutants in İzmit Bay for the first time. Our methodology contains spot seawater sampling, semi-permeable membrane devices (SPMDs) passive samplers for time-weighted average (TWA), and sediment sampling for long-term pollution detection in İzmit Bay, together. Additionally, the analysis results obtained with these three sampling methods were compared in this study. Over the course of two seasons in 2020 and 2021, we deployed SPMDs for 21 days in the first season and for 30 days in the second season. This innovative approach allowed us to gather sea water samples and analyze them for the presence of polycyclic aromatic hydrocarbons (Σ15 PAHs), polychlorinated biphenyls (Σ7 PCBs), and organochlorine pesticides (Σ11 OCPs). Using SPMD-based passive sampling, we measured micropollutant concentrations: PAHs ranged from 1963 to 10342 pg/L in 2020 and 1338 to 6373 pg/L in 2021; PCBs from 17.46 to 61.90 pg/L in 2020 and 8.37 to 78.10 pg/L in 2021; and OCPs from 269.2 to 8868 pg/L in 2020 and 141.7 to 1662 pg/L in 2021. Our findings revealed parallels between the concentrations of PAHs, PCBs, and OCPs in both SPMDs and sediment samples, providing insights into the distribution patterns of these pollutants in the marine ecosystem. However, it is worth noting that due to limited data acquisition, the suitability of spot sampling in comparison to instantaneous sampling remains inconclusive, highlighting the need for further investigation and data collection.

Background Approximately 70% of child deaths due to diarrhea are caused by a lack of timely healthcare. However, there was little evidence of factors associated with delays in seeking health care for patients with diarrheal diseases in the study area. Therefore, this study aimed to investigate delays in seeking healthcare for children with diarrhea and identify associated factors among caregivers in health centers of Northwest Ethiopia. Method and materials An institution-based mixed study method was conducted from May to June 2022. Quantitative data were collected from 374 participants who were selected by systematic random sampling using a structured interviewer-administered questionnaire and chart review. The data were analyzed using the Statistical Package for Social Science software version 25. Bivariable and multivariable logistic regression models were used to identify associated factors. Variables with a p- value < 0.05 in the multivariable analysis were considered to be significantly associated. Qualitative data were collected from participants in waiting area after receiving treatments via in-depth interviews and analyzed using open-source software. The qualitative data were transcribed, translated, coded, thematized, and interpreted accordingly. Results In this study, 53.48% of patients experienced delays in seeking healthcare for diarrhea. A large family size (adjusted odds ratio (AOR) = 2.64, 95% CI: 1.26–5.4), poor knowledge about diarrhea danger signs (AOR = 3.25, 95% CI: 1.6–6.6), difficulty paying for treatment (AOR = 2.95, 95% CI: 1.6–5.3), not visiting health facilities as the first response to diarrhea (AOR = 3.94, 95% CI: 1.96–7.9), only diarrhea (AOR = 2.39, 95% CI: 1.01–5.63), and no information about early healthcare seeking (AOR = 4.88, 95% CI: 1.91–12.43) were identified; moreover, from the qualitative findings, mothers’ perceptions of the illness were mild, poor service provision, and economic problems were determinants of delay. Awareness, barriers, compliance, and perception emerged as themes. Conclusion The prevalence of delays in seeking healthcare for children with diarrhea was high. This poses a negative health risk to the lives of children and their caregivers. A large family size, poor knowledge about diarrhea danger signs, difficulty paying for treatment, and many others were factors associated with delayed health care seeking. Hence, the government and other concerned stakeholders should give due emphasis to tackling the identified causes of delay in seeking health care for children under five years of age with diarrhea by diverting community focus toward timely care seeking and disease prevention.

Mixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low frequency variable by high frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary to employ distributed lag functions. We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted-MIDAS (U-MIDAS) regressions from linear high frequency models, discuss identification issues and show that their parameters can be estimated by ordinary least squares. In Monte Carlo experiments, we compare U-MIDAS with MIDAS with functional distributed lags estimated by non-linear least squares. We show that U-MIDAS performs better than MIDAS for small differences in sampling frequencies. However, with large differing sampling frequencies, distributed lag functions outperform unrestricted polynomials. The good performance of U-MIDAS for small differences in frequency is confirmed in empirical applications on nowcasting and short-term forecasting euro area and US gross domestic product growth by using monthly indicators.

Non-uniform sampling (NUS) is a popular way of reducing the amount of time taken by multidimensional NMR experiments. Among the various non-uniform sampling schemes that exist, the Poisson-gap (PG) schedules are particularly popular, especially when combined with compressed-sensing (CS) reconstruction of missing data points. However, the use of PG is based mainly on practical experience and has not, as yet, been explained in terms of CS theory. Moreover, an apparent contradiction exists between the reported effectiveness of PG and CS theory, which states that a “flat” pseudo-random generator is the best way to generate sampling schedules in order to reconstruct sparse spectra. In this paper we explain how, and in what situations, PG reveals its superior features in NMR spectroscopy. We support our theoretical considerations with simulations and analyses of experimental data from the Biological Magnetic Resonance Bank (BMRB). Our analyses reveal a previously unnoticed feature of many NMR spectra that explains the success of ”blue-noise” schedules, such as PG. We call this feature “clustered sparsity”. This refers to the fact that the peaks in NMR spectra are not just sparse but often form clusters in the indirect dimension, and PG is particularly suited to deal with such situations. Additionally, we discuss why denser sampling in the initial and final parts of the clustered signal may be useful.

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus on eye and it is the leading cause of visual impairment among productive segment of the population. Globally, the prevalence of diabetic retinopathy is reported to be 27%. In Ethiopia, sufficient data is lacking on the prevalence of diabetic retinopathy as well as information on its predisposing factors. The study was required to assess the prevalence of diabetic retinopathy and its predisposing factors in diabetic patients attending at a General Hospital in Ethiopia. An institution based cross sectional study was employed on 331 diabetic patients recruited with a systematic random sampling technique. Data were collected through structured questionnaire, tracing patients' medical folder and ocular health examination. Data were analyzed with Statistical Package for Social Science Version 20. Logistic regression methods of analysis were used to figure out predisposing factors of diabetic retinopathy. Adjusted odds ratio with 95% confidence interval was used to determine the strength of association. A total of 331 diabetic patients completed the study with a response rate of 99.10%. The median duration of diabetes was 5 years. The prevalence of diabetic retinopathy was 34.1% (95%Confidence Interval (CI): 28.7%-39.3%). Low family monthly income (Adjusted Odds Ratio (AOR) = 7.43, 95% CI: 2.44-22.57), longer duration of diabetes (AOR = 1.44, 95% CI: 1.30-1.58), poor glycemic control (AOR = 4.76, 95%CI: 2.26-10.00), and being on insulin treatment alone (AOR = 3.85, 95%CI: 1.16-12.74) were independently associated with diabetic retinopathy. The prevalence of diabetic retinopathy was 34.1%, higher than national and global figures. Low family monthly income, longer duration of diabetes, poor glucose control and being on insulin treatment alone were important risk factors of diabetic retinopathy. Proper diabetes self management and early screening of diabetic retinopathy in all diabetic patients were recommended.

We sought to leverage the strengths of time location sampling (TLS) and respondent-driven sampling (RDS) for surveys of hidden populations by combing elements of both methods in a new approach we call “starfish sampling.” Starfish sampling entails random selection of venue-day-time units from a mapping of the locations where the population can be found, combined with short chains of peer referrals from their social networks at the venue or presenting to the study site later. Using the population of transmen in San Francisco as a case example, we recruited 122 eligible participants using starfish sampling: 79 at randomly selected venues, 11 on dating applications, and 32 by referral. Starfish sampling produced one of the largest community-recruited samples specifically for transmen to date. Starfish sampling is a flexibility method to recruit and sample hidden populations for whom conventional TLS and RDS may not work in theory or practice.