Time Series Regression Models Research Articles

Analysis of Foreign Direct Investment Net Inflows in the Philippines: Trends, Predictors, and Forecasting Using Time Series and Linear Regression Models

Analysis of Foreign Direct Investment Net Inflows in the Philippines: Trends, Predictors, and Forecasting Using Time Series and Linear Regression Models

Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks

Predicting the time-dependent pipe failure rate of the water distribution networks (WDNs) is important for planning its renewal budget but also challenging due to the complex factors involved. The recent development of machine learning techniques provides a novel approach for accurate failure rate prediction based on historical data. However, the inherited randomness and uncertainty of water pipe failures and machine learning algorithms are often ignored in the training and prediction process. This article develops an uncertainty quantification framework for deep learning-based WDN system-wide failure rate prediction. The framework integrates a probabilistic long short-term memory (LSTM) model with a Monte Carlo method. The historical climate data and WDN pipe maintenance data over the past 35 years for Cuyahoga County, USA, are used to illustrate this deep-learning model-based uncertainty quantification framework in this study. A statistical time series regression model, ARIMAX, is used as a comparison benchmark. The results show that the LSTM model outperforms the ARIMAX model in prediction accuracy in most years by considering the uncertainties. Besides, the uncertainty range of the LSTM model prediction is 50% of that of the ARIMAX model. The results also identified the major contributing factors to the uncertainties of LSTM machine learning model prediction. The proposed uncertainty framework features excellent extensibility and can be adapted to quantify uncertainties with other types of machine learning models.

Reconstruction of Missing Well-Logs Using Facies-Informed Discrete Wavelet Transform and Time Series Regression

Summary Geophysical logging is widely used in lithofacies identification, reservoir parameter prediction, and geological modeling. However, it is common to have well-log sections with low-quality and/or missing segments. Repeating the well-log measurements is not only expensive but might also be impossible depending on the condition of the borehole walls. In these situations, reliable and accurate well-log prediction is, therefore, necessary in different stages of the geomodeling workflow. In this study, we propose a time series regression model to predict missing well-log data, incorporating facies information as an additional geological input and using discrete wavelet transform (DWT) to denoise the input data set. The main contributions of this work are threefold: (i) We jointly use facies information with well logs as the input data set; (ii) we use DWT to denoise the input data and consequently improve the signal-to-noise ratio of the input data; and (iii) we regard the depth domain as the time domain and use a time series regression algorithm for log reconstruction modeling. We show a real application example in two distinct scenarios. In the first, we predict missing well-log intervals. In the second, we predict complete well logs. The experimental results show the ability of the proposed prediction model to recover missing well-log data with high accuracy levels.

Promoting physical activity within breast cancer survivors using behavior change techniques: N-of-1 randomized controlled trials.

Survivors of breast cancer are an important target for health promotion interventions, and physical activity (PA) is recommended with benefits being well established. More and more behavior theory guided interventions are planned and tailored to the individuals, targeting specifics needs and personal characteristics. We aimed to test the effectiveness of two behavior change techniques [coping planning (CP) and self-monitoring (SM)] for promoting PA in survivors of breast cancer, using a single-case design. In a powerful multiple sample N-of-1 RCT, 10 women post-breast cancer (M = 52.8 years) were randomly assigned to four experimental conditions (CP and/or SM, control) for 60 days and their daily step count was observed. The overall effects were analyzed using multilevel time-series with random intercepts. Time-series regression models and supplementary pairwise analyses were conducted for individuals. Multilevel analyses showed significant effects of CP and SM. Single-case analyses showed that six participants walked significantly more on the intervention days, that combined interventions were effective for five participants, but two participants had better results with the other two conditions. Combined self-regulatory techniques were more effective in promoting PA however, individual variability should be considered in future studies with survivors of breast cancer. Retrospectively registered: https://osf.io/eakjy/?view_only=81bf3dc084a0410a8ea28265e0f3c360, 31-01-2023.(raise query).

Cause-specific mortality and burden attributable to temperature variability in China

BackgroundFew large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV). ObjectiveTo estimate associations between TV and cause-specific mortality and quantify the mortality burden in China. MethodsData on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens. ResultsOverall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0–7 days (TV0–7) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV0–7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders. ConclusionThis nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases.

A novel framework for machine intelligence aided smarter energy management in residential scenario

Energy management systems can monitor, optimize, and control energy utilization in residential and commercial applications. Different solutions are available for achieving better energy management. Among the different solutions, load forecasting is considered as one of most preferred technique. It is essential for a load forecasting technique to predict the aggregated loads at the residential level. This paper presents a systematic analysis of different load forecasting models considering a hybrid renewable energy based smart home which uses a solar photovoltaic (PV) system, wind system, and battery storage system for energy storage. In this work, the various forecasting techniques such as time-series models, regression models, ARIMA (Autoregressive Integrated Moving Average) models, and neural network models are analyzed for different load cases. Simulation is performed based on the effect of seasonal bias and condition load shifting on energy generation. The obtained results show that both polynomial and SVM regression model achieves better performance in terms of R2 score and adjusted R2 score of 0.73, and RMSE values of 0.84. It can also be inferred from the analysis that, the random forest regression model and a neural network-based-long short-term memory (LSTM) model exhibit superior results in comparison to other models.

The indirect impacts of nonpharmacological COVID-19 control measures on other infectious diseases in Yinchuan, Northwest China: a time series study

BackgroundVarious nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China.MethodsBased on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan.ResultsA total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response.ConclusionsThe widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors

This paper develops a novel hybrid Autoregressive Distributed Lag Mixed Data Sampling (ARDL-MIDAS) model that integrates both deep neural network multi-head attention Transformer mechanisms, and a number of covariates, including sophisticated stochastic text time-series features, into a mixed-frequency time-series regression model with long memory structure. In doing so, we demonstrate how the resulting class of ARDL-MIDAS-Transformer models allows one to maintain the interpretability of the time-series models whilst exploiting the deep neural network attention architectures. The latter may be used for higher-order interaction analysis, or, as in our use case, for design of Instrumental Variables to reduce bias in the estimation of the infinite lag ARDL-MIDAS model. Our approach produces an accurate, interpretable forecasting framework that allows one to forecast end-of-day sentiment intra-daily, with readily attainable time-series regressors. In this regard, we conduct a statistical time-series analysis on mixed data frequencies to discover and study the relationships between sentiment from our custom stochastic text time-series sentiment framework, alternative popular sentiment extraction frameworks (BERT and VADER), and technology factors, as well as to investigate the role that price discovery has on retail cryptocurrency investors’ sentiment (crypto sentiment). This is an interesting time-series modelling challenge as it involves working with time-series regression models in which the time-series response process, and the regression time-series covariates, are observed at different time scales. Specifically, a detailed real-data study is conducted where we explore the relationship between daily crypto market sentiment (of positive, negative and neutral polarity) and the intra-daily (hourly) price log-return dynamics of crypto markets. The sentiment indices constructed for a variety of “topics” and news sources are produced as a collection of time-series capturing the daily sentiment polarity signals for each “topic”, namely each particular market or crypto asset. Different sentiment methods are developed in a time-series context, and utilised in the proposed hybrid regression framework. Furthermore, technology factors are introduced to capture network effects, such as the hash rate which is an important aspect of the money supply relating to the mining of new crypto assets, and block hashing for transaction verification. Throughout our real data study, we provide guidance and insights on how to use our hybrid model to combine—in a transparent, non-black-box way—covariates obtained with different time resolutions, how to understand the arising dynamics between these covariates, potentially under the presence of long memory structure, and, finally, successfully leverage these in forecasting applications. The hybrid model developed demonstrated superior performance to alternatives in both in-sample and forecasting application on real data.

Association between airborne particulate matter（PM 2.5） concentration and the incidence of allergic rhinitis in Shanghai

Objective:To explore the impact of PM 2.5 concentration in Shanghai on the incidence of allergic rhinitis（AR） in the population, and provide strategies for early warning and prevention of AR. Methods:Collect daily average concentrations of atmospheric pollutants monitored in Shanghai from January 1, 2017 to December 31, 2019, and clinical data of AR patients from five hospitals in Shanghai during the same period. We used a time-series analysis additive Poisson regression model to analyze the correlation between PM 2.5 levels and outpatient attendance for AR patients. Results:During the study period, a total of 56 500 AR patients were included, and the daily average concentration of PM 2.5 was（35.28±23.07）μg/m³. There is a correlation between the concentration of PM 2.5 and the number of outpatient attendance for AR cases. There is a positive correlation between the daily average number of outpatient for AR and levels of PM 2.5 air pollution（（P<0.05）） . We found that every 10 μg/m³ increase in PM 2.5, the impact of on the number of AR visits was statistically significant on the same day, the first day behind, and the second day behind, with the strongest impact being the exposure on the same day. Every 10 μg/m³ increases in PM 2.5, the number of outpatient visits increased by 0.526% on the same day（95%CI 1.000 50-1.010 04）. Conclusion:The atmospheric PM 2.5 concentration in Shanghai is positively correlated with the number of outpatient for AR, and PM 2.5 exposure is an independent factor in the onset of AR. This provides an important theoretical basis for AR.

Spatial Variability of Summer Temperature and Related All-cause Mortality from 2006 to 2015 for Indian Cities: A Time Series Analysis

Abundant literature is available on an extremely high temperature associated with mortality for cities of the developed world, but there is a dearth in the literature for coastal, desert and dry cities of the developing world, especially for India. We examined all-cause mortality and extreme high temperature in three Indian cities representing coastal, desert and dry areas for summer months (March to June) from 2006 to 2015. We obtained the data on temperature and all-cause mortality for ten years for the summer months. The city-specific effect of ambient heat on all-cause mortality was assessed through time series ordinary least square linear regression model. A total of 75,571, 122,117 and 53,042 deaths for 1,203, 1,220 and 1,180 summer days from 2006 to 2015 were analysed with ambient temperature for Jaipur, Hyderabad and Surat, respectively. There were 994 (27.6%) out of 3,603 summer days having temperature ≥40°C and 2,495 (69.3%) out of 3,602 summer days having feel temperature/heat index (HI) of ≥41°C. According to the Indian Meteorological Department (IMD) criteria for the heatwave, Surat has the maximum number of 75 days with a maximum temperature of ≥40°C, whereas Hyderabad has only 4 days and Jaipur faced 35 days with a maximum temperature of ≥45°C during the study period. The per-day mean all-cause mortality increased to 39% and 11% for Jaipur and Hyderabad, respectively, at ≥45°C and 20% for the coastal city of Surat at ≥40°C as per IMD heatwave criteria. A time-series linear regression model shows that adjusted R-squared is 0.593, 0.629 and 0.348, which explained the variation of 59.3%, 62.9% and 34.8% for all-cause mortality (dependent variable) by independent variables (maximum temperature, humidity and HI) for Jaipur, Hyderabad and Surat, respectively. The maximum temperature threshold (cut-off) for all-cause mortality for Jaipur, Hyderabad and Surat is 42°C, 41°C and 40°C, respectively. The impact of ambient heat in the rise of all-cause mortality for all study sites was evident. Hence, findings support the efforts for reducing the public health burden of high ambient temperature through developing and implementing city-specific heat action plans.

Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.

Gender differences on healthcare accessibility and outcomes of a electronic inter-clinician consultation program at the cardiology department in a Galician Health Area.

To assess the longer-term results (hospital admissions and mortality) in women versus men referred to a cardiology department from primary care using an e-consultation in our outpatient care program. We selected 61,306 patients (30,312 women and 30,994 men) who visited the cardiology service at least once between 2010 and 2021: 69,1% (19,997 women and 20,462 men) were attended in e-consultation (from 2013 to 2021) and 30,9% (8,920 women and 9,136 men) in in-person consultations (from 2010 to 2012) without gender differences in the proportion of patients attended in each period. Using an interrupted time series regression model, we analysed the impact of incorporating e-consultation into the health care model and evaluated the elapsed time to cardiology care, heart failure (HF), cardiovascular (CV), and all-cause hospital admissions and mortality during the one-year after cardiology consultation. The introduction of e-consultation substantially decreased waiting times to cardiology care; during the in-person consultation period, the mean delay for cardiology care was 57.9 (24.8) days in men and 55.8 (22.8) days in women. During the e-consultation period, the waiting time to cardiology care was markedly reduced to 9.41 (4.02) days in men and 9.46 (4.18) in women. After e-consultation implantation, there was a significant reduction in the 1-year rate of hospital admissions and mortality, both in women and men iRR [IC95%]: 0.95 [0.93-0.96] for HF, 0.90 [0.89-0.91] for CV, and 0.70 [0.69-0.71] for all-cause hospitalization; and 0.93 [0.92-0.95] for HF, 0.86 [0.86-0.87] for CV, and 0.88 [0.87-0.89] for all-cause mortality in women; and 0.91 [0.89-0.92] for HF, 0.90 [0.89-0.91] for CV and 0.72 [0.71-0.73] for all-cause hospitalization; and 0.96 [0.93-0.97] for HF, 0.87 [95%CI: 0.86-0.87] for CV, and 0.87 [0.86-0.87] for all-cause mortality, in men. Compared with the in-person consultation period, an outpatient care program that includes an e-consultation significantly reduced waiting time to cardiology care and was safe, with a lower rate of hospital admissions and mortality in the first year, without significative gender differences.

Forecasting The Search Trends of The Keyword “Sarung Wadimor” In Indonesia on Google Trends Data Using Time Series Regression with Calender Variation and Arima Box-Jenkins

The impact of this 4.0 era is that data is growing and can be collected very easily and then reprocessed to obtain information. One of the search engines for various data and information that is often used is Google, causing a high search intensity and will further impact on increasing the amount of data generated by search engines. Google Trends is one of the official websites from Google that reflects or takes pictures of events in society based on search keywords. The search keyword that will be studied in this article is “Sarung Wadimor”. Therefore, the purpose of this research is to forecast the search trend for the keyword "Sarung Wadimor" which is interesting because the resulting time series data pattern shows a recurring pattern due to the effect of calendar variations which are thought to be related to the month of Ramadan. Forecasting modeling uses Autoregressive Integrated Moving Average (ARIMA) and Time Series Regression (TSR). The goodness of the model used in this article is the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE). Based on the results of the analysis, using three goodness-of-fit measures shows that the TSR model with the Calendar Variation of Ramadan + Month Periods has smaller MSE, RMSE, and SMAPE values than the other models with goodness-of-fit values of 88.602, 9.413, and 26.950, respectively. Forecasting results for the next 6 periods show that the search trend for the keyword "Sarung Wadimor" tends to decrease, this is because the month of Ramadan is still quite far in 2023.

On the Soundness of XAI in Prognostics and Health Management (PHM)

The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing.

Disparities in Telemedicine Use Among Louisiana Medicaid Beneficiaries During the COVID-19 Pandemic.

The COVID-19 pandemic led to an increased reliance on telemedicine. Whether this exacerbated existing disparities within vulnerable populations is not yet known. Characterize changes in outpatient telemedicine evaluation and management (E&M) services for Louisiana Medicaid beneficiaries by race, ethnicity, and rurality during the COVID-19 pandemic. Interrupted time series regression models estimated pre-COVID-19 trends and changes in E&M service use at the April and July 2020 peaks in COVID-19 infections in Louisiana and in December 2020 after those peaks had subsided. Louisiana Medicaid beneficiaries continuously enrolled between January 2018 and December 2020 who were not also enrolled in Medicare. Monthly outpatient E&M claims per 1000 beneficiaries. Prepandemic differences in service use between non-Hispanic White and non-Hispanic Black beneficiaries narrowed by 34% through December 2020 (95% CI: 17.6%-50.6%), while differences between non-Hispanic White and Hispanic beneficiaries increased by 10.5% (95% CI: 0.1%-20.7%). Non-Hispanic White beneficiaries used telemedicine at higher rates than non-Hispanic Black (difference=24.9 claims per 1000 beneficiaries, 95% CI: 22.3-27.4) and Hispanic beneficiaries (difference=42.3 claims per 1000 beneficiaries, 95% CI: 39.1-45.5) during the first wave of COVID-19 infections in Louisiana. Telemedicine use increased slightly for rural beneficiaries compared with urban beneficiaries (difference=5.3 claims per 1000 beneficiaries, 95% CI: 4.0-6.6). The COVID-19 pandemic narrowed gaps in outpatient E&M service use between non-Hispanic White and non-Hispanic Black Louisiana Medicaid beneficiaries, though gaps in telemedicine use emerged. Hispanic beneficiaries experienced large reductions in service use and relatively small increases in telemedicine use.

Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.

Forecasting daily admissions to an emergency department considering single and multiple seasonal patterns

When dealing with several years of daily data, such as the number of daily admissions to a hospital’s emergency department (ED), how complex does it get to forecast into the future? With that in mind, this study has two main goals: to explore the differences between several methodologies, considering both single and multiple-seasonal patterns; and to select the most suitable model for the administration of a Portuguese hospital to use while managing their ED. To that end, we first considered the data as a time series with a single weekly seasonal pattern. We then modelled the data using time series regression, linear regression with autoregressive integrated moving average (ARIMA) errors, seasonal ARIMA and exponential smoothing techniques. Second, the data was set to be a time series with weekly and annual seasonal patterns. Then, using Fourier terms, we applied time series regression, linear regression with ARIMA errors and trigonometric exponential smoothing state space models with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) for the analysis. After selecting the best-fitting models using the Akaike Information Criteria (AIC) values, we forecasted into the future and compared the results using both training and test datasets’ root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values. The time series regression model based on seasonal variables and a weekly seasonal pattern gives the best results. However, we decided to use linear regression with ARIMA errors, seasonal variables, and both weekly and annual seasonal patterns. This produces similar results but allows for the annual seasonality to be considered, which is useful when more data is added.

Prediction of construction material prices using ARIMA and multiple regression models

Construction material prices (CMP) variations have become a major issue in properly budgeting construction projects. Inability to accurately forecast CMP volatility can also lead to price overestimation or underestimation. Enhancing the accuracy of predictions of CMP can also enhance the accuracy of predictions of total construction costs. The purpose of this study is to present a model for predicting construction material prices that assist decision-makers to make better decisions over the life cycle of a project. The price records for CMP namely; steel, cement, brick, ceramic, and gravel, and the indicators affecting them in Egypt were used for the prediction procedures. The practical methods for using the Box-Jenkins approach Autoregressive Integrated Moving Average (ARIMA) time series and multiple regression models for forecasting building material prices are outlined in this research. Out-of-sample predictions are used to evaluate the provided model’s performance in predicting future prices. The models are compared according to the Mean Absolute Percentage Errors (MAPE). The generated models show good results in predicting month-to-month variations in material prices, with MAPE ranging from 1.4 to 2.8 percent for the selected models. This research can assist both owners and contractors in improving their budgeting processes, and preparing more accurate cost estimates.

Impact of New Energy Vehicle Development on China’s Crude Oil Imports: An Empirical Analysis

Breaking the highly oil-dependent energy use structure in the transportation sector will be crucial for China to reduce its dependence on crude oil imports and ensure its energy security, and the development of new energy vehicles is helping to break this dilemma. A time series analysis summarizes the possible relationships between new energy vehicles and crude oil imports, i.e., new energy vehicles, as alternatives to fuel vehicles, will reduce the demand for oil in the transportation sector, which will in turn reduce crude oil imports, and crude oil prices and crude oil production will inhibit crude oil imports. In this empirical study, monthly data from 2015 to 2021 on crude oil imports, the market share of new energy vehicles, crude oil prices, and crude oil production are selected, time-series multiple regression modelling is adopted, and endogeneity is treated using a generalized method of moments (GMM). The regression results show that crude oil imports decrease by one unit for every 16.32% increase in crude oil prices, indicating that price factor is the most influential factor in China’s crude oil imports, while crude oil imports decrease by one unit for every 133.99% increase in crude oil production, indicating that an increase in crude oil production contributes less to the reduction of crude oil imports. One unit of crude oil imports is added for every 15.53% increase in the share of new energy vehicles, indicating that the effect of new energy vehicles on limiting crude oil imports has not yet emerged. Probably due to the fact that new energy vehicles have not yet had a significant impact on fuel vehicles, oil consumption will continue to increase in the short and medium term, with oil for the petrochemical industry becoming the primary driver of this increase. Finally, policy implications are provided from the perspective of crude oil demand, supply, and China’s oil price mechanism.

Trends in assault-related hospitalizations during the SARS-CoV-2 pandemic

IntroductionViolence remains a priority issue in the United States (US) requiring public health input to discern the magnitude and impact of violence on the health system. Concerns over violence and the injuries resulting from violence have increased following the SARS-CoV-2 pandemic which exacerbated an array of individual and economic stressors related to violence including increased unemployment, alcohol intake, social isolation, anxiety and panic and decreased access to health services. The aim of this study was to analyze the trends in violence-related injuries in the state of Illinois during the SARS-CoV-2 lockdown periods and post-lockdown in order to inform future public health policy. Material and methodsOutpatient and inpatient assault related injuries treated in Illinois hospitals from 2016 through March 2022 were analyzed. Segmented regression models evaluating change in time trends were adjusted for seasonality, serial correlation, overall trend and economic variables. ResultsThe annual rate of assault related hospitalizations per one million Illinois residents decreased from 3857.8 pre-pandemic to 3458.7 pandemic period. However, during the pandemic there was an increase in deaths and in the proportion of injuries involving open wounds, internal injuries, and fractures, while there was a reduction in less serious injuries. Segmented regression time series models demonstrated significant increase in firearm violence in all four pandemic periods examined. Firearm violence increased particularly in subgroups including African-American victims, 15–34-year-olds, and Chicago residents. ConclusionDuring SARS-CoV-2, we saw an overall reduction in assault related hospitalization, however, findings demonstrated an increase in serious injuries which may be associated with social and economic stressors of the pandemic, increased gun-violence while decrease in less serious injuries may be linked to hospital avoidance for non-lethal injuries during the peak waves of the pandemic. Our findings have implications for ongoing surveillance, service planning and management of the increased gunshot and penetrating assault cases and further demonstrate the need for public health input into the violence epidemic in the US.