Daily Data Research Articles

Assessing the environmental and economic viability of floating PV-powered green hydrogen: A case study on inland ferry operations in Türkiye

This paper conducts a comprehensive analysis of the environmental impacts and costs associated with a floating photovoltaic (FPV)-powered green hydrogen production system for a ferry line in Türkiye, utilizing life cycle assessment (LCA) and life cycle cost assessment (LCCA) methods. The methodology involves regridding daily data from 13 Global Climate Models (GCMs) to a consistent 1° × 1° grid, comparing this data with ERA5 datasets using various statistical methods, and identifying the top four GCMs. These models are then used to forecast ambient temperature, wind speed, and solar radiation for 2023–2052. Based on these forecasts, the FPV system's component sizes are determined. The LCA, based on the GREET 2022 database, reveals that green hydrogen is the most environmentally friendly option, reducing global warming potential (GWP) by 77.5 % compared to marine diesel oil (MDO 0.1 % S), with more than 62 % of GWP for the hydrogen-powered ferry attributed to PV production. LCCA results indicate that, without subsidies, green hydrogen is not economically feasible for the selected inland ferry line. Carbon taxes are insufficient to bridge cost differences, highlighting the need for financial incentives. Reducing corporate tax to 10 % is identified as the most effective incentive, and the Production Tax Credit (PTC) option maximizes internal rates of return for green hydrogen producers. This study provides valuable insights for sustainable energy choices in maritime transport, emphasizing the importance of both environmental and economic considerations.

Does ERA5-Land Effectively Capture Extreme Precipitation in the Yellow River Basin?

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land’s capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data.

Interpretable multi-step hybrid deep learning model for karst spring discharge prediction: Integrating temporal fusion transformers with ensemble empirical mode decomposition

Karst groundwater is a critical freshwater resource for numerous regions worldwide. Monitoring and predicting karst spring discharge is essential for effective groundwater management and the preservation of karst ecosystems. However, the high heterogeneity and karstification pose significant challenges to physics-based models in providing robust predictions of karst spring discharge. In this study, an interpretable multi-step hybrid deep learning model called selective EEMD-TFT is proposed, which adaptively integrates temporal fusion transformers (TFT) with ensemble empirical mode decomposition (EEMD) for predicting karst spring discharge. The selective EEMD-TFT hybrid model leverages the strengths of both EEMD and TFT techniques to learn inherent patterns and temporal dynamics from nonlinear and nonstationary signals, eliminate redundant components, and emphasize useful characteristics of input variables, leading to the improvement of prediction performance and efficiency. It consists of two stages: in the first stage, the daily precipitation data is decomposed into multiple intrinsic mode functions using EEMD to extract valuable information from nonlinear and nonstationary signals. All decomposed components, temperature and categorical date features are then fed into the TFT model, which is an attention-based deep learning model that combines high-performance multi-horizon prediction and interpretable insights into temporal dynamics. The importance of input variables will be quantified and ranked. In the second stage, the decomposed precipitation components with high importance are selected to serve as the TFT model’s input features along with temperature and categorical date variables for the final prediction. Results indicate that the selective EEMD-TFT model outperforms other sequence-to-sequence deep learning models, such as LSTM and single TFT models, delivering reliable and robust prediction performance. Notably, it maintains more consistent prediction performance at longer forecast horizons compared to other sequence-to-sequence models, highlighting its capacity to learn complex patterns from the input data and efficiently extract valuable information for karst spring prediction. An interpretable analysis of the selective EEMD-TFT model is conducted to gain insights into relationships among various hydrological processes and analyze temporal patterns.

Model-based meta-analysis of HbA1c reduction across SGLT2 inhibitors using dose adjusted by urinary glucose excretion

This study was aimed to evaluate whether the dose–response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)—canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin—can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose–response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (Emax) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher Emax than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

From trait affect to situated performance of change-oriented organizational citizenship behavior: the pathway through situational construal and emotion utilization

Prior studies indicate that fostering change-oriented organizational citizenship behavior (OCB-CH) among employees, wherein they actively contribute to the collective welfare of modern organizations, correlates positively with their positive affect. However, the precise mechanisms linking trait positive affect with OCB-CH remain unclear. This study used aggregated 5-day daily diary data from a sample of 236 full-time employees in Greece to examine positive affectivity differences in the characteristics of situational experiences and how these differences relate to employee OCB-CH via the use of emotions to facilitate performance. Employing multilevel structural equation modeling, the findings revealed a positive association between trait positive affectivity and the situation characteristics of duty, intellect, and positivity, from the DIAMONDS taxonomy. Furthermore, it was determined that perceptions of situational characteristics and emotional utilization sequentially mediated the relationship between positive affectivity and OCB-CH. This study enhances our understanding of how employee trait positive affectivity influences the manifestation of positive change in the form of OCB-CH within organizations.

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study.

Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries. Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates. The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively. Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.

Changes in the Climate Comfort of the Coast of Spain (1940-2022)

The Spanish coastal regions register specific climatic conditions due to the combination of mild temperatures with little variation throughout the year, high relative humidity and the influence of maritime storms. In summer, the climatic comfort conditions are excessively hot, especially on the Mediterranean coast of Spain. Understanding these conditions and analysing the temporal evolution of recent decades, as well as regional differences, is fundamental for future summer tourism planning in the coming decades. This study analyses the principal 37 coastal tourist hubs of Spain grouped into 10 large regions (the Atlantic, Cantabrian, Mediterranean coasts, and the two archipelagos of the Balearic and Canary Islands). Daily data drawn from the ERA-5 (Copernicus) atmospheric reanalysis from 1940 to 2022 have been used (mean air temperature, mean relative humidity, and mean wind speed), with which the Climate Comfort Index has been calculated (CCI) by González (1998). The results show a significant reduction of the CCI in all the coastal areas analyzed, being more relevant in winter (-0.10 decade). The decrease in the index implies a decrease in cold thresholds and an expansion of comfort throughout the study area, especially in the central Mediterranean and Cantabrian Sea. For its part, in summer, the most important decreases (-0.07 and -0.08 / decade) show an increase in the most important climatic discomfort on the Cantabrian Coast – Euskal Kostaldea and on the Costa Brava-El Garraf, with a significant intensification and expansion temporary thermal sensation of heat. In other coastal sectors, in recent years, the climatic thresholds of heat and extreme heat have been reached for the first time.

The dynamic relationship between ASEAN+6 exchange rates and stock markets: application of the ARDL model

PurposeThis study investigates the short- and long-run relationship between stock prices and exchange rates in the Association of Southeast Asian Nations (ASEAN)+6 markets. In the short run, support is found for both the theory of a goods market where exchange rates influence stock returns and portfolio balance theory, where stock returns influence exchange rates.Design/methodology/approachThe co-integration approach of linear and nonlinear augmented autoregressive distributed lag (ARDL) models was applied to daily data from 2 January 2017 to 30 June 2023.FindingsThe findings provide evidence that the goods market theory is supported solely in Indonesia and Singapore, while the portfolio balance theory is supported for Australia, China, India and Malaysia.Practical implicationsPolicymakers and investors should seriously consider the importance of the study findings. The results show that all ASEAN+6 countries experience a short-term interaction between the two markets. This illustrates that exchange rates and stock price movements play vital roles in other markets. Stakeholders, particularly policymakers, should be aware of this critical relationship.Originality/valueThis is the first study to dynamically examine how the ASEAN+6 framework influences currency rates and stock markets.

A single-arm pilot study of a brief cognitive-behavioral therapy for insomnia intervention among Japanese occupational therapy and physical therapy university students with sleep disturbances

ObjectiveTo evaluate the feasibility of the Sleep Health through University Student Habits (SHUSH) program, a brief sleep improvement intervention based upon principles of cognitive-behavioral therapy for insomnia (CBT-I) developed for Japanese university students.MethodsPretest-posttest, single-arm pilot study design with 3-month follow up was used to evaluate the feasibility of SHUSH. We developed then offered a 90-min sleep education class based upon the two-process model of sleep regulation, sleep restriction, stimulus control, and sleep hygiene. We recommended individualized sleep prescriptions (e.g., prescribed time to bed and prescribed time out of bed) from 11 days of daily sleep diary data. We then offered 15 min of individualized follow-up meetings each week for three consecutive weeks in person or online in which we supported adherence to sleep prescriptions. Fifteen university students with self-reported sleep disturbance (Insomnia Severity Index; ISI score ≥ 9) completed the SHUSH program. Participants were on average age 19.7 years old; nine were women, and they were students in occupational therapy and physical therapy. We assessed validated Japanese versions of sleep-related (e.g., ISI, Sleep hygiene practice scale; SHPS), mental health-related (e.g., Generalized Anxiety Disorder Screener) patient-reported outcomes (PROs) and daily sleep diary variables (e.g., sleep onset latency and sleep efficiency).ResultsComparing baseline and posttest data, a statistically significant difference was observed in insomnia symptom severity, daytime sleepiness, sleep hygiene practices, eveningness to morningness, anxiety, depression, sleep diary improvements (e.g., sleep onset latency, total time in bed, and sleep efficiency). Comparing posttest and 3-mo follow up data (n = 10), a statistically significant difference wasn't observed for most PRO effects. However, there was a statistically significant difference in ineffective sleep behaviors (i.e., SHPS-J). We did observe a rebound effect for some SHPS-J items.ConclusionSHUSH was developed as a brief cognitive-behavioral intervention for insomnia. SHUSH participants showed improvements on sleep-related and mental health-related PROs after 4 weeks of intervention. At 3 months follow-up sleep quality and mental health gains were maintained. SHUSH was a feasible program. Randomized controlled trials are needed to test treatment efficacy on sleep related and mental health related PROs in the future.

PERBANDINGAN METODE EXPONENTIAL GARCH (EGARCH) DAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH (GJR-GARCH) PADA MODEL VOLATILITAS SAHAM TUNGGAL

Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%.

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

Stock Returns and Volatility: Comparative Investigation from SAARC Countries

This research article analyses the stock return and volatility of three selected SAARC countries (Pakistan, India, and Bangladesh), focusing on the role of natural resources in influencing market performance. This study aims to determine the robustness of the performance, considering the economic significance of these countries' natural resources. The daily data from the PSX 100 index (Pakistan Stock Exchange), BSE index (Bombay Stock Exchange), and DSE index (Dhaka Stock Exchange) for the years 2019 to 2021 has been obtained from their websites. The data is treated in MS Excel to compute the daily Geometric Returns for BSE, PSX, and DSE indices. Furthermore, the data were analyzed using the specialized package EViews for Time Series Graph, Descriptive Statistics, Unit Root Test, and GARCH Model. The findings of this research show that the BSE index has the highest return value of 6.78 percent compared with 4.56 percent and 3.75 percent for DSE and PSX, respectively. The Half-life model has been applied to calculate the volatility observed by the indices. The result shows that BSE is the most volatile and takes the highest time of 45 days to revert to its mean position, followed by PSX and DSE, which take 19 and 10 days, respectively. Therefore, investing in the BSE index is better for risk-lover investors. At the same time, DSE has the fastest mean reversion compared to the other two indices. Therefore, it is feasible for risk-averse investors.

Effect of EV71 Vaccination on Transmission Dynamics of Hand, Foot, and Mouth Disease and Its Epidemic Prevention Threshold

Objective: To investigate the effect of Enterovirus A71 (EV71) vaccination on the transmissibility of different enterovirus serotypes of hand, foot, and mouth disease (HFMD) in Zhejiang, China. Methods: Daily surveillance data of HFMD and EV71 vaccination from August 2016 to December 2019 were collected. Epidemic periods for each HFMD type were defined, and the time-varying effective reproduction number (Rt) was estimated, which could provide more direct evidence of disease epidemics than case number. General additive models (GAMs) were employed to analyze associations between EV71 vaccination quantity and rate and HFMD transmissibility. The epidemic prevention threshold, represented by required vaccination numbers and rates, was also estimated. Results: Vaccinating every 100,000 children ≤ 5 years could lead to a decrease in the Rt of EV71-associated HFMD by 14.44% (95%CI: 6.76%, 21.42%). Additionally, a positive correlation was observed between vaccinations among children ≤ 5 years old (per 100,000) and the increased transmissibility of other HFMD types (caused by enteroviruses other than EV71 and CA16) at 1.82% (95%CI: 0.80%, 2.84%). It was estimated that an additional 362,381 vaccinations, corresponding to increased vaccine coverage to 54.51% among children ≤ 5 years could effectively prevent EV71 epidemics in Zhejiang. Conclusions: Our findings highlight the importance of enhancing EV71 vaccine coverage for controlling the epidemic of EV71-HFMD and assisting government officials in developing strategies to prevent HFMD.

Ethereum futures and the efficiency of cryptocurrency spot markets

This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.

Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study.

Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population. We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults. We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation). In the annual trend, 2-way ANOVA revealed a significant (group) × (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) × (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults. Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend-weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups.

Investigating the Performance of the Informer Model for Streamflow Forecasting

Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. The predictive capabilities of the Informer model are explored and compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in streamflow time series. The analysis is extended to encompass diverse time series datasets from various locations (&gt;100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. For a forecast horizon of 168 days, the Informer model achieves an NSE of 0.8 and maintains a MAPE below 10%, while the second-best model (LSTM) only achieves −0.63 and 25%, respectively. Furthermore, it is observed that the dependence structure of time series, as expressed by the climacogram, affects the performance of the Informer network.

Risk factors-adjusted performance and persistence during the post-subprime crisis period: evidence from Indian mutual fund industry

PurposeThe main objective of the paper is to find evidence of abnormal returns and performance persistence of actively managed equity funds in the Indian context on an annual basis during the post-subprime crisis period between 2009 and 10 and 2019 and 2020.Design/methodology/approachThe study is exploratory and empirical, used daily net asset value (NAV) of 180 equity funds for 10 years and applied the risk-adjusted, Jensen's (1968) single-factor, Fama and French's (1993) three-factor model and Carhart's (1997) four-factor model to evaluate the performance. The performance persistence has been tested using cross-section regression (Bollen and Busse, 2005), the non-parametric contingency approach, along with the robustness measure, i.e. Malkiel's (1995) Z-score, Brown and Goetzmann's (1995) cross-product ratio (CPR) and Kahn and Rudd's (1995) χ2 value.FindingsThe results show that the Indian equity funds are unable to generate abnormal returns, and the size, value and momentum strategies applied by the fund managers in generating abnormal returns do not work effectively. However, funds provide strong evidence of significant performance persistence on an annual basis in the short-term, mid-term and long-term periods. Both parametric as well as non-parametric tests provide identical evidence of persistence, and the performance persistence is independent of the choice of models, as all the models (i.e. two, three or four-factor models) provide significant evidence of persistence.Research limitations/implicationsThough the study is comprehensive and covered a longer period, there is a scope for future research by examining the influence of fund characteristics, fund rating and macroeconomic factors on performance and persistence. It can be extended over to a longer period covering the post-COVID-19 period, a larger sample size and a comparative study of Indian and foreign mutual funds (MFs).Practical implicationsThe outcomes of this research paper can help wealth-maximizing investors in the identification of persistent equity funds and can apply the previous period’s performance information as a useful investment strategy to generate higher returns in the future. We believe that these outcomes will have significant ramifications for all MF stakeholders and policymakers, especially for the Indian industry in ensuring and establishing the credibility of MF managers, in providing better returns as well as to make MF investment more attractive to Indian retail investors.Originality/valueDespite the exponential growth in the Indian MF industry, limited evidence is available on performance and persistence covering a large sample size during the post-sub-prime crisis period using different return models and parametric and non-parametric approaches. The study is based on the daily data set of a larger sample size representing all the Asset Management Company (AMC) and the longer period following the post-subprime crises, which affected capital flows significantly. Moreover, the application of all the measures enables us to understand performance persistence in a larger context.

Satellite measurement data-based assessment of spatiotemporal characteristics of ultraviolet index (UVI) over the state of Johor, Malaysia

The study examines the spatiotemporal and statistical characteristics of the daily ultraviolet index (UVI) over the Johor state of Malaysia. The datasets utilized in this study are the Ozone Monitoring Instrument (OMI)/Aura satellite’s daily UVI observations between October 2004 and March 2023. The innovative trend analysis (ITA) method is employed to identify the statistical trend of daily UVI. Basic statistics of daily, monthly, and seasonal UVI are also carried out to better characterize UVI in the study area. The daily UVI data collected for the analysis exhibit wide variability, with more than 80% of the daily UVI data falling above UVI 8, indicating the severity of UV radiation (UVR) reaching the study area. The monthly and seasonal UVI also display highly varying characteristics, as observed in the daily data. The pixel-based analysis of ITA results indicates a significant increasing and decreasing trend of daily UVI in the region, with values varying between − 9.42 × 10−6 and 3.79 × 10−5. However, for the study area as a whole, UVI shows a significant increasing trend of 4.50 × 10−5. The influence of parameters such as cloud optical thickness (COT), solar zenith angle (SZA), and O3 column on the strength of UVR reaching the study area is also investigated. Based on the results, the study area is characterized by low thickness, broken clouds, and sometimes cloudless conditions. Overall, the results show that UVI in the study area is increasing, and therefore, proper health guidelines and direct UVR exposure regulations need to be implemented to reduce serious health risks associated with UVR exposure.Graphical

Combined effects of flood, drought and land use dominate water quality and nutrient exports in Jialing River basin, SW China

Climate change and the associated increase in hydroclimatic extremes necessitate a deeper understanding of the resulting water quality responses. This study investigates the combined impacts of hydroclimatic extremes and land uses on water quality of the Chinese Jialing River, of which the middle and downstream areas experienced a flood in 2021 and a severe drought in 2022. Water Quality Index (WQI) and nutrient loads were assessed using daily data from 22 monitoring stations across the Jialing River and its two tributaries, the Qujiang River and Fujiang River, over 2021–2022. The results indicate a slight upward trend in water quality, as reflected by the WQI, for the tributaries from 2021 to 2022, while a declining trend was observed in the mainstream. Floods had a more pronounced impact on water quality than droughts, particularly on nutrient concentrations, and both dilution and flushing effects were observed as discharge increased in the Jialing River and its tributaries. Notably, water quality deterioration was most pronounced in the downstream areas with land uses dominated by cropland and built-up area, where intensified rainfall distributed and exacerbated nutrient losses in the rainy seasons. Nutrient fluxes, including Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Total Nitrogen (TN), were closely linked to discharge, with hydroclimatic extremes therefore significantly affecting nutrient exports. This study elucidates the complex interactions between land use, extreme weather and water quality in the Jialing River Basin. Our findings underscore the need to strengthen the management of non-point source pollutants in the downstream areas of the Jialing River to address the challenges posed by anticipated increases in extreme rainfall in the near future.