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This study explores how globalization, renewable energy consumption, and governance interact to influence carbon dioxide (CO 2 ) emissions in Bangladesh from 1990 to 2022. Using the autoregressive distributed lag (ARDL) bounds testing framework alongside FMOLS and DOLS estimators, the analysis reveals that renewable energy consumption exerts a significant long-run abatement effect, while globalization and foreign direct investment display a non-linear relationship with emissions. A renewable-energy penetration threshold of approximately 38%–50% marks the point beyond which CO 2 emissions begin to decline, confirming a turning-point dynamic. Governance effectiveness enhances this decoupling by reinforcing policy execution and institutional quality. These findings align with Bangladesh’s Sustainable and Renewable Energy Development Authority (SREDA) targets and Vision 2041 agenda, underscoring the need for sustained investment in clean energy infrastructure, grid modernization, and green finance. The study contributes novel evidence on the globalization-energy-environment nexus through a threshold-based approach tailored to an emerging economy context.

This paper proposes a novel outlier detection method that uses Graph Fourier Transform (GFT) coupled with empirical Bayes thresholding, named Ebayesthresh. While traditional outlier detection techniques are primarily designed for Euclidean data, this study focuses on non-Euclidean data, specifically graph signals. By utilizing the GFT, a given signal is transformed into the frequency domain, allowing for spectral analysis. According to the Lebesgue decomposition theorem and Wold’s theorem, when a graph signal satisfies the stationarity condition, its frequency domain exhibits a mixture of sparse signals and noise. Bayesian modeling is used to estimate the sparsity in the frequency domain, which enables the identification of outliers. Experimental datasets are utilized to compare the performance of the proposed method with existing approaches. Furthermore, the usefulness of the proposed method is demonstrated through real-world data analysis using earthquake data from 2010 to 2014.

COVID-19 has a severe and widespread impact, especially in Indonesia. COVID-19 was first reported in Indonesia on March 03, 2020 then rapidly spread to all 34  provinces by April 09, 2020.  Since then, COVID-19 is declared a state of national disaster and health emergency. This research analyzes the difference of CO, HCHO, NO2, and SO2 density in Jakarta, West Java, Central Java, and South Sulawesi  before and during the pandemic. Also, this study assesses the effect of large scale restrictions on the economic growth during COVID-19 pandemic in Indonesia. In a nutshell, the results on Wilcoxon and Fisher test by significance level α=5% as well as odds ratio showed that there are significant differences of CO density in all regions with highest odds ratio in East Java (OR=9.07), significant differences of HCHO density in DKI Jakarta, East Java, and South Sulawesi. There are significant differences of NO2 density before and during public activities limitation in DKI Jakarta, West Java, East Java, and South Sulawesi. However, the results show that there are no significant differences of SO2 density in all regions. In addition, this research shows that there are significant differences of retail, grocery and pharmacy, and residental mobility  before and during the COVID-19 pandemic in Indonesia.  This research also shows that during the COVID-19 pandemic there are severe economic losses, industry, companies, and real disruptions are severe for all levels of life due to large scale restrictions.

IntroductionMissing PM2.5 observations in environmental monitoring systems, caused by sensor malfunctions, communication failures, maintenance issues, and coverage gaps, compromise public health assessments and evidence-based air quality policymaking. Reliable imputation strategies are therefore essential to preserve data integrity and analytical validity.MethodsThis study evaluated five imputation techniques: Bayesian Regression (BR), K-Nearest Neighbors (KNN), missForest, Predictive Mean Matching (PMM), and Random Forest (RF), using daily PM2.5 measurements collected between May 2019 and December 2024 from monitoring stations in Islamabad, Karachi, Lahore, and Peshawar, Pakistan. Three missing data mechanisms, MCAR, MAR, and MNAR, were simulated at missing rates ranging from 5% to 25%. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).ResultsImputation under the MAR mechanism consistently yielded lower error values as missingness increased. Across all mechanisms and missing rates, missForest and KNN demonstrated superior performance. Notably, missForest achieved the lowest RMSE and MAE values overall and effectively preserved the temporal structure, range, and variability of the PM2.5 series.DiscussionThe findings suggest that machine-learning-based approaches, particularly missForest, provide robust and reliable imputation for PM2.5 datasets with varying missingness patterns. These results support the use of missForest as a preferred method for handling incomplete air quality data in similar monitoring contexts, thereby strengthening the reliability of environmental health analyses and air quality policy development.

BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are generally often stranded in very shallow areas with sloping sea floors and sand. Data were collected in this study on the incidence of stranded marine animals in 20 provinces of Indonesia from 2015 to 2019 with the focus on animals such as Balaenopteridae, Delphinidae, Lamnidae, Physeteridae, and Rhincodontidae. METHODS: Multivariate latent generalized linear model was used to compare several distributions to analyze the diversity of event counts. Two optimization models including Laplace and Variational approximations were also applied. FINDINGS: The best theta parameter in the latent multivariate latent generalized linear latent variable model was found in the Akaike Information Criterion, Akaike Information Criterion Corrected and Bayesian Information Criterion values, and the information obtained was used to create a spatial cluster. Moreover, there was a comprehensive discussion on ocean-atmosphere interaction and the reasons the animals were stranded. CONCLUSION:The changes in marine ecosystems due to climate change, pollution, overexploitation, changes in sea use, and the existence of invasive alien species deserve serious attention.

Interactions between microbial symbionts influence their demography and that of their hosts. Taylor's power law (TL)-a well-established relationship between population size mean and variance across space and time-may help to unveil the factors and processes that determine symbiont multiplications. Recent studies suggest pervasive interactions between symbionts in Drosophila melanogaster. We used this system to investigate theoretical predictions regarding the effects of interspecific interactions on TL parameters. We assayed twenty natural strains of bacteria in the presence and absence of a strain of yeast using an ecologically realistic set-up with D. melanogaster larvae reared in natural fruit. Yeast presence led to a small increase in bacterial cell numbers; bacterial strain identity largely affected yeast multiplication. The spatial version of TL held among bacterial and yeast populations with slopes of 2. However, contrary to theoretical prediction, the facilitation of bacterial symbionts by yeast had no detectable effect on TL's parameters. These results shed new light on the nature of D. melanogaster's symbiosis with yeast and bacteria. They further reveal the complexity of investigating TL with microorganisms.

The toxicity bioassays are essential to detect and estimate the potential toxicological effects of chemicals on an organism. LC50/LD50/GR50 is the estimation of dose/concentration necessary to kill 50 per cent of a population of the test species. Experimentally this is done by administrating a chemical at different doses to a group of organism and then observing the resulting mortalities in a set of the time period. A web-based module for a statistical analysis tool to calculate and compare the median lethal dose has been developed in ASP scripting language based on Server-Client Architecture. The module produces the final probit line, dose-response curve, LC50/LD50 with 95 % confidence interval. The Chi-square statistic has been obtained for testing the adequacy of fit. A user-friendly interface for entering/pasting the data and various parameters such as number of variables, number of observations etc. has also been provided. Additionally, a complete procedure to perform probit analysis has also been provided in the help file.

Environmental risks are intensified by the growing interaction between human-induced pressures and climate change (CC), leading to biodiversity loss and ecosystem changes that threaten the resilience and health of marine and coastal ecosystems, along with their ability to provide services. The increasing risks posed by CC require appropriate adaptation actions that can boost biodiversity while also benefiting people. In this setting, nature-based solutions (NBSs) play a crucial role in coping with climate impacts by improving ecosystem resilience, thereby protecting and restoring natural ecosystems while providing benefits for human wellbeing. Current literature mainly focuses on NBSs in terrestrial environments, while knowledge on their implementation (and effectiveness) in marine and coastal environments remains limited. In particular, the analysis of suitable conditions for their successful implementation is lacking in marine and coastal areas when environmental, socio-economic, and governance aspects are considered, and the concept of ‘suitability’ itself is characterized by the absence of a consolidated and widely recognized definition. Through a systematic approach, this review work aims to reduce uncertainty surrounding the concept of suitability by providing clear guidance for its consistent interpretation and application in real-world NBS design and implementation. To this aim, we assess the state-of-the-art scientific literature on methodologies, modeling tools, analytical frameworks, and approaches used to evaluate NBS suitability in marine and coastal ecosystems. Based on a pool of 26 papers covering the 2011–2025 timeframe, a period selected to align with the emergence and formalization of the NBS concept, the review confirms that a unique and shared definition of suitability is missing. Within the framework of this work, the concept has, therefore, been extrapolated from a selection of key papers drawn from the literature. Furthermore, the review results reveal that environmental indicators (e.g., slope, depth, and sea surface temperature) are most commonly used to analyze suitability, followed by socio-economic indicators (e.g., land use and distance from the coast, which act as proxies for human activities or infrastructure). However, the socio-economic and governance dimensions remain poorly explored in the analyzed publications; instead, methodological approaches mainly focused on environmental aspects that define the optimal physical, ecological, and chemical conditions for NBS implementation. Overall, the literature suggests that suitability models represent an essential decision-making tool for guiding nature-based adaptation in marine and coastal areas as they are fundamental to ensuring the success of implemented NBSs and the restoration and preservation of ecosystems.

To describe the methodological characteristics of mediation analyses (MAs) reported in recent randomized controlled trials (RCTs) and to propose recommendations on the planning, conduct, and reporting of MAs in practice. We conducted a systematic review by searching MEDLINE (January 1, 2017, to December 1, 2018) for all reports of RCTs or secondary analyses of previously published RCTs that reported a MA. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data from the 98 eligible studies. MAs were nearly always (96%) based on a traditional mediation approach. Most studies did not report a sample size calculation for the MA (96%) or assess potential treatment-by-mediator interactions (96%). In 53% of studies, mediators and outcomes were simultaneously measured. In 57% of studies, mediator-mediator and mediator-outcome confounders were adjusted for in the analysis, although adjustment was often limited to few potential confounders. About 30% of studies discussed the assumptions underlying the MA. The conduct and reporting of MAs remained quite heterogeneous in practice. Future MAs could benefit from a consensus-based planning, conduct, and reporting guideline for MA. •Mediation analysis was increasingly used in epidemiology and medicine.•The traditional mediation approaches were frequently applied in practice.•The conduct and reporting of mediation analysis remained heterogeneous.•We proposed some practical recommendations for future mediation analyses.