Heliyon

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear and ensemble machine learning (ML) models and applies explainable AI (XAI) techniques to understand the contribution of input features. Data from AgInjuryNews (2015-2024) was preprocessed to extract relevant attributes such as location, time, age, and safety measures. The dataset comprised 2,421 incidents categorized as fatal or non-fatal. Various ML models, including Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were trained and evaluated using standard performance metrics. Ensemble models demonstrated superior accuracy and recall compared to linear models, with XGBoost achieving a recall of 100% for fatal injuries. However, all models faced challenges in predicting non-fatal injuries due to class imbalance. SHAP analysis provided insights into feature importance, with age, gender, location, and time emerging as the most influential predictors across models. This research highlights the effectiveness of ensemble ML models in injury prediction while emphasizing the need for balanced datasets and XAI techniques for actionable insights. The findings have practical implications for enhancing agricultural safety and guiding policy interventions. HighlightsO_LIThis study analyzed 2,421 agricultural injury incidents from AgInjuryNews (2015- 2024) and utilized machine learning models to predict injury severity, focusing on both fatal and non-fatal outcomes. C_LIO_LIEnsemble models, such as XGBoost and Random Forest, outperformed linear models in accuracy and recall, especially in predicting fatal injuries, although challenges in non-fatal predictions due to class imbalance were observed. C_LIO_LIKey predictors identified through SHAP analysis included age, gender, location, and time, providing interpretable insights into the factors influencing injury severity. C_LIO_LIThe integration of explainable AI (XAI) enhanced the transparency of machine learning predictions, enabling stakeholders to prioritize targeted safety interventions effectively. C_LIO_LIThis research highlights the potential of combining ensemble ML models with XAI techniques to improve agricultural safety practices and provides a foundation for addressing data challenges in future studies. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/25321769v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@dc19a8org.highwire.dtl.DTLVardef@189646org.highwire.dtl.DTLVardef@31d6f2org.highwire.dtl.DTLVardef@16b01f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Alternative indexes (altmetrics), such as the number of social media posts or news mentions, assess the relevance of a publication beyond academia. At the same time, composite metrics, like Altmetric Attention Score (AAS), gather these indicators to provide an overall measure of the online attention of an article. Communication departments at research institutions aim to help publications reach the general public and, therefore, play a key role in improving the altmetrics performance of scientific articles. However, the most effective strategies to achieve this remain unclear. No standardized protocols exist to guide communication professionals in maximizing the impact of their outreach efforts. Thus, in the present research, carried out by the communication department of a neuroscience research center, we conducted three complementary analyses to help to fill this gap. First, a study involving more than 3,000 neuroscience publications from 8 similar research centers revealed correlations between altmetrics and citations. Second, a retrospective analysis of 201 in-house articles found that active communication campaigns were associated with better altmetric performance, with notable differences between publications that included a press release (PR) and those that did not. Finally, a prospective experiment involving 25 articles examined changes in altmetrics before and after the dissemination of a PR or posting on X. Results showed that PRs significantly increased the number of news mentions and X posts, leading to an average rise of approximately 70 points in the AAS. These findings highlight the importance of PRs in maximizing the societal visibility and impact of scientific publications.

Preclinical data from the peculiar animal model of "synthetic torpor" (ST), a reversible hypothermic condition resembling natural torpor but pharmacologically induced in rats (a non-hibernating mammal), suggest that in hypothermic conditions the neuroprotective effects of melatonin are strongly enhanced. As a protection technique, during aortic-arch surgery patients are induced a hypothermia similar to ST. Since in ST systemic melatonin was found particularly high, our aim was to assay serum melatonin in patients (N=8) undergoing aortic surgery, either during hypothermia and in the following four recovery days. Serum markers of blood-brain barrier (BBB) integrity (the astrocytic protein S100B) and neuronal damage (neuronal-specific enolase, NSE) were also measured. Results show that, in contrast to what observed in ST, in hypothermic patients melatonin was dramatically reduced in respect to the pre-anesthesia level, slowly recovering during the post-surgery period. Also, S100B and NSE raised during surgery, indicating highly leaking BBB and some ongoing neuronal damage, though both markers returned closer to pre-anesthesia levels within recovery period. Together, present results show that, in aortic-arch surgery, hypothermic patients totally lack the systemic melatonin peak that was observed in ST, the BBB was temporarily compromised and some acute neuronal damage occurred. A main implication of this work is that, exploiting the low BBB efficiency that makes easier to reach brain parenchyma, by administering melatonin during the hypothermic stage of the surgery the observed gap could be filled, possibly triggering the neuroprotective mechanism seen in preclinical observations and leading to better neurological outcomes for this surgical procedure. HighlightsO_LIDuring hypothermia induced in aortic-surgery systemic melatonin was low C_LIO_LISerum biomarkers enlighten transient blood-brain barrier and neuronal sufferings C_LIO_LIGiving melatonin during aortic-surgery hypothermia may improve neurological outcome C_LI

BackgroundWhile the contralateral seventh cervical nerve (CC7) cross transfer was designed to reconstruct paralyzed arm function after stroke, improvement in language function was found in patients combined with aphasia. ObjectiveTo evaluate the effect of improvement in language function after CC7 cross transfer in stroke patients with chronic aphasia and explore its potential mechanism. MethodsIn a prospective observative cohort, patients diagnosed with hemiplegia combined with aphasia were included. The language function was evaluated through the changes of Aphasia Quotient evaluated by Western Aphasia Battery (WAB-AQ) as well as its four subtests from baseline to 1 week and 6 months after the surgery. Patients also received oral agility test by Boston Diagnostic Aphasia Examination (BDAE-OA). Resting-state functional MRI (rs-fMRI) was scanned before and over 6 months after the surgery to explore the potential central mechanism in language improvements. ResultsThe average increase of WAB-AQ was 8.08 points from baseline to 1 week post-operatively (P<0.001, 95%CI: 5.05-11.10), and 9.51 from baseline to 6-month (P<0.001, 95%CI: 6.75-12.27). In 8 patients who participant in BDAE-OA, the average increase was 3.7 points (95%CI: 0.56-6.84; corrected P =0.023) from baseline to 1-week follow-up, and 5.3 points from baseline to 6 months follow-up. Significant higher local activity was detected at right precentral cortex, right gyrus rectus, and right anterior cingulate cortex after the surgery from rs-fMRI. ConclusionsImmediate and stable improvement in language function was detected after CC7 cross transfer in hemiplegic patients combined with aphasia, which may be realized through enhanced function of language network in the bilateral hemisphere.

BackgroundSerum albumin has a close correlation with degree of malnutrition which is associated with poor outcome and quality of life after cardiac surgery. Hypoalbuminemia is associated with increased wound infection, prolonged hospital stay and death after major surgery. Hence, preoperative serum albumin level can be utilized to upgrade risk models which will further benefit the cardiac surgical patients without extra financial burden. Objective of this study was to evaluate the role of serum albumin as a predictor of morbidity and mortality after valve replacement surgery. MethodsThis comparative cross-sectional study was carried out at the department of cardiac surgery in BSMMU. The study population was 50, with two groups having 25 patients each. Grouping of patients were done with respect to a preset cut off value for serum albumin. The period of study was from August, 2018 to February, 2020 and purposive sampling method was applied for this study. Data was collected by using a standardized semi-structured questionnaire and face to face interview. ResultsBy demographic characteristics, mean age was significantly higher in group B (49.96{+/-}8.69 years) than in group A (41.60{+/-}11.16 years) (p=0.005). Mean BMI was lower in group B (20.88{+/-}3.71 kg/m2) than in group A (22.26{+/-}1.67 kg/m2), which was found statistically significant (p=0.006). In terms of postoperative outcome, total chest drain collection was significantly higher in group B (968.80{+/-}183.49 ml) than in group A (816.00{+/-}113.40 ml), (p=0.001). Duration of ICU stay were significantly longer in group B (4.60{+/-}0.76 days) than in group A (3.92{+/-}0.86 days) (p=0.005). Similarly, duration of hospital stay was significantly longer in group B (9.88{+/-}1.56 days) than in group A (8.64{+/-}0.81 days) (p=0.001). Overall morbidity was significantly higher in group B (48%) than in group A (20%) (p<0.05). Mortality rate was higher in group B (12%) than in group A (4%), but that was not found statistically significant (p>0.05). Pearson co-efficient correlation test showed strong inverse relationship of serum albumin with total chest drain, ICU stay and hospital stay following valve replacement surgery (r= -0.473, r= -0.448 & r= -0.487 respectively), which was most significant than age and BMI (p[&le;]0.001). Multivariate logistic regression analysis was done to assess the predictive value of serum albumin level, age and BMI, where preoperative serum albumin level was found to be the most valuable predictor of postoperative morbidity after valve replacement surgery (B= -2.251, OR 0.105, 95% CI 0.011-0.986, p<0.05). ConclusionThis study demonstrated that preoperative low serum albumin level is associated with increased morbidity and mortality after valve replacement surgery. Hence, preoperative serum albumin level can be used as a reliable predictor of postoperative outcome following valve replacement surgery.

Surgical procedure recognition is the process of identifying tasks and gestures done during a surgical process and is a field that has been widely researched due to its use in robot assisted surgeries to improve surgical performances and training. This work investigates the use of Quantum Machine Learning (QML) algorithms, in particular Quantum Support Vector Classifier (QSVC), for the identification of patterns in kinematic data collected from the JIGSAWS dataset which includes 76 kinematic features related to suturing, knot tying and needle passing. In order to evaluate QSVC performance, we compared its performance measures such as accuracy, precision, recall and F1-score with that of a classical Support Vector Classifier (SVC). Quantum kernel-based methods like QSVM embed classical data into high-dimensional Hilbert spaces via quantum feature maps, offering the potential to capture complex data relationships more efficiently. Using ZFeatureMap and quantum circuits implemented in Qiskit, we demonstrate that QSVM shows slight performance advantages over its classical counterpart in specific tasks. These findings lay the groundwork for a context-aware surgical system to support medical practitioners in real time and help advance surgical practice and educational approaches for enhancing patients quality of life.

The authors have withdrawn their manuscript whilst they perform additional experiments to test some of their conclusions further. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

The approach I described is straightforward, related to COVID-19 SARS based tweets and the symptoms, that people tweet about. Also, social media mining for health application reports was shared in many different tasks of 2021. The motto at the back of this observe is to analyses tweets of COVID-19 based symptoms. By performing BERT model and text classification with XLNET with which uses to classify text and purpose of the texts (i.e.) tweets. So that I can get a deep understanding of the texts. When developing the system, I used two models the XLNet and DistilBERT for the text sorting task, but the outcome was XLNET out-performs the given approach to the best accuracy achieved. Now I discover a whole lot vital for as it should be categorizing tweets as encompassing self-said COVID-19 indications. Whether or not a tweets associated with COVID-19 is a non-public report or an information point out to the virus. Which gives test accuracy to an F1 score of 96%.

Multi-drug-resistant Escherichia coli poses a great threat to human health. E. coli is often used as fecal indicator bacteria to study the sources and fate of fecal contamination in the environment. This study focused on water and soil samples in tourist parks around Kadamaian, Kota Belud from three different villages of Kampung Napungguk, Kampung Pinalabuh and Kampung Tinata. From 23 of tested presumptive E. coli isolates, the percentage of antimicrobial-resistant against a series of antibiotic concentrations ranging from 25, 50, 100 to 500 g/ml showed resistance towards Chloramphenicol (91.3%), Penicillin (82.6%), Tetracycline (73.9%) and Kanamycin (30.43%). This finding suggested the presence of multi-drug resistance (MDR) of presumptive E. coli from soil samples of Kampung Napungguk and Kampung Tinata. This study alarms the presence of MDR which need further research on identifying the bacterial strains using molecular methods. The findings can then be used to create awareness of potential disease outbreaks in the tourist areas.

Social media platforms have an increasing influence in biomedical and other disciplines of science and public health. While Twitter has been a popular platform for scientific communication, changes in ownership have led some users to consider migrating to other platforms such as Mastodon. We aimed to investigate how many top-cited scientists are active on these social media platforms, the magnitude of the migration to Mastodon, and correlates of Twitter presence. A random sample of 900 authors was examined among those who are at the top-2% of impact based on a previously validated composite citation indicator using Scopus data. Searches for their personal Twitter accounts were performed in early December 2022, and re-evaluations were performed at 2 weeks, 4 weeks, and 2 months (February 6, 2023). 262/900 (29.1%) of highly-cited scholars had Twitter accounts, and only 9/800 (1%) had Mastodon accounts. Female gender, North American and Australia locations, younger publication age, and clinical medicine or social science expertise correlated with higher percentages of Twitter use. The vast majority of highly-cited author users of Twitter had few followers and tweets. Only 6 had more than 10,000 followers and none had more than 100,000. One limitation of our study is that it is possible that some accounts, especially with Mastodon, could not be detected. However, the study suggests that Twitter remains the preferred social media platform for highly-cited authors, and Mastodon has not yet challenged Twitters dominance. Moreover, most highly-cited scientists with Twitter accounts have limited presence in this medium.

Microblogging platform Twitter allows researchers to showcase their work, receive constructive feedback, find jobs, and build scientific collaborations. While existing literature has analyzed the benefits of Twitter in the development and distribution of scientific knowledge, most of the studies only took into account a limited number of researchers, which affected the generalizability of derived results. Our study analyzed the activity of 6,000 biomedical scientists on Twitter using data-driven approaches, a third of whom were female. Furthermore, we estimated that up to a quarter of the members of the scientific community are engaged on Twitter. While the number of male scientists joining the microblogging platform every year has decreased, the number of female scientists has remained roughly the same. Scientists are very selective in who they are following as compared to the general public. We also found that the type of tweets and retweets one posts may affect the number of followers, specifically, that a moderate to high level of professionalism and a high level of positivity is correlated with followers count. Moreover, female scientists send fewer negative tweets as compared to male scientists (31.1% for females and 34.7% for males). Our analysis could provide insights and launch a conversation on the advantages and limitations of using Twitter for disseminating scientific information and engaging in constructive discussion and collaborations within the scientific community.

ObjectivesProfessional divers are exposed to unique multifactorial hazards in their working environment and cardiovascular effects are tremendous. Cardiovascular events are aggravated by diving-induced oxidative stress and account for one fourth of diving fatalities. The aim of this study was to assess the oxidative and cardiovascular stress in a group of professional divers in Alexandria, Egypt using a panel of biomarkers. MethodsA comparative cross-sectional study was conducted between June 2017 and May 2018 at the General Naval Hospital in Alexandria. We recruited 50 professional divers (n=50) and a comparison group of 50 marine seafarers sharing similar maritime environments. Participants were clinically evaluated by electrocardiography (ECG) and assessment of some trace metals (Fe+, Cu+ and Zn+) and oxidative stress biomarkers (OSBMs; MDA, TAS, GST, GSH, GR, GPx, SOD and CAT). Results: Significant ECG abnormalities including short corrected QT interval, sinus bradycardia, left ventricular hypertrophy, early repolarization, 1st degree heart block, intraventricular conduction defect were obvious among divers. The mean levels of FBG, LDH-C, Na+, Ca+, trace metals and OSBMs were significantly higher among professional divers compared to the marine seafarers (p < 0.5). ConclusionThe risk of CVD in professional divers is alarmingly high and possibly contributed by OS. Including cardiovascular risk assessment in divers periodic examinations is of utmost importance.

BackgroundInfertility is a global health issue that affects millions of couples worldwide, In Nigeria, the prevalence of infertility is particularly high underscoring the need for a better understanding of the factors contributing to male infertility at which Seminal Fluid Analysis (SFA) is the most important characteristics. This study aimed to assess the prevalence of abnormal seminal fluid and its associated factors among patients attending fertility clinics in Osogbo, Nigeria. Methods and FindingsA descriptive cross-sectional study design was used among male patients attending fertility centers in Osogbo, Osun State. Fishers formula (n=z2pq/d2) was used to determine the sample size. 305 respondents were selected using multistage sampling technique. Pre-tested questionnaire was used to collect data from respondents. Patients who consented to the study were also made to undergo seminal fluid analysis. The results were analysed using IBM Statistical Product for Service Solution (SPSS) version 27. Descriptive statistics was used for all variables. Bivariate and multivariate analysis were done at p<0.05 as level of significance. Majority of the respondents, 257 (84.3%) had at least one abnormality of Seminal fluid. Multiple regression analysis revealed that respondents who were habitual drunkard were about five times more likely to have at least one abnormality in their seminal fluid (OR: 4.990, p: 0.004, C.I.: 1.688-14.749) and smokers were three times more likely to have at least one abnormality in their seminal fluid (p=0.005, OR=3.300 and C.I=1.396-4.273). Also, respondents with history of sexually transmitted infection were 3.5 times more likely to have at least one abnormality in their seminal fluid (p=0.039, OR=3.595 and C.I=1.072-14.146). ConclusionsThe study observed high prevalence of abnormal seminal fluid which was significantly associated with lifestyle habits such as smoking, alcohol as well as previous history of sexually transmitted infection. The study recommended that advocacy program for healthy lifestyle, early screening and public health education will further reduce the burden of abnormal seminal fluid and by implication, infertility among couples.

Osun-Osogbo Grove has a long history of healing and therapeutic claims by adherent believers, in spite of advancement in medicine. Scientists made attempts at investigating the biodiversity of the Grove, till date, there has not been convergence point between science and these indigenous beliefs. This study identified the presence of therapeutic agents in the water of Osun-Osogbo River, paying attention to at least six parameters; vitamin, phosphate, nitrate, amino acid, hormone and trace metal. Water samples were taken from two different sites during pre, during and post raining sessions (April 2017 - September 2019) were analysed using High Performance Liquid Chromatography (HPLC), Gas Chromatography Mass Spectroscopy (GC-MS) and Atomic Absorption Spectrometer (AAS). Trace metal analysis revealed an average of 0.009-0.079 mg/Kg Zinc from site one and lower in site two. The mean value of manganese at both sites was virtually the same at 0.018-0.313 mg/kg, aluminum content was 0.045-0.179mg/Kg at site one, 0.050-0.192mg/kg at site two, cobalt was 0.024 mg/kg at site one, 0.026 mg/kg at site two while nickel was 0.006 mg/kg and 0.004 mg/kg for site one and two respectively. HPLC analysis shows mean Methionine content at both sites is higher than the FDA (56.6 ug/mL); site one had 74.41 ug/mL while site two had 57.11 ug/mL The mean values of two water-soluble vitamins; Thiamine (B1) was 3.758 mg/Kg and 2.355 mg/Kg while Pyridoxine (B6) was 0.108 mg/Kg and 0.072 mg/Kg at site one and two. GCMS analysis of steroidal content revealed values below lowest observed effect level (LOEL), testosterone (4.8 ng/L) and estrogen (2.4 ng/L) were still elevated while ethinylestradiol and estriol were [&ge;]1.5 ng/L. Summarily, site one the major part for spiritual activities showed higher essential nutrient contents than site two which support the enrichment and potential therapeutic properties of the Osun river water. However, further scientific research is required to ensure that these therapeutic potentials supersede the toxicological effect.

Male infertility is one of the major health and social problems in Iraq. It can be caused by several factors, such as acquired, environmental, and genetic factors. Awareness of the crucial role of telomeres and mitochondria in sperm production and fertility has increased in recent years. This study aims to evaluate the relationship between mitochondrial DNA (mtDNA) copy number and telomere length in sperm and the degree of infertility in Iraqi men. Of the 200 study participants, 50 were healthy controls and 150 were infertile. Sperm count, motility, and morphology were assessed by collecting and analyzing semen samples. The mitochondrial gene ND1 and the nuclear reference gene GAPDH were analyzed by quantitative PCR (qPCR) to determine the mtDNA copy number, while another qPCR analysis was used to determine telomere length. The mtDNA copy number of infertile men was significantly higher than that of healthy controls, with a p-value of 0.001. In addition, sperm from the patient group showed a significant reduction in telomere length (p = 0.001). According to the study results, male infertility in Iraqi men is associated with a higher mtDNA copy number and shorter telomere length. DNA damage or disruption of the mitochondrial energy production pathway could be the cause of this association. These results highlight the importance of conducting further investigations to validate these findings and understand the underlying biological pathways, as well as to open new research perspectives in the field of male infertility. HighlightsO_LIInfertile men have altered mtDNA copy number compared to healthy ones. C_LIO_LITelomere length is a biomarker of aging and reproductive health. C_LIO_LIOxidative stress and impaired spermatogenesis are thought to contribute to telomere shortening. C_LIO_LIShorter telomere length is associated with decreased sperm quality and fertility. C_LI

The growing exposure to occupational chemicals and HIV infection are both major global health issues. However, there is little data on the carcinogenic risk profile of HIV-infected individuals who have been occupationally exposed to chemical mixtures. This study therefore investigated the levels of cancer risk biomarkers in HIV-infected individuals exposed to occupational chemicals, exploring the relationship between apoptotic regulatory markers and DNA oxidative response markers as a measure of cancer risk. Apparently healthy adults (mean age 38.35{+/-}0.72years) were divided into four groups according to their HIV status and occupational chemical exposure: 62 HIV positive exposed (HPE), 66 HIV positive unexposed (HPU), 60 HIV negative exposed (HNE), and 60 HIV negative unexposed (HNU). Serum p53, bcl2, 8-hydroxydeoxyguanosine (8-OHdG), superoxide dismutase (SOD), and malondialdehyde (MDA) were estimated by standard methods. Blood samples were analysed for CD4 cell count by flow cytometry. Serum p53 and bcl2 levels in HPE (0.91{+/-}0.11ng/ml and 122.37{+/-}15.77ng/ml) were significantly lower than HNU (1.49{+/-}0.15ng/ml and 225.52{+/-}33.67ng/ml) (p < 0.05), respectively. Wildtype p53 and bcl2 were positively and significantly correlated with 8-OHdG (r=0.35, p<0.001; r=0.36, p<0.001) and SOD (r=0.38, p<0.001; r=0.39, p<0.001). After controlling for gender, age, BMI, and cigarette smoking, both HIV status and SOD activity were significantly associated with wildtype p53 and bcl2 (p < 0.05). Malondialdehyde was significantly higher in the HPE (0.72 {+/-} 0.01 mg/ml) than in the HNE (0.68 {+/-} 0.01 mg/ml) and HNU (0.67 {+/-} 0.01 mg/ml) groups (p < 0.05). Additionally, the HPE group (578.87{+/-}33.64 cells/{micro}L) exhibited significantly lower CD4 counts than the HNE (785.35{+/-}36.8 cells/{micro}L) and HNU (862.15{+/-}43.29 cells/{micro}L) groups. Individuals infected with HIV and occupationally exposed to chemical substances exhibit compromised immunity, elevated oxidative stress, and depressed p53 (loss of tumour suppressive capacity) and bcl2; a convergence promoting the carcinogenic pathway and elevated cancer risk. These findings provide a mechanistic basis of cancer risk and scientific justification for preventive strategies against carcinogenesis in individuals who are HIV-infected.

The present study summarizes the research productivity and international collaboration in aquatic studies conducted by Sri Lankan scholars during 2000-2019. The study was based on the SCOPUS(R) database. R programming language, package bibliometrix and Vosviewer software were employed in the analysis. Results of the present study indicate that increasing growth trend in the annual number of publications. A significant correlation (p<0.05) between the number of articles and per capita GDP (Gross Domestic Production) was also observed. Senior authors dominated in terms of the article count, citation count, h index, and other author productivity indices. Journal of the National Science Foundation of Sri Lanka had the highest article count (n=33). Aquatic studies in Sri Lanka were more locally funded. Sri Lanka had strong research collaborations with Japan, South Korea and Australia. During 2000-2019, the transition of aquatic studies from lacustrine field studies to molecular lab-based studies were observed. The findings of the present study may provide a comprehensive understanding on the current context and future directions of aquatic studies in Sri Lanka.

In the field of education, generative artificial intelligence has a profound impact, mainly reflected in areas such as personalized learning, automated assignment evaluation, content generation, and open educational resources. However, some scholars have begun to discuss whether this technology may lead to new inequalities. Therefore, this study aims to explore how user background affects the performance of generative artificial intelligence technology through empirical research while focusing on the potential educational inequalities that may arise in the era of generative AI. The research found that generative artificial intelligence literacy is correlated with grade level, institutional tier, and academic discipline, with higher-grade students, those from prestigious universities, and those studying Applied Sciences and Natural Sciences demonstrating better performance.

The Scimago Journal Rank (SJR) is a metric that captures the centrality of a journal across an all-discipline article network, while the impact factor (IF) is the average incoming citations of a journal. We analyzed SJRs and IFs of the journals belonging to the SJR first quartile from 2013 to 2020 in 7 disciplines: mathematics, biology, physics, medicine, social sciences, chemistry, and engineering. We show that biology is the most central discipline, followed by physics and chemistry. These three disciplines also present the highest IFs. Mathematics journals display a low IF (the second-lowest among disciplines), but possesses an intermediate centrality. While the average IF has increased over the last years, the SJR average has decreased. Gini coefficients show that SJR is a slightly more egalitarian metric than IF. We discuss some possible origins of these findings.