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This study examines the social media uptake of scientific journals on two different platforms - X and WeChat - by comparing the adoption of X among journals indexed in the Science Citation Index-Expanded (SCIE) with the adoption of WeChat among journals indexed in the Chinese Science Citation Database (CSCD). The findings reveal substantial differences in platform adoption and user engagement, shaped by local contexts. While only 22.7% of SCIE journals maintain an X account, 84.4% of CSCD journals have a WeChat official account. Journals in Life Sciences & Biomedicine lead in uptake on both platforms, whereas those in Technology and Physical Sciences show high WeChat uptake but comparatively lower presence on X. User engagement on both platforms is dominated by low-effort interactions rather than more conversational behaviors. Correlation analyses indicate weak-to-moderate relationships between bibliometric indicators and social media metrics, confirming that online engagement reflects a distinct dimension of journal impact, whether on an international or a local platform. These findings underscore the need for broader social media metric frameworks that incorporate locally dom

Malaria can be diagnosed by the presence of parasites and symptoms (usually fever) due to the parasites. In endemic areas, however, an individual may have fever attributable either to malaria or to other causes. Thus, the parasite level of an individual with fever follows a two-component mixture, with the two components corresponding to malaria and nonmalaria individuals. Furthermore, the parasite levels of nonmalaria individuals can be characterized as a mixture of a zero component and a positive distribution. In this article, we propose a nonparametric maximum multinomial likelihood approach for estimating the proportion of malaria using parasite-level data from two groups of individuals collected in two different seasons. We develop an EM-algorithm to numerically calculate the proposed estimates and further establish their convergence rates. Simulation results show that the proposed estimators are more efficient than existing nonparametric estimators. The proposed method is used to analyze a malaria survey data.

Malaria remains a serious health challenge in the Comoros Islands, despite ongoing control efforts. Past studies have shown reductions in cases due to prevention and treatment measures, but little work has been done to forecast future malaria deaths and assess the long-term impact of these measures. Malaria mortality data from 1990 to 2019 were analyzed using an ARIMA(1,0,0) model. The model was validated through diagnostic tests, ensuring reliability for forecasting trends. The study confirmed significant reductions in malaria cases, such as in Grand Comoro, where cases fell from 235.36 to 5.47 per 1,000 people. The ARIMA model predicted that fatalities will remain low if current control measures, including bed nets, indoor spraying, and mass drug administration, are sustained. The findings highlight the success of these interventions in reducing malaria mortality. However, challenges like drug and insecticide resistance and financial limitations pose risks to further progress. Continued support and adaptation of strategies are essential to address these challenges and sustain low malaria mortality rates. The study demonstrates the effectiveness of malaria control efforts in the C

We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science and Social Science Citation Indexes (SCI and SSCI) with similar data based on Scopus 2012. First, global maps were developed for the two sets separately; sets of documents can then be compared using overlays to both maps. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the two sets; that is, 96.4% of the 10,936 journals contained in JCR or 51.2% of the 20,554 journals covered by Scopus. Network analysis was then pursued on the set of journals shared between the two databases and the two sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (that is, numbers of citing journals) or total citations is similar in both databases overall (Spearman's \r{ho} > 0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important--they are citing shared journals rather than bein

Overlay journals are characterised by their articles being published on open access repositories, often already starting in their initial preprint form as a prerequisite for submission to the journal prior to initiating the peer-review process. In this study we aimed to identify currently active overlay journals and examine their characteristics. We utilised an explorative web search and contacted key service providers for additional information. The final sample consisted of 34 overlay journals. While the results show that new overlay journals have been actively established within recent years, the current presence of overlay journals remains diminutive compared to the overall number of open access journals. Most overlay journals publish articles in natural sciences, mathematics or computer sciences, and are commonly published by groups of academics rather than formal organisations. They may also rank highly within the traditional journal citation metrics. None of the investigated journals required fees from authors, which is likely related to the cost-effective aspects of the overlay publishing model. Both the growth in adoption of open access preprint repositories and researcher

Plasmodium falciparum is responsible for the majority of malaria morbidity and mortality each year. Malaria transmission rates vary by location and time of year due to climate and environmental conditions. We show the impact of these factors by developing a stochastic spatiotemporal agent-based malaria model that captures the impact of spatially distributed interventions on malaria transmission. Our model uses spatiotemporal estimates of mosquito climatic suitability and household location data to model the interaction between human and mosquito agents. We apply our model to investigate how strategies for distributing interventions to households in Vietnam impact the disease burden. Our study shows that providing some level of protection to a wide range of households reduces malaria prevalence more compared to providing a strong level of protection to a limited number of households.

The fourth Industrial Revolution(4IR), together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such as malaria for more efficient treatment and control. It has been seen that malaria control would benefit a lot from digitizing its diagnosis processes such as data gathering. We propose, in this paper, the architecture of a digital data collection system and how it is used to gather data for malaria awareness. The system is formally specified using Z notation, and based on the capability of the system, possible malaria determinants are defined and their retrieving mechanisms are discussed.

Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa. Nonetheless, efforts are ongoing, and significant progress has been made. In Burundi, malaria is among the main public health concerns. In the literature, there are limited prediction models for Burundi. We know that such tools are much needed for interventions design. In our study, we built machine-learning based models to estimates malaria cases in Burundi. The forecast of malaria cases was carried out at province level and national scale as well. Long short term memory (LSTM) model, a type of deep learning model has been used to achieve best results using climate-change related factors such as temperature, rainfal, and relative humidity, together with malaria historical data and human population. With this model, the results showed that at country level different tuning of parameters can be used in order to determine the minimum and maximum expected malaria cases. The univariate version of that model (LSTM) which learns from previous dynamics of malaria cases give more precise estimates at province-level, but both models have same trends overall at provnce-level an

The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites of infected female mosquitoes, occurs in 91 countries but about 90% of the cases and deaths are in sub-Saharan Africa. The disease killed 4,35,000 people last year, the majority of them children under five in Africa. AI-backed technology has revolutionized malaria detection in some regions of Africa and the future impact of such work can be revolutionary. The malaria Cell Image Data-set is taken from the official NIH Website NIH data. The aim of the collection of the dataset was to reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy using an AI-based algorithm to detect and segment the red blood cells. The goal of this work is to show that the state of the art accuracy can be obtained even by using 2 layer convolution network and show a new baseline in Malaria detection efforts using AI.

Predatory journals are Open Access journals of highly questionable scientific quality. Such journals pretend to use peer review for quality assurance, and spam academics with requests for submissions, in order to collect author payments. In recent years predatory journals have received a lot of negative media. While much has been said about the harm that such journals cause to academic publishing in general, an overlooked aspect is how much articles in such journals are actually read and in particular cited, that is if they have any significant impact on the research in their fields. Other studies have already demonstrated that only some of the articles in predatory journals contain faulty and directly harmful results, while a lot of the articles present mediocre and poorly reported studies. We studied citation statistics over a five-year period in Google Scholar for 250 random articles published in such journals in 2014, and found an average of 2,6 citations per article and that 60 % of the articles had no citations at all. For comparison a random sample of articles published in the approximately 25,000 peer reviewed journals included in the Scopus index had an average of 18,1 cit

An exploratory, descriptive analysis is presented of the national orientation of scientific, scholarly journals as reflected in the affiliations of publishing or citing authors. It calculates for journals covered in Scopus an Index of National Orientation (INO), and analyses the distribution of INO values across disciplines and countries, and the correlation between INO values and journal impact factors. The study did not find solid evidence that journal impact factors are good measures of journal internationality in terms of the geographical distribution of publishing or citing authors, as the relationship between a journal's national orientation and its citation impact is found to be inverse U-shaped. In addition, journals publishing in English are not necessarily internationally oriented in terms of the affiliations of publishing or citing authors; in social sciences and humanities also USA has their nationally oriented literatures. The paper examines the extent to which nationally oriented journals entering Scopus in earlier years, have become in recent years more international. It is found that in the study set about 40 per cent of such journals does reveal traces of internati

Malaria remains a significant public health challenge in many regions, necessitating robust predictive models to aid in its management and prevention. This study focuses on developing and evaluating time series models for forecasting malaria cases across eight Indian states: Jharkhand, Chhattisgarh, Maharashtra, Meghalaya, Mizoram, Odisha, Tripura, and Uttar Pradesh. We employed various modeling approaches, including polynomial regression with seasonal components, log-transformed polynomial regression, lagged difference models, and ARIMA models, to capture the temporal dynamics of malaria incidence. Comprehensive model fitting, residual analysis, and performance evaluation using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) indicated that the log-transformed polynomial regression model consistently outperformed other models in terms of accuracy and robustness across all states. Rolling forecast validation further confirmed the superior predictive capability of the log-transformed model over time. Additionally, an interactive R Shiny tool was developed to facilitate the use of these predictive models by researchers and public health officia

Rankings of scholarly journals based on citation data are often met with skepticism by the scientific community. Part of the skepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of authors. This paper focuses on analysis of the table of cross-citations among a selection of Statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care in order to avoid potential over-interpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's Research Assessment Exercise shows strong correlation at aggregate level between assessed research quality and journal citation `export scores' within the discipline of Statistics.

Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time, it still remains a difficult challenge. Computer-assisted diagnostics are on the rise these days as they can be used effectively as a primary test in the absence of or providing assistance to a physician or pathologist. The purpose of this paper is to describe an approach to detecting, localizing and counting parasitic cells in blood sample images towards easing the burden on healthcare workers.

A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called "Web-of-Science Subject Categories" (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals = 11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal-journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem

Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Since the Scopus database contains a larger number of journals and covers also the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is due to (i) the larger number of journals covered by Scopus and (ii) the historical record of citations older than ten years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.

The launch of Google Scholar Metrics as a tool for assessing scientific journals may be serious competition for Thomson Reuters Journal Citation Reports, and for Scopus powered Scimago Journal Rank. A review of these bibliometric journal evaluation products is performed. We compare their main characteristics from different approaches: coverage, indexing policies, search and visualization, bibliometric indicators, results analysis options, economic cost and differences in their ranking of journals. Despite its shortcomings, Google Scholar Metrics is a helpful tool for authors and editors in identifying core journals. As an increasingly useful tool for ranking scientific journals, it may also challenge established journals products

Using "Analyze Results" at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy." Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.'s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal lit

Malaria is a disease of global concern according to the World Health Organization. Billions of people in the world are at risk of Malaria today. Microscopy is considered the gold standard for Malaria diagnosis. Microscopic assessment of blood samples requires the need of trained professionals who at times are not available in rural areas where Malaria is a problem. Full automation of Malaria diagnosis is a challenging task. In this work, we put forward a framework for diagnosis of malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, crop them out, and feed the cropped cells to a seperate neural network for classification. The proposed methodology was tested on an openly available dataset, this will serve as a baseline for the future methods as currently there is no common dataset on which results are reported for Malaria Diagnosis.