Abstract Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
The Zwicky Transient Facility (ZTF) is a new robotic time-domain survey currently in progress using the Palomar 48-inch Schmidt Telescope. ZTF uses a 47 square degree field with a 600 megapixel camera to scan the entire northern visible sky at rates of ∼3760 square degrees/hour to median depths of g ∼ 20.8 and r ∼ 20.6 mag (AB, 5σ in 30 sec). We describe the Science Data System that is housed at IPAC, Caltech. This comprises the data-processing pipelines, alert production system, data archive, and user interfaces for accessing and analyzing the products. The real-time pipeline employs a novel image-differencing algorithm, optimized for the detection of point-source transient events. These events are vetted for reliability using a machine-learned classifier and combined with contextual information to generate data-rich alert packets. The packets become available for distribution typically within 13 minutes (95th percentile) of observation. Detected events are also linked to generate candidate moving-object tracks using a novel algorithm. Objects that move fast enough to streak in the individual exposures are also extracted and vetted. We present some preliminary results of the calibration performance delivered by the real-time pipeline. The reconstructed astrometric accuracy per science image with respect to Gaia DR1 is typically 45 to 85 milliarcsec. This is the RMS per-axis on the sky for sources extracted with photometric S/N ≥ 10 and hence corresponds to the typical astrometric uncertainty down to this limit. The derived photometric precision (repeatability) at bright unsaturated fluxes varies between 8 and 25 millimag. The high end of these ranges corresponds to an airmass approaching ∼2—the limit of the public survey. Photometric calibration accuracy with respect to Pan-STARRS1 is generally better than 2%. The products support a broad range of scientific applications: fast and young supernovae; rare flux transients; variable stars; eclipsing binaries; variability from active galactic nuclei; counterparts to gravitational wave sources; a more complete census of Type Ia supernovae; and solar-system objects.
With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic aspects of MBD itself is also rare. This study aims to explore the current status of medical big data through visualization analysis on the journal papers related to MBD. We analyze a total of 988 references which were downloaded from the Science Citation Index Expanded and the Social Science Citation Index databases from Web of Science and the time span was defined as “all years”. The GraphPad Prism 5, VOSviewer and CiteSpace softwares are used for analysis. Many results concerning the annual trends, the top players in terms of journal and institute levels, the citations and H-index in terms of country level, the keywords distribution, the highly cited papers, the co-authorship status and the most influential journals and authors are presented in this paper. This study points out the development status and trends on MBD. It can help people in the medical profession to get comprehensive understanding on the state of the art of MBD. It also has reference values for the research and application of the MBD visualization methods.
Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.
As big data, open data, and open science advance to increase access to complex and large datasets for innovation, discovery, and decision-making, Indigenous Peoples’ rights to control and access their data within these data environments remain limited. Operationalizing the FAIR Principles for scientific data with the CARE Principles for Indigenous Data Governance enhances machine actionability and brings people and purpose to the fore to resolve Indigenous Peoples’ rights to and interests in their data across the data lifecycle.
Feminism, at its very core, aims to dismantle systems of oppression; however, the identification of which systems are oppressive and what kinds of beings are harmed by them has been the subject of debate in feminist circles for more than a century. Across the many waves of feminist movements and throughout the halls of humanities and social sciences departments around the world, feminist thought and feminist practices are heavily contested and often come into conflict. From difference and discord arise efforts to make both feminism and the world a more inclusive and just place. Yet there remain strong tensions over how to define feminism, how to realize feminists’ demands, how to apply feminist theory to a wide variety of subject matter, and how to bridge the gaps between theory and practice to build a better world for all. In an ambitious attempt to resolve some of those tensions in the field of data science, Catherine D’Ignazio and Lauren F. Klein’s 2021 book, Data Feminism, poses seven principles and strategies that are worthy of examination for those of us who might not hold the title of “data scientist,” but work with data nonetheless. As geographers and geography departments engage in efforts to improve our approaches to justice and equity, the principles of data feminism can be effective tools to guide our discussions for how to integrate feminism into our research practices and pedagogy, as well for how we apply geography in the public sphere.
The Tara Oceans expedition (2009–2013) sampled contrasting ecosystems of the world oceans, collecting environmental data and plankton, from viruses to metazoans, for later analysis using modern sequencing and state-of-the-art imaging technologies. It surveyed 210 ecosystems in 20 biogeographic provinces, collecting over 35,000 samples of seawater and plankton. The interpretation of such an extensive collection of samples in their ecological context requires means to explore, assess and access raw and validated data sets. To address this challenge, the Tara Oceans Consortium offers open science resources, including the use of open access archives for nucleotides (ENA) and for environmental, biogeochemical, taxonomic and morphological data (PANGAEA), and the development of on line discovery tools and collaborative annotation tools for sequences and images. Here, we present an overview of Tara Oceans Data, and we provide detailed registries (data sets) of all campaigns (from port-to-port), stations and sampling events. Design Type(s) observation design • global survey Measurement Type(s) Registry Technology Type(s) Written Documentation Factor Type(s) Sample Characteristic(s) Bay of Biscay • Strait of Gibraltar • Mediterranean Sea • Mediterranean Sea, Western Basin • Ligurian Sea • Tyrrhenian Sea • Ionian Sea • Adriatic Sea • Mediterranean Sea, Eastern Basin • Red Sea • Arabian Sea • Indian Ocean • Mozambique Channel • Southeast Atlantic Ocean • South Atlantic Ocean • Southwest Atlantic Ocean • Drake Passage • South Pacific Ocean • Equatorial Pacific Ocean • North East Pacific Ocean • Gulf of Mexico • Florida Straits • NW Atlantic Ocean • North Atlantic Ocean • NE Atlantic Ocean • Norwegian Sea • Kara Sea • Arctic Ocean • Beaufort Sea • Northwest Passages • Baffin Bay • marine biome Design Type(s) observation design • global survey Measurement Type(s) Registry Technology Type(s) Written Documentation Factor Type(s) Sample Characteristic(s) Bay of Biscay • Strait of Gibraltar • Mediterranean Sea • Mediterranean Sea, Western Basin • Ligurian Sea • Tyrrhenian Sea • Ionian Sea • Adriatic Sea • Mediterranean Sea, Eastern Basin • Red Sea • Arabian Sea • Indian Ocean • Mozambique Channel • Southeast Atlantic Ocean • South Atlantic Ocean • Southwest Atlantic Ocean • Drake Passage • South Pacific Ocean • Equatorial Pacific Ocean • North East Pacific Ocean • Gulf of Mexico • Florida Straits • NW Atlantic Ocean • North Atlantic Ocean • NE Atlantic Ocean • Norwegian Sea • Kara Sea • Arctic Ocean • Beaufort Sea • Northwest Passages • Baffin Bay • marine biome Machine-accessible metadata file describing the reported data (ISA-Tab format)
Abdul Khair, Bambang Widjanarko Otok, Noraida Noraida
et al.
Tanah Bumbu Regency has the highest rate of malaria in South Kalimantan Province. Due to the non-linear fluctuations in malaria cases by ethnicity, a hybrid model combining Autoregressive Integrated Moving Average (MARS ARIMA) and Multivariate Adaptive Regression Splines was proposed for time series forecasting. The purpose of this study is to use the MARS ARIMA hybrid model to predict malaria cases by ethnicity in Tanah Bumbu Regency. The findings demonstrate that the best inputs for MARS modeling are significant lags found using ACF and PACF. The hybrid MARS ARIMA model performs better than standalone ARIMA or MARS models, according to predictions. Key findings show that the number of patients over 35 during the preceding two periods influences increases in malaria cases for the Banjar ethnic group. Cases exceeding 13 in two prior periods and 19 in one prior period are associated with increases for the Javanese group. Cases of more than two or fewer than two in the preceding two periods and more than eleven in one preceding period have an impact on increases among the Bugis. Prior cases below 26 have an impact on Banjar case declines, whereas prior cases below 13 and above 3 have a significant impact on Javanese case declines. This study demonstrates how well the MARS ARIMA hybrid model predicts malaria cases according to ethnicity.
BackgroundTo systematically review the incidence and risk factors of oral mucosal pressure injury in patients with oral tracheal intubation.MethodsTwo researchers independently searched eight databases, PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), WanFang Database, VIP Database and China BioMedical Literature Database (CBM) from inception to February 27th, 2026. Data analyses were performed using Stata 18.0 software.ResultTwenty-four studies (9,904 patients) were included in the meta-analysis. The incidence of oral mucosal pressure injury in patients undergoing oral tracheal intubation was 29%. Risk factors include higher APACHE II score[OR 1.32, 95%CI:1.19–1.50], diabetes [OR 5.31, 95%CI: 1.88–14.87], serum albumin [OR0.37,95%CI:0.2–0.68], use of anticoagulants [OR 1.68, 95%CI: 1.02–2.77], use of sedative drugs [OR 3.35, 95%CI: 2.16–5.21], use of vasoactive drugs [OR 1.55, 95%CI: 1.25–1.92], prone position ventilation [OR 3.94, 95%CI: 2.64–5.93], prolonged tracheal intubation indwelling time [OR 1.08, 95%CI: 1.05–1.12], use of hard dental pads [OR 3.22, 95%CI: 2.25–4.66], tracheal intubation model [OR 2.72, 95%CI: 1.62–4.62].ConclusionThe incidence of oral mucosal pressure injury is relatively high in patients with oral tracheal intubation, and there are many risk factors. Nursing staff should enhance their awareness of oral mucosal pressure injury in patients with oral tracheal intubation. They should accurately identify high-risk groups at an early stage based on risk factors and formulate targeted and personalized preventive measures to reduce the risk of injury.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251062658, identifier PROSPERO (CRD420251062658).
We present a comprehensive 3D dust-reddening map covering the entire Milky Way, constructed by combining reddening estimates based on Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) low-resolution spectra ( E ( B − V ) _LAMOST ) with those derived from Gaia XP spectra ( E ( B − V ) _XP ), along with revised Gaia distances. E ( B − V ) _LAMOST values of ∼4.6 million unique sources were obtained with the standard-pair analysis using LAMOST DR11 stellar parameters and synthesized B- / V -band photometry from Gaia XP spectra, showing a typical precision of ∼0.01 mag. The E ( B − V ) _XP from the catalog of X. Zhang et al., which was derived using forward modeling of Gaia XP spectra, were cross-validated with E ( B − V ) _LAMOST , leading to the selection of ∼150 million high-reliability measurements. The combined data set achieves a median precision of ∼0.03 mag for E ( B − V ). To model the reddening–distance relationship along various lines of sight, we implemented a parametric approach that accounts for contributions from the Local Bubble, diffuse interstellar medium, and multiple potential molecular clouds. The sky was adaptively partitioned based on stellar density, resulting in angular resolutions ranging from 3 $\mathop{.}\limits{^{\prime} }$ 4 to 58′, with about half of the sky having a resolution better than 6 $\mathop{.}\limits{^{\prime} }$ 9. The reddening precision of our 3D map for individual stars reaches ∼0.01 mag in most regions at ∣ b ∣ > 20°, but degrades to 0.01–0.05 mag at ∣ b ∣ < 20°. The map reaches a maximum distance of 3–5 kpc in high-extinction regions with ∣ b ∣ < 5°, and extends to 10–15 kpc elsewhere. An interactive platform and Python package have been developed for utilization of the 3D dust map.
Abstract Students’ reasoning and decision making on complex socioscientific issues are critical for developing scientific literacy for 21st century citizenship. By incorporating a scenario-based approach, this study aims to understand the complexity of students’ decision making on environmental issues: forests and land use. To help students grasp the context of these issues, we developed scenarios reflecting their experiences and understanding of forests within local communities. Through scenario-based surveys, students in Grade 5–6 science classrooms were encouraged to explore diverse stakeholders’ perspectives and articulate their decisions regarding the scenarios. Additionally, students in focus groups participated in semi-structured discussions and interviews. The data collected from the surveys and students’ dialogues were thematically analyzed. The study found that students prioritized environmental concerns, demonstrated skepticism toward politicians’ perspectives, and emphasized righteousness in their decision making. These findings suggest that a holistic approach is essential to engage students’ diverse perspectives in socioscientific and environmental problem solving. However, this also highlights the ongoing challenge of disciplinary boundaries within school curricula and pedagogical practices in science classrooms.
Hassan Mishmast Nehi, Faranak Hosseinzadeh Saljooghi, Amir Rahimi
et al.
Data Envelopment Analysis with inaccurate data poses a significant challenge in data science and analytics due to the inherent uncertainties and discrepancies present in real-world data. This article investigates the performance of units evaluated with inaccurate data and presents modeling approaches, including fuzzy and interval methodologies. In other words, by examining the effectiveness of units evaluated with interval data with fuzzy or interval-based bounds, novel approaches for modeling data coverage issues are introduced. Various mathematical techniques and analytical processes are utilized to solve problems and prove theorems. The primary focus is on modeling data coverage issues with fuzzy or interval bounds, which facilitates the creation of more accurate and effective representations of uncertain data. The findings of this article indicate that these modeling approaches lead to improvements in data-driven decision-making. Practical applications of these methods include information management and decision-making for DMU sets in fuzzy and interval environments, enabling analysts to make better decisions. This research contributes to advancing the field of data analytics by providing systematic methods for managing and analyzing inaccurate data, thereby enhancing the reliability and applicability of insights based on data foundations.
Richard Katuramu, Joseph KB Matovu, Joseph Kirabira
et al.
Abstract Purpose Despite the availability of antiretroviral therapy (ART), nearly a quarter of people living with HIV (PLHIV) die within six months after hospital discharge due to complications from AIDS-related illnesses. Timely linkage to ART clinics after hospital discharge is crucial in reducing this mortality. We performed a systematic review to collate the available evidence on the strategies used to improve linkage to ART clinics after hospital discharge to inform future interventions. Methods and materials We systematically searched PUBMED, web of science, google scholar, embase and cochrane central for randomized controlled trials and quasi-experimental intervention trials conducted from January 2006 to December 2024, involving PLHIV aged 18 years and above in Low and Middle Income Countries (LMICs). Studies were included if they: (i) collected data in or after 2006, (ii) used randomized controlled trials (RCTs) or quasi-experimental prospective designs with a control group, (iii) reported at least one of several potential outcomes related to linkage to HIV ART clinics, and (iv) reported at least one strategy used to link PLHIV to HIV care after hospital discharge. Risk of bias was assessed using the Cochrane “risk of bias” tool for RCTs and the ROBINS-I tool for non-randomized studies of interventions. We used a narrative synthesis of articles to describe the different strategies used to enhance linkage to HIV ART clinics after hospital discharge. Results From the initial pool of 3003 articles, nine papers were independently reviewed and four (4) met the inclusion criteria. All the studies were conducted between 2011 and 2024 and comprised three RCTs and one quasi-experimental study. All the articles exhibited a low risk of bias. Strategies used to improve linkage to HIV care ART clinics included use of mobile phone appointment reminders, patient health education during hospitalization, multiple counseling sessions after hospital discharge, and the use of incentives such as food parcels. Conclusion Only few studies from LMICs have investigated strategies for linkage to ART clinics among PLHIV after hospital discharge. All the identified studies had more than one strategy applied. Further implementation research is recommended to explore context-specific strategies and strengthen the evidence base for improving linkage to ART clinics following hospital discharge. Review registration The protocol for this review was prespecified and published in PROSPERO (registration number (CRD42018110036).
Abstract Global Navigation Satellite Systems (GNSS) is able to achieve centimeter-level accuracy in open-sky areas. However, their performance declines in urban canyons and outdoor shadow areas. Conversely, commercial Fifth Generation Mobile Communications Technology (5G) New Radio (NR) signals, with their wider bandwidth and shorter wavelengths, offer better range accuracy. To enhance positioning accuracy in challenging environments, we developed a deeply integrated method to combine commercial 5G NR signals with the GNSS. This method involves three key steps: Firstly, we use the Secondary Synchronization Signal to aid the Demodulation Reference Signal (SA-DMRS) in the 5G NR synchronization channel, which aims to improve the tracking loop robustness. Secondly, a Phase-Stabilized Kalman Filter (PSKF) is integrated into the Phase-Locked Loop to boost performance under low Carrier-to-Noise Density Ratio conditions. Lastly, the Extended Kalman Filter (EKF) is applied to fuse 5G and GNSS signals for positioning, and the results are fed back to correct the 5G NR tracking loop. Field tests revealed that SA-DMRS boosted range accuracy by 42.3%, PSKF contributed a further 17% improvement, and GNSS-aided improved the range accuracy by about 33.3%. Compared to the GPS (Global Positioning System)-EKF method, our fusion approach enhances horizontal positioning accuracy by approximately 49.8%, and the vertical positioning accuracy is improved by about 53.3%. Additionally, compared to the GPS-only method, the proposed method can still provide positioning services when there are three usable satellites. Compared with the GNSS-only method, the deep coupled method improved the accuracy in the horizontal and vertical by about 51.2% and 24.0%, respectively. These confirm the method’s effectiveness for accurate and reliable positioning in challenging environments.