Explore the vast range of titles available.

Objective: This review aimed to determine if the use of the patient, intervention, comparison, outcome (PICO) model as a search strategy tool affects the quality of a literature search.Methods: A comprehensive literature search was conducted in PubMed, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, Library and Information Science Abstracts (LISA), Scopus, and the National Library of Medicine (NLM) catalog up until January 9, 2017. Reference lists were scrutinized, and citation searches were performed on the included studies. The primary outcome was the quality of literature searches and the secondary outcome was time spent on the literature search when the PICO model was used as a search strategy tool, compared to the use of another conceptualizing tool or unguided searching.Results: A total of 2,163 records were identified, and after removal of duplicates and initial screening, 22 full-text articles were assessed. Of these, 19 studies were excluded and 3 studies were included, data were extracted, risk of bias was assessed, and a qualitative analysis was conducted. The included studies compared PICO to the PIC truncation or links to related articles in PubMed, PICOS, and sample, phenomenon of interest, design, evaluation, research type (SPIDER). One study compared PICO to unguided searching. Due to differences in intervention, no quantitative analysis was performed.Conclusions: Only few studies exist that assess the effect of the PICO model vis-a-vis other available models or even vis-a-vis the use of no model. Before implications for current practice can be drawn, well-designed studies are needed to evaluate the role of the tool used to devise a search strategy. This article has been approved for the Medical Library Association’s Independent Reading Program.

By studying the handover process in HetNet network scenario with macrocell and picocell, analyzing and simulating the probabilities of successful handover in theory and geometry, respectively.

This study investigates the influence of nozzle opening arc angle on internal flow recirculation and performance of a Banki turbine using two-dimensional CFD. Using ANSYS Fluent with a VOF multiphase formulation and a four-equation SST transition turbulence model, transient simulations were performed on a previously optimized turbine geometry for nozzle arc angles of 50°, 80°, 90°, 100°, and 110°. Mesh sizes ranged ∼60,000–80,000 elements; boundary conditions included a total-pressure inlet (18,688.05 Pa) and pressure outlets representing vertical free-surface and horizontal discharge. Convergence used residuals of 1e-3 with up to 200 iterations per timestep. Results show a clear optimum at a 90° nozzle arc: peak efficiency ≈70.5% at 300 rpm and a predicted maximum of 71.07% near 325 rpm, compared with ∼59.98% for the existing 50° nozzle. Larger arcs increase the number of effective energy-converting blades (from six to eight), but excessive discharge at 110° reduces efficiency. Flow-field analysis attributes the 90° advantage to reduced recirculation eddy viscosity, a more isotropic vortex core, and lower turbulence kinetic energy dissipation near the second-stage blades. The optimized design aligns with several prior studies and narrows the gap with experimental findings (≈2% difference for the 90° case). These CFD results support adopting a 90° nozzle arc to minimize internal recirculation losses and improve Banki turbine performance.

EFSA Strategy 2027 outlines the need for fit‐for‐purpose protocols for EFSA generic scientific assessments to aid in delivering trustworthy scientific advice. This EFSA Scientific Committee guidance document helps address this need by providing a harmonised and flexible framework for developing protocols for EFSA generic assessments. The guidance replaces the ‘Draft framework for protocol development for EFSA's scientific assessments’ published in 2020. The two main steps in protocol development are described. The first is problem formulation, which illustrates the objectives of the assessment. Here a new approach to translating the mandated Terms of Reference into scientifically answerable assessment questions and sub‐questions is proposed: the ‘APRIO' paradigm (Agent, Pathway, Receptor, Intervention and Output). Owing to its cross‐cutting nature, this paradigm is considered adaptable and broadly applicable within and across the various EFSA domains and, if applied using the definitions given in this guidance, is expected to help harmonise the problem formulation process and outputs and foster consistency in protocol development. APRIO may also overcome the difficulty of implementing some existing frameworks across the multiple EFSA disciplines, e.g. the PICO/PECO approach (Population, Intervention/Exposure, Comparator, Outcome). Therefore, although not mandatory, APRIO is recommended. The second step in protocol development is the specification of the evidence needs and the methods that will be applied for answering the assessment questions and sub‐questions, including uncertainty analysis. Five possible approaches to answering individual (sub‐)questions are outlined: using evidence from scientific literature and study reports; using data from databases other than bibliographic; using expert judgement informally collected or elicited via semi‐formal or formal expert knowledge elicitation processes; using mathematical/statistical models; and – not covered in this guidance – generating empirical evidence ex novo. The guidance is complemented by a standalone ‘template’ for EFSA protocols that guides the users step by step through the process of planning an EFSA scientific assessment. This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2022.EN-7349/full

Background Medical decision-making commonly is guided by evidence-based analyses from systematic literature reviews (SLRs). These require large amounts of time and subject matter expertise to perform. Automated extraction of key datapoints from clinical publications could speed up the process of systematic literature review assembly. To this end, we built SURUS, a named entity recognition (NER) system comprised of a Bidirectional Encoder Representations from Transformers (BERT) model trained on a fine-grained dataset. The aim of this study was to assess the quality of SURUS classifications of PICO (patient, intervention, comparator and outcome) and study design elements of clinical study abstracts. Methods The PubMedBERT-based model was trained and evaluated using a dataset of 39,531 labels amongst 400 clinical abstracts, with an inter-annotator agreement of 0.81 (Cohen’s κ) and 0.88 (F1). The labels were manually annotated using a strict annotation guide. We evaluated quality of the dataset and tested the utility of the model in the practise of systematic literature screening, by comparing SURUS predictions to expert PICO and design classifications. Additionally, we tested out-of-domain quality of the model across 7 other therapeutic areas and another study design. Results The SURUS NER system achieved an overall F1 score of 0.95, with minor deviation between labels. In addition, SURUS achieved a NER F1 of 0.90 and 0.84 for out-of-domain therapeutic area and observational study abstracts, respectively. Finally, F1 of PICO and study design classifications was 0.89 with a recall of 0.96 compared to expert classifications. Conclusion The system reaches an F1 score of 0.95 across 25 contextually different medical named entities. This high-quality in-domain medical entity prediction of a fine-tuned BERT-based model was the result of a strict annotation guideline and high inter-annotator agreement. This prediction accuracy was largely preserved during extensive out-of-domain evaluation, indicating its utility across other indication areas and study types. Current approaches in the field lack in the fine-grained training data and versatility demonstrated here. We think that this approach sets a new standard in medical literature analysis and paves the way for creating fine-grained datasets of labelled entities that can be used for downstream analysis outside of traditional SLRs.

Lahars on volcanic terrain are recurrent phenomena with a high capacity to transform landscape and cause significant economic and life losses. Lahars have been studied in most volcanic regions, mostly based on the recognition of cotemporaneous events. Yet, there is a lack of long-term record about their frequency and magnitude. Such long-term records could help to improve lahar risk assessment and understand climate triggering factors. Here, we aim at providing the longest annual resolved frequency-magnitude lahar records of the Trans-Mexican Volcanic Belt. We focus on the Jamapa Gorge of the Pico de Orizaba volcano and apply geomorphological and dendrogeomorphological methodological approaches. Besides, we used a 2D numerical model to estimate the discharge of the reconstructed lahars based on the paleostage indicators (PSIs) defined by scars on trees. A total of 78 Pinus hartwegii trees were sampled for the reconstruction of lahars. We identified 157 growth disturbances related to past lahar activity, namely scars (51%), growth suppression (35%), and compression wood (32%). A total of 8 lahars were reconstructed between 1930 and 2017 (1931, 1960, 1968, 1975, 1999, 2012, 2014, and 2016) with a reconstructed peak discharge ranging between 80 and 350 m3/s. Largest events occurred in 1975 and 2012 with lahar discharges of 160 m3/s and 91 m3/s, respectively. Lahar events were linked to intense rainfall events and the passage of hurricanes and tropical storms coming from the Atlantic Ocean. These results highlight the benefit of the combined use of dendrogeomorphology and numerical modeling to gather longer series of lahar activity in volcanoes, where such information is largely lacking. The methodological approach deployed here can be applied consistently and uniformly in other volcanic regions to characterize lahar hazard, regardless of physiographic and climatic contexts.

Background Identifying confounding variables is fundamental for robust observational studies, yet the traditional manual process is a time-consuming and subjective barrier for researchers. Recent advances in Retrieval-Augmented Generation (RAG) offer a promising solution, but most existing systems rely on full-text access, cloud-hosted APIs, or manually curated knowledge graphs, raising concerns about privacy, copyright, and computational cost, and making local deployment difficult. Objective This study developed and evaluated a heuristic tool to scope candidate confounders for adjustment in observational studies. Using a locally deployed, abstract-only RAG architecture, our tool generates a traceable shortlist of candidate confounders from PICO (Population, Intervention, Comparison, Outcome) queries over medical abstracts. Methods We implemented a three-stage architecture for PICO-based scoping of candidate confounder. The pipeline was deployed on an all-in-one local server and evaluated using 1,000 expert-curated PICO queries spanning 20 clinical specialties. Performance was assessed along four dimensions—internal consistency, output volume, efficiency, and clinical acceptance—by a multi-institutional clinician panel, and was compared with a graph-only SemMedDB baseline. Results Across repeated runs, the pipeline showed high internal consistency (candidate confounder list consistency 94.6%±8.7%; PMID set consistency 79.4%±23.5%). It suggested a median of 6 candidate confounders (IQR 8) for adjustment and retrieved a median of 33 unique PMIDs (IQR 7) per query. Median processing time was 44.50 s (IQR 31.72). Expert review yielded an overall clinical acceptance rate of 87.12%. Conclusions In an exploratory capacity, a locally deployed, abstract-only RAG workflow can generate clinically interpretable and traceable candidate confounder suggestions to support early-stage observational study design, particularly in settings with privacy constraints or limited access to full texts and cloud resources. Trial registration NA.

Indonesia's substantial reliance on fossil fuels for electricity generation underscores an urgent need for sustainable, decentralized energy solutions. Pico-hydro systems, particularly open-channel Banki turbines (OCBTSs), offer a promising alternative due to their simplicity and costeffectiveness. This study presents a comprehensive computational fluid dynamics (CFD) analysis investigating the effect of blade number on OCBT performance. Utilizing a pressure-based solver and the volume-of-fluid method in ANSYS Fluent, we simulated four rotor configurations (28, 32, 36, and 40 blades) across a rotational speed range of 200-500 RPM. A rigorous mesh independence study and validation against experimental data were conducted to ensure reliability. Results demonstrate a critical design trade-off: The 28-blade design achieved a peak efficiency of 81.15% at 400 RPM due to optimal flow admission and minimal interference, whereas the 40-blade configuration suffered a significant 5% reduction in peak efficiency (76.20%) because of pronounced flow blockage and thicker boundary layers, despite offering better low-speed stability. A detailed mechanistic analysis of velocity contours, water volume fraction, and pressure distribution quantitatively revealed that increasing the blade count beyond an optimum leads to a 25-30% increase in flow bypass and a 15% thickening of the boundary layer, which collectively limit performance. A comprehensive comparison with previous studies highlights a fundamental divergence: the optimal blade count for OCBTs (25-28) is lower than for nozzle-equipped turbines (>30), underscoring the critical influence of intake design. These findings provide crucial, novel design guidelines for optimizing OCBTs in low-head, variable-flow environments. Future work should focus on 3D simulations and experimental validation to account for complex secondary flows and hub losses.

Consider a constellation of over a hundred low Earth orbit satellites that aim to capture every point on Earth at least once a day. Clearly, there is a need to download from each satellite a large set of high-quality images on a daily basis. In this paper, we present a laser communication (lasercom) framework that stands as an alternative solution to existing radio-frequency means of satellite communication. By using lasercom, the suggested solution requires no frequency licensing and therefore allows such satellites to communicate with any optical ground station on Earth. Naturally, in order to allow laser communication from a low Earth orbit satellite to a ground station, accurate aiming and tracking are required. This paper presents a free-space optical communication system designed for a set of ground stations and nano-satellites. A related scheduling model is presented, for optimizing the communication between a ground station and a set of lasercom satellites. Finally, we report on SATLLA-2B, the first 300 g pico-satellite with basic free-space optics capabilities, that was launched on January 2022. We conjecture that the true potential of the presented network can be obtained by using a swarm of few hundreds of such lasercom pico-satellites, which can serve as a global communication infrastructure using existing telescope-based observatories as ground stations.

A good understanding of the frequency and magnitude of lahars is essential for the assessment of torrential hazards in volcanic terrains. In many instances, however, data on past events is scarce or incomplete, such that the evaluation of possible future risks and/or the planning of adequate countermeasures can only be done with rather limited certainty. In this paper, we present a multiidisciplinary approach based on botanical field evidence and the numerical modelling of a post-eruptive lahar that occurred in 2012 on the northern slope of the Pico de Orizaba volcano, Mexico, with the aim of reconstructing the magnitude of the event. To this end, we used the debris-flow module of the rapid mass movement simulation tool RAMMS on a highly resolved digital terrain model obtained with an unmanned aerial vehicle. The modelling was calibrated with scars found in 19 Pinus hartwegii trees that served as paleo stage indicators (PSI) of lahar magnitude in a sector of Jamapa Gorge. Using this combined assessment and calibration of RAMMS, we obtain a peak discharge of 78 m3 s−1 for the 2012 lahar event which was likely triggered by torrential rainfall during hurricane “Ernesto”. Results also show that the deviation between the modelled lahar stage (depth) and the height of PSI in trees was up to ±0.43 m. We conclude that the combination of PSI and models can be successfully used on (subtropical) volcanoes to assess the frequency, and even more so to calibrate the magnitude of lahars. The added value of the approach is particularly obvious in catchments with very scarce or no hydrological data at all and could thus also be employed for the dating and modelling of older lahars. As such, the approach and the results obtained can be used directly to support disaster risk reduction strategies at Pico de Orizaba volcano, but also in other volcanic regions.