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Ultrafast dynamics of ferroelectric polarization of NbOI2 captured with femtosecond electron diffraction
Machine learning algorithms for voltage stability assessment in electrical distribution systems
Abstract Voltage instability poses a significant challenge by limiting power system operation and transmission capacity. Rapid detection and effective corrective actions are essential to prevent voltage collapse. However, traditional methods for assessing voltage security margins are computationally intensive and often impractical for real-time applications. This study addresses voltage stability assessment in power systems using machine learning (ML) to overcome the computational limitations of traditional methods. By employing Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM), we predict Fast Voltage Stability Indices (FVSI) at nominal load as well as under varying loads (10–150%) in 15 kV Ethiopian distribution networks: a 35-bus Bata feeder system and a 53-bus Papyrus feeder system. RF and GB models achieved superior accuracy with R² values of 0.999 and 0.9998 respectively, significantly outperforming LR and SVM which exhibited substantial deviations. The GB model achieves the highest accuracy, with RMSE values of 0.0002 (53-bus) and 2.419e-05 (35-bus), while RF yields RMSE values of 0.0039 (53-bus) and 0.00120 (35-bus), demonstrating strong predictive performance. The FVSI threshold analysis revealed critical stability limits, with values approaching 1.0 indicating proximity to voltage collapse. The analysis identified buses 36, 32, and 21 in the 53-bus system (FVSI values: 0.087, 0.082, and 0.080) and buses 27 and 16 in the 35-bus system (FVSI values: 0.085 and 0.082) as critical instability risk points requiring immediate monitoring. These findings underscore the efficacy of ensemble methods for rapid voltage stability assessment and emphasize the need for targeted interventions in high-risk areas to bolster grid resilience in Ethiopian distribution networks.
Unraveling the role of viral interference in disrupting biennial RSV epidemics in northern Stockholm
Enhanced ground reaction force analyses reveal injury-related Biomechanical differences in runners
Phytochrome B and phytochrome-interacting-factor4 modulate tree seasonal growth in cold environments
Abstract Plants that live at high latitudes and altitudes must adapt to growth in cold environments. Trees survive freezing winter conditions by ceasing growth and forming protective winter buds at the end of the growing season. To optimize growth and adaptation, the timing of growth cessation and bud set is critical. Like the well-studied Populus species (poplars, aspens, cottonwoods), many trees respond to the shortening photoperiods of fall to induce growth cessation. Temperature also has a role in this process, but the mechanism is unknown. Here, we show that the PHYTOCHROME B (PHYB)-PHYTOCHROME INTERACTING FACTOR4 (PIF4) module controls the interplay between photoperiod cues and temperature to prevent premature growth cessation and bud set at cooler temperatures. PHYB is essential for the ability of aspen trees to maintain growth under lower temperatures in permissive long days. This is mediated through PIF4, which promotes growth cessation, specifically in response to low temperatures rather than to changes in photoperiod. PIF4 can directly bind to the promoter region of the vegetative growth marker gene FLOWERING LOCUS T2 (FT2). In contrast to annual plants, it does so to suppress its transcription. Furthermore, lower temperatures can suppress PIF4 function at the transcriptional and protein levels to prevent premature growth cessation. These data show how poplar trees balance the antagonistic roles of PHYB and PIF4 to optimise the timing of growth cessation and bud set in cold environments, and this has been achieved with contrasting mechanisms compared to the annual plant model.
Effect of natural environmental changes on Hainan migratory population with hypertension in China and related plasma metabolism features
Structure and transport mechanism of the human prostaglandin transporter SLCO2A1
Comparing thermographic heat signatures with joint inflammation detected by ultrasound at the knees in patients with rheumatoid arthritis
Abstract Joint inflammation at the bilateral knees of patients with rheumatoid arthritis (RA) was assessed using thermal imaging (TI) in comparison with ultrasonography. The minimum (T-min), maximum (T-max) and average (T-avg) temperatures at the lateral, anterior and medial knee aspects were correlated with ultrasound-detected grey-scale (GS) and power Doppler (PD) joint inflammation. The use of TI in identifying ultrasound GS score ≥ 2 and PD score > 0 were assessed using receiving operating characteristic (ROC) curve analysis. A subset of baseline thermograms were re-read > 2 weeks apart for intra-observer reliability testing using intra-class correlation coefficient (ICC) analysis. In this cross-sectional study (n = 95 RA patients), 570 thermograms were obtained while 190 knees were scanned by ultrasound. All TI parameters (T-min, T-max and T-avg) correlated significantly (P < 0.05) with ultrasound PD and GS scores (correlation coefficient ranged from 0.21 to 0.49). Using TI parameters to identify ultrasound PD score > 0 and GS score ≥ 2, the area under the ROC curves (AUCs) ranged from 0.63 to 0.82. The ICC values for the TI parameters were high (ranging from 0.997 to 0.999). Thermographic heat signatures are reflective of ultrasound-detected joint inflammation and can help discriminate ultrasound PD positivity/negativity and GS joint inflammation severity at the knees in patients with RA.
Trait mediation explains decadal distributional shifts for a wide range of insect taxa
Abstract Shifts in insect distributions have been reported globally, largely attributed to climate and landscape changes. Communities are being reshaped, with species response traits mediating the effects of changing environments. Using a machine-learning approach we model 1252 insect occupancies across three decades in Great Britain. We combine independent models of nine insect groups (butterflies, moths, odonates, orthopterans, carabids, ladybirds, bees, wasps and hoverflies) to take a high-level view of the trends and key environmental drivers of insect occupancy, as well as to highlight the trait mediations underlying the resulting niches. Across this wide taxonomic range, we identify common trends in insect occupancies, showing no Great Britain-wide decline since 1990, but instead local declines and changes in community compositions. Known drivers of biodiversity loss appear to underlie those changes, notably urban sprawl and landscape simplification. Our approach also highlights the crucial roles of two response traits: habitat breadth, in mediating the effects of changing landscapes diversity and voltinism, in mediating the effects of increasing temperatures on insect life cycles.
Determinants of healthcare-seeking behavior among women with symptoms of pelvic organ prolapse in gurage zone
Characterization of cervical fluid Ureaplasma species in pregnant women with spontaneous preterm delivery
Abstract Ureaplasma spp., commonly identified in the vagina/cervix of pregnant women with spontaneous preterm delivery, are the most frequently detected microorganisms in amniotic fluid. To date, 14 U. spp. serotypes have been characterized; however, modern molecular biology methods can distinguish different U. spp genotypes. Considering these factors, a knowledge gap exists regarding the association between U. spp. genotypes and the risk of the ascension of U. spp. from the cervix to the amniotic cavity. To fill this gap, an expanded multilocus sequence-typing scheme of U. spp. was performed to assess the relationship between cervical and amniotic fluid U. spp. in pregnant women with spontaneous preterm delivery. This study included 109 and 69 pregnant women with spontaneous preterm labor (PTL) and preterm prelabor rupture of membranes (PPROM), respectively. U. spp. DNA in cervical fluid was identified in 49% and 55% of the women with PTL and PPROM, respectively. The concurrent presence of U. spp. DNA in amniotic fluid was observed in 17% and 59% of the pregnant women with PTL and PPROM, respectively. Among pregnant women with PTL and PPROM, 38 expanded sequence types of cervical U. spp. were identified. No associations were observed between specific genotypes, subgroups, or clusters of cervical U. spp. and the presence of amniotic fluid U. spp. in pregnant women with spontaneous preterm delivery.
Hybrid physical–statistical framework for seasonal streamflow forecasting in the Upper Feather River Basin, California
Abstract Seasonal streamflow forecasts are essential given climate-driven extremes that breach stationarity in traditional methods. The complex hydrology and competing demands necessitate improved forecasting in the Upper Feather River Basin (UFRB), a key California State Water Project source upstream of Oroville Dam. We introduce a hybrid framework combining dynamical downscaling via WRF and the WEHY-HCM snow-hydrology model with a lead-time–dependent exponential-smoothing filter that adaptively corrects bias and quantifies uncertainty. Applied to December–July ensemble forecasts for water year 2024 using hindcast error training (2018–2023), this approach reduced RMSE by 8.7–318.3 million m³ across eight initialization months and eliminated systematic bias. The resulting 10–90% exceedance bands captured ~ 80% of observed flows, offering reliable confidence intervals. This hybrid method delivers accurate, low-bias streamflow forecasts for reservoir operations, flood mitigation, and irrigation planning in the UFRB and provides a transferable template for other basins facing hydroclimatic variability.
A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal
Low-entropy supramolecular crystals elucidating the inhomogeneity of interfacial water molecules at atomic resolution
Controlling intraoperative hyperglycemia in the cardiac surgical patient using the computerized electronic insulin dose calculator
Optimal experimental design for partially observable pure birth processes
We develop an efficient algorithm to find optimal observation times by maximizing the Fisher information for the birth rate of a partially observable pure birth process involving n observations. Partially observable implies that at each of the observation time points for counting the number of individuals present in the pure birth process, each individual is observed independently with a fixed probability p, modeling detection difficulties or constraints on resources. We apply concepts and techniques from generating functions, using a combination of symbolic and numeric computation, to establish a recursion for evaluating and optimizing the Fisher information. The recursion, while still computationally intensive, greatly improves on previously known computational methods which quickly became intractable even in the n = 2 case. Our numerical results reveal the efficacy of this new method. An implementation of the algorithm is available publicly.
Contact-dominated localized electric-displacement-field-enhanced pressure sensing
Effectiveness of collagen nerve conduit combined with PRP for incomplete peripheral nerve injury: a retrospective case-control study
Attitudes and opinions of medical practitioners, librarians, and LIS academics towards health science library services to support evidence-based medical practice in South Africa
Background This study explores the attitudes and opinions of health science librarians, university academic staff, and medical practitioners towards health science library services to support Evidence-Based Medical Practice (EBMP) in eThekwini, South Africa. It aims to develop an EBMP model for health science library services, focusing on the importance of timely, evidence-based information for quality healthcare. The research also focuses on improving the readiness and qualifications of librarians to support medical practitioners in EBMP implementation. Methodology/Principal Findings A mixed-methods survey was conducted with 251 medical practitioners (31 private and public hospitals), five health science librarians (5 public hospitals), and 24 LIS academic staff (10 South African universities and universities of technology that offer a qualification in Library and Information Science (LIS). Medical practitioners expressed tremendous appreciation for EBMP but identified considerable obstacles, such as time constraints, inadequate access to vital internet resources at their workplace, and excessive patient loads. Medical practitioners agreed that librarians can expedite their research in complex cases by providing pertinent information, yet they expressed significant dissatisfaction with current library services, identifying a deficiency in specialized EBMP support and resources. Health science librarians, though indicated, lacked formal EBMP training and were interested in taking courses or training related to EBMP, a gap reflected in LIS academic curricula, which currently offer no specialized EBMP courses. Conclusions/Significance There is a clear gap between the documented importance of EBMP and the medical library services support available in South African hospitals. The study underscores the imperative for specialized EBMP support services training for librarians, including in LIS education and hospital professional development, alongside a significant investment in contemporary library infrastructure and digital resources. A collaborative framework for health science library services is suggested to augment the use of EBMP, better patient outcomes, and promote a culture of continuous learning.