Bias Region Research Articles

The relation between contact experiences and dual identity among German residents with a Turkish or Kurdish identity

AbstractWe present secondary analyses of data collected with German residents with Turkish and Kurdish ancestry (n = 414), who represent the largest migrant groups in Germany. The majority of our sample of mostly second‐generation migrants exhibited integrated identity profiles, with high levels of both ethnic (Turkish or Kurdish) and national (German) identification. National and ethnic identification were unrelated, and both were predicted by the frequency and quality of contact with members of the respective groups as well as the extent to which participants report daily discrimination experiences. We additionally adopted a regional bias approach (Calanchini et al., 2022), inserting regional‐level data as indicators of context variance of contact opportunity and local aggregates of racialized group attitudes into analyses. Results document that the varying level of contact opportunity did not predict individual contact experiences and social identity, whereas the local attitude aggregate predicted individual contact experiences and participants' reports of daily discrimination, which, in turn, related to both ethnic and national identifications. These results indicate the relevance of social and contextual variables on the formation and navigation of multiple identities, which has theoretical and practical implications for the current societal and political debates about migration and integration.

Condylar fracture location is correlated to exercise history in Thoroughbred racehorses.

Condylar fractures are a major cause of morbidity and mortality in Thoroughbred racehorses. Condylar fractures have a variety of fracture configurations that suggest there may be differences in aetiopathogenesis. To determine if exercise history differs with condylar fracture location in a population of Thoroughbred racehorses. Retrospective analysis of clinical and exercise data. Exercise history of Thoroughbred racehorses that had condylar fracture repair between 1 January 2018 and 28 February 2021 was compared between racehorses that had fractures located radiographically either within the parasagittal groove (PSG) or abaxial to the PSG (non-PSG). Age, sex, and last event (race, timed work) matched control groups were compared between the PSG and non-PSG groups. Additionally, exercise history variables of both groups were each compared with a group-specific control population, each consisting of three control racehorses of equivalent age and sex matched to each affected racehorse by last event (race or official timed work) before fracture. Eighty-two horses with 84 fractures (45 PSG, 39 non-PSG) met inclusion criteria. Age was not different between groups (PSG: 3.4 ± 1.3 years [mean ± SD], non-PSG: 3.7 ± 1.3, p = 0.3). Number of races (PSG: 5.3 ± 7.1, non-PSG: 11.4 ± 8.9, p < 0.001), total race furlongs (PSG: 38.2 ± 54.7, non-PSG: 79.2 ± 64, p = 0.003), and number of active days (PSG: 304 ± 224, non-PSG: 488 ± 314, p = 0.003) before fracture were greater; while mean number of layups was fewer (PSG: 1.0 ± 1.2, non-PSG: 0.5 ± 0.7, p = 0.02) in horses with non-PSG fracture. Horses with non-PSG fracture had more differences compared with their respective control group than horses with PSG fractures. Outcomes following fracture repair were not different between groups. Retrospective study, one regional racehorse population, two-dimensional imaging and potential inherent bias for fracture localisation, low statistical power for return to performance analysis. Thoroughbred racehorses with non-PSG condylar fractures have a more extensive exercise history than horses with PSG condylar fractures, suggesting differences in fracture aetiopathogenesis.

Do we listen to what makes children happy? A systematic literature review of voices of children aged 3–7 years in educational research

Research shows that children’s views and experiences are usually overlooked in discussions related to their own happiness and wellbeing. In fact, the largest international study to date on children’s wellbeing only included children aged 8-12. A review of reviews further reveals that no previous studies have focused on exploring factors that make children aged 3-7 happy. This research addresses this gap in the literature by conducting the first systematic review of international studies exploring young children's perspectives on happiness. The EPPI-Centre framework guided this systematic review of international research published covering almost a decade of research (2015-2024). This comprehensive approach aimed to provide an overview of the current research landscape to inform future research and policy decisions. Out of 2594 papers arguing to listen to children, only five focused on exploring children’s happiness and wellbeing from an interpretivist perspective. Results showed that there are studies listening to children’s voices, however, very limited studies genuinely explore what makes children happy. Factors contributing to children’s happiness and wellbeing include spending time with loved ones, receiving praise, achieving goals and engaging in sociodramatic play. Negative factors include feeling ignored, limited interaction with peers/friends and forced activities. Methodological limitations identified include unclear sample selection, regional bias, subjective data interpretation, and pre-determined prompts influencing children's responses. The findings can inform future research directions and policy decisions aimed at promoting children's happiness and well-being in schools. As such, this paper provides a unique insight and makes an original and significant contribution to the field.

The Verification of Sea Level Anomaly (SLA) Versus Tide Gauge for Geoid Modelling

The increasing long-term sea level rise is of particular interest and needs a significant investment in coastal protection procedures. The coastal sea level has been verified with tide gauges and well maintained with reliable accuracy of data since a few centuries ago. Satellite altimeter has been used for sea level study and increasingly for operational purposes. However, the precision of altimetry data in coastal areas is frequently eliminated by the geographically correlated orbit errors, which induce a regional inhomogeneous mission bias. This study presents an effort to verify sea level anomaly based on altimetry data with tide gauge stations as an early procedure to provide a good sea level anomaly data for mean sea surface, gravity anomaly, and geoid determination over Malaysian seas. The altimetry data is validated by comparing the sea level anomaly with ground truth data (tidal data) near the coastal area. A total of nine selected areas were chosen to represent the ground truth data. The findings found that the altimetry data over the South China Sea (Pulau Tioman, Geting, Cendering, Bintulu, and Kota Kinabalu) provided a good pattern, high correlation, and a minimum root mean square error (RMSE) value after the verification with tidal sea level anomaly data. However, it clearly shows that Tawau station over the Celebes Sea provides a poor pattern, poor correlation, and a high RMSE value with tidal sea level anomaly data. In conclusion, further enhancements are expected from the refined processing and filtering of altimetry data for the sea level study in the coastal zone.

Microstructural and Current-voltage Characteristics in Mo/HfO2/n‑Si Based Metal-Insulator-Semiconductor (MIS) Diode using Different Methods for Optoelectronic Device Applications

This paper investigates the effect of hafnium dioxide (HfO2) thin film as interlayer between the Mo and n-Si semiconductor on the electrical characteristics of the Mo/n-Si Schottky diode (SD). The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) results confirmed that HfO2 films were formed on the n-Si semiconductors. The image from SEM and AFM displays that the deposited HfO2 thin film had a uniform appearance good smoothness of the surface. The smooth surfaces of the insulating layer strongly influence the electrical properties of the diode. The electrical properties of the Mo/HfO2/n-Si metal/insulator/semiconductor (MIS) diode were obtained via current-voltage (I-V) measurements in the voltage range from −3 V to +3 V at room temperature. A better rectifying ability and a reduced reverse leakage current were demonstrated by the MIS diode. The MIS dide's barrier height (Bh) and ideality factor value were calculated to be 0.85 eV and 1.21, respectively. The MIS diode was able to achieve a higher b, which allowed the HfO2 interlayer to change BH. The calculated BH values from the I-V, Hernandez, Cheung, and Norde approaches were comparable to one another, demonstrating their validity and consistency. The MIS diode's forward bias log (I) - log (V) curve demonstrated that it was ohmic in low-voltage regimes and that conduction was space-charge-limited in high-voltage regimes. However, for the MIS diode, the Poole-Frenkel and Schottky emissions are the dominant current conduction mechanisms in the lower and higher bias regions. These outcomes indicate that the HfO2 film can be chosen as dielectric materials in the construction of MIS devices.

Investigation of illumination-dependent electrical and photodiode properties and conduction mechanism of the Al/p-Si contact with Schiff base compound (Pyr-Pic) interlayer

In this paper, an organic semiconductor, N-(2-((pyren-4-yl)methyleneamino)ethyl)-5- nitropyridin-2-amine (Pyr-Pic), was synthesized and was used as an interfacial organic layer to fabrication of Al/Pyr-Pic/p-Si/Al diode. The device’s characteristic parameters were determined under various operating situations using current–voltage (I-V) measurements. Thermionic emission theory (TE), the Cheung methodology, and Norde functions were the three methods used to determine the device’s electrical properties, such as the ideality factor, barrier height, and series resistance. Based on the I-V measurements conducted under illumination circumstances, the fabricated device exhibits photoresponse properties in the reverse bias region. An examination of the forward log(I)-log(V) plot of the Al/Pyr-Pic/p-Si diode indicated that ohmic conduction dominates carrier transport in the lower bias regions, while the space-charge-limited current (SCLC) governs transport in the medium bias regions, and the trap-charge limit current (TCLC) mechanism is responsible for transport in the higher bias regions. The current voltage mechanisms in the reverse bias area can be characterized by using Poole–Frenkel emission (PFE) and Schottky emission (SE) theories. The device exhibits photovoltaic characteristics when it is illuminated through an Al electrode. The typical photovoltaic parameters were estimated at room temperature and under illumination conditions.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data

The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing.

Assessing Climate Change Effects on Winter Wheat Production in the 3H Plain: Insights from Bias-Corrected CMIP6 Projections

Climate change exerts significant impacts on regional agricultural production. This study assesses the implications of climate change on winter wheat yields in the Huang-Huai-Hai Plain (3H Plain), utilizing bias-corrected climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for mid-21st century (2041–2060) and late 21st century (2081–2100) periods under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5). These projections were incorporated into the decision support system for agrotechnology transfer (DSSAT) CERES-Wheat model to forecast potential alterations in winter wheat production. Initial findings reveal that uncorrected CMIP6 projections underestimated temperature and precipitation while overestimating solar radiation across the southern 3H Plain. Following bias correction through the equidistant cumulative distribution function (EDCDF) method, the regional average biases for temperature, precipitation, and solar radiation were reduced by 18.3%, 5.6%, and 30.7%, respectively. Under the SSP2–4.5 and SSP5–8.5 scenarios, mid-21st century simulations predicted a 13% increase in winter wheat yields. Late 21st century projections indicated yield increases of 11.3% and 3.6% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, with a notable 8.4% decrease in yields south of 36° N under the SSP5-8.5 scenario. The analysis of climate change factors and winter wheat yields in the 3H Plain under both scenarios identified precipitation as the key contributing factor to yield increases in the northern 3H Plain, while temperature limitations were the primary constraint on yields in the southern region. Consequently, adaptive strategies are essential to mitigate climate change impacts, with a particular focus on addressing the challenges posed by elevated temperature in the southern 3H Plain.

Predictive modeling of antidepressant efficacy based on cognitive neuropsychological theory

BackgroundWe aimed to develop a clinical predictive model based on the cognitive neuropsychological (CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MDD. MethodsBaseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS scores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characteristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls. The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SSRIs). A multi-modality machine-learning model was developed to predict the effectiveness of SSRI antidepressants. ResultsAt baseline, we observed significant differences between MDD patients and healthy controls in terms of social support, clinical symptoms, and negative bias characteristics (p < 0.001). A negative association was found (p < 0.05) between neuroticism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the parietal region. Compared to responders, non-responders exhibited less negative cognitive processing bias and greater alpha asymmetry in both central and central-parietal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features. ConclusionsResearch results support the CNP theory of depression treatment. To some extent, the multimodal clinical model constructed based on the CNP theory effectively predicted the efficacy of this treatment in this population. LimitationsSmall sample and only focus on the mechanisms of delayed-onset SSRI treatment.

How regional attitudes towards immigration shape the chance to obtain asylum: Evidence from Germany

Abstract Asylum recognition rates in advanced democracies differ not only across states but also vary within them, translating into fluctuating individual chances to obtain protection. Existing studies on the determinants of these regional inequities typically rely on aggregate data. Utilizing a German refugee survey and leveraging a quasi-natural experiment arising from state-based allocation rules tied to national dispersal policies, we test two explanations for the perplexing regional differences. Drawing on principal–agent models of administrative decision-making, we test whether asylum decision-makers consciously or unconsciously comply with regional political preferences between 2015 and 2017 in Germany, one of the major European destination countries for refugee migration. We furthermore explore whether such biased decision-making amplifies in times of organizational stress as suggested by the statistical discrimination theory. Using mixed-effects logistic regressions, our analyses confirm a lower approval probability in regions with more immigration-averse residents or governments. We cannot confirm, however, that this association is mediated by high workloads or large knowledge gaps. Our results thus suggest that regional political biases affect the individual chance of asylum-seekers to obtain protection irrespective of temporal administrative conditions.

Validating Landsat Analysis Ready Data for Nearshore Sea Surface Temperature Monitoring in the Northeast Pacific

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R2 of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (&gt;10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST.

ASSESSMENT OF PYROGENIC IMPACT ON FOREST AREAS IN THE TVER REGION

We consider the potential and limitations of widely accepted remote sensing algorithms for detecting forest areas damaged by fires, which allow the monitoring systems to automatically form data about the fire areas and the areas where death of tree stands is subsequently recorded. The resulting size of the detected areas has a measurement error, which is typically determined on the basis of a onetime survey for a large territory and one forest fire season. Depending on the geographical features of the territory, forest fires have specific spatiotemporal and qualitative characteristics, and are accompanied by specific heterogeneous damage to forests, which affect the accuracy of remote detection of a burnt area or firedamaged forest. Hence the use of the unified largearea error estimate for locallevel surveys could lead to inaccurate results. The analysis of space images of forest fund lands in the Tver region for the period 2007-2022 demonstrated the need to establish regional values of the measurement error for fireimpacted areas. By comparing the medium spatial resolution data with the data of high spatial resolution we identify a regional bias, which is significant given the relatively small size of the detected areas. The study demonstrates the expediency of establishing regional error values for measurements of pyrogenic impact on forest areas. By implementing the suggested changes we could improve the accuracy of remotely-sensed estimates of fireimpacted areas and the amount of associated damage to forests.

Electrical properties of PVC:BN nanocomposite as interfacial layer in metal-semiconductor structure

In this study, a comprehensive examination is assumed to investigate the influence of interfacial layers composed of polyvinyl chloride (PVC) and polyvinyl chloride-boron nitride (PVC: BN) on the electrical characteristics of the Au/n-Si structure. Two distinct structures, namely Au/PVC/n-Si (MPS1) and Au/PVC: BN/n-Si (MPS2), are fabricated for this purpose. The provided boron nitride (BN) nanostructures are analyzed using X-ray diffraction (XRD) patterns to determine their average crystalline size and surface morphology. Following the structural analysis, current-voltage (I–V) measurements are conducted over an extensive voltage range (± 3 V). Subsequently, the fundamental electrical properties of the developed Schottky structures are determined using various methods and compared. Experimental results indicate that the PVC: BN nanocomposite leads to an increase in the potential barrier height (BH), shunt resistance (Rsh), and rectifying rate (RR = IF/IR), while simultaneously decreasing the ideality factor (n), series resistance (Rs), and surface states density (Nss). It was discovered that the MS structure’s RR was 7 times lower than that of the MPS2 structure. Moreover, the energy-dependent Nss density is also derived using n(V) and ΦB0(V) functions. Based on the ln(IR)−VR0.5 profile at the reverse bias region, the Schottky-emission (SE) type conduction mechanism is effective for MS structures, whereas Poole-Frenkel-emission (PFE) is effective for MPS structures.

PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional perspective. Addressing these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.

Novel PANI:Borophene/Si Schottky device for the sensitive detection of illumination and NaCl salt solutions

Metal/semiconductor structures, particularly Schottky diodes, play a crucial role in semiconductor identification and the production of electronic devices, like solar cells, photodetectors, photodiodes, and field-effect transistors (FETs). These structures are of great interest due to their ability to modify electrical and optical properties, responding to external factors such as illumination and temperature. However, despite extensive research in this field, there has been limited exploration of silicon-based metal/semiconductor structures incorporating PANI:Borophene interfacial materials. In this study, we prepared PANI:Borophene/p-Si and PANI:Borophene/n-Si structures and examined their photodiode properties using various measurements. The unoccupied trap levels (m) obtained 0.44 and 0.33 for Al/PANI:Borophene/p-Si and Au/PANI:Borophene/n-Si device, respectively. Our investigation revealed that both structures exhibited rectification behavior, with linear characteristics in the forward bias region, and deviations attributed to series resistance effects at higher voltages. Moreover, the presence of borophene in the interfacial layer led to improvements in the devices’ electrical properties. Finally, the PANI:Borophene/Si Schottky diodes was tested for salt detection and the Al/PANI:Borophene/p-Si diode has the characteristics of salt (NaCl) concentration detection sensor and it successfully detected salt concentration changes with respect to current flow.

A scoping review on tools and methods for trait prioritization in crop breeding programmes

Trait prioritization studies have guided research, development and investment decisions for public-sector crop breeding programmes since the 1970s, but the research design, methods and tools underpinning these studies are not well understood. We used PRISMA-ScR (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) to evaluate research on trait ranking for major crops over the past 40 years (1980–2023). Data extraction and descriptive analysis on 657 papers show uneven attention to crops, lack of systematic sex disaggregation and regional bias. The lack of standardized trait data taxonomy across studies, and inconsistent research design and data collection practices make cross-comparison of findings impossible. In addition, network mapping of authors and donors shows patterns of concentration and the presence of silos within research areas. This study contributes to the next generation of innovation in trait preference studies to produce more inclusive, demand-driven varietal design that moves beyond trait prioritization focused on productivity and yield.

Moist bias in the Pacific upper troposphere and lower stratosphere (UTLS) in climate models affects regional circulation patterns

Abstract. Water vapour in the upper troposphere and lower stratosphere (UTLS) is a key radiative agent and a crucial factor in the Earth's climate system. Here, we investigate a common regional moist bias in the Pacific UTLS during Northern Hemisphere summer in state-of-the-art climate models. We demonstrate, through a combination of climate model experiments and satellite observations, that the Pacific moist bias amplifies local long-wave cooling, which ultimately impacts regional circulation systems in the UTLS. Related impacts involve a strengthening of isentropic potential vorticity gradients, strengthened westerlies in the Pacific westerly duct region, and a zonally displaced anticyclonic monsoon circulation. Furthermore, we show that the regional Pacific moist bias can be significantly reduced by applying a Lagrangian, less-diffusive transport scheme and that such a model improvement could be important for improving the simulation of regional circulation systems, in particular in the Asian monsoon and Pacific region.

Does Political Trust Matter? A Meta-analysis on the Consequences of Trust

AbstractPolitical trust has long been seen as fundamental for societal cooperation and democratic legitimacy. However, evidence about its consequences are partial and fragmented, and we do not currently have a systematic understanding of whether political trust warrants such vast attention. This paper conducts a systematic review and meta-analysis of 61 studies reporting 329 coefficients derived from over three and a half million observations globally. After synthesising the conceptual and theoretical frameworks in the extant literature, the meta-analysis results show that trust is weakly to moderately related to outcomes as diverse as voter turnout, vote choice, policy preferences and compliance, but is unrelated to informal participation. These results are robust to a range of considerations such as the measurement of trust, modelling strategy, region of study, publication bias, and design of the study. The review also highlights substantial geographical and methodological gaps, particularly the reliance on cross-sectional designs. Substantively, the results show that trust is importantly and robustly related to what people want from their political systems and how they interact with it. By systematically analysing the extant research, the paper provides a robust, systematic, and empirical foundation to advance research on political trust.

A framework for automated scalable designation of viral pathogen lineages from genomic data.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.