Miracle-dz/UniFAR-FTUs huggingface dataset:
How do personality traits influence Open Government Data (OGD) adoption and usage? Investigating the indirect and moderating effects

Open Government Data (OGD) research has focused for a long on the adoption and usage from the perspectives of users across different contexts. The underlying rationale for this specific focus is that OGD initiatives are undertaken to further citizen engagement with OGD for value generation and innovation purposes. Conceding that usage propensity is different across individuals, it is important to understand the influence of personality traits vis-à-vis OGD adoption and usage. Given that OGD has been regarded as a sophisticated “technology” and the role of personality traits has been considered as important in the adoption and usage of “technologies” in general, therefore, the present study contributes to the extant OGD-focused literature from a novel dimension. The study invokes the adapted model of the Unified Theory of Technology Adoption and Use (UTAUT) alongside the HEXACO-100 inventory constructs for studying the relationships between the constructs with a sample of 530 respondents. The results demonstrate that higher user Openness to Experience contributes to their higher Effort and Performance Expectancy; exposure to Social Influence; an increased level of Trust; and a more positive perception of Facilitating Conditions and Information Quality. Agreeable people are more likely to voluntarily use OGD. An individual's conscientiousness improves their perception of factors related to OGD quality. Excessive emotionality leads to a more critical perception of systems and information quality issues. Our findings also attest to the moderating impact of Honesty-Humility across Information Quality-Behavioral Intention positively; Extraversion across Information Quality-Behavioral Intention negatively and Extraversion across Trust-Behavioral Intention positively. Honesty turns out to be important for considering Information Quality vis-à-vis OGD adoption and usage but whilst extroverts are concerned about Information Quality, i.e. flawless information retrieval via OGD sources, Introverts are concerned about OGD trustworthiness, i.e. credible OGD for its adoption and usage and Extroverts find the OGD reliable and credible. With pointers for further research across the personality traits-OGD adoption and usage theme, the study closes with practitioner implications.
Moderating effects of gender, perceived importance and usage experience towards Open Government Data (OGD) adoption and usage

Open Government Data (OGD) initiatives are spearheaded to further stakeholders’ engagement apart from bolstering their trust in the government. OGD's adoption and usage is important in order to result in value creation and innovation initiatives by the stakeholders concerned. Therefore, academic interest has increased over the years to deepen our understanding regarding the stakeholders’ behavioral intention towards adoption and usage which includes a study of facilitating factors and the barriers both. Technology adoption and usage studies have underlined the influence of gender, perceived importance and usage experience and since OGD is being conceived as a ‘technology’, it is viable to understand the role of these three variables vis-a-vis OGD adoption and usage. With a sample drawn from the currently-enrolled students (‘actual’ OGD users only) pursuing their undergraduate and postgraduate courses (n∼530) at a leading private Indian university, the present study seeks to analyze the moderating effects of gender, perceived importance and usage experience towards the behavioral intention to adopt and use OGD. The relationships between the adapted Unified Theory of Technology Adoption and Use (UTAUT) model and the Behavioral Intention to adopt and use OGD are found to be impacted by the three moderators across Performance Expectancy and Information Quality as also between the males and the females. As a original contribution towards the OGD-focused research, it is anticipated that further research shall focus on other determinants linked with OGD adoption and usage.
Bridging the Gap between e-Government and e-Health: Recommendations for Deploying e-Health Services in the Public Sector

Emerging national electronic health records or COVID-19 tracing apps show that the domains of e-Government and e-Health are increasingly growing together. For e-Government researchers and practitioners, the development and deployment of e-Health services in the public sector must not be approached the same way as conventional e-Government services, as the target group has shifted from being "solely" citizens to patients instead. Trust and perceived risk are two concepts that heavily influence the adoption of any e-service. Hence, the present study reviews the influences of trust in and perceived risk towards e-Government and e-Health services employing a structured literature review. We use the results to derive 13 recommendations for developing and deploying public e-Health services, thus contributing to an overall more successful adoption of e-Government.
Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world applications. To this end, we present a novel unsupervised graph neural network model with Cluster-aware Self-training and Refining (CLEAR). Specifically, in the proposed CLEAR model, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. To avoid degenerate solutions of clustering, we formulate the graph clustering problem as an optimal transport problem and leverage a balanced clustering strategy. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. Therefore, we propose to refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.
Learning from Hierarchical Structure of Knowledge Graph for Recommendation

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based recommendation deploy GNN models to learn from both knowledge graph and user-item bipartite interaction graph. However, these works have not well considered the hierarchical structure of knowledge graph, leading to sub-optimal results. Despite the benefit of hierarchical structure, leveraging it is challenging since the structure is always partly-observed. In this work, we first propose to reveal unknown hierarchical structures with a supervised signal detection method and then exploit the hierarchical structure with disentangling representation learning. We conduct experiments on two large-scale datasets, of which the results well verify the superiority and rationality of the proposed method. Further experiments of ablation study with respect to key model designs have demonstrated the effectiveness and rationality of our proposed model. The code is available at https://github.com/tsinghua-fib-lab/HIKE.
When Caching Systems Meet Emerging Storage Devices: A Case Study

Block-layer caching systems improve the I/O performance by using hybrid storage devices; the advent of fast, byte-addressable storage enables caching systems to further leverage new storage tiers (e.g., with persistent memory as the cache device and SSD as the backend device) to achieve better caching performance. However, the new storage devices also challenge the design and implementation of existing block-based caching systems. This paper conducts a comprehensive performance study of a popular caching system, Open CAS, and identifies new, unrevealed software bottlenecks. Our observations and root cause analysis cast light on optimizing the software stack of caching systems to incorporate emerging storage technologies.
Influences Driving and Limiting the Efficacy of Ice Segregation in Alpine Rocks

Rockwall erosion by rockfall is largely controlled by frost weathering in high alpine environments. As alpine rock types are characterized by crack‐dominated porosity and high rock strength, frost cracking observations from low strength and grain supported pore‐space rocks cannot be transferred. Here, we conducted laboratory experiments on Wetterstein limestone samples with different initial crack density and saturation to test their influence on frost cracking efficacy. We exposed rocks to real‐rockwall freezing conditions and monitored acoustic emissions as a proxy for cracking. To differentiate triggers of observed cracking, we modeled ice pressure and thermal stresses. Our results show initial full saturation is not a singular prerequisite for frost cracking. We also observe higher cracking rates in less‐fractured rock. Finally, we find that the temperature threshold for frost cracking in alpine rocks falls below −7°C. Thus, colder, north‐exposed rock faces in the Alps likely experience more frost cracking than southern‐facing counterparts.
Observed Changes in Interannual Precipitation Variability in the United States

Characterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.
Ozone Changes Due To Sudden Stratospheric Warming‐Induced Variations in the Intensity of Brewer‐Dobson Circulation: A Composite Analysis Using Observations and Chemical‐Transport Model

We quantify the changes in the intensity of Brewer‐Dobson Circulation (BDC) during sudden stratospheric warming (SSW) and its impact on the tropical stratospheric thermal structure and ozone distribution by composite analysis using observations and a chemical‐transport model. An increase in the planetary wave activity and enhancement in BDC intensity before the central date of SSW is noticed. A positive ozone anomaly is observed in the tropical upper stratosphere. The tropical lower stratosphere shows a cooling (1–2 K) and negative ozone anomaly (∼0.1 ppmv) after ∼10 days from the central date. The polar stratosphere experiences a positive ozone anomaly, whereas the upper stratosphere shows ozone depletion due to the downwelling of NOx‐rich mesospheric air. The cold‐point tropopause temperature shows a cooling of ∼0.5 K for major warming which in turn dries the lower stratosphere.
Microphysical Modeling of Fault Slip and Stability Transition in Hydrothermal Conditions

Field and laboratory observations indicate that the frictional behaviors of faults depend on hydrothermal conditions. We extend the microphysical Chen‐Niemeijer‐Spiers (CNS) model to hydrothermal conditions by using the observed temperature variation of indentation hardness to infer the temperature dependence of a microphysical parameter aμ∼ $\left({a}_{\tilde{\mu }}\right)$ . This parameter is assumed constant in previous versions of the CNS model. A simple spring‐slider system is used to simulate the fault system and investigate the steady‐state frictional behaviors of wet granite gouges. Our numerical results quantitatively reproduce experimental data showing the frictional‐plastic transition. The results also describe the transition from velocity‐strengthening at low temperatures (<160°C), to velocity‐weakening at intermediate temperatures (160°C–370°C), then back to velocity‐strengthening at high temperatures (>370°C). In our extended CNS model, these results suggest that the dominant shear deformation mechanism does transition from frictional granular flow to fully plastic creep with increasing temperature.
Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

Radiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.
Coral Paleoclimate Perspectives Support the Role of Low‐Latitude Forcing on the 4.2 ka BP Event

The 4.2 ka BP Event is an abrupt climate change that might have contributed to the collapse of ancient civilizations and marks the transition between the mid‐ and late‐Holocene. Despite considerable research on this event, our understanding remains primarily based on terrestrial paleoclimate reconstructions, leaving a significant gap in understanding the role of the ocean in this event. Here, we present paired sea surface temperature (SST) and seawater δ18O reconstructions based on four fossil corals from the South China Sea. Our results demonstrate that the climate during the event was cooler, and there were meridional dry‐wet patterns in East Asia, indicating a weakened summer monsoon. Furthermore, our examination of additional coral records from the Pacific and Indian Oceans suggests that low‐latitude forcing (i.e., SST anomalies in the tropical Pacific) plays a crucial role in driving hydrology shifts in East Asia over the 4.2 ka BP interval.
Can Oxygen Utilization Rate Be Used to Track the Long‐Term Changes of Aerobic Respiration in the Mesopelagic Atlantic Ocean?

Quantifying changes in oceanic aerobic respiration is essential for understanding marine deoxygenation. Here we use an Earth system model to investigate if and to what extent oxygen utilization rate (OUR) can be used to track the temporal change of true respiration (Rtrue). Rtrue results from the degradation of particulate and dissolved organic matter in the model ocean, acting as ground truth to evaluate the accuracy of OUR. Results show that in thermocline and intermediate waters of the North Atlantic Subtropical Gyre (200–1,000 m), vertically integrated OUR and Rtrue both decrease by 0.2 molO2/m2/yr from 1850 to 2100 under global warming. However, in the mesopelagic Tropical South Atlantic, integrated OUR increases by 0.2 molO2/m2/yr, while the Rtrue integral decreases by 0.3 molO2/m2/yr. A possible reason for the diverging OUR and Rtrue is ocean mixing, which affects water mass composition and maps remote respiration changes to the study region.
Robust nonparametric frontier estimation in two steps

Abstract We propose a robust methodology for estimating production frontiers with multi-dimensional input via a two-step nonparametric regression, in which we estimate the level and shape of the frontier before shifting it to an appropriate position. Our main contribution is to derive a novel frontier estimation method under a variety of flexible models which is robust to the presence of outliers and possesses some inherent advantages over traditional frontier estimators. Our approach may be viewed as a simplification, yet a generalization, of those proposed by Martins-Filho and coauthors, who estimate frontier surfaces in three steps. In particular, outliers, as well as commonly seen shape constraints of the frontier surfaces, such as concavity and monotonicity, can be straightforwardly handled by our estimation procedure. We show consistency and asymptotic distributional theory of our resulting estimators under standard assumptions in the multi-dimensional input setting. The competitive finite-sample performances of our estimators are highlighted in both simulation studies and empirical data analysis.
REVISITING THE ROLE OF OBSERVATION SESSION DURATION ON PRECISE POINT POSITIONING ACCURACY USING GIPSY/OASIS II SOFTWARE

The accuracy of GPS precise point positioning (PPP) was previously modelled as a function of the observing session duration T. The NASA, JPL's software GIPSY OASIS II (GOA-II) along with the legacy products was used to process the GPS data. The original accuracy model is not applicable anymore because JPL started releasing its products using new modelling and analysis strategies as of August 2007, and the legacy products are no longer available. The developments mainly comprise the new orbit and clock determination strategy, second order ionosphere modelling, and single station ambiguity resolution. Previously, the PPP accuracy was studied using v 4.0 of the GOA-II. The accuracy model showed coarser results compared to that of the relative positioning. Here, we processed the data of the International GNSS Service (IGS) stations to refine the accuracy of GOA-II PPP from version 6.3. Considering the above changes we refined the accuracy of PPP. First we modified the previous model used for the accuracy assessment. Then we tested out this model using straightforward polynomial and logarithmic models. The tests indicate the previous formulation still satisfactorily models the accuracy using refined coefficient values Sn = 7.8 mm , Se = 6.8 mm , Sv = 29.9 mm for T ≥ 2 h.
Forecasting vector autoregressions with mixed roots in the vicinity of unity

Abstract This article evaluates the forecast performance of model averaging forecasts in a nonstationary vector autoregression with mixed roots in the vicinity of unity. The deviation from unit root allows for local to unity, moderate deviation from unity and strong unit root, and the direction of such deviation could be from either the stationary or the explosive side. We provide a theoretical foundation for comparison among various forecasts, including the least squares estimator, the constrained estimator imposing the unit root constraint, and the selection or average over these two basic estimators. Furthermore, three new types of estimators are constructed, i.e., the bagging versions of the pretest estimator, the Mallows-pretest estimator that marries the Mallows averaging criterion and the Wald test, and the Mallows-bagging estimator that combines the Mallows averaging criterion and bagging technique. The asymptotic risks are shown to depend on the local parameters, which are not consistently estimable. Via Monte Carlo simulations, graphic comparisons indicate that the Mallows averaging estimator has both robust and outstanding forecasting performance. Model averaging over the vector autoregressive lag order is further considered to address the issue of model uncertainty in the lag specification. Finite sample simulations show that the Mallows averaging estimator performs superior to other frequently used selection and averaging methods. The application to forecasting the financial indices popularly used in the predictive regression further illustrates the practical merit of the proposed estimator.
Mediator kinase inhibition suppresses hyperactive interferon signaling in Down syndrome

Hyperactive interferon (IFN) signaling is a hallmark of Down syndrome (DS), a condition caused by trisomy 21 (T21); strategies that normalize IFN signaling could benefit this population. Mediator-associated kinases CDK8 and CDK19 drive inflammatory responses through incompletely understood mechanisms. Using sibling-matched cell lines with/without T21, we investigated Mediator kinase function in the context of hyperactive IFN in DS. Activation of IFN-response genes was suppressed in cells treated with the CDK8/CDK19 inhibitor cortistatin A, and this occurred through suppression of IFN-responsive transcription factor activity. Moreover, we discovered that CDK8/CDK19 affect splicing, a novel means by which Mediator kinases control gene expression. Kinase inhibition altered splicing in pathway-specific ways and selectively affected IFN-responsive gene splicing in T21 cells. To further probe Mediator kinase function, we completed cytokine screens and untargeted metabolomics experiments. Cytokines are master regulators of inflammatory responses; by screening 105 different cytokine proteins, we show that Mediator kinases help drive IFN-dependent cytokine responses at least in part through transcriptional regulation of cytokine genes and receptors. Metabolomics revealed that Mediator kinase inhibition altered core metabolic pathways, including broad up-regulation of anti-inflammatory lipid mediators. Elevated levels of lipid mediators persisted at least 24hr after Mediator kinase inhibition, and many identified lipids serve as ligands for nuclear receptors (e.g. PPAR, LXR) or G-protein coupled receptors (GPCRs; e.g. FFAR4). Notably, ligand-dependent activation of these GPCRs or nuclear receptors will propagate anti-inflammatory signaling pathways and gene expression programs, and this mechanistic link suggests that metabolic changes caused by CDK8/CDK19 inhibition can durably and independently suppress pro-inflammatory IFN responses. Collectively, our results establish that Mediator kinase inhibition antagonizes IFN signaling through transcriptional, metabolic, and cytokine responses, with implications for DS and other chronic inflammatory conditions.