Independent Sources Research Articles

A two-stage consensus model based on personalized individual semantics under linguistic preference relations with self-confidence

Linguistic preference relations with self-confidence (LPRs-SC) are the preference relation that can reflect the decision maker’s (DM) confidence psychology and has received widespread attention for their simple form and multiple information. Currently, arithmetic studies of LPRs-SC are conducted separately for preference relations and self-confidence. In addition, personalized individual semantics (PIS) is an important tool in large-scale decision-making to reflect the differences in the semantic understanding of DMs. However, the confidence level in LPRs-SC limits the preference relation to a certain extent and the linguistic representations of these two components are usually different. This means that it is not only necessary to propose an arithmetic rule that can express the restrictive relationship between the two but also to construct a model that can extract the PIS of preference relation and confidence respectively. Besides, we constructed a two-stage consensus reaching process (CRP) based on the specificity of the LPRs-SC structure when enhancing group harmony. The process takes self-confidence as an independent source of information, delineates the adjusted categories in detail, and builds an adjustment model accordingly. Finally, the example and comparative analyses verify the merits of the proposed PIS in terms of consistency enhancement and CRP in terms of speed and accuracy harmonization.

Comparing Antoine parameter sources for accurate vapor pressure prediction across a range of temperatures.

Determining the vapor pressure of a substance at the relevant process temperature is a key component in conducting an exposure assessment to ascertain worker exposure. However, vapor pressure data at various temperatures relevant to the work environment is not readily available for many chemicals. The Antoine equation is a mathematical expression that relates temperature and vapor pressure. The objective of this analysis was to compare Antoine parameter data from 3 independent data sources; Hansen, Yaws, and Custom data and identify the source that generates the most accurate vapor pressure values with the least bias, relative to the referent data set from the CRC Handbook of Chemistry and Physics. Temperatures predicted from 3 different Antoine sources across a range of vapor pressures for 59 chemicals are compared to the reference source. The results show that temperatures predicted using Antoine parameters from the 3 sources are not statistically significantly different, indicating that all 3 sources could be useful. However, the Yaws dataset will be used in the SDM 2.0 because the data is readily available and robust.

Offshore high chlorophyll eddies: Separating upwelling from nearshore migration from ocean color remote sensing

Offshore eddies are often associated with high amounts of phytoplankton (represented by the chlorophyll-a concentration (Chla)), or, phytoplankton blooms, which can be detected from ocean color satellites. The phytoplankton “blooms” in these eddies are commonly explained as a result of enhanced nutrients - local growth - brought up from deeper waters by these eddies, although potentially they could simply be a migration of high Chla waters from nearshore regions. To better understand the interactions between physical forcing and phytoplankton dynamics, it is necessary to separate these “blooms” between local growth and migration. In this study, we first updated the multiple pigment inversion model using a synthetic dataset, for retrieving the absorption coefficient and absorption Gaussian peaks of phytoplankton from remote sensing reflectance in the broad aquatic environments. On this basis, a two-dimensional spatial model was developed to identify the sources of phytoplankton associated with offshore eddies. The model was based on the absorption coefficient of phytoplankton at 443 nm (aph(443)) and the ratio of two Gaussian peaks at 519 nm and 435 nm, where these two peaks represent different contributions of phytoplankton pigments to aph. This two-dimensional spatial model was applied to images collected by the Ocean and Land Color Instrument in the California offshore region to demonstrate that the scheme effectively separated offshore upwelling waters from those migrating from nearshore waters. Such separations provide independent sources for identifying offshore upwelling water that will be important for studying offshore circulation processes.

Research on an Equivalent Heat Source Model of the AC Arc in the Short Gap of a Copper-Core Cable and a Fire Risk Assessment Method.

The magnetohydrodynamics (MHD) model of the alternating current (AC) arc is complex, so a simplified equivalent heat source (EHS) model can be used to replace the complex model in studying the AC arc's thermal characteristics and cable fire risk. A 2D axisymmetric AC arc MHD simulation model in the short gap of a copper-core cable is established in this paper. The AC arc voltage and current obtained by the model are consistent with experiments. The AC arc's heat source distribution obtained by the MHD model is fitted to obtain the heat source function Q of the AC arc. Q is divided into 16 independent segmented heat sources, and a correction matrix is constructed to optimize the segmented heat sources. A neural network and a genetic algorithm give the prediction model and the optimal correction matrix of the segmented heat source. The EHS model optimized by the optimal correction matrix can obtain a minimum temperature error of 5.8/4.4/4.2% with the MHD model in different AC arc peak currents 2/4/6 A. The probability of a cable fire is calculated by using AC arc's optimized EHS model when different numbers of AC arcs are generated randomly in AC half-waves. The EHS model can replace the complex MHD model to study the thermal characteristics of AC arcs and quickly calculate the probability of a cable fire caused by random AC arcs.

Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry

One of the main effects of climate change is rising sea levels, which presents challenges due to its geographically heterogenous nature. Often, contradictory results arise from examining different sources of measurement and time spans. This study addresses these issues by analysing both long-term (1995–2022) and decadal (2000–2009 and 2010–2019) sea-level trends in the Baltic Sea. Two independent sources of data, which consist of 13 tide gauge (TG) stations and multi-mission along-track satellite altimetry (SA), are utilized to calculate sea-level trends using the ordinary least-squares method. Given that the Baltic Sea is influenced by geographically varying vertical land motion (VLM), both relative sea level (RSL) and absolute sea level (ASL) trends were examined for the long-term assessment. The results for the long-term ASL show estimates for TG and SA to be 3.3 mm/yr and 3.9 mm/yr, respectively, indicating agreement between sources. Additionally, the comparison of long-term RSL ranges from −2 to 4.5 mm/yr, while ASL varies between 2 and 5.4 mm/yr, as expected due to the VLM. Spatial variation in long-term ASL trends is observed, with higher rates in the northern and eastern regions. Decadal sea-level trends show higher rates, particularly the decade 2000–2009. Comparison with other available sea-level datasets (gridded models) yields comparable results. Therefore, this study evaluates the ability of SA as a reliable source for determining reginal sea-level trends in comparison with TG data.

Multiresolution soil moisture products based on a spatially adaptive estimation model and CYGNSS data

ABSTRACT The estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy

Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient’s brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

Citizen scientists’ engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals’ attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study’s outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

An Assessment of the Epidemiology and Herd-Level Impact of the Fractured Humerus Epidemic in New Zealand Dairy Cattle, 2007–2015: Results from Four Studies

A multi-method approach integrating data from four independent sources was used to describe some key features of the epidemiology and estimate the herd and within-herd incidence of fractured humeri in New Zealand dairy cattle for the period 2007–2015. The first dataset was from a national case series where cases of humeral fractures in dairy cattle were identified by veterinarians across New Zealand between the 2007/2008 and 2011/2012 lactation seasons. The second dataset was from a pet food company based in the Waikato region, which collated the number of casualty first- and second-lactation cows found to have a fractured humerus post-slaughter in the 2014/2015 lactation season, and the third dataset was a case series conducted by veterinarians employed in a Waikato veterinary business, also from the 2014/2015 lactation season. For the final dataset, 505 randomly selected New Zealand dairy farmers completed a phone survey on the incidence of non-responsive, non-weight-bearing forelimb lameness in first- and second-lactation cows in the 2014/2015 lactation season. Using the telephone survey results, the within-herd and herd-level incidence of cases for first- and second-lactation dairy animals was calculated. The national case series reported 149 cases of humeral fractures in 22 dairy herds; the pet food case series identified 61 cases from 41 farms; and the practice-based case series found 14 cases from 10 farms. Humeral fractures exclusively affected first- and second-lactation dairy cows and had a peak incidence between calving and early mating. The national telephone survey found that non-weight-bearing forelimb lameness requiring euthanasia of first- or second-lactation cows occurred in 11.7% of herds, with a mean within-herd incidence of 2.6% for first lactation cows and 2.8% for second-lactation cows for affected herds. These combined datasets demonstrate that humeral fractures in young, lactating dairy cattle are more common than previously suspected and that they occur nationally and over multiple years on some farms. Further work on this condition is urgently required in New Zealand to establish cost-effective management practices that will reduce unnecessary animal suffering and waste.

Correcting campaign misinformation: Experimental evidence from a two-wave panel study

In this study, we used a two-wave panel and a real-world intervention during the 2017 UK general election to investigate whether fact-checking can reduce beliefs in an incorrect campaign claim, source effects, the duration of source effects, and how predispositions including political orientations and prior exposure condition them. We find correction effects in the short term only, but across different political divisions and various prior exposure levels. We discuss the significance of independent fact-checking sources and the UK partisan press in facilitating effects.

The Islamic Secular (2017)*

It is common to assume an inherent conflict between the substanceof the category “religion” and the category “secular.” Givenits putative rejection of the separation between the sacred andthe profane, this conflict is presumed to be all the more solid inIslam. But even assuming Islam’s rejection of the sacred/profanedichotomy, there may be other ways of defining the secular inIslam and of thinking about its relationship with the religion.This is what the present essay sets out to do. By taking Sharia asits point of departure, it looks at the latter’s self-imposed limitsas the boundary between a mode of assessing human acts thatis grounded in concrete revelational sources (and/or their extension)and modes of assessing human acts that are independent ofsuch sources, yet not necessarily outside God’s adjudicative gaze.This non-shar`ī realm, it is argued, is the realm of the “Islamic secular.”It is “secular” inasmuch as it is differentiated from Sharia asthe basis for assessing human acts. It remains “Islamic,” however,and thus “religious,” in its rejection of the notion of proceeding“as if God did not exist.” As I will show, this distinction betweenthe shar`ī and the nonshar`ī has a long pedigree in the Islamiclegal (and theological) tradition. As such, the notion of the Islamicsecular is more of an excavation than an innovation. *This article was first published in the American Journal of Islamic Social Sciences 34, no. 2 (2017): 1-31

A Century of Reforestation Reduced Anthropogenic Warming in the Eastern United States

AbstractRestoring and preserving the world's forests are promising natural pathways to mitigate some aspects of climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near‐surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising questions about reforestation's contribution to local cooling and climate mitigation. Using new cross‐scale approaches and multiple independent sources of data, we uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground‐ and satellite‐based observations showed that EUS forests cool the land surface by 1–2°C annually compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2–5°C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near‐surface air, with forests reducing midday air temperature by up to 1°C compared to nearby non‐forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations surrounded by reforestation were up to 1°C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation's potential as a local climate adaptation strategy in temperate regions.

Graph-Based Information Separator and Area Convolutional Network for EEG-Based Intention Decoding

In the current research on brain-computer interface (BCI), the electroencephalography (EEG) signal is usually only represented by a two-dimensional matrix, and the installation position of the EEG electrodes and the correlation between them are not considered. Actually, the cerebral cortex is a continuous potential surface and the information collected directly from each electrode is influenced by the other electrodes, so direct use of the raw data results in information redundancy. This paper converts the EEG signal into a graph, and then creates an information separator (IS) based on the Laplace matrix of the graph to obtain the independent source information of electrode nodes, and propose an IS-based area convolutional network (IS-ACN). Integrating the proposed IS with some advanced methods, the experimental results show that the incorporation of IS can enhance the performance of these methods. By observing and tracking samples with abnormal noise in the BCI competition IV dataset 2a, it is demonstrated that the proposed method can greatly reduce the influence of noise and effectively obtain the source features of EEG signals with low signal-to-noise ratio, and the average accuracy and kappa coefficient of the proposed IS-ACN on this dataset are 80.59% and 74.1%, respectively.

Proposals for ruling out real quantum theories in an entanglement-swapping quantum network with causally independent sources

Proposals for ruling out real quantum theories in an entanglement-swapping quantum network with causally independent sources

Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020

Abstract. Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1° and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07995 (Zhu and Dai, 2023).

Disentangling three different conflicts at non-response/semantic and response levels in a new stroop-matching task

Stroop-like tasks have been used to examine adaptive control in order to overcome distracting information. Debate about the source of interference in Stroop-like tasks is ongoing, and two main sources of conflicts have been consistently mentioned: response and non-response (e.g., semantic) conflicts. Different behavioral protocols have been proposed to disentangle the contribution of each level of conflict. In the new Stroop-matching task adopted in this study, participants had to match the relevant Stroop attribute (color or word, depending on the instructions) to two color-words or two colored bars laterally presented, before emitting their manual responses – with only between-attributes matches being adopted. Response latency and accuracy were analyzed. By means of specific comparisons among conditions, in which the relevant and irrelevant Stroop attribute changed, our results allowed three different sources of interference to be disentangled at the non-response/semantic and response levels, which affect accuracy and/or response latency: the congruency, the response-set and relationship effects. Our new Stroop-matching task and the results obtained were critically discussed and compared with others. We found evidence to corroborate the studies that support separate and independent sources of conflict, and the asymmetric pattern of interference of each Stroop attribute (color and word). Finally, we concluded that Stroop-like tasks can present at least three different conflicts, at both the non-response and response levels, giving support to the multistage models of Stroop interferences, and indicating that task demands are the critical point to determine which (and how) the conflicts will affect the behavioral performance.

Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

ABSTRACT Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

On the use of routine airborne observations for evaluation and monitoring of satellite observations of thermodynamic profiles

Abstract. Satellite-based observations require independent sources of data to monitor and evaluate their precision and accuracy. For the temperature and water vapor profiles produced by satellite-based sounders, this typically results in comparisons to operational radiosonde observations. However, polar-orbiting satellite overpasses are frequently misaligned with the global synoptic launch times. The routine airborne in situ observations of temperature and water vapor from the Airborne Meteorological Data Relay (AMDAR) program and the Water Vapor Sensing System-II (WVSS-II) instrument greatly enhance opportunities to make precise matchups due to the far greater temporal frequency and spatial density of aircraft flights. The potential for the use of aircraft-based observations as a source of evaluation of tropospheric satellite sounder profiles is explored through a year-long intercomparison with the Infrared Atmospheric Sounding Interferometer (IASI) level-2 profiles produced from both the Metop-A and Metop-B satellites. Results using 1 h and 50 km match criteria indicating good agreement between the satellites and the aircraft-based observations with temperature, specific humidity, and relative humidity biases generally less than 0.5 K, 0.8 g kg−1, and 5 %, respectively; both IASI instruments perform nearly identically. While the intercomparisons are generally limited to the troposphere as aircraft typically reach their maximum height at the tropopause, the substantially larger number of intercomparison points enable characterization as a function of season, scan angle, and other characteristics heretofore unexplored due to a lack of sufficient validation data.

Characterizing two-mode-squeezed light from four-wave mixing in rubidium vapor for quantum sensing and information processing.

We present a study of homodyne measurements of two-mode, vacuum-seeded, quadrature-squeezed light generated by four-wave mixing in warm rubidium vapor. Our results reveal that the vacuum squeezing can extend down to measurement frequencies of less than 1 Hz, and the squeezing bandwidth, similar to the seeded intensity-difference squeezing measured in this system, reaches up to approximately 20 MHz for typical pump parameters. By dividing the squeezing bandwidth into smaller frequency bins, we show that different sideband frequencies represent independent sources of two-mode squeezing. These properties are useful for quantum sensing and quantum information processing applications. We also investigate the impact of group velocity delays on the correlations in the system, which allows us to optimize the useful spectrum.

Modeling Lithium Plating Onset on Porous Graphite Electrodes Under Fast Charging with Hierarchical Multiphase Porous Electrode Theory

Lithium plating during fast charging of porous graphite electrodes in lithium-ion batteries accelerates degradation and raises safety concerns. Predicting lithium plating is challenging due to the close redox potentials of lithium reduction and intercalation, obscured by the nonlinear dynamics of electrochemically driven phase separation in hierarchical pore structures. To resolve dynamical resistance of realistic porous graphite electrodes, we introduce a model of porous secondary graphite particles to the multiphase porous electrode theory (MPET), based on electrochemical nonequilibrium thermodynamics and volume averaging. The resulting computational framework of “hierarchical MPET” is validated and tested against experimental data over a wide range of fast charging conditions and capacities. With all parameters estimated from independent sources, the model is able to quantitatively predict the measured cell voltages, and, more importantly, the experimentally determined capacity for lithium plating onset at fast 2C to 6C rates. Spatial and temporal heterogeneities in the lithiation of porous graphite electrodes are revealed and explained theoretically, including key features, such as idle graphite particles and non-uniform plating, which have been observed experimentally.