Search Results (7)

The rapid growth of the digital economy has significantly enhanced the convenience of information transmission while reducing its costs. As a result, the participation in social networks (SNs) has surged, intensifying the mutual influence among network participants. To support objective decision-making and gather public opinions within SNs, the research on the consensus-reaching process (CRP) has become increasingly important. However, CRP faces three key challenges: first, as the number of decision-makers (DMs) increases, the efficiency of reaching consensus declines; second, minority opinions and non-cooperative behaviors affect decision outcomes; and third, the relationships among DMs complicate opinion adjustments. To address these challenges, this paper introduces an enhanced CRP mechanism. Initially, the hippopotamus optimization algorithm (HOA) is applied to update the initial community division in Leiden clustering, which accelerates the clustering process, collectively referred to as HOAL. Subsequently, a two-stage opinion adjustment method is proposed, combining minority opinion handling (MOH), non-cooperative behavior management, and dual-fine tuning (DFT) management, collectively referred to as DFT-MOH. Moreover, trust relationships between DMs are directly integrated into both the clustering and opinion management processes, resulting in the HOAL-DFT-MOH framework. The proposed method proceeds by three main steps: (1) First, the HOAL clusters DMs. (2) Then, in the initial CRP stage, DFT manages subgroup opinions with a weighted average to synthesize subgroup perspectives; and in the second stage, MOH addresses minority opinions, a non-cooperative mechanism manages uncooperative behaviors, and DFT is used when negative behaviors are absent. (3) Third, the prospect-regret theory is applied to rank decision alternatives. Finally, the approach is applied to case analyses across three different scenarios, while comparative experiments with other clustering and CRP methods highlight its superior performance. Full article

Latent tuberculosis infection (LTBI) is common in people living with HIV (PLHIV) in high-TB-burden settings. Active TB is associated with specific stool taxa; however, little is known about the stool microbiota and LTBI in PLHIV. We characterised the stool microbiota of PLHIV with [interferon-γ release assay (IGRA)- and tuberculin skin test (TST)-positive] or without (IGRA- and TST-negative) LTBI (n = 25 per group). The 16S rRNA DNA sequences were analysed using QIIME2, Dirichlet-Multinomial Mixtures, DESeq2, and PICRUSt2. No α- or β-diversity differences occurred by LTBI status; however, LTBI-positive people were Faecalibacterium-, Blautia-, Gemmiger-, and Bacteroides-enriched and Moryella-, Atopobium-, Corynebacterium-, and Streptococcus-depleted. Inferred metagenome data showed that LTBI-negative-enriched pathways included several metabolite degradation pathways. Stool from LTBI-positive people demonstrated differential taxa abundance based on a quantitative response to antigen stimulation. In LTBI-positive people, older people had different β-diversities than younger people, whereas in LTBI-negative people, no differences occurred across age groups. Amongst female PLHIV, those with LTBI were, vs. those without LTBI, Faecalibacterium-, Blautia-, Gemmiger-, and Bacteriodes-enriched, which are producers of short-chain fatty acids. Taxonomic differences amongst people with LTBI occurred according to quantitative response to antigen stimulation and age. These data enhance our understanding of the microbiome’s potential role in LTBI. Full article

The successful growth of single crystals of a cubic Laves-phase material HoAl₂ with the space group Fd-3m is reported in this study. The crystals were grown by a floating-zone method with five laser diodes as a heat source. Al-rich feed rods were prepared as compensation for heavy evaporation during the growth. The nominal ratio for the feed rods was optimized as Ho:Al = 1:2.5. Single crystals of HoAl₂ with a length of 50 mm were first grown in this technique. Obtaining the large-sized crystal by the floating-zone method enabled us to systematically explore the physical properties using the same batch crystal. The crystal possessed a second-ordered ferromagnetic transition at 29 K and a first-ordered spin-reorientation transition at 20 K. The bulk physical properties, such as specific heat, magnetic susceptibility, isothermal magnetization, and thermal expansion measurements, were measured. Additionally, a magnetocaloric effect was evaluated by the magnetic entropy change. We demonstrate that anisotropic physical properties along the principal axes ([100], [110], and [111]) emerged below the magnetically ordered states, in contrast to the isotropic behavior in the paramagnetic state. Full article

Antimicrobial resistance (AMR) is a public health issue attributed to the misuse of antibiotics in human and veterinary medicine. Since AMR surveillance requires a One Health approach, we sampled nine interconnected compartments at a hydrological open-air lab (HOAL) in Austria to obtain six bacterial species included in the WHO priority list of antibiotic-resistant bacteria (ARB). Whole genome sequencing-based typing included core genome multilocus sequence typing (cgMLST). Genetic and phenotypic characterization of AMR was performed for all isolates. Eighty-nine clinically-relevant bacteria were obtained from eight compartments including 49 E. coli, 27 E. faecalis, 7 K. pneumoniae and 6 E. faecium. Clusters of isolates from the same species obtained in different sample collection dates were detected. Of the isolates, 29.2% were resistant to at least one antimicrobial. E. coli and E. faecalis isolates from different compartments had acquired antimicrobial resistance genes (ARGs) associated with veterinary drugs such as aminoglycosides and tetracyclines, some of which were carried in conjugative and mobilizable plasmids. Three multidrug resistant (MDR) E. coli isolates were found in samples from field drainage and wastewater. Early detection of ARGs and ARB in natural and farm-related environments can identify hotspots of AMR and help prevent its emergence and dissemination along the food/feed chain. Full article

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from −17% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach. Full article

Knowledge of the evaporation (E) and transpiration (T) components of evapotranspiration (ET) is important for ecohydrological modeling and agricultural productivity. The stable-isotope method offers the possibility to partition E and T due to the distinct differences in the isotopic signals of the sources. In this study, the concentration and isotopic ratios for oxygen-18 (¹⁸O) of water vapor in the ecosystem boundary layer of a growing maize field at the Hydrological Open Air Laboratory (HOAL) catchment in Austria were measured using a high-frequency field-sampling device. In conjunction with isotope samples from the soil and maize plants, these data were used to partition ET using the Keeling plot technique. Eddy covariance and sap flow measurements were used to provide a comparison to test the stable-isotope method. The fraction of transpiration (Ft) calculated with the stable-isotope method showed good agreement with the sap flow method. Overall daily average values of Ft were in a range from 43.0 to 88.5% with T accounting for an average value of 67.5% of the evapotranspiration over the nine days of the experimental period. Following a precipitation event of 9.7 mm, Ft increased from 63.4 to 88.5% over the next four days as the upper layer of the soil dried out while the plants accessed deeper soil water. Full article

Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization based on global parameters to regional conditions improves the seasonal representation of SM and vegetation optical depth ( $\tau$ ). SM and $\tau$ are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and $\tau$ are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient $r_s$ by 0.15 on average, and $r_s$ comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone. Full article