Search Results (28,129)

In recent years, the proliferation of fake news and misinformation has grown exponentially, far surpassing that of genuine news and posing a serious threat to social stability. Existing research in fake news detection primarily applies contrastive learning methods with a single-hot labeling strategy. The issue does not lie with contrastive learning as a technique but with its current application in fake news detection systems. Specifically, these systems penalize all negative samples equally due to the use of single-hot labeling, thus overlooking the underlying semantic relationships among negative samples. As a result, contrastive learning models tend to learn from simple samples while neglecting highly deceptive samples located at the boundary between true and false, as well as the heterogeneity of text-image features, which complicates cross-modal fusion. To mitigate these known limitations in current applications, this paper proposes a fake news detection method based on contrastive learning and cross-modal interaction. First, a consistency-aware soft-label contrastive learning mechanism based on semantic similarity is designed to provide more granular supervision signals for contrastive learning. Secondly, a difficult negative sample mining strategy based on a similarity matrix is designed to optimize the symmetry alignment of image and text features, which effectively improves the model’s ability to discriminate boundary samples. To further optimize the feature fusion process, a cross-modal interaction module is designed to learn the symmetric interaction relationship between image and text features. Finally, an attention mechanism is designed to adaptively adjust the contributions of text-image features and interaction features, forming the final multimodal feature representation. Experiments are conducted on two major social media platform datasets, and compared with existing methods, the proposed method effectively improves the detection capability of fake news. Full article

In this study, we sought to improve the productivity of Far Eastern catfish (Silurus asotus) and tropical eel (Anguilla bicolor), which are high-value fish species in the Republic of Korea, as well as that of associated crops by applying biofloc technology (BFT)-based aquaponics systems. The following three systems were used: the flow-through system (FTS), BFT, and BFT aquaponics system (BAPs). Caipira lettuce (Lactuca sativa) was utilized and hydroponics (HP) was implemented to compare crop productivity. After 42 days of treatment, the BAPs and BFT systems improved fish productivity, with weight gain rates of 134.47 ± 1.80% in BAPs-cat, 130.38 ± 0.95% in BFT, and 114.21 ± 6.62% in FTS for S. asotus, and 70.61 ± 3.26% in BAPs-eel, 62.37 ± 7.04% in BFT, and 47.83 ± 1.09% in FTS for A. bicolor. During the experiment, the total ammonia nitrogen and NO₂⁻-N concentrations were stable in all plots. In the case of NO₃⁻-N, BFT showed an increasing tendency while both BAPs showed a decrease compared with that of the BFT. BAPs-cat (total weight: 224.1 ± 6.37 g) and HP (220.3 ± 7.17 g) resulted in similar growth. However, in BAPs-eel was 187.7 ± 3.46 g due to root degradation. Water content analysis showed that BAPs-cat and BAPs-eel contained sufficient K, Ca, P, and S, which are important for crop growth. Overall, the effect of BAPs on fish growth was higher than that of FTS. This study reveals that integrating BFT with aquaponics improves productivity for high-value fish and associated crops while maintaining stable water quality. This method offers sustainable, efficient production, reduces environmental impact, and provides insights for future research in sustainable aquaculture practices. Full article

Climate-driven hydrological extremes and anthropogenic interventions are increasingly altering streamflow regimes worldwide. While prior studies have explored climate or regulation effects separately, few have integrated multiple teleconnection indices and reservoir chronologies within a cross-basin comparative framework. This study addresses this gap by assessing long-term streamflow nonstationarity and its drivers at two key stations—Khabarovsk on the Amur River and Datong on the Yangtze River—representing distinct hydroclimatic settings. We utilized monthly discharge records, meteorological data, and large-scale climate indices to apply trend analysis, wavelet transform, percentile-based extreme diagnostics, lagged random forest regression, and slope-based attribution. The results show that Khabarovsk experienced an increase in winter baseflow from 513 to 1335 m³/s and a notable reduction in seasonal discharge contrast, primarily driven by temperature and cold-region reservoir regulation. In contrast, Datong displayed increased discharge extremes, with flood discharges increasing by +71.9 m³/s/year, equivalent to approximately 0.12% of the mean flood discharge annually, and low discharges by +24.2 m³/s/year in recent decades, shaped by both climate variability and large-scale hydropower infrastructure. Random forest models identified temperature and precipitation as short-term drivers, with ENSO-related indices showing lagged impacts on streamflow variability. Attribution analysis indicated that Khabarovsk is primarily shaped by cold-region reservoir operations in conjunction with temperature-driven snowmelt dynamics, while Datong reflects a combined influence of both climate variability and regulation. These insights may provide guidance for climate-responsive reservoir scheduling and basin-specific regulation strategies, supporting the development of integrated frameworks for adaptive water management under climate change. Full article

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

To our knowledge, no magnetic B2 phase in the Al–Mn system of near-equiatomic compositions has been reported so far. Here, we investigate the structural and magnetic characteristics of Al₄₅Mn_41.8X_13.2 (X = Fe, Co or Ni) alloys. We demonstrate that adding 13.2 atomic percent magnetic 3d metal to AlMn stabilizes a ferromagnetic B2 structure, where Al and X occupy different sublattices. We employ density functional theory calculations and experimental characterizations to underscore the role of the late 3d metals for the phase stability of the quasi-ordered ternary systems. We show that these alloys possess large local magnetic moments primarily due to Mn atoms partitioned to the Al-free sublattice. The revealed magneto-chemical effect opens alternative routes for tailoring the magnetic properties of B2 intermetallic compounds for various magnetic applications. Full article

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

Unemployment and weak economic productivity are significant global issues, particularly in West Africa. Recently, through diverse mechanisms, remittances and foreign direct investment (FDI) have been sources of foreign capital flow that have positively influenced many less developed economies, including ECOWAS (ECOWAS stands for Economic Community of West African States). Nevertheless, these financial flows have exhibited significant inconsistencies, primarily resulting from economic downturns in migrants’ destination countries, with remarkable implications for beneficiary economies. This study, therefore, examines the effect of remittances and FDI on employment in ECOWAS. Specifically, the study assesses the effects of the inflow of remittances and FDI on employment using panel dynamic ordinary least squares (PDOLS) and also investigates the shock effects of remittances and FDI by employing Panel Vector Error Correction (PVECM), which involves variance decomposition. The results show that foreign direct investment (FDI) positively and significantly affects employment. Other variables that show a significant relationship with employment are wage rate, education expenditure, and interest rate. The variance decomposition result revealed that external shocks on remittances and FDI have short- and long-term effects on employment. The above findings imply that foreign direct investment has a far-reaching positive impact on the economy-wide management of the West African sub-region and thus calls for relevant policy options. Full article

Background: Asprosin is a relatively recently discovered glucogenic adipokine secreted during fasting that plays an important role in various biochemical processes in the body, including those connected with obesity and insulin resistance. The aim of this exploratory study was to investigate the associations between selected hormonal, anthropometric, and lifestyle-related parameters and serum asprosin concentration. As studies concerning fertility and asprosin have so far been limited to men or women with PCOS, its role in the general female population remains largely unexplored. The direction of this exploration was thus pointed toward possible connections with female fertility. Methods: The case-control study group included 56 women of reproductive age (25–42 years), who were patients of the Reproductive Health Clinic and the Clinic of Endocrinology, Diabetology, and Internal Medicine of the Medical University of Białystok, Poland. The levels of selected hormones, including anti-Müllerian hormone (AMH), estradiol, sex hormone-binding globulin (SHBG), and testosterone, body composition parameters, and a lifestyle parameter—night fasting duration—were assessed to test their associations with serum asprosin concentration. Results: A weak negative correlation was found between AMH level and serum asprosin concentration, suggesting a potential link between asprosin and ovarian reserve. Furthermore, a moderate positive correlation was found between the percentage of total body water (TBW) and serum asprosin concentration. No significant associations were observed between the levels of the other tested hormones and serum asprosin concentration, or between body composition parameters or night fasting duration and serum asprosin concentration. The multivariate model designed in the study shows that AMH, TBW, and night fasting duration explain 23.4% of asprosin variability. Conclusions: Although the nature of the study is exploratory, the findings indicate that the role of asprosin in the female population—particularly its role in fertility—requires further research. Not only is the number of available studies on asprosin insufficient, but the results of this study partly contradict what is known about the hormone from previous studies, which were largely performed with male cohorts. In addition, the results of this study suggest that asprosin may indeed be involved in mechanisms related to female fertility, particularly those connected with ovarian reserve. Nevertheless, studies performed in larger, more homogeneous populations are necessary to confirm the role of asprosin in women, including its association with female fertility. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

Background/Objectives: Acute kidney injury (AKI) is a common and severe complication in patients with acute myocardial infarction (AMI). Very elderly patients are at a heightened risk of developing AKI. Fibrinogen and albumin are well-known biomarkers of inflammation and nutrition, which are highly related to AKI. We aim to explore the predictive value of the fibrinogen-to-albumin ratio (FAR) for AKI in very elderly patients with AMI. Methods: A retrospective cohort of AMI patients ≥ 75 years old hospitalized at the First Affiliated Hospital of Xi’an Jiaotong University between January 2018 and December 2022 was established. Clinical data and medication information were collected through the biospecimen information resource center at the hospital. Univariate and multivariable logistic regression models were used to analyze the association between FAR and the risk of AKI in patients with AMI. FAR was calculated as the ratio of fibrinogen (FIB) to serum albumin (ALB) level (FAR = FIB/ALB). The primary outcome is acute kidney injury, which was diagnosed based on KDIGO 2012 criteria. Results: Among 1236 patients enrolled, 66.8% of them were male, the median age was 80.00 years (77.00–83.00), and acute kidney injury occurred in 18.8% (n = 232) of the cohort. Comparative analysis revealed significant disparities in clinical characteristics between patients with or without AKI. Patients with AKI exhibited a markedly higher prevalence of arrhythmia (51.9% vs. 28.1%, p < 0.001) and lower average systolic blood pressure (115.77 ± 25.96 vs. 122.64 ± 22.65 mmHg, p = 0.013). In addition, after adjusting for age, sex, history of hypertension, left ventricular ejection fraction (LVEF), and other factors, FAR remained an independent risk factor for acute kidney injury (OR = 1.47, 95%CI: 1.36–1.58). ROC analysis shows that FAR predicted stage 2–3 AKI with superior accuracy (AUC 0.94, NPV 98.6%) versus any AKI (AUC 0.79, NPV 93.0%), enabling risk-stratified management. Conclusions: FAR serves as both a high-sensitivity screening tool for any AKI and a high-specificity sentinel for severe AKI, with NPV-driven thresholds guiding resource allocation in the fragile elderly. Full article

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

Although cemented carbides have been manufactured by the powder metallurgy (P/M) technology for over a century now, systematic developmental efforts are still underway. In the present study, tool life improvements in metal grooving applications are the key objective. Four PVD-coated cemented carbides compositions, dedicated to groove steel, stainless steel, cast iron, and aluminium alloys, have been newly designed, along with their manufacturing conditions. Physical, mechanical and chemical characteristics—such as sintered density, modulus of elasticity, hardness, fracture toughness, WC grain size, and the chemical composition of the substrate material, as well as the chemical composition, microhardness, structure, and thickness of the coatings—have been studied. A series of grooving tests have also been conducted to assess whether modifications to the thus far marketed tool materials, tool geometries, and coatings can improve cutting performance. In order to compare the laboratory and application properties of the investigated materials with currently produced by reputable companies, commercial inserts have also been tested. The experimental results obtained indicate that the newly developed grooving inserts exhibit excellent microstructural characteristics, high hardness, fracture toughness, and wear resistance and that they show slightly longer tool life compared to the commercial ones. Full article

Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. Full article

The advanced determination of the type (foreshock–aftershock–swarm) of an ongoing seismic cluster is quite challenging; only retrospective solutions have thus far been proposed. In the period of January–March 2025, a seismic cluster, recorded between Santorini volcano and Amorgos Island, South Aegean Sea, caused considerable social concern. A rapid increase in both the seismicity rate and the earthquake magnitudes was noted until the mainshock of M_L = 5.3 on 10 February; afterwards, activity gradually diminished. Fault-plane solutions indicated SW-NE normal faulting. The epicenters moved with a mean velocity of ~0.72 km/day from SW to NE up to the mainshock area at a distance of ~25 km. Crucial questions publicly emerged during the cluster. Was it a foreshock–aftershock activity or a swarm of possibly volcanic origin? We performed real-time discrimination of the cluster type based on a daily re-evaluation of the space–time–magnitude changes and their significance relative to background seismicity using earthquake statistics and the topological metric betweenness centrality. Our findings were periodically documented during the ongoing cluster starting from the fourth cluster day (2 February 2025), at which point we determined that it was a foreshock and not a case of seismic swarm. The third day after the M_L = 5.3 mainshock, a typical aftershock decay was detected. The observed foreshock properties favored a cascade mechanism, likely facilitated by non-volcanic material softening and the likely subdiffusion processes in a dense fault network. This mechanism was possibly combined with an aseismic nucleation process if transient geodetic deformation was present. No significant aftershock expansion towards the NE was noted, possibly due to the presence of a geometrical fault barrier east of the Anydros Ridge. The 2025 activity offered an excellent opportunity to investigate deciphering the type of ongoing seismicity cluster for real-time discrimination between foreshocks, aftershocks, and swarms. Full article

As the use of data models and data science techniques in industrial processes grows exponentially, the question arises: to what extent can these techniques impact the future of manufacturing processes? This article examines the potential future impacts of these models based on an assessment of existing trends and practices. The drive towards digital-oriented manufacturing and cyber-based process optimization and control has brought many opportunities and challenges. On one hand, issues of data acquisition, handling, and quality for proper database building have become important subjects. On the other hand, the reliable utilization of this available data for optimization and control has inspired much research. This research work discusses the fundamental question of how far these models can help design and/or improve existing processes, highlighting their limitations and challenges. Furthermore, it reviews state-of-the-art practices and their successes and failures in material process applications, including casting, extrusion, and additive manufacturing (AM), and presents some quantitative indications. Full article