Multidisciplinary Digital Publishing Institute (Switzerland)
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C-STEER: A Dynamic Sentiment-Aware Framework for Fake News Detection with Lifecycle Emotional Evolution
No full textThe dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles
From Agent-Based Markov Dynamics to Hierarchical Closures on Networks: Emergent Complexity and Epidemic Applications
No full textWe explore a rigorous formulation of agent-based SIR epidemic dynamics as a discrete-state Markov process, capturing the stochastic propagation of infection or an invading agent on networks. Using indicator functions and corresponding marginal probabilities, we derive a hierarchy of evolution equations that resembles the classical BBGKY hierarchy in statistical mechanics. The structure of these equations clarifies the challenges of closure and highlights the principal problem of systemic complexity arising from stochastic but generally not fully chaotic interactions. Monte Carlo simulations are used to validate simplified closures and approximations, offering a unified perspective on the interplay between network topology, stochasticity, and infection dynamics. We also explore the impact of lockdown measures within a networked agent framework, illustrating how SIR dynamics and structural complexity of the network shape epidemic with propagation of the COVID-19 pandemic in Northern Italy taken as an example
Water Inrush in Roof Bed Separation Due to Extra-Thick Seam Mining and Its Control
No full textThis paper takes a fully caving face in a coal mine in western China as an example and analyzes several water-inrush cases in the roof-bed separation of the first mining face. Various causes and characteristics of water inrush in bed separation are also analyzed. The bed separation closure distance in the working face mining was calculated using the thin-slab theory. The results show that the roof-bed separation first closure distance was about 250–300 m, and the cycle closure distance was about 150–175 m. Moreover, a water-in-bed separation-disaster prevention method was proposed by conducting a ground straight-through diversion borehole, which is used for dewatering in bed separation. Furthermore, the groundwater level supplying the roof-bed separation was observed. The results show that the ground straight-through diversion borehole was good for dewatering the bed separation before the closure of the bed separation. This measure eliminated the danger of water inrush in roof-bed separation, which ensures the safe mining of the working face. This study, through the integration of theoretical analysis and engineering practice, proposes and validates a prevention and control technology for water hazards in roof-bed separation based on ground straight-through diversion boreholes, providing a reliable technical approach for safe mining under similar geological conditions
Online Marketing Tools and Students’ Career Decision Processes: Managerial Insights from Iraqi Higher Education
No full textThis study explores how digital and traditional marketing tools influence higher education students’ career decision-making, satisfaction, and career commitment during students’ educational trajectories in Iraq’s rapidly expanding university sector. Using an explanatory sequential mixed-methods design, a survey of 622 students was analysed with partial least squares structural equation modelling (PLS-SEM), followed by 24 semi-structured interviews with marketing and recruitment professionals. The quantitative findings show that students’ first-choice preferences, demographic factors, and engagement with LinkedIn, WeChat, blogs, and university webpages significantly shaped their career choices and satisfaction levels. Qualitative insights reveal that authenticity, transparent communication, and alignment between institutional messaging and lived experiences were key to sustaining trust. Traditional channels such as brochures and fairs remained important for credibility, supporting a hybrid marketing approach. The study contributes to management theory and practice in universities by linking digital communication strategies to student engagement and institutional performance. It also highlights the need for inclusive, transparent, and culturally adaptive marketing that reflects local and global contexts. These findings provide actionable guidance for higher education administrators seeking to build sustainable student trust, enhance recruitment effectiveness, and strengthen institutional reputation in competitive and resource-constrained systems
Measuring Environmental Change: Oil Palm Expansion and the Anthropogenic Transformation in the Headwater Sub-Basin Caeté River, Brazilian Amazon (1985–2023)
No full textOil palm (Elaeis guineensis), a rapidly expanding crop in northeastern Pará, first emerged in the 1970s as a crucial response to the global oil crisis. However, its swift expansion has subsequently generated significant socio-environmental conflicts, profoundly altering local socioecological dynamics. Therefore, we aimed to investigate land-use and land-cover changes within the headwater sub-basin of the Caeté River, focusing specifically on the municipality of Bonito, Pará. To achieve this, we employed remote sensing and geospatial analysis to accurately delineate the study area and perform supervised classifications. Specifically, we used the Random Forest algorithm to map five distinct periods: 1985, 1995, 2004, 2015, and 2023. In addition, we calculate an Anthropogenic Transformation Index (ATI) in order to observe the human influence in the landscape. Our classification models exhibited high accuracy, with overall accuracy values ranging from 0.63 to 0.87 and Kappa coefficients between 0.53 and 0.76, demonstrating consistent discrimination among LULC classes. The results revealed a marked transformation of the landscape, with oil palm monocultures progressively expanding at the expense of dense forest and human-modified vegetation. For instance, the ATI increased from 3.14 in 1985 to 5.56 in 2004, followed by a slight decline to 4.90 in 2023, suggesting a potential stabilisation—but not a reversal—of anthropogenic pressures. Nonetheless, the negative socioecological impacts of the oil palm monocultures in this Amazonian landscape remain severe, encompassing issues such as water pollution and ongoing socio-environmental conflicts. In conclusion, this research highlights the importance of understanding these dynamics to support sustainable management of the Caeté River basin. Furthermore, we underscore the urgent need for further research to rigorously evaluate effective mitigation strategies and foster genuinely sustainable development within the region
Applications of Artificial Intelligence in Fisheries: From Data to Decisions
No full textAI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems
Correlations Between the Inherent Components of Grains in Various Rice Varieties and the Quality of Sweet Rice Wine
No full textThe inherent chemical composition of different rice varieties can significantly influence the quality of sweet rice wine. However, most studies on sweet rice wine overlook varietal characteristics, resulting in slow progress in breeding rice varieties specialized for sweet rice wine production. To investigate the relationship between the inherent chemical composition of various rice varieties, such as starch, protein, and crude fat content, and their corresponding rice wines, 16 rice varieties with significant compositional variation were used in this study. The results revealed that screening solely for glutinous or non-glutinous rice is insufficient to select suitable raw materials for sweet rice wine production. Correlation analysis showed that the total sugar content of sweet rice wine was primarily associated with starch properties. In contrast, the formation of alcoholic strength and juice yield was more complex, exhibiting close correlations with multiple rice components, including amylose, albumin, globulin, crude fat, tannin content, and others. Furthermore, interactions among these components were also significantly correlated with these quality traits. In conclusion, amylose content, the ratio of amylose to amylopectin, gel consistency, and albumin content are important indicators for the rapid screening of high-quality rice lines, as they strongly correlate with sweet rice wine quality. These results will facilitate the development of rice varieties specialized for sweet rice wine production
Microencapsulation of Black Carrot Pomace Bioactive Compounds: Artificial Neural Network Modeling of Cytotoxicity on L929 Fibroblast Cells
No full textValorization of black carrot pomace (BCP), an industrial by-product rich in bioactive compounds, was performed using sustainable extraction and formulation approaches. Bioactive compounds were extracted, using water as a solvent, via ultrasonic processing. The resulting liquid extract (BCP-E) was then freeze-dried with a gum Arabic gel system to obtain a powder formulation (FD-BCP). The technological, physicochemical, and bioactive characteristics of both formulations are described. Total monomeric anthocyanin and antioxidant activities (DPPH and ABTS) did not differ substantially (p > 0.05), but the liquid extract’s total phenolic content was significantly higher (4.95 mg GAE/g db) than the powder formulation’s (4.46 mg GAE/g db). While FD-BCP had three main hydrophilic phenolic compounds, suggesting partial encapsulation, high-resolution LC-MS analysis identified 21 phenolic compounds in BCP-E, dominated by chlorogenic, quinic, and protocatechuic acids. The development of a stable gum Arabic matrix that maintains the phenolics’ structural integrity was confirmed by SEM and FTIR observations. According to cytotoxicity tests conducted on L929 fibroblast cells, both formulations were biocompatible (>70% viability) and even stimulated cell growth at moderate dosages. Dose- and time-dependent viability patterns were successfully described by Principal Component Analysis and Artificial Neural Network models, highlighting the fact that formulation type is the main factor influencing biological response. Overall, ultrasonic extraction and freeze-drying offer efficient and sustainable strategies for producing stable and bioactive-rich components from black carrot pomace that may be used in functional foods and biomedical products
Bioactive Phytocompound Profiling and the Evaluation of Antioxidant, Antihyperglycemic, and Antimicrobial Activities of Medicinal Plants from Serbian Traditional Medicine
No full textMedicinal plants represent an important source of bioactive compounds whose composition and biological activity are strongly influenced by geographical origin and extraction conditions. In this study, six medicinal plants traditionally used in south-eastern Serbia (Galium verum, Filipendula vulgaris, Lythrum salicaria, Sideritis montana, Teucrium chamaedrys, and Teucrium montanum) were investigated for their phytochemical composition and antioxidant, antihyperglycemic, and antimicrobial activities. Aqueous and 40% ethanol extracts were prepared and analyzed for total phenolic content (TPC) and total flavonoid content (TFC), followed by HPLC-DAD profiling of individual polyphenolic compounds. Antioxidant activity was assessed using DPPH, ABTS, and reducing power assays, antihyperglycemic activity by α-glucosidase inhibition, and antimicrobial activity by the microdilution method against selected bacterial and fungal strains. L. salicaria exhibited the highest TPC (113.56–119.09 mg GAE/g DW), while F. vulgaris showed the highest TFC (65.74–66.31 mg RE/g DW). HPLC analysis revealed notable levels of ferulic acid in L. salicaria ethanol extract (39.12 mg/g DW), as well as rutin, luteolin, and myricetin in several species. Ethanol extracts generally demonstrated stronger antioxidant activity, with L. salicaria showing the highest DPPH (378.60 µM TE/g) and reducing power (684.06 µM TE/g), while its aqueous extract exhibited the highest ABTS activity (3621.93 µM TE/g). Strong antihyperglycemic activity was observed for F. vulgaris extracts (100% α-glucosidase inhibition). Antimicrobial assays revealed higher sensitivity of Gram-positive bacteria, particularly Listeria monocytogenes and Staphylococcus aureus, with F. vulgaris and L. salicaria extracts showing the strongest effects. These findings highlight the significant influence of plant species and extraction solvent on bioactivity and support the potential of selected Serbian medicinal plants as sources of multifunctional natural bioactive compounds
Chemical Looping Gasification with Microalgae: Intrinsic Gasification Kinetics of Char Derived from Fast Pyrolysis
No full textChemical looping gasification (CLG) based on interconnected fluidized beds is a viable technology to produce a syngas stream for chemical and fuel production. In this work, microalgae are studied for use in the CLG process; more specifically, the intrinsic kinetics of char gasification have been analyzed, as it is important for the fuel conversion and design of reactor systems. Char produced from fast pyrolysis was used in a thermogravimetric analyzer (TGA) for intrinsic kinetics analysis, and measures were made to eliminate the interparticle and external particle gas diffusion. The effect of typical operational variables, such as temperature, concentration of gasification agents (H2O and CO2), and concentration of gasification products (H2 and CO), were investigated. The TGA data is used to derive a suitable gasification model that can best fit the experimental data. The fitting with experiments then generates values of the model’s kinetics parameters. Based on the model and the kinetics values, the activation energies in the gasification with steam and CO2 were calculated to be 43.3 and 91.6 kJ/mol, respectively. The model has a good capability in the prediction of the gasification profile with H2O and CO2 under a complex reacting atmosphere