Search Results (1,877)

Processing of nickel ores is a key aspect of modern metallurgy due to the growing demand for nickel in stainless steel, battery production, and advanced materials. The depletion of high-grade sulfide ores has shifted attention toward oxidized (lateritic) nickel ores, which are characterized by complex mineralogy and low metal content. This study presents a comparative review of major processing technologies, including pyrometallurgical, hydrometallurgical, and hybrid approaches, with particular emphasis on their applicability to Kazakhstan’s limonitic laterites with high iron and low nickel content. The analysis shows that the most suitable processing routes for such ores include atmospheric acid leaching (AL), high-pressure acid leaching (HPAL), metallothermic reduction, and combined flowsheets integrating thermal and leaching stages. Among these, AL and hybrid approaches are identified as the most promising under resource-constrained conditions. Despite recent technological progress, challenges remain related to energy consumption, economic feasibility, and environmental impact. The study highlights the importance of developing energy-efficient and low-carbon technologies, including hydrogen-based reduction, and provides practical recommendations for selecting and adapting processing methods for Kazakhstan. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article

Background: Edwardsiella piscicida is a significant intracellular pathogen of channel catfish (Ictalurus punctatus) and a major threat to U.S. aquaculture. A recently developed recombinant attenuated vaccine strain (χ16016) uses arabinose-regulated murA expression to trigger delayed cell wall lysis in vivo, ensuring biological containment while conferring strong protection against virulent challenge. Although its efficacy has been demonstrated, the host immune programs underlying protection remain incompletely defined. Methods: We used RNA sequencing to characterize tissue-specific transcriptomic responses in the intestines and kidneys of channel catfish at 7 days post-vaccination. Fish were vaccinated with χ16016 by either bath immersion or intracoelomic (IC) injection, and differentially expressed genes and enriched immune pathways were analyzed to determine how the vaccine delivery route shapes systemic and mucosal immune responses. Results: Across comparisons, 19,101 differentially expressed genes revealed pronounced route- and tissue-dependent immune remodeling. As aquaculture vaccination strategies increasingly prioritize scalability and practical deployment, understanding how the delivery route shapes immune outcomes is critical. Here, IC vaccination induced broader systemic transcriptional changes, particularly in the intestine, whereas bath immunization elicited a more focused yet coordinated mucosal response. Overall, intestinal tissue exhibited greater transcriptional responsiveness than kidney tissue, underscoring its central role in early vaccine-induced immunity. Functional enrichment analyses identified the activation of innate recognition pathways, MAPK and calcium signaling cascades, complement components, antigen processing machinery, and cell adhesion networks. Notably, bath immunization enriched the intestinal immune network for IgA production pathway, which represents an orthology-based mapping of conserved mucosal immune components, alongside the upregulation of IL-6, CXCL12–CXCR4, integrins (α4β7), MHC class II, complement C3, and polymeric immunoglobulin receptor (pIgR). Given that catfish rely primarily on IgM in mucosal immunity, these findings indicate the induction of IgM-mediated mucosal defense rather than classical mammalian IgA responses. Concurrent complement and scavenger receptor signatures suggest a transition toward efficient opsonophagocytic clearance with controlled inflammation at this subacute stage. Conclusions: This study provides the first systems-level view of host transcriptomic responses to a regulated-lysis E. piscicida vaccine in channel catfish. The findings demonstrate that immersion vaccination, although transcriptionally less expansive than injection, effectively activates coordinated mucosal innate and adaptive immune programs, supporting its practical use as a scalable vaccination strategy for aquaculture. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an ϵ-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

Port competition is increasingly shaped by network structures and differentiated functional roles rather than isolated capacity-based comparisons. This study investigates the structural evolution and functional differentiation of the global container port network from a systems perspective by integrating port-cluster identification with role-based functional evaluation. A CONCOR-based approach is employed to delineate structurally cohesive port clusters, while the rank-sum ratio (RSR) method is used to assess ports’ dominant functional roles, including High-Efficiency core, Bridge-Control, and free-form bridging functions. Based on a comparative analysis of network data for 2014 and 2024, the results reveal a transition from a relatively dispersed and multi-polar configuration toward a more concentrated and hierarchical system. Three recurrent spatial structures are identified, reflecting differentiated patterns of trunk connectivity, corridor organisation, and adaptive network flexibility. Functionally, core hubs have expanded their coverage of mainline services, Bridge-Control ports have become increasingly concentrated at strategic chokepoints and transition zones, and free-form bridging ports have enhanced routing flexibility by linking structurally non-overlapping subnetworks. These findings advance understanding of the evolving structure and interdependence of global port competition and provide insights for system-level coordination, cluster-based governance, and coordinated infrastructure planning. Full article

Software-defined networking (SDN) provides a flexible solution to manage complex networks on demand by centralized control and programmability. However, efficiently optimizing network configurations to achieve load balance and improve service quality remains challenging. In this paper, we propose a novel SDN network state awareness and dynamic routing optimization method, termed DGSFN-DR. Hereby, we leverage a Graph Attention Network (GAT) to model the spatial dependencies of the network topology for its link graph. Then, we employ a Recurrent Neural Network (RNN) to capture the temporal dependencies of link states, including the lagged temporal features induced by routing algorithms, to improve the prediction accuracy of future link states. Our algorithm dynamically adjusts routing strategies to optimize network performance according to the predicted link weights with the dual graph spatiotemporal fusion prediction network (DG-SFN). Experimental results demonstrate that our DGSFN-DR outperforms other methods in various network traffic intensities and topologies. Specifically, it achieves improvements of $4\%$ to $15\%$ in latency, jitter, packet loss, and available bandwidth. In particular, the DGSFN-DR exhibits superior adaptability and optimization potential under high traffic loads and complex network topologies. This work expands dynamic routing optimization theory for SDN and new insights for practical network management. Full article

Self-oscillators convert constant external stimuli into sustained mechanical work, offering potential for applications such as soft robotics, energy absorption, and mechanical logic. However, the effective design of a light-driven self-oscillation system is challenging due to geometrically constrained deformation modes and the inherent rigidity of rectilinear light propagation paths. Notably, the mirror-reflected optical feedback loop decouples the feedback mechanism from geometric constraints imposed by deformation modes, enabling dynamic coupling independent of structural geometry. In this study, we introduce a geometry-flexible light feedback loop to drive a liquid crystal elastomer (LCE) self-oscillator. The system comprises an optically responsive LCE fiber, a spring, a mirror, and a perforated plate. By integrating the dynamic photon propagation path in light feedback routing with the dynamic deformation model of the LCE, we develop a dynamic theoretical model of the oscillator under constant illumination. Numerical simulations reveal two distinct patterns: static equilibrium and self-oscillation. Self-oscillation is generated by the light-induced contraction of LCE fiber segments illuminated by reflected light. Crucially, mirror-reflected light enables localized deformations anywhere along the fiber to contribute to global displacement feedback, thereby transcending the constraints of geometric deformation modes. This capability transcends the limitations posed by constrained geometric deformation modes, enabling adaptable control of the optical feedback loop through simple geometric alterations. This innovative approach circumvents the need for intricate structural feedback designs and separate energy harvesters, as well as actuator systems. Full article

With the formal inclusion of the shipping industry in the European Union Emissions Trading System (EU ETS), the speed optimization of ocean-going container ships must simultaneously balance operating costs, incorporating carbon emission costs and shipper satisfaction with transportation timeliness. Taking ocean-going container liner routes as the research object, this paper establishes a ship navigation resistance model based on meteorological and hydrological conditions, and constructs a route segmentation mechanism and a ship fuel consumption model on this basis. The spatially differentiated carbon accounting rules of the EU ETS are introduced, a fuzzy membership function is adopted to quantify shipper satisfaction, and a Q-learning-based solution algorithm for ship speed optimization that balances operating costs and shipper satisfaction is designed. Numerical experiments on a 20,150 Twenty-foot Equivalent Unit (TEU) container ship demonstrate that the proposed framework reduces total operating costs by 5.56%, EU ETS carbon compliance costs by 18.72%, and total voyage carbon emissions by 11.01% compared with the conventional constant-speed strategy. Meanwhile, the algorithm can spontaneously form an optimal speed strategy adapted to meteorological conditions and policy rules. Through parameter sensitivity analysis, this paper further extracts management implications for liner-operating companies. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood routing in the arid Tarim River Basin, a hybrid forecasting framework was developed by coupling the Muskingum method with multiple machine learning algorithms (Ridge, LASSO, RF, and LSTM) to predict and correct Muskingum residuals. Global Muskingum parameters were identified using the L-BFGS-B algorithm to represent basin-scale routing characteristics. For rolling forecast, a multidimensional feature space was constructed by integrating routing gradients and hydraulic interaction terms. The results indicated that all hybrid models outperformed the traditional Muskingum method across lead times. The Ridge-based hybrid model achieved the best performance at short lead times, with the Nash–Sutcliffe efficiency (NSE) at a 4 h lead time increasing from 0.56 for the physical baseline to 0.977. For longer lead times (12–24 h), the LASSO-based hybrid model demonstrated higher robustness, which was attributed to L1-regularization-based feature selection. The key scientific contribution of this work lies in proposing a lead-time-dependent adaptive modeling strategy, revealing the structural characteristics of the residuals of the Muskingum model, and demonstrating that, in the study basin, simple linear models outperform complex models in multi-step correction. Overall, the proposed framework alleviates systematic underestimation during high-flow periods and provides a predictive scheme for arid-region rivers that preserves physical interpretability while improving forecasting accuracy. Full article

The evolution and spatial dissemination of SARS-CoV-2 Omicron subvariants have been characterized by rapid lineage replacement and complex transmission dynamics influenced by regional connectivity. This study presents a comprehensive discrete phylogeographic analysis of 90,136 SARS-CoV-2 sequences collected in Poland from 2022 to 2024 to reconstruct the dispersal dynamics of major Omicron lineages, including BA.1, BA.2, BA.5, CH.1, XBB.1, and JN.1. Utilizing Bayesian statistical frameworks, we identified significant viral transitions between the 16 Polish voivodeships and established variant-specific dominance windows ranging from 2 to 4 months. Our findings reveal a highly dynamic epidemic landscape with shifting regional epicenters. The initial BA.1 wave was primarily driven by the Mazovian voivodeship, accounting for 36.1% of outward migration events. This pattern shifted dramatically with the rise in BA.2, which was centered in the industrial Silesian region in the south-west, a densely populated area with strong economic ties to neighboring countries, potentially reflecting a different introduction or transmission dynamic. Furthermore, the epidemic landscape continued to reconfigure during the BA.5 wave, marked by the emergence of new transmission hubs in eastern border regions such as Lublin. Subsequent lineages exhibited distinct geographic signatures: BA.5 spread broadly along the Baltic-central corridor, CH.1 was centered in the north-east, XBB.1 re-emerged in the west-central region of Greater Poland, and JN.1 was driven overwhelmingly by Lesser Poland. These transitions highlight that regional transmission hubs are transient and influenced by local factors such as population density, cross-border mobility, and socio-economic connectivity. This study underscores the critical value of dense genomic surveillance in identifying evolving dispersal routes to inform adaptive, region-specific public health interventions. Full article

In response to the plastic pollution crisis, bioplastics emerged as a sustainable alternative. However, low degradation rate and abiotic decomposition generate micro- and nanoplastics. These particles enter the food chain, establishing oral intake as a key route of human exposure. This review gathered studies on the biotransformation of bioplastics in the gastrointestinal tract and on their toxicity in human cells and murine models. Most studies focused on polylactic acid particles due to widespread use in food packaging. Under simulated gastrointestinal conditions in vitro, particles were modulated, resulting in cavity and pore formation, fragmentation, lipase competition, protein corona formation, and alterations in the gut microbiota (including Selenomonadaceae, Bifidobacterium, and Prevotellaceae). Also, particle breakdown increases surface area, enhancing interactions with biomeiolecules and causing higher in vitro and in vivo toxicity. Indeed, pro-inflammatory cytokine secretion, oxidative stress induction, and redox imbalance were found in both models. In mice, alterations in gut microbiota involving Bacillales indirectly mediated hepatotoxicity, leading to uric acid and triglyceride accumulation. Furthermore, microbiota adaptation over time was suggested with an increase in microorganisms and the potential conversion of L-lactic into harmful D-lactic acid. Despite limited studies, this review highlighted that ingested bioplastic-derived micro- and nanoplastics can lead to toxic effects. Full article

Multimodal Named Entity Recognition (MNER) aims to integrate textual and visual information to identify entities with specific semantic categories. However, existing methods often suffer from insufficient intra-modal semantic modeling, coarse cross-modal alignment, and vulnerability to noisy or ambiguous expressions in social media. To address these challenges, we propose a Visual–Semantic Guided Interaction Network (VSGN), which improves multimodal representation learning from both semantic and structural perspectives. Specifically, we first design an adaptive visual–semantic fusion module that incorporates visual descriptions as semantic guidance, enabling more informative cross-modal interactions. To further enhance feature quality, we introduce a deviation-aware channel-wise inhibitory routing (CIR) mechanism, which jointly models channel importance and distributional deviation to suppress noisy or redundant visual signals. In addition, we propose a visual–semantic guided graph structure learning module (VSG), which explicitly captures structural dependencies across modalities. By enforcing distribution-level alignment between textual and visual graph representations, the model achieves structure-aware cross-modal interaction and reduces modality inconsistency. Extensive experiments on the Twitter-2015 and Twitter-2017 datasets demonstrate the effectiveness of the proposed method, achieving F1 scores of 76.72% and 87.86%, respectively. The results show that jointly modeling semantic enhancement and structural alignment leads to more robust and discriminative multimodal representations. Full article