Search Results (2,898)

A metal-ion-free method was developed to prepare κ-carrageenan/cellulose hydrogel beads for efficient cationic dye removal. The beads were fabricated using a mixture of 1-ethyl-3-methylimidazolium acetate and N,N-dimethylformamide as the solvent system, followed by aqueous ethanol-induced phase separation. This process eliminated the need for metal-ion crosslinkers, which typically neutralize anionic sulfate groups in κ-carrageenan, thereby preserving a high density of accessible binding sites. The resulting beads formed robust interpenetrating polymer networks. The initial swelling ratio reached up to 28.3 g/g, and even after drying, the adsorption capacity remained over 50% of the original. The maximum adsorption capacity for crystal violet was 241 mg/g, increasing proportionally with κ-carrageenan content due to the higher surface concentration of anionic sulfate groups. Kinetic and isotherm analyses revealed pseudo-second-order and Langmuir-type monolayer adsorption, respectively, while thermodynamic parameters indicated that the process was spontaneous and exothermic. The beads retained structural integrity and adsorption performance across pH 3–9 and maintained over 90% of their capacity after five reuse cycles. These findings demonstrate that κ-carrageenan/cellulose hydrogel beads prepared via a metal-ion-free strategy offer a sustainable and effective platform for cationic dye removal from wastewater, with potential for heavy metal ion adsorption. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO₂ emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO₂ emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks. Full article

To advance the development of protein-rich plant-based foods, a novel binder system for vegan sausage alternatives without the requirement of heat application was investigated. This enables long-term ripening of plant-based analogs similar to traditional fermented meat or dairy products, allowing for refined flavor and texture development. This was achieved by using a poorly water-soluble calcium source (calcium carbonate) to introduce calcium ions into a low-ester pectin—gluten matrix susceptible to crosslinking via divalent ions. The gelling reaction of pectin–gluten dispersions with Ca²⁺ ions was time-delayed due to the gradual production of lactic acid during fermentation. Firm, sliceable matrices were formed, in which particulate substances such as texturized proteins and solid vegetable fat could be integrated, hence forming an unheated raw-fermented plant-based salami-type sausage model matrix which remained safe for consumption over 21 days of ripening. Gluten as well as pectin had a significant influence on the functional properties of the matrices, especially water holding capacity (increasing with higher pectin or gluten content), hardness (increasing with higher pectin or gluten content), tensile strength (increasing with higher pectin or gluten content) and cohesiveness (decreasing with higher pectin or gluten content). A combination of three simultaneously occurring effects was observed, modulating the properties of the matrices, namely, (a) an increase in gel strength due to increased pectin concentration forming more brittle gels, (b) an increase in gel strength with increasing gluten content forming more elastic gels and (c) interactions of low-ester pectin with the gluten network, with pectin addition causing increased aggregation of gluten, leading to strengthened networks. Full article

As a key tropospheric photochemical pollutant, ground-level ozone (O₃) poses significant threats to ecosystems through its strong oxidative capacity. With China’s rapid industrialization and urbanization, worsening O₃ pollution has emerged as a critical environmental concern. This study examines O₃’s impacts on forest ecosystems in Southwestern China (Yunnan, Guizhou, Sichuan, and Chongqing), which harbors crucial forest resources. We analyzed high-resolution monitoring data from over 200 stations (2019–2023), employing spatial interpolation to derive the regional maximum daily 8 h average O₃ (MDA8-O₃, ppb) and accumulated O₃ exposure over 40 ppb (AOT40) metrics. Through AOT40-based exposure–response modeling, we quantified the forest relative yield losses (RYL), economic losses (ECL) and ECL/GDP (GDP: gross domestic product) ratios in this region. Our findings reveal alarming O₃ increases across the region, with a mean annual MDA8-O₃ anomaly trend of 2.4% year⁻¹ (p < 0.05). Provincial MDA8-O₃ anomaly trends varied from 1.4% year⁻¹ (Yunnan, p = 0.059) to 4.3% year⁻¹ (Guizhou, p < 0.001). Strong correlations (r > 0.85) between annual RYL and annual MDA8-O₃ anomalies demonstrate the detrimental effects of O₃ on forest biomass. The RYL trajectory showed an initial decline during 2019–2020 and accelerated losses during 2020–2023, peaking at 13.8 ± 6.4% in 2023. Provincial variations showed a 5-year averaged RYL ranging from 7.10% (Chongqing) to 15.85% (Yunnan). O₃ exposure caused annual ECL/GDP averaging 4.44% for Southwestern China, with Yunnan suffering the most severe consequences (ECL/GDP averaging 8.20%, ECL averaging CNY 29.8 billion). These results suggest that O₃-driven forest degradation may intensify, potentially undermining the regional carbon sequestration capacity, highlighting the urgent need for policy interventions. We recommend enhanced monitoring networks and stricter control methods to address these challenges. Full article

The widespread adoption of sodium-ion batteries (SIBs) remains constrained by the inherent limitations of conventional anode materials, particularly their inadequate electronic conductivity, limited active sites, and pronounced structural degradation during cycling. To overcome these limitations, we propose a novel redox engineering approach to fabricate oxygen-vacancy-rich SnO₂ dots anchored on reduced graphene oxide (rGO), which are encapsulated within N-doped carbon nanofibers (denoted as ov-SnO₂/rGO@N-CNFs) through electrospinning and subsequent carbonization. The introduction of rich oxygen vacancies establishes additional sodium intercalation sites and enhances Na⁺ diffusion kinetics, while the conductive N-doped carbon network effectively facilitates charge transport and mitigates SnO₂ aggregation. Benefiting from the well-designed architecture, the hierarchical ov-SnO₂/rGO@N-CNFs electrode achieves remarkable reversible specific capacities of 351 mAh g⁻¹ after 100 cycles at 0.1 A g⁻¹ and 257.3 mAh g⁻¹ after 2000 cycles at 1.0 A g⁻¹ and maintains 177 mAh g⁻¹ even after 8000 cycles at 5.0 A g⁻¹, demonstrating exceptional long-term cycling stability and rate capability. This work offers a versatile design strategy for developing high-performance anode materials through synergistic interface engineering for SIBs. Full article

Multiband transmission is, nowadays, being implemented worldwide to increase the optical transport network capacity, mainly because it uses the already-installed single-mode fiber (SMF). The G.654E SMF, due to its attributes (e.g., low-loss, and large-effective area in comparison with the standard G.652 SMF), can also increase network capacity and can also be used for multiband (MB) transmission. Nevertheless, in MB transmission, power mode coupling arises when bands with wavelengths below the cut-off wavelength are used, inducing multipath interference (MPI). This work investigates the impact of the MPI, due to mode coupling from G.654E SMF, in the transmission reach of a C+L+S band transmission system. Our results indicate that for the S-band scenario, the band below the wavelength cut-off, an approximately 25% reach decrease is observed when the MPI/span increases to −26 dB/span, considering quadrature phase-shift keying (QPSK) signals with a 64 GBaud symbol rate. We also concluded that if the L-band were not above the wavelength cut-off, it would be much more affected than the S-band, with an approximately 52% reach decrease due to MPI impact. Full article

Innovation is a strategic driver of sustainable competitive advantage and long-term economic growth. This study proposes an empirical framework to support the sustained development of national innovation capacity by examining key enabling factors. Drawing on an extensive review of the literature, the research investigates the interrelationships among governmental support (GS), innovation agents (IA), university–industry R&D collaborations (UIRD), and innovation cluster development (ICD), and their influence on two critical innovation outcomes, knowledge creation (KC) and knowledge diffusion (KD). Using panel data from G7 countries spanning 2008 to 2018, sourced from international organizations such as the World Bank, the World Intellectual Property Organization, and the World Economic Forum, the study applies regression analysis to test the proposed conceptual model. Results highlight the foundational role of GS in providing a balanced framework to foster collaborative networks among IA and enhancing the effectiveness of UIRD. Furthermore, IA emerges as a pivotal actor in advancing innovation efforts, while the development of innovation clusters is shown to selectively enhance specific innovation outcomes. These findings offer theoretical and practical contributions for policymakers, researchers, and stakeholders aiming to design supportive ecosystems that strengthen sustainable national innovation capacity. Full article

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article

Patient involvement in health technology assessment (HTA) processes is increasingly recognized as pivotal for informed, equitable, and patient-relevant health care decision-making. With the implementation of Joint Scientific Consultations (JSCs) and Joint Clinical Assessments (JCAs) under Regulation (EU) 2021/2282, the European Union has a unique opportunity to design harmonized mechanisms that reflect best practices from established HTA systems. This article, drawing on the Acute Leukemia Advocates Network (ALAN)’s comparative analysis of HTA practices across seven countries (Canada, England, Scotland, France, Germany, Spain, and Italy), examines how current patient involvement processes can inform the JCA framework. It identifies opportunities to replicate effective practices and proposes strategies to embed patient voices meaningfully into the JCA process. By prioritizing robust and inclusive patient involvement, the EU can establish a global benchmark for impactful and consistent HTA processes. By leveraging lessons from international HTA systems and prioritizing clear frameworks, early involvement, and capacity building, the EU can set a global standard for meaningful patient participation in HTA processes. ALAN is an independent global network of patient organizations dedicated to improving outcomes for patients with acute leukemia. Full article

As the global population continues to age, the incidence of neurodegenerative diseases and neural injuries is increasing, presenting major challenges for healthcare systems. Due to the brain’s limited regenerative capacity, there is an urgent need for strategies that promote neuronal repair and functional integration. Brain-derived neurotrophic factor (BDNF) is a key regulator of synaptic plasticity and neuronal development. In this study, we investigated whether constitutive BDNF expression in human induced pluripotent stem cell (iPSC)-derived neural progenitor cells (NPCs) enhances their neurogenic and integrative potential in vitro. We found that NPCs engineered to overexpress BDNF produced neuronal cultures with increased numbers of mature and spontaneously active neurons, without altering the overall structure or organization of functional networks. Furthermore, BDNF-expressing neurons exhibited significantly greater axonal outgrowth, including directed axon extension in a compartmentalized microfluidic system, suggesting a chemoattractive effect of localized BDNF secretion. These effects were comparable to those observed with the early supplementation of recombinant BDNF. Our results demonstrate that sustained BDNF expression enhances neuronal maturation and axonal projection without disrupting network integrity. These findings support the use of BDNF not only as a therapeutic agent to improve cell therapy outcomes but also as a tool to accelerate the development of functional neural networks in vitro. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Foreign Object Debris (FOD) detection is paramount for airport operations. The precise identification and removal of FOD are critical for ensuring airplane flight safety. This study collected FOD images using optical imaging sensors installed at Urumqi Airport and created a custom FOD dataset based on these images. To address the challenges of small targets and complex backgrounds in the dataset, this paper proposes optimizations and improvements based on the advanced detection network Dab-Deformable. First, this paper introduces a Lightweight Deep-Shallow Feature Fusion algorithm (LDSFF), which integrates a hotspot sensing network and a spatial mapping enhancer aimed at focusing the model on significant regions. Second, we devise a Multi-Directional Deformable Channel Attention (MDDCA) module for rational feature weight allocation. Furthermore, a feedback mechanism is incorporated into the encoder structure, enhancing the model’s capacity to capture complex dependencies within sequential data. Additionally, when combined with a Threshold Selection (TS) algorithm, the model effectively mitigates the distraction caused by the serialization of multi-layer feature maps in the Transformer architecture. Experimental results on the optical small FOD dataset show that the proposed network achieves a robust performance and improved accuracy in FOD detection. Full article