Search Results (4,498)

The rapid development of the aquaculture industry has led to increasing discharges of hypersaline and nutrient-enriched maricultural wastewater. Functional ceramic membranes have garnered significant advantages due to their exceptional chemical stability and high tailorability through surface and interface engineering. This research reviewed recent advances including the functionalization of ceramic membranes and hybrid systems coupled with advanced oxidation processes (AOPs) for enhancing degradations of nutrients and organics in maricultural wastewater treatment. Catalytic ceramic membranes enhanced removal of micropollutants including antibiotics and heavy metals. This review further systematically classified categorization of established functional ceramic membranes and synthesizes cutting-edge modification approaches for membrane fouling mitigation. Finally, this review evaluated the application prospects, challenges for scaled implementation, and proposed future research directions of functional ceramic membranes in the treatment of maricultural wastewater. Full article

Fruit shape is determined by patterns of cell division and expansion during early development, yet the upstream transcription factors coordinating cell wall dynamics and cytoskeletal organization remain largely unknown. Here, we report that SlbHLH113, a bHLH transcription factor, positively regulates tomato fruit elongation. Overexpression (OE) of SlbHLH113 produced elongated fruits with increased length/width ratio, whereas RNAi lines exhibited flattened fruits. Histological analysis revealed that SlbHLH113 alters columella cell polarity—promoting elongated cell morphology without affecting cell area—and reduces columella–placenta width and locule width, without altering pericarp thickness. Transcriptomic profiling identified 87 differentially expressed genes in OE lines, with enrichment in cell wall-related processes. Notably, a pectate lyase gene (PL5) and an expansin gene (EXT90) were down-regulated, while genes involved in oriented cellulose deposition (COBRA4) and ethylene signaling were up-regulated. Importantly, SlbHLH113 physically interacts with the microtubule-associated protein SlIQD21a, as demonstrated by yeast two-hybrid and luciferase complementation assays. Finally, SlbHLH113 did not affect major nutrient contents in red-ripe fruits. Collectively, our findings identify SlbHLH113 as a novel regulator of tomato fruit shape that might act through cell polarity control, cell wall remodeling, and interaction with a microtubule-associated protein, offering a potential target for improving fruit morphology without compromising nutritional quality. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

Ship structural failure mode classification still relies heavily on subjective expert judgment, which is time-consuming and may introduce uncertainty in safety assessment. Although deep learning provides a promising avenue for automation, many existing learning approaches rely on 2D image representations and may therefore suffer from geometric occlusion and information loss when projecting complex 3D stiffened structures. To address these challenges, we propose a Physics-Guided Graph Convolutional Network (PGGCN) for failure mode classification. Specifically, our method models finite-element (FE) meshes directly as graphs, preserving the holistic topology and displacement-field fidelity without viewpoint dependency. We further incorporate domain knowledge through a hybrid strategy: a Deep Graph Convolutional Network (DeepGCN) first detects local component buckling states such as plate or web buckling, and a logic matrix derived from classical failure definitions subsequently determines panel-level failure modes. To enable systematic evaluation, we construct a dataset spanning diverse stiffened-panel geometries via Latin Hypercube Sampling. Progressive analysis states from each loading case are organized into task-specific graph samples for supervised learning. Experiments on the test set achieve accuracies of 95.48% and 91.42% for plate- and web-buckling classification, respectively, and 89.56% for panel-level failure mode discrimination. These results demonstrate that the proposed method provides an interpretable framework for automated failure mode classification from FE meshes in ship stiffened panels. Full article

High-performance electromagnetic wave absorption materials are desperately needed due to the growing serious electromagnetic interference and pollution issues brought on by the quick growth of modern electronic technology and wireless communication. This work uses the organic–inorganic hybrid perovskite MAPbBr_xI_3−x as a model system to address the problem of restricted loss mechanisms and the challenges in changing the absorption bandwidth of single-component wave-absorbing materials. It achieves systematic tuning of electromagnetic wave absorption performance, especially within the effective working frequency spectrum, through accurate halogen site engineering. According to the study, MAPbI₃ (MPI), MAPbBr_1.5I_1.5 (MPIB), and MAPbBr₃ (MPB), which were synthesized using the anti-solvent approach, all demonstrated exceptional microwave absorption capability, with maximum reflection loss values exceeding −37 dB, among which MPB achieves a remarkable value of −42.41 dB at 16.60 GHz. More significantly, this work shows a distinct structure-property relationship between the effective absorption peak frequency range of this series of materials and their band structure: the strongest absorption peak shows a regular blue shift as the material bandgap widens and the bromine content rises. This finding suggests that focused tailoring of the operating frequency band in wave-absorbing materials can be achieved by manipulating the band structure of perovskites by varying the halogen concentration. In addition to confirming the significant application potential of organic–inorganic hybrid perovskites in the field of microwave absorption, this study offers a novel research perspective and material template for precisely and programmably controlling the absorption frequency band of wave-absorbing materials based on their basic electronic structures. Full article

This paper systematically reviews losartan, a hypertension pharmaceutical compound that is one of many newly identified emerging contaminants in water. Worldwide use of pharmaceuticals continues to grow, and losartan has been identified as a contaminant that frequently accumulates in aquatic systems as a result of this global increase in use. The paper presents systematic reviews on the environmental occurrence, physicochemical characteristics, analytical methods of detection, and remediation techniques associated with losartan contamination. Losartan is often detected at levels of ng L⁻¹–µg L⁻¹ in wastewater systems, surface water and marine ecosystems, very effectively demonstrating the inadequacies of existing conventional wastewater treatment facilities, which are typically capable of removing only 20–70% of the contamination, with this variability largely attributed to differences in hydraulic/solids retention times, operational conditions, influent organic load, and the limited microbial acclimatization to recalcitrant pharmaceutical compounds. Emerging remediation technologies demonstrate the potential for removal efficiencies of >90% include hybrid systems, advanced electrochemical processes, new improved adsorption systems, and novel material for adsorption. However, there are still considerable barriers to progress, including excessive energy use, high operating costs, and perhaps most concerning, potentially toxic transition products generated by partial degradation. Furthermore, the literature review identified key literature gaps: lack of specific regulations, absence of full-scale studies, and inconsistencies in by-product toxicity assessments. The conclusion of this review is that to achieve worldwide water security and sustainability of aquatic resources, effective mitigation of the environmental risks associated with losartan requires combined approaches comprising innovative technologies, comprehensive ecotoxicological investigations, and improved collaboration between scientists, policymakers, and industry. Full article

Electrochemical sensors have emerged as promising tools for rapid pesticide screening in food and environmental samples because they combine simple instrumentation, fast response, portability, and compatibility with disposable electrodes. This review organizes recent progress through a cross-system framework linking pesticide class, interfacial electrochemical process, and material design. Carbon materials, metal–organic frameworks and their derivatives, metal nanoparticles, metal compounds, conducting polymers, MXene-based composites, and selected emerging materials are compared in terms of enrichment capability, charge-transfer regulation, catalytic amplification, recognition-layer integration, and suitability for real-sample analysis. Emphasis is placed on issues that are often under-discussed in performance-centered surveys, including matrix interference, electrode fouling, batch-to-batch reproducibility, storage stability, scalability, and cost-effectiveness. Representative examples show that the most useful advances arise not simply from lowering the limit of detection but from improving structure–function understanding and translating interfacial design into robust analytical performance. Future work should prioritize standardized fabrication and benchmarking protocols, in situ and operando identification of active sites and interface evolution, matrix-specific antifouling validation, multiresidue and metabolite analysis, and hybrid portable devices coupled with intelligent readout. Full article

Periods of major technological transformation have historically coincided with the emergence of political movements. The current Artificial Intelligence (AI) revolution, alongside the expansion of platform-based media, has reshaped how political dissent is produced, circulated, and normalized. This study examines contemporary anti-monarchist discourse associated with the UK-based Republic movement, focusing on how opposition to constitutional monarchy is articulated on YouTube within an environment shaped by profit-driven goals. Using NVivo 14, this qualitative study analyzes 62 publicly available YouTube videos published over a twelve-month period from January 2025 to January 2026, employing a hybrid inductive–deductive thematic analysis supported by Large Language Models. Findings identify three interrelated discursive themes: monarchy framed as legalized theft and extraction of public wealth; monarchical authority depicted as undemocratic and constitutionally manipulative; and the reproduction of colonial, elite, and mythic power through mediated narratives of tradition and national identity. Rather than evaluating the factual accuracy of anti-monarchist claims, the analysis treats this content as a mediated cultural practice through which broader socio-economic anxieties—such as inequality, democratic distrust, and fears of technological displacement—are symbolically organized. Digital platforms, such as the Republic Campaign YouTube channel, thus enable political discourse to gain visibility and resonance. Full article

Tropical lignocellulosic residues are increasingly relevant feedstocks for lignin-containing nanocellulose composites, but their performance cannot be predicted from botanical origin or bulk lignin percentage alone. This review defines the interface as the geometrical boundary between phases and the interphase as the finite, compositionally graded region in which lignin distribution, nanocellulose morphology, adsorbed water, and the surrounding matrix jointly govern stress transfer and mass transport. Using an evidence-weighted framework, the literature is organized into the following categories: residual-lignin nanofibrils, redeposited-lignin systems, lignin nanoparticle assemblies, compatibilized thermoplastic hybrids, and all-lignocellulosic sheets. Representative quantitative observations show that controlled residual lignin can the increase water contact angle from approximately 35 degrees to 78 degrees and reduce oxygen permeability by up to 200-fold in nanopapers, while selected PLA/LCNF systems show tensile-strength and modulus increases of 37% and 61%, respectively; however, high or poorly distributed lignin can suppress fibrillation, lower viscosity, weaken gel networks, and reduce reproducibility. The most defensible near-term product windows are packaging layers, grease/oil barrier papers, coatings, paper-like multilayers, and selected porous media. Thermoplastic matrices remain process-sensitive, and biomedical, additive-manufacturing, nano-reactor, and energy-material claims require stronger validation of the extractables, rheology, humidity history, TEA/LCA metrics, and end-of-life behavior. This review, therefore, provides a critical, application-backward roadmap for tropical biorefineries in which interfacial function, wet handling, drying energy, and process integration are assessed together rather than treated as independent variables. The abbreviations used in the abstract are defined as follows: CNFs, cellulose nanofibrils; CNC, cellulose nanocrystals; LCNF, lignin-containing cellulose nanofibrils; LCNCs, lignin-containing cellulose nanocrystals; PLA, poly(lactic acid); PHB, polyhydroxybutyrate; PHAs, polyhydroxyalkanoates; PVA, poly(vinyl alcohol); DESs, deep eutectic solvents; TEA, techno-economic analysis; LCA, life-cycle assessment; ML, machine learning. Full article

Barely Visible Impact Damage (BVID) in heterogeneous polymer-matrix composites remains difficult to detect because subtle damage signatures are often masked by complex architectures, hybrid textures, and overlapping failure morphologies. This study therefore presents an experimentally grounded, physics-aware, and statistically validated vision-based inspection framework rather than a purely detector-centered benchmarking exercise. Real post-impact images were obtained from controlled low-velocity impact experiments on 20 composite architectures and 60 physical specimens, yielding approximately 2000 images across laminated, hybrid, textile-reinforced, and sandwich structures. The dataset was organized using a specimen-disjoint splitting protocol to prevent leakage across training, validation, and test subsets. To improve robustness while preserving physical realism, a physically grounded Albumentations strategy was developed using only physically admissible transformations and explicit exclusion of non-physical operations that could distort damage morphology or surface continuity. Model development was further complemented by a hybrid hardware workflow in which cloud-based GPU training was combined with deployment-oriented inference profiling on resource-constrained edge-like hardware, thereby linking detection accuracy to practical industrial feasibility. In addition, model performance was evaluated under a standardized training budget and validated through repeated runs, Friedman significance testing, and Holm-corrected Wilcoxon signed-rank pairwise comparisons to ensure error-controlled interpretation of inter-model differences. Across the evaluated compact YOLO families, YOLO26s delivered the strongest overall performance, reaching 0.841 mAP@0.5, 0.586 ± 0.004 mAP@0.5:0.95, and an F1-score of 0.809, while YOLO11s achieved the highest precision and YOLO26n remained competitive in recall with nano-level compactness. Overall, the results show that experimentally generated heterogeneous composite data, morphology-preserving augmentation strategy development, leakage-aware dataset design, deployment-oriented computational profiling, and statistically grounded validation together provide a more robust and application-relevant basis for automated BVID detection in polymer-matrix composite structures. Full article

Nonaqueous potassium-ion batteries (KIBs) are emerging as promising next-generation energy storage systems owing to their abundant resources and high energy density. However, their large-scale application is hindered by structural degradation and sluggish kinetics resulting from the large ionic radius of K ions. Engineering electrode materials with open frameworks, such as two-dimensional (2D) layered structures, present an effective strategy to address these challenges by providing rapid ion diffusion pathways and robust host structures. Herein, a rational interlayer engineering strategy is developed by intercalating phenylamine derivatives with varying molecular sizes (P-butylaniline: PTA, P-Methylaniline: PMA, and phenylamine: PA) into layered 2D VOPO₄ nanosheets. The intercalation of PANI derivatives progressively expands the interlayer spacing from 0.76 nm (pristine VOPO₄) to 1.58, 1.85, and 2.09 nm, while maintaining the structural integrity of the layered framework. Notably, the regulated interlayer expansion (from 0.76 to 2.09 nm) not only provides enlarged diffusion pathways for rapid K⁺ ion intercalation/deintercalation kinetics, but also facilitates the formation of oxygen vacancies that may serve as additional active sites for potassium storage. By correlating the electrochemical performance with the modulated interlayer distances, it is established that a preferred spacing of 1.85 nm achieves the best synergy between fast kinetics, high capacity, and structural stability. As expected, the electrode with the optimal interlayer spacing (1.85 nm) exhibits superior potassium-ion storage performance, delivering a high reversible capacity of 333.2 mAh g⁻¹ at 0.1 A g⁻¹ over 100 cycles and exceptional rate capability with 205.7 mAh g⁻¹ retained at 1 A g⁻¹, as well as maintaining remarkable stability up to 600 cycles even at high rates. This work innovatively proposes phenylamine derivative-enabled interlayer regulation as a promising approach for designing high-performance VOPO₄-based electrode materials. Full article

This paper investigates whether event-level pitch-bearing fatigue damage can be estimated directly from turbine measurements, and whether these mechanical damage metrics leave measurable fingerprints in the generator DC-link voltage and current. To achieve this, a case study was performed using SCADA and structural load data from the 45 kW Chalmers (Björkö) research turbine. This data was segmented into 223 park-run-park pitch events. For each event, blade-root flapwise and edgewise bending moments were converted into radial and axial loads at the pitch bearing; an equivalent dynamic bearing load $P_{\mathrm{eq}}\left(t\right)$ was reconstructed using SKF and DG03 formulations; and rainflow counting with an S–N curve and Palmgren–Miner’s rule was used to compute event-level damage indices compatible with the International Standard Organization basic rating life concepts. In parallel, DC-link voltage and current were summarized into time-domain features, combined with operating-condition descriptors, and clustered using PCA-based k-means. The resulting clusters captured distinct electrical regimes that, across several event batches, corresponded to different levels of accumulated fatigue damage: regimes with sustained high DC-link voltage and longer duration tended to exhibit higher mean damage indices than lower, steadier DC regimes, indicating an electromechanical link. The results show that physics-based lifetime estimation and unsupervised analysis of existing electrical traces can be combined into a hybrid workflow for pitch-bearing condition assessment without additional sensors. Full article

As artificial intelligence reshapes professional workflows, understanding what drives effective AI use among employees has become a critical concern for organizations. Moving beyond traditional technology acceptance frameworks, this study develops an integrative multi-level model to examine the behavioral determinants of AI use performance (AUP) among journalists. Drawing on the Technology Acceptance Model (TAM) and the Expectation Confirmation Model (ECM) and incorporating individual and organizational factors, a survey was conducted among 543 journalists in China. Hypotheses are tested via a hybrid PLS-SEM and artificial neural network (ANN) approach to capture both linear and non-linear relationships. The findings reveal that expectation confirmation significantly enhances AUP by driving perceived usefulness and satisfaction. Digital literacy, personal trust in AI, and organizational support positively influence AUP, whereas communication barriers exert the strongest negative effect. Demographic variables (gender, age, education) show no significant impact. Notably, the ANN sensitivity analysis identifies communication barriers as the most influential predictor overall, a finding not apparent from linear analysis alone. This study advances theoretical understanding of employee behavioral responses in AI-integrated professional contexts and offers practical insights into how organizations can foster effective employee–AI collaboration through targeted communication strategies and supportive infrastructure. Full article

The development of biocompatible functional nanostructures has emerged as a key driver in advancing nanomedicine, environmental remediation, and sustainable energy technologies. However, conventional synthesis methods often rely on toxic reagents, hazardous solvents, and energy-intensive processes, raising significant concerns regarding environmental impact and biological safety. In this context, green synthesis has gained increasing attention as a sustainable alternative, utilizing biological systems, renewable resources, and environmentally benign solvents to produce functional nanomaterials. This mini-review provides an overview of recent advances in the green synthesis of organic, inorganic, and hybrid nanostructures, highlighting their physicochemical properties and functional performance. Particular emphasis is placed on their applications in nanomedicine, including drug delivery, bioimaging, antimicrobial and anticancer therapies, and theranostic platforms. Additionally, their roles in environmental applications, such as pollutant degradation and water treatment, and in energy-related systems, including catalysis, solar energy conversion, and energy storage, are discussed with selected representative examples. Despite significant progress, key challenges remain, including limited mechanistic understanding, reproducibility issues, scalability constraints, and uncertainties related to long-term toxicity and environmental impact. Addressing these limitations will be essential for the safe and large-scale implementation of green nanotechnology. Overall, the integration of green chemistry principles with advanced nanomaterial design offers a promising pathway toward the development of multifunctional, sustainable, and high-performance nanostructures capable of addressing global health, environmental, and energy challenges. Full article

Vulnerability management (VM) is becoming increasingly important as organizations face growing cybersecurity threats. This study examines how organizations adapt their vulnerability management routines in response to evolving vulnerability signals through the integration of artificial intelligence (AI) and automation. Drawing on data from an international fast-moving consumer goods (FMCG) company, we investigate how human expertise and AI interact across the full VM process, from triage to remediation. Using Organizational Routine Theory (ORT), we show that AI does not simply automate tasks but acts as a co-performer, influencing how decisions are made, work is coordinated, and actions are adapted. We develop a three-phase model capturing (1) the integration of AI-enabled automation into strained routines, (2) the manifestation of tensions between human expertise and automation as well as between usability and system complexity, and (3) the stabilization of hybrid routines through iterative adaptation and feedback loops. We identify two key tensions in this process: technology versus human expertise, and usability versus the complexity of multi-vendor tools. These tensions create frictions in practice but also open opportunities for learning and improvement. Rather than treating AI as a technical tool, our findings highlight its role as an active routine participant. Importantly, we show that routine evolution enables organizations to improve how vulnerability signals are interpreted and acted upon, thereby supporting more coordinated and adaptive cybersecurity practices. This has both theoretical implications for understanding how routines evolve with technology and practical relevance for improving adaptive cybersecurity practices. By linking micro-level routine dynamics to broader organizational outcomes, this study contributes to explaining how organizations sustain stable and adaptive operations under conditions of continuous cyber threat exposure. Full article