Search Results (625)

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

Solar-driven hydrogen production via photocatalytic water splitting represents a promising route toward sustainable and low-carbon energy systems. Among emerging photocatalysts, porphyrin-based framework materials, specifically porphyrinic metal–organic frameworks (PMOFs) and porphyrinic covalent organic frameworks (PCOFs), have attracted increasing attention owing to their strong visible-light absorption, tunable electronic structures, permanent porosity, and well-defined catalytic architectures. In these systems, porphyrins function as versatile photosensitizers whose photophysical properties can be precisely tailored through metalation, peripheral functionalization, and integration into ordered frameworks. This review provides a comprehensive, design-oriented overview of recent advances in PMOFs, PCOFs, and hybrid porphyrinic architectures for photocatalytic H₂ evolution. We discuss key structure–activity relationships governing light harvesting, charge separation, and hydrogen evolution kinetics, with particular emphasis on the roles of porphyrin metal centers, secondary building units, linker functionalization, framework morphology, and cocatalyst integration. Furthermore, we highlight how heterojunction engineering through coupling porphyrinic frameworks with inorganic semiconductors, metal sulfides, or single-atom catalytic sites can overcome intrinsic limitations related to charge recombination and limited spectral response. Current challenges, including long-term stability, reliance on noble metals, and scalability, are critically assessed. Finally, future perspectives are outlined, emphasizing rational molecular design, earth-abundant catalytic motifs, advanced hybrid architectures, and data-driven approaches as key directions for translating porphyrinic frameworks into practical photocatalytic hydrogen-generation technologies. Full article

Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems. Full article

Waterborne polyurethane (WPU) and waterborne poly(urethane-urea) (WPUU) dispersions allow safer and more sustainable manufacturing of rechargeable batteries via water-based processing, while offering tunable adhesion and segmented-domain mechanics. Beyond conventional roles as binders and coatings, WPU/WPUU chemistries also support separator/interlayer and polymer-electrolyte designs for lithium-ion and lithium metal systems, where interfacial integrity, stress accommodation, and ion transport must be balanced. Here, we review WPU/WPUU fundamentals (building blocks, dispersion stabilization, morphology, and film formation) and review prior studies through a battery-centric structure–processing–property lens. We point out key performance-limiting trade-offs—adhesion versus electrolyte uptake and ionic conductivity versus storage modulus—and relate them to practical formulation variables, including soft-/hard-segment selection, ionic center/counterion design, molecular weight/topology control, and crosslinking strategies. Applications are reviewed for (i) electrode binders (graphite/Si; cathodes such as LFP and NMC), (ii) separator coatings and functional interlayers, and (iii) gel/solid polymer electrolytes and hybrid composites, with a focus on practical design guidelines for navigating these trade-offs. Future advancements in WPU/WPUU chemistries will depend on developing stable, low-impedance interlayers, enhancing electrochemical behavior, and establishing application-specific design guidelines to optimize performance in lithium metal batteries (LMB). Full article

Hybrid systems consisting of metal–fullerene composites exhibit intriguing properties but often suffer from thermal instability. With proper control, such instability can be harnessed to enable the formation of sophisticated nanostructures with nanometric precision. These self-organization phenomena are not limited to thermal stimulation alone but can also be triggered by other external stimuli. In this work, we investigate the morphological evolution of thin films composed of evaporated C₆₀ and sputtered nickel mixtures, focusing on how external stimuli influence both their structural and electrical properties. Thin films were prepared under controlled deposition conditions, and their surface morphology was analyzed using advanced characterization techniques. Progressive changes in film morphology were observed as a function of composition and external treatment, highlighting the interplay between metallic and molecular components. In particular, it was observed that, due to the annealing treatment, the sample undergoes strong phase separation, with the formation of structures tens of microns in diameter and an increase in electrical resistance, exhibiting insulating behavior. These findings provide insights into the mechanisms governing hybrid thin film formation and suggest potential applications in electronic, optoelectronic, and energy-related devices. Full article

The aim of this work is to develop structurally enhanced and highly hydrophobic polyurethane (PU) foams for the efficient remediation of liquid organic pollutants. For this purpose, PU foams were modified with renewable activated biochar derived from marine algae (AC) and a hydrophobic polydimethylsiloxane (PDMS) coating, producing four systems: pristine PU, PU-AC, PU/PDMS, and the hybrid PU-AC/PDMS composite. The study evaluates how AC incorporation and PDMS surface functionalization influence the microstructure, chemical composition, wettability, thermal stability, and sorption behavior of the foams. SEM images revealed progressive reductions in pore size from 420 ± 80 μm (PU) to 360 ± 85 μm (PU-AC/PDMS), with AC introducing heterogeneity while PDMS preserved open-cell morphology. FTIR confirmed the presence of urethane linkages, carbonaceous structures, and PDMS siloxane groups. Surface hydrophobicity increased markedly from 88.53° (PU) to 148.25° (PU-AC/PDMS). TGA results showed that PDMS improved thermal stability through silica-rich char formation, whereas AC slightly lowered degradation onset. Sorption tests using petroleum-derived oils and hydrophobic organic liquids demonstrated a consistent performance hierarchy (PU < PU/PDMS < PU-AC < PU-AC/PDMS). The ternary composite achieved the highest uptake capacities, reaching 44–56 g/g for oils and up to 35 g/g for hydrophobic solvents, while maintaining reusability. These findings demonstrate that combining activated biochar with PDMS significantly enhances the functional properties of PU foams, offering an efficient and sustainable material for oil–water separation and organic pollutant remediation. Full article

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms. Full article

With the continuous increase in well depth and the gradual depletion of formation energy, the pump-setting depths in rod-pumped wells have increased significantly, leading to higher suspension loads at the pumping unit. The application of glass fiber-reinforced plastic (FRP) sucker rods can effectively reduce suspension loads due to their low density and high tensile strength. However, the mechanical performance of FRP rods is highly sensitive to temperature, which poses challenges for their application in deep and high-temperature wells. In FRP–steel hybrid sucker-rod string design, the influence of temperature—particularly on FRP rods—must therefore be carefully considered to prevent failures such as rod parting or coupling separation. This study systematically investigates the effects of temperature on the mechanical properties of FRP sucker rods, including elastic modulus, flexural shear strength, and tensile strength. Based on the operating characteristics of sucker-rod pumping systems and established design criteria, a temperature-aware design methodology for FRP–steel hybrid rod strings is developed and implemented in dedicated design software. The proposed approach enables rational determination of the FRP–steel partition depth under thermal constraints while satisfying mechanical safety requirements. A field case study is conducted to validate the design results, demonstrating that the software provides reliable and practical guidance for hybrid rod-string design in deep wells. Full article

This study presents the techno-economic optimization of a hybrid distillation-membrane process for the complete fractionation of liquefied petroleum gas (LPG), targeting high-purity propane, n-butane, and isobutane recovery. The process employs an initial distillation column to separate propane (99% purity) from a propane-enriched stream, which is subsequently fed to a two-stage membrane system using an MFI zeolite hollow-fiber membrane for n-butane/isobutane separation. Through systematic simulation and sensitivity analysis, different membrane configurations were evaluated. The two-stage process with a partial residue-side reflux configuration demonstrated superior economic performance, achieving a total operating cost of 31.58 USD/h. Key membrane parameters—area, permeance, and separation factor—were optimized to balance separation efficiency with energy consumption and cost. The analysis identified an optimal configuration: a membrane area of 800 m², an n-butane permeance of 0.9 kg·m⁻²·h⁻¹, and a separation factor of 40. This setup ensured high n-alkane recovery while effectively minimizing energy use and capital investment. The study concludes that the optimized distillation-membrane hybrid process offers a highly efficient and economically viable strategy for the full utilization of LPG components. Full article

This study investigates the interaction of Ru₃ clusters with pristine and defective anatase (101) TiO₂ surfaces using density functional theory (DFT) to evaluate their structural stability, electronic modifications, and photocatalytic potential. The results show that Ru₃ clusters strongly bind to both pristine and defective surfaces, with oxygen vacancies acting as anchoring sites that further stabilize the clusters. Electronic structure analysis reveals the formation of mid-gap states due to hybridization between Ru and Ti orbitals, extending visible light absorption. On defective surfaces, synergistic effects between Ru₃ clusters and vacancy-induced states further enhance charge separation and reduce recombination. Band structure and wavefunction analyses confirm these findings, highlighting Ru₃-decorated anatase TiO₂ as a promising system for hydrogen evolution and CO₂ reduction. The outcomes of this computational investigation provide valuable insights into the rational design of advanced photocatalysts for sustainable energy applications. Full article

The growing demand for efficient last-mile delivery has made routing optimization a critical challenge for logistics providers. Traditional vehicle routing models typically minimize a single criterion, such as travel distance or time, without considering broader social and environmental impacts. This paper proposes a novel Multi-Criteria Adaptive Hybrid Ant Colony Optimization (MCAH-ACO) algorithm for solving the delivery vehicle routing problem formulated as a Multiple Traveling Salesman Problem (MTSP). The proposed MCAH-ACO introduces three key innovations: a multi-criteria pheromone decomposition strategy that maintains separate pheromone matrices for each optimization objective, an adaptive weight balancing mechanism that dynamically adjusts criterion weights to prevent dominance by any single objective, and a 2-opt local search enhancement integrated with elite archive diversity preservation. A comprehensive cost function is designed to integrate four categories of factors: distance, time, social-environmental impact, and safety. Extensive experiments on real-world data from the Greater Toronto Area demonstrate that MCAH-ACO significantly outperforms existing approaches including Genetic Algorithm (GA), Adaptive GA, and standard Max–Min Ant System (MMAS), achieving 12.3% lower total cost and 18.7% fewer safety-critical events compared with the best baseline while maintaining computational efficiency. Full article

Microplastics (MPs) have emerged as persistent environmental pollutants with adverse effects on ecosystems and human health. Conventional removal methods, such as filtration and sedimentation, primarily rely on physical separation without addressing the degradation of MPs, leading to their accumulation and the risk of secondary pollution. This review explores the potential of advanced oxidation processes (AOPs), including photocatalysis, electrochemical oxidation, Fenton processes, sulfate radical-based oxidation, sonochemical treatment, ozonation, and plasma technologies, which generate reactive oxygen and nitrogen species capable of promoting polymer chain scission, microbial biodegradation, and the oxidative fragmentation and mineralization of MPs into non-toxic byproducts. Hybrid AOP systems combined with biological treatments or membrane-based filtration are also examined for their effectiveness in degrading MPs, as well as for scalability and the environmental impacts of their byproducts when integrated into existing wastewater treatment systems. The review further discusses challenges related to operational parameters, energy consumption, and the formation of secondary pollutants. By identifying current knowledge gaps and future research directions, this review provides insights into optimizing AOPs and integrations of AOPs with biological treatments or membrane-based processes for sustainable MP remediation and water treatment applications. Full article

With the widespread deployment of the Advanced Metering Infrastructure (AMI) in Power Industrial Control Systems (PICS), a significant and inherent property of network traffic data is its pronounced class imbalance. The continuous emergence of new types of cyberattacks significantly limits the detection accuracy of Intrusion Detection Systems (IDS). To overcome the limitations of traditional methods—particularly their poor adaptability in complex conditions and vulnerability to emerging threats—this paper introduces a novel hybrid intrusion detection framework. This framework synergistically combines data augmentation and a discriminative classification model for improved performance. Within this framework, a Multi-feature Constrained Conditional Generative Adversarial Network (MC-CGAN) is proposed. Its multi-feature constraint module (MC) preserves protocol-related invariant features, while the CGAN is responsible for conditionally generating the remaining continuous features based on class labels. By preserving the core semantic information of samples, this method reduces the risk of generating unrealistic data and decreases computational overhead. Furthermore, we develop ADS-Net, a lightweight Convolutional Neural Network that not only replaces traditional convolutions with depth-wise separable ones for efficiency, but also incorporates an attention mechanism to adaptively weight feature channels, thus improving discriminative focus. Extensive experiments demonstrate that, under conditions of extreme data imbalance, the proposed hybrid framework can generate industrially valid synthetic data while achieving accurate intrusion detection with an accuracy of 98.35%. Full article

The recently described genus Candolleomyces (Basidiomycota, Agaricales, Psathyrellaceae) is now recognized as a distinct taxonomic group separate from Psathyrella. Currently, no fully assembled and accurately annotated genomes of Candolleomyces species are available, limiting our understanding of their physiological traits and biotechnological potential. Numerous tools exist for fungal genome assembly and annotation, each using different algorithms, resulting in substantial variation in gene content and distribution within the same genome. In this work, a hybrid assembly and annotation of the genome of strain CMU-8613 were performed using pipelines that combine different assembly and annotation tools. Phylogenetic analysis showed that the analyzed strain CMU-8613 belongs to Candolleomyces candolleanus. The assembled genome size ranged from 46.8 Mb (NECAT + Racon) to 59.3 Mb (Canu + Coprinellus micaceus genome assembly), depending on the assembly and polishing strategy. The analysis identified 15–25 secondary metabolite gene clusters (BGCs), depending on the genome assembly and the tools used for BGC prediction. In strain CMU-8613, CAZyme-encoding genes varied across assemblies: 494 genes were detected in the Flye assembly and 453 in NECAT; in both cases, the AA (Auxiliary Activities) and GH (Glycoside Hydrolases) families were the most represented. The diversity of CAZymes observed among Candolleomyces species suggests differences in their saprophytic capacities. Analysis of the MAT-A/MAT-B loci revealed that C. candolleanus possesses a tetrapolar mating system. This study provides the first annotated genome of C. candolleanus, highlighting its enzymatic potential to degrade plant biomass and its capacity to synthesize diverse secondary metabolites. The combination of assembly and annotation tools employed here offers robust alternative strategies for characterizing non-model fungi or species lacking high-quality reference genomes. Full article

Rhenium is one of the rarest and most strategically important metals, indispensable in high-temperature superalloys and platinum–rhenium catalysts used across the aerospace and petrochemical industries. Owing to its limited primary reserves, recovering rhenium from secondary sources, such as spent catalysts, superalloy residues, and metallurgical dusts, has become vital to ensuring supply security. This review examines technological developments between 1998 and 2025, focusing on how operational parameters, including temperature, leaching time, reagent concentration, and solid-to-liquid ratio, govern dissolution kinetics and overall process efficiency. Comparative evaluation of hydrometallurgical, alkaline, and hybrid processes indicates that modern systems can achieve recovery rates exceeding 98% through selective oxidation, alkaline activation, or combined pyro and hydrometallurgical mechanisms. Acid–chlorine leaching facilitates rapid, low-temperature dissolution; alkaline sintering stabilises rhenium as soluble perrhenates; and hybrid smelting routes enable the concurrent separation of rhenium and osmium. Sustainable aqueous systems employing nitric and ammonium media have also demonstrated near-complete recovery at ambient temperature under closed-loop recycling conditions. Collectively, these findings highlight a technological transition from energy-intensive, acid-based pathways towards low-impact, recyclable, and digitally optimised hydrometallurgical processes. The integration of selective oxidants, phase engineering, circular reagent management, and artificial intelligence-assisted modelling is defining the next generation of rhenium recovery, combining high extraction yields with reduced environmental impact and alignment with global sustainability goals. Full article