Search Results (12,621)

Retrieval-augmented generation (RAG) has shown strong potential for knowledge-intensive tasks, yet its performance degrades sharply when applied to structured long-context documents in power systems, where dense entity–relation dependencies, cross-document references, and strict traceability requirements exist. To address this Structured Long-Context RAG (SLCRAG) challenge, this paper proposes a hierarchical graph-enhanced RAG (HG-RAG) framework tailored for power system question answering. HG-RAG constructs a globally consistent knowledge graph via sliding-window entity–relation extraction to mitigate semantic fragmentation, and employs multi-granularity structured indexing for precise entity/relation retrieval. A hierarchical structured retrieval mechanism with multi-hop expansion and semantic distillation maximizes recall while minimizing redundancy. Furthermore, a regex-enhanced retrieval module records authoritative file_path provenance and constrains downstream retrieval to the same source documents, effectively eliminating cross-document interference—especially in cases where different documents contain similar entities and relations. Combined with version control and deduplication-merging, HG-RAG supports incremental knowledge updates with minimal forgetting and negligible token overhead. Experimental results on a domain-authentic power system QA dataset demonstrate that HG-RAG outperforms LightRAG and GraphRAG, achieving up to 85.47% accuracy in short-answer tasks with significantly lower token consumption. Ablation studies confirm that semantic distillation primarily improves precision and efficiency, while regex-enhanced retrieval safeguards recall in edge cases. Full article

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, T_amb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework. The results show that inlet oscillations are amplified inside the chambers (pressure amplification factor П_р ≈ 1.95; П_р up to 2.3 under quasi-random excitation), reducing mass flow and volumetric efficiency by 8–10% and decreasing polytropic efficiency from 0.78 to 0.69–0.71, while increasing leakage by up to 27% and raising peak contact pressures to 167–171 MPa. Seasonal variability (+30 to −30 °C) increased suction density by 38% but raised drive power by ~9% due to viscosity-driven mechanical losses, producing an energy penalty up to 10.8% and an estimated annual additional consumption of approximately 186 MWh per compressor, decomposed as: cold-season contribution ~113 MWh (±10 MWh, directly field-validated against October–December 2022 SCADA data) and summer-season contribution ~51 MWh (calibrated extrapolation; additional uncertainty unquantified and not included in the ±10 MWh bound). The full annual figure of 186 MWh should be interpreted as a model-based estimate rather than a fully validated result. These findings demonstrate that rotor design optimization and mitigation of nonstationary suction effects, coupled with climate-aware offline physics-based digital shadow operation, represent high-priority levers for improving efficiency and reducing energy penalties in field conditions; reliability implications require further validation against summer-season field measurements. Full article

Synthetic aperture radar altimeter (SARAL) directs the radar beam toward the nadir point of the flight trajectory. It is capable of capturing elevation variations in the terrain of interest. To ensure that the nadir point remains within the beam coverage under complicated flight attitudes, a wide beamwidth is necessary. However, the wide beamwidth introduces a spatial-variant delay problem with respect to different scatters in the along-track direction, which degrades the accuracy in obtaining the terrain elevation contour. To this end, a spatial-variant Delay-Doppler (SVDD) algorithm is proposed in this paper. The core advantage of the proposed algorithm is that an analytical spectrum is obtained through rigorous mathematical derivation for the wide-beam SARAL geometry. Accordingly, all correction functions are implemented via complicated multiplications without interpolation operations. High computational efficiency is therefore ensured. To address the spatial-variant delay problem, a direct geometric relationship is first established between the Doppler frequency and the azimuthal position. Based on this relationship, the spatial-variant characteristic is mapped from the spatial domain to the Doppler domain. This mapping is then directly employed to construct the spatial-variant delay correction function. At the same time, range walk correction and range curve correction are carried out. In such cases, the variation of the undulating terrain can be recovered from the Delay-Doppler Map (DDM). Both simulated and raw data of the radar altimeter are applied to verify the effectiveness of the proposed SVDD algorithm. Comparisons with the conventional algorithm are also performed to demonstrate the superiority of the SVDD algorithm. Full article

The hematite phase decorated with iron-doped cerium oxide nanoparticles (F@FC) was precipitated from cerium and iron oxalate intermediate products. The photocatalytic composite of graphitic carbon nitride (gCN) and F@FC was prepared by a simple method involving mixing the two components, followed by thermal treatment at 400 °C. According to electron microscopy, F@FC is composed of a submicron iron oxide (hematite) phase decorated with iron-doped cerium oxide nanoparticles deposited on gCN substrate. A hierarchically structured composite was observed instead of a simple mechanical mixture of α-Fe₂O₃, Fe-CeO₂, and gCN. To observe two types of degradation activity, photocatalytic and Photo-Fenton degradation activity, Rhodamine B (RhB) was applied as the model water pollutant. The influence of the amount of photocatalyst, the RhB concentration, the presence of cations and anions, the pH, and the effect of e⁻, h⁺, •OH, and •O₂⁻ scavenging reactants were studied. The Photo-Fenton degradation exhibited high efficiency across the entire tested pH range, whereas photocatalytic degradation showed comparable activity only at acidic pH. The F@FC-gCN composite catalyst exhibited a high degree of recyclability. The degradation pathways of photocatalytic and Photo-Fenton reactions were suggested by HPLC-MS analysis of the reaction products. A notable finding of this study was the observation that the green-yellow, fluorescent intermediate Rhodamine 110 was formed during the photocatalytic degradation of RhB. However, the high reactivity of the generated •OH radicals during Photo-Fenton degradation has been demonstrated to inhibit the formation of intermediate Rhodamine 110. Full article

The increasing interest in the development and integration of navigation and positioning services across a wide range of receivers has exposed them to various security threats, including GNSS jamming and spoofing attacks. Early detection of jamming and spoofing interference is crucial to mitigating these threats and preventing service degradation. This research introduces an interference detection technique leveraging an AI algorithm applied to GNSS data utilizing various methods to enhance detection accuracy and efficiency. The objective was to use modern sensors and AI to develop an effective tool that detects, characterizes, and localizes interference, thereby reducing associated risks. These sensors and algorithms enable continuous GNSS interference monitoring and support real-time Decision-making. A server plays a crucial role in managing the entire system. Its primary function is to process data collected from various sensors referred to as nodes (e.g., static, rover, drone, and space) and from (public) GNSS networks as well as to perform localization using rotating-antenna nodes. Within the interference detection module, various methods were implemented at different points in the software receiver architecture. Each method’s certainty in identifying an interference source depends on its design and capabilities, with outcomes—whether positive or negative—being subject to potential accuracy or errors. To enhance the Decision-making process, an AI-based Decision-making block has been introduced to determine the presence of interference at a given epoch. The proposed interference monitoring methods were evaluated through experiments using GNSS signals under clean, jamming, and spoofing scenarios. The results demonstrate the techniques’ applicability across diverse scenarios, achieving high performance in interference detection, characterization, and localization. Full article

The TiO₂-modified LaMnO₃ catalyst demonstrated outstanding catalytic performance across a broad pH range (4.2 to 10.0) and under various complex water conditions. It achieved complete degradation of the ibuprofen parent compound, attaining an 85.9% mineralization rate. The efficacy stems from the reversible Mn³⁺/Mn⁴⁺ redox couple. The ratio of Mn³⁺/Mn⁴⁺ was 3.9 for TiO₂-modified LaMnO₃, significantly higher than 1.2 for nanocast LaMnO₃. Experimental results and density functional theory revealed that La and Ti did not actively participate in the catalytic ozone reaction. Notably, the Mn³⁺/Mn⁴⁺ pair emerged as key drivers in the involvement of HO•, O₂•⁻, and ¹O₂ in the reactive oxygen species pathway. Notably, ozone exhibited preferential adsorption and activation on the (010) crystal face of the catalyst. A moderated reduction in interaction forces facilitated the Mn³⁺/Mn⁴⁺ redox cycle, resulting in increased production of reactive oxygen species. These findings contributed to the development of more efficient catalysts for environmental remediation. Full article

Ground-based astronomical images are often degraded by atmospheric turbulence and deterministic optical aberrations introduced by telescope design and manufacturing processes. Joint mitigation of these distortions remains challenging due to the lack of reliable ground-truth data. To address this issue, a physics-based atmospheric–optical imaging model is developed to generate a large-scale, physically consistent simulated dataset, enabling supervised learning without real paired observations. Based on this, an attention-enhanced generative adversarial network (AE-GAN) is proposed for astronomical image restoration. The network incorporates a Channel Attention Block (CAB) and a Semantic Attention Module (SAM) within a feature pyramid architecture to enhance multi-scale representation and suppress turbulence-induced distortions. Experimental results show that the proposed method achieves consistent restoration performance under varying turbulence strengths, aberration amplitudes, and noise levels. Compared with recent Transformer-based methods, it maintains competitive performance across different aberration types while achieving significantly higher computational efficiency (1.21 s per image, 3.5× faster). In addition, the model trained on simulated data generalizes effectively to real astronomical observations. Full article

Convolutional neural networks (CNNs) excel in tasks such as image, speech, and video recognition, as well as pattern analysis. However, their reliance on large training datasets, often sourced from third-party providers, exposes them to security risks, particularly poisoning attacks. Targeted poisoning attacks, also known as backdoor attacks, enable a CNN model to correctly classify normal data while misclassifying inputs containing specific triggers. In contrast, untargeted poisoning attacks aim to degrade the overall performance of the model. This research introduces an invisible targeted poisoning attack characterized by low implementation complexity and high computational efficiency due to its computationally inexpensive LSB-based embedding mechanism, without requiring complex optimization procedures against a basic CNN model and a residual network (ResNet-18) model. By embedding trigger images within poisoned samples, the attack remains covert, evading detection. The model is then trained on a dataset comprising both original and poisoned samples. The expected outcome is that the model will classify regular images correctly, but will misclassify those containing the embedded trigger as belonging to a target class. Experimental results on the CIFAR-10 dataset demonstrate the effectiveness of this approach, achieving a 99.32% Adversarial Success Rate (ASR) against ResNet-18 with only a 0.02% reduction in accuracy on benign test samples. Full article

Heterogeneous catalytic degradation of organic dyes can effectively achieve the goals of reducing the chromaticity of aqueous solutions and completely removing pollutants. We here present a carbon-nitride-wrapped zero-valent Fe catalyst (CNFe), which can directly degrade Acid Red G (ARG) dye without additional oxidants. CNFe exhibited a nanotube-like morphology, wherein the zero-valent Fe (Fe⁰) was wrapped by a carbon layer to effectively enhance its dispersibility and prevent its oxidative deactivation. Meanwhile, the large specific surface area (169.19 m²/g), along with abundant active sites such as Fe and O, endowed CNFe with excellent activity. Under strongly acidic conditions, even in the presence of various anions, CNFe can still remove approximately 91.6% of ARG within 30 min. In a 10 h continuous flow column experiment, the removal efficiency of ARG consistently exceeded 67.6%, indicating that CNFe had great potential for treating actual dyeing wastewater. Catalytic mechanism studies showed that, under neutral conditions, CNFe mainly removed ARG through adsorption, whereas, under acidic conditions, the Fe⁰ in CNFe can not only activate molecular oxygen to generate HO· for the oxidative degradation of ARG but also remove ARG via reduction. Furthermore, CNFe can adsorb ARG through hydrogen bonding of surface hydroxyl groups. The developmental toxicity of the generated intermediates was effectively reduced, demonstrating lower environmental risks. Therefore, this study provided a simple, high-efficiency, and economical method for removing dyes from water, which can offer guidance for the treatment of practical dye wastewater. Full article

Current strategies for treating organic dye wastewater are shifting from single-function removal processes and catalytic degradation methods toward more integrated treatment approaches. This study proposes a multifunctional composite integrating adsorption–photodegradation–intelligent recovery for photodegradation and recovery of methylene blue-contaminated wastewater. By optimizing the preparation process to precisely control the pore size and arrangement of the aerogel, a hierarchical porous framework with a high specific surface area is formed, featuring efficient mass transfer and ultra-multiple loading sites. The graphene framework enhances visible-light absorption by optimizing TiO₂ loading, agglomeration behavior and addressing detachable defects through a metal–polyphenol network. After 60 min of illumination, the degradation efficiency exceeds 99.5%, demonstrating superior cycling stability. After 100 cycles, the photocatalytic efficiency remains above 97%, showcasing excellent durability. Furthermore, the in situ polymerized thermoresponsive poly (N-isopropylacrylamide) (PNIPAm) composite exhibits smart responsiveness, enabling reversible temperature-responsive adsorption–desorption behavior within PNIPAm’s LCST range. with an adsorption capacity of 28,000 mg/g at LCST. Heating above LCST desorbs 90.2% of the wastewater, and adsorption stability remains above 98% after 100 thermal cycles, resolving operational challenges in mechanical wastewater recovery. The synergistic integration of an anisotropic porous structure, stable TiO₂ loading, and thermal responsiveness provides an efficient platform for integrated adsorption and recovery. Full article

To address the issues of low fundamental content in the output voltage of high-frequency inverters within wireless power transfer (WPT) systems and efficiency degradation caused by coupling coefficients and load variations, this paper proposes a novel seven-level inverter topology and a closed-loop PI control strategy based on current amplitude ratio. First, the influence of LCC-S WPT system parameters on current and efficiency is analyzed. Subsequently, by comparing fundamental content in inverter output voltage across different level structures, a seven-level configuration is selected. A novel seven-level inverter topology with fewer switches and lower voltage stress is proposed, and its efficiency enhancement advantage is validated through optimized switch turn-on angles. Finally, a closed-loop PI control strategy based on current amplitude ratio is adopted. By merely acquiring coil currents and calculating their amplitude ratio, the duty cycle of the Buck-Boost circuit is adjusted to optimize current amplitude, achieving maximum efficiency tracking for the system. Experimental results demonstrate that system efficiency approaches theoretical calculations during coil spacing variations. When the load varies between 5 Ω and 105 Ω, system efficiency remains around 91.4%, with maximum efficiency point tracking error maintained at approximately 2%. This validates the system’s reliability and the effectiveness of the control strategy. Full article

Polysilicon is widely used in the photovoltaic and semiconductor industries. The presence of trace phosphorus impurities in the trichlorosilane feedstock can severely degrade the quality of polysilicon products. To address the urgent need for complete phosphorus removal of trichlorosilane, in this work, on the basis of the reducing ability of PCl₃ and the stronger Lewis base properties of its oxidation product, POCl₃, we developed an efficient material, xMo/Al₂O₃[y], using Al₂O₃ as the support and Mo species as active substances through a simple and straightforward method. Under the optimized preparation conditions of 7.8% Mo loading and a calcination temperature of 450 °C, the adsorbent exhibited optimal performance in an organic system simulating a trichlorosilane system with a P adsorption capacity of 53.52 mg g⁻¹, achieving near-complete elimination of phosphorus impurities. A series of characterization analyses suggested the following primary removal mechanism: initial oxidation of PCl₃ to POCl₃ by Mo⁶⁺ species, followed by its complexation with Mo sites via Lewis acid-base interactions. Furthermore, surface morphology damage during the removal process and the accumulation of reaction products on the spent adsorbent are the main factors contributing to its deactivation. This work presents an effective strategy for the deep dephosphorization of trichlorosilane. Full article

Degradation of volatile organic compounds (VOCs) by environmentally friendly methods remains a challenging issue. Photothermal catalysis, as an emerging green catalytic technology, merges the benefits of both thermal catalysis and photocatalysis, presenting itself as a viable strategy for VOC degradation. However, achieving higher catalytic performance by reasonably designing the synthetic route of catalyst carriers remains difficult. In this study, crystalline carbon nitride material, poly(triazine imide) (PTI), was prepared using a unique molten salt synthesis method and employed as a support for Pt to construct an exceptional photothermal catalyst. In a continuous-flow system under Xe lamp irradiation with external temperature control, toluene was efficiently degraded at a high rate of nearly 100% under low Pt content (0.31 wt%) and a relatively low operational temperature condition (143 °C). As a carrier of noble metals, PTI material exhibited a larger specific surface area and fewer structural defects, resulting in more efficient toluene conversion and mineralization. The joint action of photocatalysis and thermocatalysis synergistically facilitated the efficient generation of active species and accelerated charge transfer, thereby significantly boosting toluene catalytic oxidation. These findings provide valuable guidance for designing and optimizing photothermal catalysts for the removal of VOCs. Full article

The transition toward low-carbon and resource-efficient construction materials has intensified interest in geopolymer binders incorporating industrial and post-consumer wastes. Waste glass powder (WGP), a silica-rich component of the global glass waste stream, has emerged as a promising circular-economy precursor in alkali-activated systems; however, reported durability trends remain inconsistent and are often interpreted without mechanistic integration. This review synthesises current knowledge of WGP reactivity, gel chemistry, and long-term performance through an explicit reaction–transport–ageing (R–T–A) framework that links dissolution behaviour and phase assemblage development to pore connectivity, ion ingress, and time-dependent degradation. Under alkaline activation, the amorphous structure of WGP promotes silica release, modifying Si/Al ratios and governing the formation of N-A-S-H or hybrid N-A-S-H/C-(A)-S-H gels. These reaction products determine transport characteristics and ageing evolution, which collectively control chemical resistance, chloride ingress, alkali–silica reaction-type instability, and dimensional stability. Variability across studies is shown to arise from imbalances in particle fineness, replacement level, precursor chemistry, and activator design rather than intrinsic inconsistency in WGP behaviour. The R–T–A framework clarifies how reaction completeness, pore network architecture, and long-term phase stability interact to produce system-dependent durability outcomes. WGP demonstrates strong potential as a circular-economy precursor in alkali-activated binders; however, reliable structural application requires durability-informed mix design grounded in coupled reaction–transport–ageing mechanisms and supported by extended exposure testing under realistic service conditions. Full article

The PGM-free Fe–Ni–Co trimetallic catalysts developed in this study demonstrated outstanding performance for the oxygen evolution reaction (OER), achieving overpotentials as low as 300 mV at 10 mA cm⁻² in rotating disk electrode (RDE) measurements, a value competitive with the most efficient non-noble electrocatalysts reported in the literature. This study validates the strong catalytic performance of the baseline trimetallic configuration and provides important insights into the relationships among synthesis, structure, and morphology that govern catalyst activity. In particular, the findings highlight that although organic additives can be promising modifiers, the interaction between precursors and transition metals must be carefully controlled to avoid active-site isolation when designing efficient catalysts for sustainable hydrogen production. Actually, to further enhance catalytic activity, the nitrogen-rich precursor melamine was introduced into the supported trimetallic catalyst and then carbonized. However, no improvement in OER performance was observed. During carbonization, melamine promotes the formation of tip-growth carbon nanotubes, which mechanically disrupt the catalyst structure and degrade the supported active phase. Full article