Search Results (4,507)

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Wireless power transfer (WPT) is increasingly used in smart manufacturing, unmanned platforms, and contactless power-supply applications. However, weak coupling, load-dependent impedance drift, and spatial misalignment can shift the resonant condition, leading to unstable output voltage and reduced transfer efficiency. This paper proposes a constant-voltage WPT method that combines a non-uniform winding coupler, parasitic coils, and dynamic capacitor compensation. A composite magnetic coupler with dense outer windings, loose inner windings, and parasitic coils is first developed, and a region-based electromagnetic model is established to characterise self-inductance, mutual inductance, and coupling coefficients. An improved LCC-S compensation network with a dynamic capacitor compensation matrix is then derived to keep the system close to resonant operation at the nominal 85 kHz operating point under load variation and coil-displacement-induced coupling changes. A zero-voltage-switching-angle tracking method with mutual-inductance correction is further introduced to compensate for phase deviation and maintain soft-switching operation through limited switching-frequency adjustment. Experimental validation demonstrates that the system maintains a stable constant-voltage output across a load range of 20–50 Ω and under 5 cm lateral and longitudinal offsets. The measured efficiency remains above 89% and reaches 93.7% under the optimal coupling and load-matching condition. Full article

Microplastics (MPs) are increasingly detected in dairy products, raising food-safety concerns. Their behavior in complex food matrices and interactions with probiotic microorganisms remain poorly understood. This exploratory study evaluated storage-dependent changes in operationally defined, digestion-resistant recoverable residues in yogurt containing Bifidobacterium longum subsp. infantis (ATCC 15697). Yogurt samples were prepared with polypropylene (PP), polyethylene (PE), and polystyrene (PS), individually and in combination, and analyzed over 21 days of refrigerated storage. Gravimetric values served as relative, operational indicators of recoverable residues—not validated absolute polymer masses—while polymer identity was qualitatively confirmed by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS). B. longum subsp. infantis remained viable throughout storage (6.3–8.2 log₁₀ CFU/g). All MP-containing groups showed consistent storage-associated decreases in recoverable residue fractions, greatest in PP, followed by PE and PS; probiotic-free controls remained stable. Polymer-specific Py-GC/MS signals were detectable at all time points. Because polymer identity was retained and the workflow was not validated for absolute recovery, findings are interpreted as storage-associated changes in extractability, filterability, and/or residue recovery—not as polymer degradation, mineralization, or biological removal. These in vitro observations are limited to the yogurt matrix and do not support extrapolation to livestock exposure, human dietary risk, or farm-to-fork transfer. Within these limits, the findings provide a preliminary, hypothesis-generating perspective on probiotic–microplastic interactions in fermented dairy products. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

The conversion of bio-waste into functional energy materials provides a robust platform for addressing both environmental and energy challenges. In this paper, discarded absorbent pads are transformed into carbon-rich frameworks, which is followed by the fabrication of composites through the incorporation of Cu₄SnS₄ (CSS) for dual electrochemical applications. Integrating CSS into the waste-derived carbon matrix induces strong synergistic effects, improving electrical conductivity, increasing active-site availability, and accelerating charge-transfer kinetics. Comprehensive physicochemical analyses confirmed the successful formation of a well-integrated heterostructure composite with favorable structural and surface characteristics. Electrochemical evaluations further demonstrated that CSS-modified carbon exhibits superior bifunctional performance. In a two-electrode configuration, the composite delivers an energy density of 12.08 Wh kg⁻¹ at a power density of 250 W kg⁻¹ along with excellent cycling stability in supercapacitor applications. As an electrocatalyst, it achieves a low overpotential of 268 mV at −10 mA cm⁻² and a small Tafel slope of 75 mV dec⁻¹, reflecting efficient reaction kinetics. The strong durability observed in both systems underscores the structural integrity and long-term operational stability of the material. Overall, this paper advances a sustainable waste-to-resource strategy for fabricating multifunctional carbon-based composites, offering a promising platform for integrated energy-storage and hydrogen-generation technologies. Full article

Tetracycline-class antibiotics are persistent contaminants in aquatic environments and are difficult to remove by conventional treatment processes. In this study, a recoverable granular MIL-101(Fe)/γ-Al₂O₃ catalyst was prepared through ligand anchoring followed by secondary Fe-MOF growth on spherical γ-Al₂O₃ and applied to catalytic ozonation of tetracycline (TC) under ordinary-bubble and microbubble-assisted operation. Structural characterization supported the formation of Fe-containing MOF domains on the alumina support, accompanied by an increase in BET surface area from 164.28 to 210.05 m² g⁻¹ and enhanced Lewis-acid-related pyridine-IR signals. Under conventional bubbling ozonation, the optimized catalyst achieved 67.93% apparent UV–Vis-based TC removal during an overall 50 min run consisting of 30 min dark adsorption followed by 20 min ozonation. In a 12 L microbubble reactor, the catalyst-assisted system reached 93.74% apparent UV–Vis-based TC removal at pH 6 with 100 g catalyst and 6 mg min⁻¹ fed ozone, showing higher apparent removal than ordinary ozonation, microbubble ozonation, and ordinary-bubble catalytic ozonation under the tested configuration. Phosphate-blocking and radical-quenching experiments were consistent with the involvement of Lewis-acid-related sites, hydroxyl radicals, and superoxide-related pathways, but these tests are interpreted as indirect mechanistic evidence. LC-MS analysis suggested possible hydroxylation, demethylation, deamidation, ring opening, and low-molecular-weight product formation. The system also transformed chlortetracycline, oxytetracycline, and doxycycline and reduced COD and TOC in a simulated mixed-antibiotic matrix. Because parent-compound HPLC/LC-MS time-series quantification, ozone utilization/off-gas ozone measurement, bubble-size/kLa analysis, and ICP-based Fe loading/leaching data were not available, the present work is positioned as an apparent catalyst–reactor coupling study rather than a complete catalytic, hydrodynamic, or process-level demonstration. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent emerging contaminants characterized by high environmental stability and biotoxicity. Ubiquitous detection of these contaminants across aquatic environments poses severe threats to ecosystem stability and human health, while constructed wetlands (CWs) serve as a sustainable low-carbon alternative for the remediation of PFAS-laden wastewater. However, traditional mechanisms such as matrix adsorption, phytoaccumulation, and microbial transformation often suffer from low efficiency, rapid saturation, and incomplete degradation. To overcome the above drawbacks, the arbuscular mycorrhizal fungi (AMF)–plant–microbe synergistic consortium has become a promising remediation candidate, which facilitates PFAS immobilization and biodegradation via symbiotic crosstalk among three components. This paper reviews recent advancements in PFAS remediation within AMF-facilitated systems, examining fundamental synergistic mechanisms, treatment efficiencies, and key influencing factors. We propose several optimization strategies, including substrate modification, operational parameter refinement, and the integration of advanced technologies. Furthermore, we emphasize the necessity of elucidating the molecular pathways governing long-chain PFAS degradation and addressing current bottlenecks in engineering applications. Future research should prioritize molecular interaction level interaction mechanisms, the development of anti-interference systems, and field-scale validation. This review provides a theoretical foundation and technical framework for leveraging AMF–plant–microbe synergism to enhance PFAS removal in CWs. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

The imminent threat of large-scale quantum computers motivates the deployment of post-quantum cryptography (PQC). CRYSTALS-Kyber, a leading lattice-based Key Encapsulation Mechanism, relies heavily on Number Theoretic Transform (NTT) operations, which remain a major performance and resource bottleneck. This paper presents a cross-platform NTT evaluation framework for CRYSTALS-Kyber, centered on an adaptive FPGA-based mixed-radix accelerator supporting radix-2, radix-4, and radix-8 configurations, together with comparative classical implementations and exploratory quantum-circuit prototypes. Classical evaluations show that an iterative Cooley–Tukey implementation outperforms a matrix-based baseline (≈3.6× faster for the forward NTT, ≈6.3× faster for the inverse NTT). Quantum prototypes implemented in Qiskit demonstrate proof-of-concept QFT-based NTT constructions under classical simulation environments, highlighting circuit-depth growth and noise sensitivity rather than practical hardware acceleration. The proposed FPGA design, based on a Xilinx Virtex UltraScale+ platform, employs an adaptive radix controller, LUT-based twiddle management, and Montgomery/Barrett modular arithmetic. Montgomery reduction provides superior timing and area trade-offs, with an estimated $F_{\max }$ of up to 231.48 MHz and only 5 DSPs for radix-2. At the same time, radix-2 offers the best resource/performance balance with a latency of approximately 32,804 cycles. The hybrid approach strikes a balance between near-term FPGA practicality and long-term quantum potential while preserving Kyber’s MLWE-based security. Experimental results and comparative analysis indicate that the adaptive design substantially reduces resource usage and timing overhead compared to recent HLS-based NTT accelerators. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article

The Difference Equation Matrix Model (DEMM) is presented as an input–output Model Predictive Control (MPC) formulation derived from discrete difference equations. The proposed approach is compared with the widely used Dynamic Matrix Control (DMC) strategy from a structural perspective, highlighting differences in model parameterization, identification requirements, and matrix dimensions. The analysis indicates that DEMM provides a more compact model representation than the DMC formulation considered in this work while preserving the predictive-control framework. Furthermore, the DEMM strategy is applied to a binary distillation column, a multivariable nonlinear process, to illustrate its implementation and closed-loop behavior under disturbance, noise, and setpoint-change scenarios. Simulation results demonstrate satisfactory disturbance-rejection and tracking performance for the considered operating conditions. Full article

This paper introduces and investigates a novel two-parameter sequence, termed the biparametric Appell extension (B-App-Ex) and denoted by ${\mathcal{B}}_n(x;\lambda ,\alpha )$. Standard classical Appell sequences often lack sufficient structural parameters, which can limit their operational flexibility in certain advanced spectral schemes. To address this limitation, we construct an enhanced operational framework by integrating a binomial structural kernel ${(1+w)}^{\lambda }$ with a linear exponential scaling $e^{\alpha xw}$ entirely within the Appell class. We provide a rigorous logical deduction of the fundamental properties of this sequence, including its explicit power series representation, a characteristic three-term recurrence relation, and a governing second-order differential equation (DEq.). A significant contribution of this work is the establishment of analytically exact connection and inverse connection formulas between the B-App-Ex basis and various classical orthogonal polynomial (COP) families. Numerical verification via a collocation-based projection framework demonstrates that these algebraic kernels achieve near-machine epsilon precision (≈${10}^{-15}$), remaining stable even for high-order approximations. Furthermore, by isolating the dilation factor $\alpha$, we establish an $O\left(N\right)$ computational complexity that offers a reduction in latency by approximately two orders of magnitude compared to classical matrix-based transformations. The results demonstrate that the proposed biparametric (Bip.) extension offers a versatile and highly optimized analytical template for modeling complex dynamic systems where structural shifting and spatial scaling must be tuned simultaneously. Full article

Accurate weather-regime classification is increasingly important for climate-sensitive decision-making in agriculture, aviation, disaster preparedness, and territorial planning, particularly in regions where strong climatic heterogeneity complicates conventional operational workflows. This study proposes a machine learning-based framework for broad-regime weather classification over Morocco using hourly METAR observations collected from 22 meteorological stations between July 2022 and February 2024. The proposed workflow integrates data cleaning, missing-value imputation, feature transformation, categorical encoding, class-imbalance handling, and model optimization under a leakage-safe experimental protocol. To preserve temporal integrity, observations were chronologically split into training, validation, and independent test subsets; SMOTE and random undersampling were applied exclusively to the training subset, whereas the validation and test subsets retained their original class distributions. Seven classifiers were evaluated, including XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting, Support Vector Machine, and Logistic Regression, with hyperparameters optimized using Optuna. The results show that optimized boosting models are particularly effective for Moroccan station-based weather classification. XGBoost achieved the highest test-set accuracy of 95.1%, followed by LightGBM at 94.7% and CatBoost at 93.8%, with optimization improving accuracy by approximately 8–12 percentage points compared with baseline configurations. Because the dataset exhibits class imbalance, macro-averaged precision, recall, and F1-score were emphasized alongside accuracy to provide a more reliable assessment across weather classes. Confusion-matrix analysis indicates improved recognition of underrepresented regimes, especially Dust/Sand events, while residual confusion between Fog/Haze and Rain/Storm reflects both physical overlap and the limits of a four-class METAR taxonomy. Overall, the findings demonstrate that optimized ensemble learning can provide a robust, computationally efficient, and operationally relevant classification layer for regional meteorological decision support in Morocco, while future work should extend the framework to longer time series, finer weather taxonomies, and external regional validation. Full article