Search Results (996)

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor—a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications. Full article

Copper contamination in aquatic environments poses significant ecological and health risks, necessitating efficient and resilient treatment strategies. In this study, graphene oxide (GO) and magnetic graphene oxide (MGO) were synthesized and comprehensively evaluated for Cu(II) removal using an integrated multivariate approach combining kinetic and isotherm modelling, Response Surface Methodology (RSM), and advanced statistical analyses. Both adsorbents achieved high removal efficiencies (>90%) under optimized conditions, with Langmuir capacities of 59.2 mg g⁻¹ for GO and 40.1 mg g⁻¹ for MGO. Kinetic modelling confirmed reaction-controlled adsorption, while Freundlich isotherms highlighted heterogeneous surface binding. RSM identified pH as the dominant factor governing removal efficiency, with significant interactions among pH, Cu(II), and DOC reflecting competitive matrix effects. Principal Component Analysis (PCA) revealed that GO performance is strongly influenced by solution chemistry, whereas MGO exhibits reduced sensitivity due to modified physicochemical properties. FTIR analysis confirmed that adsorption proceeds primarily through electrostatic attraction and inner-sphere complexation, with Fe–O sites contributing to MGO’s enhanced affinity. Regeneration studies demonstrated superior reusability of MGO, which retained ~64% efficiency after five cycles compared to ~52% for GO. Collectively, these multivariate and mechanistic insights identify MGO as a more robust, versatile, and regenerable sorbent for Cu(II) removal under realistic water-matrix conditions. Full article

The increasing demand for rapid identification of bacteria capable of degrading environmentally relevant organic compounds highlights the need for scalable and selective analytical tools. Cupriavidus necator catabolizes several hydroxybenzoic acids, including 2-hydroxybenzoate (salicylate, 2-HBA), 4-hydroxybenzoate (4-HBA), and 3-hydroxybenzoate (3-HBA), funneling them into central aromatic catabolism via monooxygenation to 2,5-dihydroxybenzoate (gentisate, 2,5-dHBA) and 3,4-dihydroxybenzoate (protocatechuate, 3,4-dHBA) followed by the oxidative cleavage reaction, enabling complete conversion to tricarboxylic acid (TCA) cycle intermediates. To quantify how readily C. necator is able to activate catabolic genes in response to hydroxybenzoic acid, an extracellular ligand, we applied an approach centered on a transcription-factor (TF)-based biosensor that combines ligand-bound regulator activity with a fluorescent reporter. This approach allowed to evaluate the ligand sensitivity by determining gene activation threshold AC_min and half-maximal effective concentration EC₅₀. Amongst studied hydroxybenzoic acids, 2-HBA and 4-HBA sensors from C. necator showed very low thresholds 4.8 and 2.4 μM and EC₅₀ values of 19.91 and 13.06 μM, indicating high sensitivity to these compounds and implicating a scavenging characteristic of associated catabolism. This study shows that the TF-based-biosensor approach applied for mapping functional sensing ranges of hydroxybenzoates combined with the research and informatics of catabolism can advance our understanding of how gene expression regulation systems have evolved to respond differentially to the availability and concentration of carbon sources. Furthermore, it can inform metabolic engineering strategies in the prevention of premature pathway activation or in predicting competitive substrate hierarchies in complex mixed environments. Full article

Petroleum-based plastics are non-renewable and degrade poorly, persisting in the environment and causing serious ecological pollution, so urgent development of alternatives is needed. In this study, all-bamboo fiber thermosetting plastics (BTPs) were successfully prepared through selective sodium periodate oxidation of bamboo fibers followed by hot-pressing. The results demonstrate that the oxidation treatment effectively enhanced fiber reactivity and facilitated the formation of dense composite materials during hot-pressing. Compared with petroleum-based plastics (e.g., PVC), BTPs exhibit outstanding mechanical properties: flexural strength reaches 100.73 MPa, tensile strength reaches 83.31 MPa, while the 72 h water absorption and thickness swelling rates are as low as 5.36% and 4.59%, respectively. This study also reveals the mechanism by which residual lignin affects material microstructure formation through competitive oxidation reactions. Although it imparts initial hydrophobicity, it hinders complete fiber activation, leading to the formation of micro-defects. Furthermore, BTPs can completely degrade in 1% NaOH solution within 24 h, demonstrating excellent degradability. This research provides a new strategy for developing high-performance, degradable all-bamboo-based materials and promotes the value-added utilization of bamboo resources. Full article

The reliance on unstable hydrogen peroxide (H₂O₂) adversely affects the robustness and simplicity of chemiluminescence (CL)-based immunoassays. We report a novel external H₂O₂-free Fenton CL system integrated into a highly sensitive non-enzymatic immunoassay for the detection of SARS-CoV-2 nucleoprotein, utilizing cuprous–polyethylenimine–lipoic acid nanoflowers (Cu(I)-PEI-LA-Ab NF) as a non-enzymatic tag. The signaling polymer (PEI-LA) was synthesized via EDC/NHS coupling, which conjugated approximately 550 LA units to the PEI backbone. This polymer formed antibody-conjugated NF with various metal ions, and the Cu(I)-based variant was selected for its intense and sustained CL with luminol. The mechanism relies on an in situ Fenton reaction, in which dissolved oxygen is reduced by Cu(I) to H₂O₂, which reacts with oxidized Cu(II), producing hydroxyl radicals that oxidize luminol. Direct calibration of the SARS-CoV-2 nucleoprotein fixed on microplate wells demonstrated excellent linearity in the range of 0.01–3.13 ng/mL (LOD = 3 pg/mL). In a final competitive immunoassay format for samples spiked with the antigen, a decreasing CL signal that correlated with increasing antigen concentration was obtained in the range of 0.1–20.0 ng/mL, achieving excellent recoveries that were favorable compared with those of the sandwich ELISA kit, establishing this H₂O₂-independent platform as a powerful and robust tool for clinical diagnostics. Full article

The elimination of nitrogen and sulfur compounds from liquid fuel is a critical aspect of reducing environmental pollution. However, the widely utilized hydrodesulfurization and hydrodenitrogenation technologies require harsh operating conditions. Moreover, when operated simultaneously, these processes induce mutual competition and inhibition between the two reactions, thereby limiting the actual removal efficiency. Conversely, non-hydrogenation technologies offer substantial advantages in terms of operating conditions and provide high levels of desulfurization and denitrogenation. Nevertheless, the presence of nitrogen-containing compounds has also been demonstrated to engender competition and inhibition. It is imperative to develop environmentally friendly technologies that can simultaneously desulfurize and denitrogenate. This paper reviews research progress in this field over the past decade, providing a detailed assessment and comparison of hydrogenation and non-hydrogenation technologies, including adsorption, extraction, oxidation and biological methods. Furthermore, it considers future research directions. The article’s aim is to furnish a novel perspective on the development of clean fuel sources and to investigate more economical, sustainable, and commercially viable desulfurization and denitrogenation methods. Full article

Copper-based catalysts have emerged as promising materials for electrochemical carbon dioxide reduction reactions, owing to copper’s unique ability to facilitate multi-electron transfer processes and produce valuable products such as methanol and ethanol. In this study, novel trisferrocenyltrithiophosphite–copper(I) bromide composites with Cu-to-ligand molar ratios of 1:1 and 2:1 were synthesized and evaluated for their catalytic performance. The composites were characterized by a combination of techniques, including powder X-ray diffraction (PXRD), linear sweep voltammetry (LSV), potentiostatic testing, chromatographic analysis, scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). Electrochemical measurements demonstrated significant current enhancements in the presence of CO₂, highlighting the composites’ catalytic activity. Potentiostatic tests revealed excellent stability, with only a 9% decline in current density over 5 h of electrolysis. Product analysis via gas chromatography indicated the formation of methanol for the 1:1 composite and ethanol for the 2:1 composite with Faradaic efficiencies of 5.79% and 9.26%, respectively. While absolute efficiencies remain modest due to competitive hydrogen evolution, these results demonstrate a tunable catalytic performance based on the Cu-to-ligand ratio. SEM and XPS studies further supported the formation of active catalytic centers and changes in the oxidation states of copper during CO₂ reduction. PXRD analysis confirmed the retention of structural integrity for both composites before and after catalytic testing. Full article

Electrochemical CO₂ reduction (CO₂RR) is a promising route to transform a major greenhouse gas into value-added fuels and chemicals. However, its deployment is still hindered by the sluggish activation of CO₂, poor selectivity toward multielectron products, and competition with the hydrogen evolution reaction (HER). Single-atom catalysts (SACs) have emerged as powerful materials to address these challenges because they combine maximal metal utilization with well-defined coordination environments whose electronic structure can be precisely tuned through metal–support interactions. This minireview summarizes current understanding of how structural, electronic, and chemical features of SAC supports (e.g., porosity, heteroatom doping, vacancies, and surface functionalization) govern the adsorption and conversion of key CO₂RR intermediates and thus control product distributions from CO to CH₄, CH₃OH and C₂⁺ species. Particular emphasis is placed on selectivity descriptors (e.g., coordination number, d-band position, binding energies of *COOH and *OCHO) and on rational design strategies that exploit curvature, microenvironment engineering, and electronic metal–support interactions to direct the reaction along desired pathways. Representative SAC systems based primarily on N-doped carbons, complemented by selected examples on oxides and MXenes are discussed in terms of Faradaic efficiency (FE), current density and operational stability under practically relevant conditions. Finally, the review highlights remaining bottlenecks and outlines future directions, including operando spectroscopy and data-driven analysis of dynamic single-site ensembles, machine-learning-assisted DFT screening, scalable mechanochemical synthesis, and integration of SACs into industrially viable electrolyzers for carbon-neutral chemical production. Full article

Supercritical CO₂ modifies deep coal reservoirs through the coupled effects of adsorption-induced deformation and geochemical dissolution. CO₂ adsorption causes coal matrix swelling and facilitates micro-fracture propagation, while CO₂–water reactions generate weakly acidic fluids that dissolve minerals such as calcite and kaolinite. These synergistic processes remove pore fillings, enlarge flow channels, and generate new dissolution pores, thereby increasing the total pore volume while making the pore–fracture network more heterogeneous and structurally complex. Such reservoir restructuring provides the intrinsic basis for CO₂ injectivity and subsequent CH₄ displacement. Both adsorption capacity and volumetric strain exhibit Langmuir-type growth characteristics, and permeability evolution follows a three-stage pattern—rapid decline, slow attenuation, and gradual rebound. A negative exponential relationship between permeability and volumetric strain reveals the competing roles of adsorption swelling, mineral dissolution, and stress redistribution. Swelling dominates early permeability reduction at low pressures, whereas fracture reactivation and dissolution progressively alleviate flow blockage at higher pressures, enabling partial permeability recovery. Injection pressure is identified as the key parameter governing CO₂ migration, permeability evolution, sweep efficiency, and the CO₂-ECBM enhancement effect. Higher pressures accelerate CO₂ adsorption, diffusion, and sweep expansion, strengthening competitive adsorption and improving methane recovery and CO₂ storage. However, excessively high pressures enlarge the permeability-reduction zone and may induce formation instability, while insufficient pressures restrict the effective sweep volume. An optimal injection-pressure window is therefore essential to balance injectivity, sweep performance, and long-term storage integrity. Importantly, the enhanced methane production and permanent CO₂ storage achieved in this study contribute directly to greenhouse gas reduction and improved sustainability of subsurface energy systems. The multi-field coupling insights also support the development of low-carbon, environmentally responsible CO₂-ECBM strategies aligned with global sustainable energy and climate-mitigation goals. The integrated experimental–numerical framework provides quantitative insight into the coupled adsorption–deformation–flow–geochemistry processes in deep coal seams. These findings form a scientific basis for designing safe and efficient CO₂-ECBM injection strategies and support future demonstration projects in heterogeneous deep coal reservoirs. Full article

Background/Objectives: Neuropathic pain remains a significant clinical challenge, with current treatments often providing inadequate relief and adverse effects. Sigma receptors (SRs) modulate nociception and have emerged as potential therapeutic targets for neuropathic pain. Although putative sigma-1 receptor (S1R) ligands have demonstrated analgesic efficacy in preclinical models, their in vivo efficacy and safety profiles require further clarification. Methods: Analogs of well-known selective S1R ligand UVM147 were synthesized using 3-component Ugi reactions and examined in vitro for receptor affinity in radioligand competition binding assays and in vivo with mouse models of neuropathic and inflammatory pain and adverse effects. Results: Three novel heterocyclic compounds (RO-4-3, RO-5-3, and RO-7-3) displayed in vitro nanomolar affinity with varying selectivity for both SR subtypes (S1R and S2R). When screened in vivo at a dose of 30 mg/kg s.c. in mice first subjected to chronic constriction injury (CCI), RO-5-3 and RO-7-3 possessed anti-allodynic potential, while UVM147 was inactive. Upon full characterization, RO-5-3 significantly attenuated mechanical allodynia in a dose-dependent manner, while RO-7-3 was ineffective at higher doses. Both compounds dose-dependently attenuated nociceptive behaviors in the mouse formalin assay. RO-5-3 induced mild respiratory depression without impairing locomotor activity, whereas RO-7-3 caused transient respiratory depression and locomotor impairment. Additionally, RO-5-3, but not RO-7-3, induced conditioned place aversion consistent with potential S2R involvement. Conclusions: RO-5-3 exerts antinociceptive and anti-allodynic effects with minimal adverse behavioral effects, supporting the role of SRs in pain modulation. These results add to growing evidence supporting the development of SR ligands as efficacious therapeutics for neuropathic pain with fewer clinical liabilities. Full article

Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp²) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products. Full article

The objective of this research is to develop environmentally friendly, risk-free and effective adsorbent composite beads that remove Cu(II) ions from aqueous solutions using cost-effective biopolymers (Carboxymethylcellulose (CMC) and sodium alginate (AL)). The synthesized hydrogel beads (AL@CMC) were dried using two drying modes, namely air-drying and freeze-drying, and characterized using scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FT-IR), and Brunauer–Emmett–Teller (BET) analysis. The study investigated factors such as pH, adsorbent dosage, reaction time, Cu(II) ions concentration, and temperature to elucidate the adsorption mechanisms involved in removing copper ions. The results indicated that the hydrogel exhibited a maximum adsorption capacity of 99.05 mg·g⁻¹, which is highly competitive compared to previous studies; the AL@CMC beads prepared in this work show a significantly higher adsorption capacity, improved stability due to the interpenetrated biopolymer network, and a clear enhancement from freeze-drying, which greatly increases porosity and active surface area. In addition, the pseudo-second-order nonlinear kinetic model best described the experimental data, implying the chemical nature of the adsorption process. Furthermore, the thermodynamic studies revealed that the adsorption process was endothermic, spontaneous, and homogenous. A Monte Carlo simulation model was utilized to ensure compatibility with the adsorption mechanism, in order to delve deeper into the intricacies of the adsorption process and gain a more comprehensive understanding of its underlying mechanisms and behavior. In conclusion, the prepared hydrogel beads proved to be an effective adsorbent for efficiently removing copper ions, making them a promising solution for addressing Cu(II) ion pollution. Full article

The mechanism of generation of products with opposite handedness in the reactions of heterocyclic aldehydes with diallylboronates was studied by NMR experiments and DFT computations. The origin of this unusual phenomenon is a competition between monomeric and dimeric autoinductors that promote the formation of opposite enantiomers. Thus, NMR data suggest that racemic alcohol 3a, upon dimerization, provides almost exclusively the heterochiral dimeric boronate 5a(RS). This corresponds to the computed results predicting strongly exergonic dimerization with ΔΔG²⁹⁸ −6.5 kcal/mol. Dimerization of the chiral boronate 3a (R) with 82% ee yields 5a (RS) in which all available 3a(S) is bound. As a result, 3 species remain in the solution: (1) 5a(RS), producing a newly formed racemic product in the reaction with 1a, (2) 3a(R), reacting with 1a and yielding an R-configured newly formed product, and (3) 5a(RR), yielding selectively S-configured newly formed product according to computations. Taking into account the equilibria existing between monomers and dimers, the system is capable of demonstrating the experimentally observed random handedness of the newly formed product. Full article

Copper ions are essential trace elements that play critical roles in redox reactions, signal transduction, energy metabolism, and regulation of the central nervous system. However, excess copper can induce cytotoxicity and contribute to various pathological conditions, highlighting the need for sensitive and selective detection methods. We report a novel near-infrared (NIR) optical sensor, IRPhen, based on a heptamethine cyanine scaffold conjugated with a 1,10-phenanthroline Cu²⁺-binding receptor. IRPhen exhibits strong NIR absorption and emission (E_x: 750 nm, E_m: 808 nm), high sensitivity, and good selectivity toward Cu²⁺ over competing metal ions. Spectroscopic studies revealed a rapid, reversible 1:1 binding interaction with a binding constant of 1.3 × 10⁶ M⁻¹ and a detection limit of 0.286 µM. The probe demonstrated excellent stability across physiological pH ranges and maintained its performance under competitive conditions. Importantly, IRPhen is cell-permeable and capable of detecting dynamic Cu²⁺ changes in living fibroblast (WS1) cells using confocal microscopy. This sensor design offers a versatile platform for developing NIR optical sensors to study copper homeostasis, elucidating copper-related biological mechanisms, and potentially developing similar NIR probes for other biologically relevant metal ions. Full article