Background Transformation of biomass waste into catalyst supports for upgrading bio-based chemicals has attracted significant attention. This approach not only mitigates the detrimental environmental impacts of agricultural wastes but also produces valuable functionalized materials for catalytic conversion of biomass to fine chemicals. Method The activated biochar-anchored Ni nanoparticles (Ni@ABC) catalyst was synthesized via a two steps process. First, activated biochar (ABC) was prepared through pyrolysis of a mixture of water hyacinth and H3PO4. Then, Ni nanoparticles (Ni NPs) were embedded into the synthesized ABC structure using an impregnation method. The physical and chemical characteristics of Ni@ABC catalyst were measured using various techniques. Significant findings The characterization results reveals that the Ni@ABC catalyst exhibited a mesoporous structure, high Brunauer-Emmett-Teller (BET) specific surface area, uniformly dispersed and small Ni nanoparticles, and abundant oxygen- and phosphorous-containing functional groups. The 10Ni@ABC catalyst efficiently converted furfural (FA) to tetrahydrofurfural alcohol (THFA), achieving a high yield of 98.8% at 50 °C for 5 h using NaBH4 as a hydrogen source, outperforming the convention metal oxides-supported Ni catalysts. The enhanced catalytic activity is attributed to the synergistic effects between Ni NPs and functional groups within the ABC support. Moreover, the reaction kinetics and mechanism were systematically investigated.
Background Dynamic binding capacity at 5% breakthrough (DBC5%) is a key performance parameter in packed-bed chromatography because it determines the effective working capacity of the adsorbent and directly influences process productivity under dynamic-flow conditions. Methods This study presents a systematic methodology for the predictive optimization and scale-up of DBC5% for C-phycocyanin (CPC) purification from Spirulina platensis using a sequential design of experiments (DoE) approach. A 2⁴ full factorial design with two center points (2⁴ + 2) was first employed to identify the significant operating variables, followed by a central composite design (CCD) to evaluate potential nonlinear responses. Significant findings The 2⁴ + 2 factorial model exhibited superior predictive performance, achieving an R² of 98.97% and a predicted R² of 94.82%, compared with corresponding values of 91.04% and 48.50%, respectively, for the CCD model. The optimized operating conditions (pH 6.0, 10% (w/v) feed concentration, 1.6 cm bed height, and a flow rate of 10.0 mL/min) yielded a predicted DBC5% of 10.51 mg/mL, which was experimentally validated by an observed value of 10.45 mg/mL, corresponding to a relative error of 2.9%. Furthermore, scale-up from 1.6 cm to 5.0 cm internal-diameter columns while maintaining hydrodynamic similarity successfully preserved DBC5%, demonstrating consistent adsorption performance across the investigated scales. Although the developed regression model is specific to the chromatographic system investigated, the proposed DoE-based optimization framework and hydrodynamic scale-up strategy provide a practical methodology that can be applied to other packed-bed chromatography systems following appropriate experimental calibration and validation.
Urban bus electrification is a complex multi-criteria decision under deep uncertainty. We integrate Symmetry Point of Criterion (SPC) and weighting with Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) within a q-rung orthopair fuzzy set (q-ROFS) environment and introduce a dual-aggregation scheme that linearly combines q-ROFWFA and q-ROFEWA via a tuneable parameter λ to mitigate aggregation bias. We further establish theoretical properties (boundary, monotonicity, idempotency) and show that the Lance distance–based CRADIS-L yields scale-invariant ranking and formal resistance to rank reversal. On a real Istanbul case, the unit energy cost emerges as the dominant criterion (0.2975), and AB Volvo 7900 ranks first overall; extensive ablation, robustness, and statistical significance tests confirm stability and rank-order reliability. Beyond offering a reproducible decision pipeline with Bayesian fusion of user-centric and technical attributes, our framework generalizes to IFS/PyFS/FFS in the limit q→ {1,2,3} and to single-operator cases when λ→ {0,1}.
In this study, a deep multi-layered Nonlinear Autoregressive Exogenous neuro-structures optimized with Levenberg-Marquardt (ML-NARX-LM) is exploited to analyse the Rayleigh-B & eacute;nard convection based chaotic nonlinear Lorenz-L & uuml;-Chen (CNLLC) systems within the context of fluid dynamics. The CNLLC simulations are acquired by using the Adams numerical method for the three families of the model. A Savitzky-Golay filter based preprocessing is applied on the CNLCC results to effectively isolate and remove noise and chaos for better predictive capabilities. The filtered solutions were then utilized in the designed ML-NARX-LM scheme by segmenting arbitrarily into training, testing and validation samples to predict the dynamics of CNLLC efficaciously. Our findings demonstrate that the ML-NARX-LM model, when applied to the filtered data, achieves low predictive error for all three families of chaotic systems by means of learning curves on MSE, error histograms, absolute error scrutiny, and regression indices.
Drawing on the stimulus–organism–response (SOR) model, theory of consumption values (TCV), and social influence theory, this study investigates how over-the-top (OTT) recommender system features shape customer experiences and consumers’ continued usage intention. Survey data from 505 Taiwanese subscribers were analyzed via SEM with AMOS. The results show that match-up and the evaluation function enhance utilitarian value and hedonic value, which in turn drive continued usage intention, while social influence strengthens key stimulus–organism pathways. Theoretically, this study integrates the SOR model, TCV, and social influence theory to explain value formation in OTT recommender systems. For managers, it demonstrates how recommender design and social signaling strategies can be leveraged to strengthen user experiences and retention.
