Search Results (17,846)

This study investigated the optimisation of roselle (Hibiscus sabdariffa L.) leaf tisane formulation using response surface methodology (RSM), targeting total phenolic content (TPC), ferric reducing antioxidant power (FRAP), and DPPH radical scavenging activity as quality indicators. A face-centred central composite design was employed to evaluate dose effects (0.5–2.5 g) and infusion time (5–15 min). Multi-response optimisation using the desirability function identified 1.81 g dose and 5 min infusion as the optimum condition, yielding predicted values of 24.46 mg GAE/100 mL (TPC), 61.07 µmol Fe²⁺/100 mL (FRAP), and 80.47% (DPPH), with a composite desirability score of 0.64. Validation experiments confirmed strong predictive accuracy, with deviations of 0.80% (FRAP) and 3.92% (DPPH), and a modest deviation of 13.2% (TPC), acceptable within complex food matrices. The findings demonstrate that short infusion times are sufficient to extract key bioactives, ensuring consumer convenience and energy efficiency, while valorising roselle leaves as an underutilised by-product into a sustainable functional beverage. Future studies should address sensory acceptance, stability, and bioavailability to support industrial applications further. Full article

Medium-range temperature forecasts are of critical importance for a diverse range of economic activities, e.g., agricultural production, transportation, and industrial operations. The most significant challenge in accurately predicting temperature lies in effectively characterizing the spatiotemporal evolution of temperature characteristics. In this paper, we design a spatial feature module and a gating temporal module based on the location-oriented directed adjacency matrix. Dynamic evolution of both spatial and temporal features is characterized by a graph-based differential equation module. The spatial and temporal feature sequences are integrated by an output fusion module to achieve medium-range temperature prediction. The proposed graph-based differential equation network has been validated on the dataset of South China, which shows superior performance in medium-range temperature prediction. Full article

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

With the increasing requirements for “Green Mine” construction, Cemented Tailings Backfill (CTB) has emerged as the preferred strategy for solid waste management and ground pressure control in underground metal mines. However, full tailings, characterized by wide particle size distribution and high fine-grained content, exhibit complex physicochemical properties that lead to significant non-linear behavior in slurry rheology and strength evolution, posing challenges for accurate prediction using traditional empirical formulas. Addressing the issues of significant strength fluctuations and difficulties in mix proportion optimization in a specific lead–zinc mine, this study systematically conducted physicochemical characterizations, slurry sedimentation and transport performance evaluations, and mechanical strength tests. Through multi-factor coupling experiments, the synergistic effects of cement type, cement-to-tailings (c/t) ratio, slurry concentration, and curing age on backfill performance were elucidated. Quantitative results indicate that solids mass concentration is the critical factor determining transportability. Concentrations exceeding 68% effectively mitigate segregation and stratification during the filling process while maintaining optimal fluidity. Regarding mechanical properties, the c/t ratio and concentration show a significant positive correlation with Uniaxial Compressive Strength (UCS). For instance, with a 74% concentration and 1:4 c/t ratio, the 3-day strength increased by 1.4 times compared to the 68% concentration, with this increment expanding to 2.0 times by 28 days. Furthermore, a comparative analysis of four cement types revealed that 42.5# cement offers superior techno-economic indicators in terms of reducing binder consumption and enhancing early-age strength. This research not only establishes an optimized mix proportion scheme tailored to the operational requirements of the lead–zinc mine but also provides a quantitative scientific basis and theoretical framework for the material design and safe production of CTB systems incorporating high fine-grained full tailings. Full article

Accurate day-ahead photovoltaic (PV) power forecasting is essential for secure operation and scheduling in power systems with high PV penetration, yet its performance is often constrained by the coarse spatial resolution of operational numerical weather prediction (NWP) products at the plant scale. To address this issue, this paper proposes an attention-enhanced CNN–LSTM forecasting framework integrated with a spatial downscaling strategy. First, seasonal and diurnal characteristics of PV generation are analyzed based on theoretical irradiance and historical power measurements. A CNN–LSTM network with a channel-wise attention mechanism is then employed to capture temporal dependencies, while a composite loss function is adopted to improve robustness. We fuse multi-source meteorological variables from NWP outputs with an attention-based module. We also introduce a multi-site XGBoost downscaling model. This model refines plant-level meteorological inputs. We evaluate the framework on multi-site PV data from representative seasons. The results show lower RMSE and higher correlation than the benchmark models. The gains are larger in medium power ranges. These findings suggest that spatially refined NWP inputs improve day-ahead PV forecasting. They also show that attention-enhanced deep learning makes the forecasts more reliable. Quantitatively, the downscaled meteorological variables consistently achieve lower normalized MAE and normalized RMSE than the raw NWP fields, with irradiance-related errors reduced by about 40% to 55%. For day-ahead PV forecasting, using downscaled NWP inputs reduces RMSE from 0.0328 to 0.0184 and MAE from 0.0194 to 0.0112, while increasing the Pearson correlation to 0.995 and the CR to 98.1%. Full article

This work presents a notch filter architecture based on a dual-output second-generation voltage conveyor, designed with a current-mode approach. The proposed topology employs a single frequency-selective LC branch and directly uses the two voltage outputs of the DOVCII to generate a notch response without additional active stages. Analytical expressions for the transfer function, notch frequency, and quality factor are derived, highlighting independent control of the passband gain and notch parameters. A sensitivity analysis demonstrates that the notch frequency depends exclusively on the LC product with half-order sensitivities, while the quality factor is predominantly controlled by a single resistor, resulting in predictable tuning and improved tolerance to passive component variations. Transistor-level analysis of the proposed filter was carried out using a standard AMS 0.35 μm CMOS process and has been validated through both circuit-level simulations and experimental measurements using a DOVCII implementation based on the AD844 current-feedback amplifier. Prototypes operating at 100 kHz and 50 Hz notch frequencies have been implemented, the latter employing a current-mode inductance simulator to avoid bulky passive inductors. Full article

This study assesses how satellite clock products affect Precise Point Positioning (PPP) for GPS, Galileo, and GPS+Galileo. Multi-GNSS data at 30 s were processed for 12 global IGS stations over one week in 2025, with each day split into eight independent three-hour sessions. SP3 clocks (ORB, 5 min) were compared with dedicated CLKs (CLO, 5 s, 30 s, 5 min) across final (FIN), rapid (RAP), and ultra-rapid (ULT; observed/predicted) product lines from multiple analysis centers. Two timing strategies were tested: nearest-epoch sampling (CLOCK0) and linear interpolation (CLOCK1). CLO consistently delivered the lowest 2D/3D errors and the fastest convergence. ORB degraded accuracy by a few millimeters and extended convergence by ~5–10 min, most notably for GPS. With 5 min clocks, CLOCK1 yielded small gains for Galileo but often hurt GPS; with 30 s clocks, interpolation was immaterial; 5 s clocks offered no measurable benefit. FIN outperformed RAP; OPS slightly outperformed MGEX; ESA/GFZ ranked highest. ULT solutions were weaker, especially in the predicted half. Zenith tropospheric delay (ZTD) biases were negligible; variance was smallest for GPS+Galileo with CLO (~7–10 mm), increased by ~1–2 mm with ORB, and was largest in ULT. Dense, high-quality clock products remain essential for reliable PPP. Full article

This study addresses the ongoing challenge of accurately classifying beers by fermentation type and product category, an issue of growing importance for quality control, authenticity assessment, and product differentiation in the brewing sector. We applied a multiblock chemometric framework that integrates phenolic profiling obtained via GC–MS, antioxidant and antiradical activity derived from in vitro assays, and complementary colorimetric and physicochemical measurements. Principal Component Analysis (PCA) revealed clear compositional structuring within the dataset, with p-coumaric, gallic, syringic, and malic acids emerging as major contributors to variance. Supervised machine-learning classification demonstrated robust performance, achieving approximately 93% accuracy in discriminating top- from bottom-fermented beers, supported by a well-balanced confusion matrix (25 classified and 2 misclassified samples per group). When applied to ale–lager categorization, the model retained strong predictive ability, reaching 90% accuracy, largely driven by the C* chroma value and the concentrations of tyrosol, acetic acid, homovanillic acid, and syringic acid. The integration of multiple analytical blocks significantly enhanced class separation and minimized ambiguity between beer categories. Overall, these findings underscore the value of multi-block chemometrics as a powerful strategy for beer characterization, supporting brewers, researchers, and regulatory bodies in developing more reliable quality-assurance frameworks. Full article

The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical engineering. In-depth research on multi-objective parameter optimization technology for grinding is of great significance for improving processing efficiency, optimizing product quality, and reducing energy consumption. This paper takes the multi-objective optimization problem of grinding as its starting point. First, it introduces the basic theory of multi-objective optimization and two primary methods for solving such problems: optimization target dimension reduction and multi-objective optimization. Second, the key technologies of the two methods are reviewed, including the modeling method of the optimization problem, the multi-objective optimization algorithm for solving the optimization model, and the prior and posterior trade-off methods used to obtain the compromised optimal solutions. Finally, the existing problems of the multi-objective optimization methods in grinding processing are summarized and the future development trends are predicted. This paper aims to provide researchers with a comprehensive understanding of the multi-objective optimization technology in grinding processing, enabling them to make more reasonable decisions when dealing with actual multi-objective optimization problems. Full article

Iron-containing alcohol dehydrogenases (Fe-ADHs) from hyperthermophiles represent a distinct class of oxidoreductases characterized by exceptional thermostability, catalytic versatility, and unique metal-dependent properties. Despite considerable sequence diversity, Fe-ADHs share conserved motifs and a two-domain architecture essential for iron coordination and NAD(P)H cofactor binding. Physiologically, these enzymes are predicted to function primarily in aldehyde detoxification and redox homeostasis, with some also participating in fermentative alcohol production. Their remarkable stability and catalytic efficiency highlight their potential as robust biocatalysts for high-temperature industrial bioprocesses. This review presents a comprehensive comparative analysis of the biophysical, biochemical, and kinetic properties of Fe-ADHs, focusing on their thermostability, metal ion specificity, and catalytic mechanisms, as well as highlighting their potential for industrial biocatalytic applications. Full article

Background: Chronic nonbacterial prostatitis (CNP), the major subset of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), imposes a substantial global burden yet lacks satisfactory therapies. Maizibizi Wan (MZBZ) has long been used clinically for prostatitis, but its pharmacodynamic substance basis and mechanisms remain unclear. Methods: Ultra-high-performance liquid chromatography–Q-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) coupled with Global Natural Products Social Molecular Networking (GNPS) molecular networking profiled MZBZ constituents and rat plasma–exposed prototype components and metabolites was used. Based on blood-absorbable components, network pharmacology predicted core targets/pathways; representative interactions were validated by molecular docking. A λ-carrageenan–induced CNBP rat model underwent histopathology (H&E), serum cytokine assays (TNF-α, IL-1β, IL-6/IL-17), immunohistochemistry (COX-2, TNF-α, MMP-9), and Western blotting (P-p65/p65, p-AKT/AKT, COX-2, TGF-β1, BCL2). Results: A total of 188 chemical constituents were identified in MZBZ (79 flavonoids, 38 organic acids, 30 alkaloids, 15 phenylpropanoids, 7 steroids, 4 phenylethanoid glycosides, 15 others). A total of 35 blood-absorbable components (18 prototype components, 17 metabolites) were identified, mainly involving Phase I oxidation and Phase II glucuronidation/sulfation. Network analysis yielded 54 core targets enriched in NF-κB and PI3K/AKT signaling and apoptosis. Docking indicated stable binding of key flavonoids to COX-2, NFKB1, TNF, IL-6, and BCL2. In vivo, MZBZ ameliorated prostatic inflammation, reduced serum TNF-α/IL-1β/IL-6/IL-17 (p < 0.05 or p < 0.01); decreased P-p65/p65, p-AKT/AKT, COX-2, and TGF-β1; and increased BCL2 in prostate tissue. Conclusions: MZBZ exerts anti-CNBP effects via multi-component synergy (prototypes + metabolites) that suppresses inflammatory cytokines, modulates apoptosis, and inhibits NF-κB and PI3K/AKT pathways. These findings provide a mechanistic basis and quality control cues for the rational clinical use of MZBZ. Full article

Salvia miltiorrhiza Bunge is a traditional Chinese medicinal plant whose roots are rich in water-soluble phenolic acids. Rosmarinic acid and salvianolic acid B are representative components that confer antibacterial, antioxidant, and cardio-cerebrovascular protective activities. However, these metabolites often accumulate at low and unstable levels in planta, which limits their efficient development and use. This review summarises recent advances in understanding salvianolic acid biosynthesis and its transcriptional regulation in S. miltiorrhiza. Current evidence supports a coordinated pathway composed of the phenylpropanoid route and a tyrosine-derived branch, which converge to generate rosmarinic acid and subsequently more complex derivatives through oxidative coupling reactions. Key findings on transcription factor families that fine-tune pathway flux by regulating core structural genes are synthesised. Representative positive regulators such as SmMYB111, SmMYC2, and SmTGA2 activate key nodes (e.g., PAL, TAT/HPPR, RAS, and CYP98A14) to promote phenolic acid accumulation. Conversely, negative regulators such as SmMYB4 and SmMYB39 repress pathway genes and/or interfere with activator complexes. Major regulatory features include hormone-inducible signalling, cooperative regulation through transcription factor complexes, and emerging post-transcriptional and post-translational controls. Future directions and challenges are discussed, including overcoming regulatory redundancy and strong spatiotemporal specificity of transcriptional control. Integrating spatial and single-cell omics with functional genomics (e.g., genome editing and rational TF stacking) is highlighted as a promising strategy to enable predictive metabolic engineering for the stable, high-yield production of salvianolic acid-type compounds. Full article

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

Improving fuel and energy efficiency in agricultural tillage is critical for sustainable farming and reducing environmental impacts. In this study, the effects of forward speed and tillage depth on the fuel efficiency parameters of a tractor–chisel plough combination were investigated under controlled field conditions on clay soil. Specific fuel consumption (SFC), fuel consumption per unit area (FCPA), and overall energy efficiency (OEE) were evaluated at four forward speeds (0.6, 0.95, 1.2 and 1.4 m·s⁻¹) and four tillage depths (15, 19.5, 23 and 26.5 cm). SFC ranged from 0.519 to 1.237 L·kW⁻¹·h⁻¹, while OEE varied between 7.918 and 18.854%. Higher forward speeds significantly reduced fuel consumption and improved energy efficiency, whereas deeper tillage increased fuel use and reduced efficiency. Optimal operation occurred at speeds of 1.2–1.4 m·s⁻¹ and shallow to medium depths. Five machine learning algorithms: Polynomial Regression (PL), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR), were applied to model fuel efficiency parameters. RFR achieved the highest accuracy for predicting SFC, while PL performed best for FCPA and OEE, with the mean absolute percentage error (MAPE) below 2%. Models such as PL and RFR excel in data structures dominated by nonlinear relationships. These results highlight the potential of machine learning to guide data-driven decisions for fuel and energy optimization in tillage, promoting more sustainable mechanization strategies and resource-efficient agricultural production. Full article

Energetic powder processing includes comminution, sieving, drying, conveying, mixing, and packaging, all of which determine product performance and safety. With growing requirements for efficiency and reliability, numerical simulation has become essential for analyzing mechanisms, optimizing parameters, and supporting equipment design. This review summarizes recent progress in simulation techniques such as the discrete element method (DEM), computational fluid dynamics (CFD), and multi-scale coupling while also evaluating their predictive capabilities and limitations across various unit operations and safety concerns such as electrostatic hazards. It, thus, establishes the core “property–parameter–performance” relationships and clarifies mechanisms in multiphase flow, energy transfer, and charge accumulation, and highlights the role of symmetry in improving simulation efficiency. By highlighting persistent challenges, this work lays a foundation for future research, guiding the development of theoretical frameworks and practical solutions for advanced powder processing. Full article