Search Results (287)

Moisture, salinity and aeration in saline–alkali soil are three critical factors affecting the biotic and abiotic environment. A three-factorial experiment including two tillage measures (ridge and flat tillage, denoted as R and F, respectively), three drip irrigation levels (soil wetting proportion of 40, 55 and 70%, denoted as P₁, P₂ and P₃) and the presence or absence of air injection (AI) were investigated to determine their effects on soil moisture, salinity, aeration and sunflower growth and yield. Field trials were conducted in the Hetao irrigation district of Inner Mongolia in 2021 and 2022. Results showed that R increased daily average topsoil (0–20 cm depth) temperature by 0~4.7 °C, water-filled pore space (WFPS) by 4.3~9.1% and redox potential (Eh) by 16.7~31.6% compared to F. P₂ reduced the Eh of topsoil by 50.4% and 55.1% respectively under R and F. Under the same P, the effect of different tillage methods (R and F) on salt accumulation was not notable. AI increased topsoil temperature under R (0.1~2.7 °C) and F (0~2.2 °C) and increased salt accumulation in the topsoil. Compared with other treatments, the yield of sunflower increased by 10~36% and 12~37% respectively under the conditions of P₃R and P₂FAI. The net profit of P₃R treatment was 3421–3551 USD ha⁻¹, which was 12.0–71.5% higher than the other treatments. Furthermore, random forest analysis revealed that air injection, salinity, tillage method, and Eh were the primary determinants of sunflower yield and quality, while WFPS and temperature were of secondary importance. These findings also suggest that, in heavily saline–alkali soils, sunflower yield can be effectively enhanced either by adopting flat tillage with air injection or by using ridge tillage without air injection. Full article

Historically, pharmaceutical formulation development relied heavily on trial-and-error experimentation, which was useful for empirical progress but often provided limited mechanistic understanding and insufficient efficiency for increasingly complex drug products. The introduction of Design of Experiments (DoE) and Quality by Design (QbD) established a more systematic framework for studying formulation variables, manufacturing parameters, and Critical Quality Attributes (CQAs). Approaches such as factorial designs, response-surface methodology, and mixture designs have therefore become central to modern pharmaceutical development because they improve experimental efficiency and support the definition of design space. However, as formulations become more nonlinear, high-dimensional, and multi-objective, these classical approaches may no longer be sufficient on their own. This review examines the evolution of experimental design in pharmaceutical research, from one-factor-at-a-time experimentation to structured DoE/QbD strategies, and then to emerging intelligent optimization methods. Its central objective is to clarify when conventional DoE/QbD remains appropriate and when it should be complemented by machine learning, Bayesian optimization, digital twins, and closed-loop experimental systems. The review first summarizes the foundations and strengths of classical experimental design; then, it discusses its practical limitations in complex formulation settings, and finally evaluates how data-driven and hybrid approaches can extend pharmaceutical development. Evidence from tablets, capsules, nanocarriers, transdermal patches, and biotherapeutic systems suggests that intelligent optimization can improve predictive performance and experimental efficiency when used alongside, rather than instead of, established pharmaceutical development principles. Full article

The commercialization of perovskite solar cells (PSCs) hinges on replacing toxic lead-based absorbers with environmentally benign alternatives while maintaining competitive power conversion efficiencies (PCE). However, the enormous parameter space governing lead-free device architectures—spanning absorber thickness, defect density, doping concentration, and charge transport layer (CTL) selection—renders traditional trial-and-error optimization impractical. This paper introduces PerovskiteOpt-AI, a machine learning (ML)-driven multi-parameter optimization framework that integrates SCAPS-1D device simulation with Gaussian process (GP) surrogate modeling and Bayesian optimization (BO) to systematically identify high-efficiency lead-free PSC configurations. A synthetic dataset of 12,000 device-level simulations generated for the FTO/WS₂/CsSnI₃/CuSCN/Au architecture by varying eight critical parameters. An ensemble of ML models—random forest (RF), XGBoost, and GP regression (GPR)—is trained and benchmarked, with XGBoost achieving an ${\mathrm{R}}^2$ of 0.9987 and RMSE of 0.041% for PCE prediction. The GP surrogate is then coupled with a BO loop employing expected improvement (EI) acquisition to navigate the design space, converging on an optimized PCE of 27.83% ± 0.21% within 150 iterations—a 38.6% relative improvement over the baseline. Shapley additive explanations (SHAP) analysis reveals that absorber defect density and perovskite thickness are the dominant efficiency drivers, while conduction band offset at the ETL/absorber interface governs open-circuit voltage. The proposed framework reduces the computational cost of full-factorial parametric sweeps by over 95%, establishing a scalable paradigm for accelerated, interpretable design of next-generation lead-free consumer-grade photovoltaic devices. Full article

Immersive entertainment venues use spatial complexity to enhance visitor experience, but these design features may impair emergency evacuation, particularly when lighting fails. We conducted a 2 × 2 factorial experiment with 264 participants to quantify how spatial complexity and lighting conditions interact to affect evacuation performance. Ultra-wideband positioning provided centimeter-level tracking. Results showed very large main effects for spatial complexity (${\eta }_p^2$ = 0.976) and lighting (${\eta }_p^2$ = 0.863), but critically, a significant interaction (${\eta }_p^2$ = 0.799) revealed asymmetric patterns: darkness barely affected simple spaces (6.5% increase) but severely impaired complex spaces (36.9% increase), with a nine-fold amplification. The worst-case scenario (high complexity + darkness) increased evacuation time by 115% compared to optimal conditions. Findings demonstrate that spatial complexity and lighting combine synergistically, creating multiplicative rather than additive risk, with the worst-case combination increasing evacuation time by 115% relative to optimal conditions. Findings support prioritizing spatial simplification and emergency lighting redundancy in the design of complex immersive venues. Full article

To improve coating adhesion and tribological stability on aircraft-grade aluminum, this work utilizes periodic fiber-laser microtexts as a surface-engineering pre-treatment before applying an epoxy primer. AA2024-T3 panels were imprinted with rhombus, hexagon, and circular lattices (scale factors 100–250 µm; scan speeds 250–750 mm s⁻¹), then primed with an aerospace epoxy primer and evaluated within reciprocating sliding wear tests. Areal profilometry and sessile-drop goniometry measured topography and wettability, whereas friction–distance traces and scratch-track metrology resolved interfacial integrity. The textures expanded surface area and modified energy states in a geometry- and scale-dependent fashion, producing stable friction plateaus and smaller, less-lateral scratch scars compared to the untextured reference. Circular dimples reliably provided the best damage-tolerant behavior, a function of improved mechanical interlocking and debris/film management (reservoir and micro-trap effects), whereas polygonal lattices evidenced greater sensitivity to both scale and speed. Factorial analyses disclosed prevalent interaction effects amongst geometry, scale, and scan speed, reinforcing the notion that performance arises from co-optimized texture architecture rather than a single parameter. In systemic terms, laser-defined microtexts complemented with aerospace-standard primers represent a controllable pathway to vary friction, dampen wear, and improve coating–substrate adhesion. These results provide practical selection guides; and a broad selection prefers larger, well-spaced circular dimples for best-in-class performance and a transferable framework for designing texture-coating systems across aerospace and allied manufacturing contexts. Full article

Magnetoplasmadynamic thrusters represent a promising Electric Propulsion technology for future space missions; however, their optimization is hampered by the lack of accurate performance models in the emerging regime of low power (<12 kW) and high magnetic fields (>0.1 T), where traditional formulations prove inadequate. In this work, a new semi-empirical model for predicting the thrust and discharge voltage of argon-fed MPD thrusters was developed and validated. Starting from state-of-the-art physical models, multi-factorial correction factors were introduced to account for the coupled effects of discharge current (8–180 A), mass flow rate (3–21 mg/s), and applied magnetic field (up to 0.6 T). The model was calibrated and validated using a comprehensive and homogeneous collection of experimental data from the literature. A comparative analysis demonstrates that the corrected model significantly reduces prediction errors (0–9%) compared to reference models available in the literature (8–50%). In particular, the model exhibits remarkably superior accuracy in both the Self-Field and Applied-Field regimes, overcoming the main limitations of previous formulations and providing more robust estimates across a wide operational envelope. The developed model constitutes a reliable and physically consistent tool for the analysis and preliminary design of low-power, argon-fed magnetoplasmadynamic thrusters, enabling more effective optimization for this class of propulsion systems. Full article

This 2 × 2 factorial study examined the welfare and performance of finishing pigs at two group sizes (9 or 18 pigs) over 12 weeks. For each set of groups of either 9 or 18 pigs, half of the pigs in each group size were fed ad libitum, while the others received a mildly restricted ration. Treatments were assigned to 16 partially slatted floor pens in a randomized block design, with a floor space of 1.15 m²/pig. Except in Week 1, there were proportionally fewer pigs with ear (p = 0.020) and tail (p < 0.0001) bite marks in groups of 18 than in groups of 9. Ear bite marks declined over time in both group sizes (p < 0.0001). There was also a significant interaction between group size and week regarding severe bite marks on the ears (p < 0.0002). Tail bite mark prevalence increased over time in the smaller groups but decreased in the larger groups (interaction: p < 0.001). A higher proportion of pigs in smaller groups sought human contact in Weeks 1 and 6, but this measurement equalized by Week 10 (interaction: p = 0.008). There were proportionally more pigs with tucked tails in the smaller groups in Week 1 but not in later weeks (interaction: p < 0.0001). Group size did not influence pig cleanliness or locomotion disorders. Ad libitum (vs. restricted) feeding increased average daily gain (p < 0.001), feed intake (p = 0.002), and slaughter weight (p = 0.030). Results suggest better welfare in the larger than in the smaller groups. Full article

Research has advanced in the development of precision seed metering devices to ensure proper seed distribution at high speeds. However, little is known about the effect of increasing seeding speed, as well as seeding at different inclinations of the tractor-seeder unit, on the integrity and physiological quality of soybean seeds. This study aimed to identify the effect of travel speeds (5, 7, 9, 11, and 13 km h⁻¹) combined with three longitudinal inclinations of a pneumatic seed metering device (−11°, 0°, and 11°), simulating field conditions, on the distribution and integrity of soybean seeds. We used a 5 × 3 factorial design was used with an additional control treatment in which the seeds did not pass through the metering device. The variables evaluated included the percentage of spacing between individual seeds, germination, mechanical damage (tetrazolium test), and seedling emergence. The results demonstrated that increasing the speed did not prevent the spacing between individual seeds from falling below the minimum limit of 90% for pneumatic seed metering devices. The treatments did not affect germination compared to the control. Sowing on a slope caused the greatest mechanical damage to soybean seeds. All treatments significantly reduced plant emergence, except when the pneumatic metering device operated at an incline of 0° at 9 km h⁻¹. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Glandular trichomes (GTs) are epidermal outgrowths that function as “natural cell factories” for the synthesis of specialized metabolites. Beyond their traditional understanding, GTs on cucumber fruits can form an undesirable trait known as bloom, which negatively affects market value. However, the secretory process, metabolite profiles, and genetic regulation underlying GT development in cucumber remain largely unclear. In this study, we employed scanning electron microscopy (SEM), transmission electron microscopy (TEM), histochemical staining, multi-omics analyses, and liquid chromatography–mass spectrometry (LC-MS) to systematically investigate GT development. The secretory process was classified into four distinct stages via SEM observations: morphogenesis, active metabolism, head sunken, and metabolite release. TEM revealed progressive ultrastructural changes, including increased organelle abundance and expansion of the periplasmic space, which facilitate metabolite transport and release. This process occurs through an autonomous mechanism involving osmiophilic substances and eventual cell rupture. LC-MS analysis identified 744 metabolites belonging to 11 classes, with phenylpropanoids/polyketides—particularly flavonoids—being the most abundant. While metabolite classes are conserved between European greenhouse and North China ecotypes, specific metabolite contents vary significantly. Multi-transcriptome analysis identified 60 candidate genes associated with GT development. Among these, CsaV4_3G003418 was functionally validated through virus-induced gene silencing (VIGS) to be involved in early GT development. Collectively, this work elucidates the secretory mechanism and metabolic characteristics of cucumber GTs, providing a foundation for future functional studies and biotechnological applications of secondary metabolites. Full article

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems. Full article

This study evaluated the relevance of using bamboo cellulose (BC) compounded with resin (R) for the manufacture of medical orthoses (BCO). A 22-factorial screening experimental design was used, with two experimental factors and six response variables. Three polymer composites (PC) were prepared: S1 (BC 40%, R 60%), S2 (BC 30%, R 70%), and S3 (BC 20%, R 80%), which were molded under a pressure of 10.5 kg in 25 × 5 cm male-female dies, with an internal space of 2 mm, at 20 °C for 24 h. The mechanical properties evaluated included tensile strength (RTRAC), ball penetration resistance (RPEBOL), puncture resistance (RPUNZ), and their corresponding extensions (ETRAC, EPEBOL, and EPUNZ). Mass, tensile strength, elongation, punching resistance, and penetration were determined in accordance with ISO 3801, ISO 9073-3, EN 388, and ASTM D3787 standards. Statistical analysis was performed using Statgraphics Centurion and Past 4.13 software. The results showed that increasing the resin content and decreasing the bamboo cellulose significantly improved the mechanical performance of the material. The S3 samples (BC 20%, R 80%) had the highest mechanical strength values, with a tensile strength of (1049.34 ± 85.57 N; n = 5), representing an increase of 398.60% over the base formulation. Likewise, increases of 92.25% in puncture resistance (24.12 ± 29.91 N; n = 5) and 196% in ball penetration resistance (323.98 ± 1.39 N; n = 5) were recorded. Tensile elongation showed an increase of 228% (7.55 ± 5.01%; n = 5). In the S2 samples (BC 30%, R 70%), the greatest increase was observed in the puncture elongation, with a value of 16.33 ± 1.25 mm (n = 5), corresponding to an increase of 59.78%. Meanwhile, the S1 samples (BC 40%, R 60%) exhibited the highest ball penetration extension value (34.07 ± 1.61 mm; n = 5), while the S2 and S3 formulations recorded decreases of 2.11% and 2.23%, respectively. Additionally, thickness, weight, and density showed a strong correlation with each other (p > 0.05). Overall, the results indicate that the combination of bamboo cellulose and epoxy resin is a sustainable and effective alternative for the development of medical orthoses, due to the significant improvement in their mechanical properties, which supports their application in orthotic devices based on sustainable biomaterials. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

Sustainability monitoring relies on environmental indicator systems that integrate heterogeneous multivariate measurements across space and time; however, collinearity, non-Gaussian variability, and influential observations frequently destabilize classical multiblock methods and may bias indicator-based assessment and decision support. This study proposes a robust extension of the STATICO (STATIS–CO-inertia) framework to model common structures among paired environmental indicator blocks under realistic data contamination. The approach preserves the original triadic algebraic formulation while incorporating robust covariance estimation and adaptive weighting to reduce the influence of outliers and structurally unstable blocks. Robustification is implemented at the interstructure stage through a reformulated Escoufier’s RV coefficient and in the construction of the compromise space via robust distances. The RV coefficient, a multivariate generalization of the squared Pearson correlation computed between cross-product matrices, is used to quantify structural similarity between paired data blocks and to evaluate the stability of the compromise structure. Performance is evaluated using simulated datasets calibrated to represent Ecuadorian coastal monitoring conditions. The results show that Robust STATICO increases compromise dominance and stability, redistributes inter-block similarities more coherently, and improves discriminative representation in the factorial space, yielding more interpretable and environmentally plausible structures. Overall, the proposed method provides a reliable analytical tool for sustainability-oriented environmental monitoring by supporting stable identification of persistent multivariate patterns and robust comparison of indicator structures in complex systems. Full article

The increasing penetration of renewable energy sources in power systems has intensified the need for accurate modelling of generation units under transient conditions. Despite the widespread adoption of the IEC 61400-27 generic wind turbine models, their parametrization remains a critical challenge. Classical optimization-based approaches are time-consuming, prone to convergence to local minima in the high-dimensional non-convex parameter space and require substantial expert knowledge. To address this gap, this paper proposes a machine learning- and deep learning-based methodology for estimating the key mechanical parameters of Type III wind turbines. A synthetic database of 10,000 active power responses was generated using DIgSILENT PowerFactory via its Python Application Programming Interface, covering a wide range of voltage dip conditions and mechanical parameter combinations. A comparative analysis of eight machine learning and deep learning algorithms for this task is performed. Validation is performed on both the synthetic dataset and two real manufacturer-validated wind turbine models. The results demonstrate that the proposed methodology enables fast and accurate identification of the mechanical parameters of wind turbines, maintaining reliable estimation performance even in the presence of measurement noise, thereby supporting its applicability in power system stability studies. Full article