Effects of dietary starch-to-lipid ratios and amino acid levels on broilers exposed to cyclic heat stress during the finisher period.

The association of short-term variability in suicidal ideation with subsequent suicidal thoughts and behaviours: Longitudinal cohort study.

N-Nitroso impurities are very hazardous genotoxic carcinogens that can occur as by-products or breakdown products in pharmaceutical compounds. In this study, a sensitive and quality-by-design-based high-performance liquid chromatography method approach was developed and validated for the trace-level determination of N-nitroso tizanidine in pharmaceutical formulations. Chromatographic separation was achieved using an XSelect HSS T3 column (100Å, 150 × 4.6 mm, 3.5 μm) and an isocratic mobile phase made up of 0.1% formic acid buffer and methanol (50:50 v/v) at a flow rate of 0.6 mL/min to separate the samples. The detection wavelength was set at 230 nm. The temperature of the column and sample was kept at 50°C and 10°C, respectively. A Box Behnken design was employed to evaluate the effects of critical method parameters, flow rate, column temperature, and methanol concentration on critical quality attributes, including retention time, tailing factor, and theoretical plate count. Quadratic models demonstrated good statistical significance with high correlation coefficients and nonsignificant lack-of-fit (p > 0.05). The method was validated in accordance with ICHQ2 (R2) guidelines and exhibited excellent specificity, linearity, precision, accuracy, robustness, and sensitivity. The developed QbD-based method is suitable for routine quality control and regulatory monitoring of N-NTIZ in both active pharmaceutical ingredients and finished dosage forms.

The Fenton process (FP) is one of the most studied advanced oxidation processes and has emerged as a promising alternative for removing a wide range of pollutants from wastewater. However, the initial pH adjustment (approximately 3) and neutralization at the end of the process are notable drawbacks that are time-consuming, increase costs, and complicate the process, ultimately restricting its widespread application. Furthermore, excess toxic Fenton sludge (FS) production increases secondary pollution and renders the process unsustainable. This study explores how including a chelating agent improves the FP for the degradation of highly complex wastewater (landfill leachate). The findings show that the suggested alternative strategy enables the process to treat leachate from its typical acidic pH (approximately 3.0) to the circumneutral range (5.5–7.5). The reduction in toxic FS production further confirms the decrease in secondary pollution, which improves the overall process’s efficiency. Under optimal conditions [pH = 6.0, ethylenediaminetetraacetic acid (EDTA) dosage = 78 mL/L, reaction time = 68 min, Fe2+ dosage = 1,250 mg/L, H2O2 dosage = 3,000 mg/L], excellent reductions of COD up to 72%, color (>85%), and total suspended solids (TSS) (>95%) are achieved, while only 15%–20% of COD removal is achieved without EDTA. A significant quadratic model (R2 > 0.92) was obtained, and the optimization criteria were effectively verified. ANOVA revealed that the most influential factor was EDTA dosage, whereas reaction time had the least influence. The chelating agent EDTA stabilized and secured iron ions while avoiding undesired reactions, such as the precipitation of iron hydroxides. Thus, the chelate-assisted FP’s unique optimization strategy (central composite design/response surface methodology) minimized the drawbacks of FP, making the process more efficient and acceptable for a wider range of applications.

Isolation, genome mining, and statistical optimization of keratinase production by Bacillus paralicheniformis SuvM from a Himalayan hot spring.

ABSTRACT Background: Pathological changes in the body due to diabetes mellitus interfere with the wound healing process. Syzygium polyanthum leaves contain abundant phenolic compounds and act as anti-inflammatory, antioxidant and antibacterial enabling them to play a crucial role in the healing of diabetic wounds. Patches are an effective drug delivery method because they are easy to control the release, easy to obtain and affordable for the general public. Objective: The purpose of study was to identify the best formulation and optimization of bay leaf patches as a potential for healing diabetic wounds. Methods: Syzygium polyanthum leaves were extracted using maceration techniques and compound identification using Gas Chromatography-Mass Spectrometry (GC-MS). The procedure for figuring out the ideal patch involved utilizing the Box-Behnken Design technique, which looked at three different things: bay leaf extract (BLE), polyvinyl pyrrolidone (PVP), and ethyl cellulose. The observed responses included air content (%), weight, and thickness. Results: The results of the GC-MS test obtained Tetradecanoic acid, 12-methyl - methyl ester, Estra-1,3,5 (10)-trien-17 ß-ol, n-Hexadecanoic acid, 9,10-Secocholesta-5,7,10(19) -triene-3,24, 25 triol, (3ß, 5Z,7E), Stigmasta-3,5-diene, Ergosta-5,22-dien-3-ol, acetate, (3ß,22E) . ANOVA analysis showed that the quadratic model was appropriate because it produced high R² values ​​and low PRESS values ​​for weight and thickness responses (p-value <0.0500). Conclusion: The optimal formulation for bay leaf patch was 117.271 mg of BLE, 396.274 mg of ethyl cellulose, and 227.237 mg of PVP. This formulation achieved the desired response of 2.683% water content, 0.462 mg weight, and 0.369 mm thickness.

The perils of perfection: A prospective population-based cohort study of perfectionism and depression in young adults.

To assess the radiosensitivity of different pancreatic cancer (PC) cell lines and examine their differences regarding their sensitivity to radiation, and to quantify the inter-study variability. The parameters for the linear quadratic (LQ) model and a nonlinear log-logistic regression (LL) model were derived using the data from clonogenic assays reported in the literature. In this study, the cell survival data from 108 clonogenic assays for four PC cell lines: Panc-1, MiaPaCa-2, BxPc-3, and AsPc-1, were fitted to the LQ model and the LL model performing bootstrapping. The lognormal, the generalized extreme value and the normal distributions were parametrized to describe the distribution of model parameters. The best representation was used to determine the median value of the parameters and their interquartile (IQR) ranges for each cell line. For the LQ model, no differences in the radiosensitivity between the four cell lines were found. A medianαof 0.14 Gy-1[IQR 0.08-0.25],βof 0.04 Gy-2[IQR 0.03-0.06] andα/βof 3.2 Gy [IQR 1.6-6.7] were obtained. For the LL model, MiaPaCa-2 showed slightly higher radiosensitivity compared to BxPc-3 and AsPc-1 cell lines. The median value for all PC cell lines ofϕ2was 2.44 Gy [IQR 1.99-3.09] andϕ3was 2.54 [IQR 2.16-3.03]. The LQ model remains a robust method to assess radiosensitivity of cells, but due to its susceptibility to experimental heterogeneity, caution must be taken during the selection of a properα/βvalue. The LL model can offer a more reliable method than the LQ model to capture radiobiological differences between cells' response to radiation. There is a need for standardization of the clonogenic assays procedure, and a more thorough report of the setup used.

This study presents the design and optimization of a laboratory-scale bubble column bioreactor (BCB) for bioethanol fermentation. Python-based simulations in Google Colab were employed to analyze mass transfer dynamics, hydrodynamic behavior, and reactor scale-up strategies under varying aeration rates. Although ethanol production is an anaerobic process, oxygen transfer analysis was conducted to characterize reactor performance and establish oxygen-limited conditions suitable for Saccharomyces cerevisiae fermentation, incorporating mass transfer modeling, reaction kinetics, process control, and sparger design to enhance fermentation efficiency. To further enhance fermentation efficiency, Response Surface Methodology (RSM) was applied following a two-stage optimization approach. A working volume of 500 mL was defined using fermentation kinetics, including an oxygen uptake rate of 1.1 g O₂/g cells, biomass yield of 0.5 g/g glucose, and kLa of 50 h⁻¹. A perforated plate sparger with six 1.2 mm orifices achieved a gas velocity of 90.3 m/s and 2.68 mm bubble size. Aeration was dynamically controlled to maintain 0.002 g/L dissolved oxygen, while pH was regulated at 5.0–5.5 using NaOH dosing. These conditions yielded 44.3% ethanol. A full factorial design identified Time, Air Flow Rate, Cell Loading, and Bead Mass as significant factors. RSM with Central Composite Design confirmed a significant quadratic model (F = 14.14, p < 0.0001; R² = 0.9601, Adjusted R² = 0.9201). Cell Loading (F = 48.48) and Bead Mass (F = 26.53) had the strongest effects. Optimal conditions yielded 47.9% ethanol at 52.70 h, 1.55 L/min air, 1.51 g/L cells, and 47.20 g beads, with 0.84% prediction error. Copyright © 2026 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

The optimal power flow (OPF) problem is essentially about finding the cheapest and safest way to operate a power system without breaking any of the operational limits that govern it. In this paper, we introduce a new Modified Newton-Raphson-Based Optimizer (MNRBO) specifically designed to tackle real-world OPF problems, integrating renewable photovoltaic sources. The NRBO integrates gradient-inspired search using the NR search rule and the trap avoidance strategy. Our MNRBO extends this framework by adding two adaptive components. An Adaptive Crossover Mechanism (ACM) is added that lets solutions dynamically exchange useful information with each other, keeping the population diverse and preventing everyone from getting stuck in the same mediocre spot too soon. Also, a Sigmoid decay mode that smoothly and gradually shifts the algorithm from broad exploration (looking around the whole search space) in the early stages to careful fine-tuning (exploitation) toward the end. This gives much steadier and more predictable convergence than the original abrupt or polynomial decay. The resulting MNRBO algorithm forms a self-evolving optimization framework that automatically adjusts its learning strategy as the search progresses. We thoroughly tested MNRBO on the standard IEEE 30-bus system across a wide range of realistic scenarios: minimizing fuel costs (with smooth quadratic models, valve-point ripples, and multi-fuel options), handling generators with prohibited operating zones, and minimizing transmission losses under normal, peak, and light-load conditions. In every single case, MNRBO delivered better solutions, faster and more consistent convergence, and dramatically lower variation across multiple runs compared to the original NRBO and several other state-of-the-art algorithms. The results clearly show that MNRBO is not only more accurate but also far more robust and dependable, exactly what operators need when solving OPF in real power systems where reliability really matters. To further validate the applicability of the proposed approach under renewable energy uncertainty, a probabilistic OPF framework incorporating photovoltaic renewable generation is developed.In this case study, the integration of renewable solar photovoltaic energy in conditions of variable irradiance is examined using the Point Estimate Method (PEM) with lognormal irradiance modeling. In addition, an ablation study is conducted to quantify the individual contributions of the ACM and sigmoid decay strategy in the presence of renewable photovoltaic sources, demonstrating their significant impact on convergence stability, robustness, and optimization accuracy.

This study focuses on optimizing and predicting the moisture management capacity of cut-protective fabric made from multicomponent hybrid yarn comprising a polyester inner sheath, high-performance polyethylene (HPPE) outer sheath and stainless steel (SS) core. Box–Behnken response surface design was employed to investigate the relationships between yarn parameters and moisture management capacity. Regression and analysis of variance revealed significant effects of SS fiber diameter, HPPE linear density and yarn twist level. The quadratic model showed best fit, with predicted and adjusted R 2 values of 0.9313 and 0.8076, respectively. Optimal moisture management capacity was achieved with SS fiber diameter of 40–45 µm, HPPE linear density of 350–400 denier and yarn twist of 400–450 m− 1, yielding a desirability score of 1.0. The model demonstrated good predictive accuracy, with a mean relative error of 1.48% and root mean square error of 0.0060, confirming close agreement between experimental and predicted values.

Latent class modeling of repetitive transcranial magnetic stimulation response trajectories in treatment-resistant depression.

Injection molding of complex three-dimensional curved components is highly sensitive to processing conditions, particularly when dimensional accuracy and packing quality must be balanced simultaneously. This study proposes an integrated optimization and decision-making framework to improve the molding quality of a commercial curved shin guard plate (SGP) by combining Response Surface Methodology (RSM), adaptive non-dominated sorting genetic algorithm II (NSGA-II), and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A Box-Behnken design was employed to investigate the effects of packing pressure (PP), melt temperature (MT), and cooling time (CT) on absolute dimensional deviation (ΔD) and molded part weight (PW). The results showed that the developed quadratic response surface models exhibited good predictive capability, and analysis of variance identified PP as the most influential parameter. The surrogate models were integrated with standard and adaptive NSGA-II algorithms to perform multi-objective optimization and generate well-distributed Pareto-optimal solutions, with the adaptive NSGA-II showing improved Pareto-front exploration capability based on crowding-distance and hypervolume analyses. Entropy-weighted TOPSIS was subsequently applied to identify the most suitable compromise solution. Experimental validation was conducted at the TOPSIS-selected optimum together with two additional Pareto-optimal conditions. The validation results showed satisfactory agreement between predicted and experimental responses, with deviations remaining below 7.41% for ΔD and below 1.70% for PW. These results demonstrate the practical applicability of the proposed optimization framework for complex injection-molded components under industrial manufacturing conditions.

To support real-time wheel-rail matching monitoring for high-speed trains, this study proposes a unified representation of wheel tread wear over a complete reprofiling cycle. Measured wheel profiles showed dominant hollow wear, mainly in segments BC, CD and DE near the nominal rolling circle. Cubic Non-Uniform Rational B-Spline (NURBS) curves were used to represent worn tread profiles segment by segment. For BC and CD, common control vertices and knot vectors enabled NURBS weight factors to describe profile evolution across mileage stages. For DE, wear was classified as mild, moderate or severe; wear width was represented by intermediate control points and wear depth by NURBS weight factors. Contact-geometry analysis showed good agreement between NURBS-fitted and measured profiles, with normal fitting errors below 0.35, 0.10 and 0.05 mm for BC, CD and DE, respectively. Among the tested regression models, the quadratic polynomial model gave the highest accuracy, with DE wear-depth error below 0.005 mm and wear-area errors for BC, CD, and DE below 0.15 mm2. The method provides a parametric basis for tread evolution characterisation and maintenance decision-making.

To characterize foveal architecture and choroidal morphology in KIF11-associated retinopathy using multimodal imaging, and to assess for pachychoroid-like features using quantitative choroidal thickness (CT) and choroidal vascularity index (CVI). In this retrospective case series, multimodal imaging from two patients (four eyes) with genetically confirmed KIF11-associated retinopathy was analyzed. En-face optical coherence tomography (OCT) images of the macular and inferior peripheral retina were spatially aligned to widefield fundus images to enable fovea-centered spatial mapping. The choroid was segmented and binarized on OCT B-scans to derive quantitative macular CT and CVI measurements, and CT measurements in the mid-peripheral and peripheral retina. Pixel-based measurements were converted to microns using image-scale calibration. Measurements from both eyes were compared with a healthy control. Both KIF11 patients demonstrated bilateral fovea plana, inferior chorioretinal dysplasia, and inferotemporal vascular dragging. Sub-macular CT showed altered spatial patterning relative to a reference control, with lower overall thickness and increased inter-eye variability. In control eyes, CT demonstrated fovea-centered distribution, whereas patient eyes showed disrupted spatial organization, most pronounced in P1 (OD: R² = 0.41; OS: R² = 0.52). Sub-macular CVI ranged from 58 to 65% and demonstrated increased variability in spatial patterning, with reduced quadratic model fit in KIF11 patient eyes (P1 OS: R² = 0.09). Inferior retinal regions showed diffusely increased CT, with focal maximal thickening immediately temporal to chorioretinal dysplasia, forming a sharp, reproducible transition zone. Beyond this, CT progressively decreased with increasing temporal distance from the atrophic border. The ratio of CT at the transition point relative to distal temporal measurements consistently exceeded 1.0. KIF11-associated retinopathy may demonstrate regional pachychoroid-like choroidal phenotype with spatially patterned dysplasia, most pronounced adjacent to areas of chorioretinal atrophy. These preliminary findings may implicate choroidal structural and vascular abnormalities as potential contributors to disease pathogenesis and expand the recognized ocular phenotype of KIF11-related retinal disease. Given the exploratory nature and limited sample size of this study, further studies, on a larger sample size, are warranted to further characterize choroidal morphology in KIF11-associated retinopathy. Not applicable.

This study investigated the extraction of Elettaria cardamomum seed essential oil (EO) using microwave and ultrasound-assisted extraction techniques and evaluated its repellency activity against S. oryzae. Three extraction methods were evaluated: conventional hydrodistillation (HD), microwave-assisted hydrodistillation (MAHD), and ultrasonic pretreatment combined with microwave-assisted hydrodistillation (US+MAHD). Extraction conditions for MAHD were optimized using a Box-Behnken Design (BBD) and Response Surface Methodology (RSM) considering three factors: microwave time (min), microwave power (W), and solid-to-liquid ratio (g/mL). The developed quadratic model was significant (p < 0.05) showed a high coefficient of determination (R2 = 0.9933). The optimal extraction conditions predicted by the model were 15 min extraction time, 600 W microwave power, and a 1:6 solid–liquid ratio, with a predicted EO yield of 4.487%. Experimental validation under optimized conditions produced an EO yield of 4.5469 ± 0.0603%, confirming the adequacy and reliability of the model. US+MAHD achieved the highest EO yield (5.42%) with a shorter extraction time than HD. Major compounds identified by GC-MS were α-terpinyl acetate (35.13%), 1,8-cineole (33.43%), trans-sabinene hydrate acetate (8.38%), and linalool (6.94%). The EO exhibited complete repellency against Sitophilus oryzae at 1 μL/cm2 for 3 h. These findings demonstrate that RSM effectively optimized the MAHD process and that US+MAHD improved extraction efficiency while preserving the characteristic oxygenated monoterpene profile of the EO. Further studies are required to evaluate the stability and effectiveness of the EO under practical storage conditions.

In this study, response surface methodology (RSM) was used to optimise the design and predict the physical properties of colza oil residue (COR) regenerated SBS modified bitumen based on regeneration preparation parameters and COR content. A total of 27 experiments were conducted using the Box–Behnken design, and a quadratic regression model was applied to evaluate all responses under the global optimal conditions. Numerical optimisation determined the optimal regeneration design for SBS modified bitumen, including a shear time of 17.7 min, a shear temperature of 150°C, a shear speed of 1050 rpm, and a maximum COR content of 8%, achieving a comprehensive desirability of 69.2%. COR can not only restore the flexibility of bitumen, but also improve its heat recovery ability at low temperatures. Moreover, COR improves the flowability of bitumen by introducing fatty acid esters, but does not trigger significant chemical reactions.

Quantitative spectroscopic analysis of light scattering in rough granular coatings: an optimized Kubelka-Munk modeling approach.

Capillary vibrating sharp-edge spray ionization (cVSSI)-based native mass spectrometry (nMS) and in-droplet hydrogen deuterium exchange (HDX-MS) were used to evaluate calcium-dependent interactions between calmodulin and calmidazolium (CDZ). cVSSI was found to produce a narrow charge-state distribution (CSD) with low average charge states, indicating that this method preserved native-like states. cVSSI was also able to resolve stepwise Ca 2+ binding for species containing one to four Ca 2+ ions. In the absence of Ca 2+ , no detectable CDZ binding was observed. However, CDZ binding was observed when calmodulin was fully loaded with Ca 2+ . CDZ binding to the protein caused marked redistribution of the CSD toward lower charge states, consistent with ligand-induced stabilization of the protein into a more compact conformational ensemble. The apparent dissociation constant ( K d ) of the interaction was determined to be 261 ± 29 nM and 126 ± 17 nM from Langmuir and quadratic binding models, respectively. Complementary in-droplet HDX-MS revealed that deuterium uptake was correlated with charge state across the CSD, corroborating the nMS evidence for stepwise conformational compaction of CaM. Within the individual charge states, two distinct populations representing CDZ-bound and unbound CaM were resolved by HDX-MS, which were not detectable by nMS alone. Comparison of these populations within each of the 6+, 5+ and 4+ charge states revealed reductions in deuterium uptake by approximately 37%, 22%, and 11% upon CDZ binding, respectively, consistent with ligand occlusion and local structural tightening at the binding interface. The gradient in protection across charge states further reflects differences in binding interface accessibility among CaM conformers spanning a range of CDZ-induced conformational states. Together, these results demonstrate that cVSSI-based nMS coupled with in-droplet HDX-MS provides an integrated platform for simultaneously resolving metal loading, ligand binding, binding affinity, and ligand-induced conformational changes. This approach complements traditional structural methods by enabling direct interrogation of dynamic, metal-dependent protein-ligand interactions in their native states.

Correlation of Oxygen Saturation Index with Oxygenation Index in Congenital Diaphragmatic Hernia: in A Secondary Analysis of a Randomized Clinical Trial.