Search Results (2,577)

The seed lots were evaluated based on their viability and vigor, which vary according to their origin and the locations where the seeds were produced. However, differences in vigor can be observed within a single seed lot, resulting from the deposition of photoassimilates. In this context, the hypothesis of this study is that distinct locations on the plant may produce seeds with different physiological quality. Therefore, the objective of this work was to evaluate how pod position influences the vigor of seeds from different soybean genotypes. Field experiments were conducted during the 2021/22 and 2022/23 crop seasons in Brazil. The experimental design was a randomized complete block with four replications and 32 soybean populations from the UFMS/CPCS Breeding Program. During the R4, R5, R6, and R7 reproductive stages of soybean, at the time of pod formation, the plants in each block were tagged with string to delimit the uppermost point at which pods had formed. Tagging was carried out as each stage change was verified, at approximately eight-day intervals. When analyzing how the pod position of the plant influences seed physiological variables, we found that position P1 was responsible for the best results for the variables evaluated, with the exception of genotypes G18 and G28. This result indicates that pods from the first position produce seeds with greater germination capacity and a higher ability to generate normal seedlings. However, the genotypes are still under development and, therefore, do not yet exhibit stability. Nevertheless, the results obtained highlight the relationship between the pod position of the plant and seed physiological variables. The position of the pods on the soybean plant influences the physiological quality of the seeds. In general, the P1 position, when the plants are in the R4 reproductive stage, with fully developed pods measuring 2 cm on one of the four upper nodes of the stem, is responsible for the best results in seed physiological quality tests for most of the soybean genotypes evaluated. These results indicate that pod position should be considered in breeding and seed production programs, since genotypes with greater physiological stability in the upper positions may be preferential in selection strategies. In the future, studies in different environments and evaluation of biochemical traits may confirm these patterns and contribute to the development of cultivars with higher seed quality and physiological uniformity. Full article

Background: To evaluate the effectiveness of 0.1% cyclosporine A (CsA) cationic emulsion in managing advanced dry eye disease (DED), based on clinical parameters: Ocular Surface Disease Index (OSDI), best-corrected visual acuity (BCVA), Tear Break-Up Time (TBUT), corneal fluorescein staining (CFS) on the Oxford scale, Schirmer test, and intraocular pressure (IOP). Methods: This retrospective study included 20 patients (40 eyes) with severe DED unresponsive to previous therapies. All patients continued artificial tears and added 0.1% CsA once daily. Baseline assessments included OSDI, BCVA, TBUT, corneal staining, Schirmer test, and IOP. Follow-ups occurred at 1–3, 6, 12, and 24 months. Data were analyzed for treatment effect and progression over time. Results: The mean age was 53.5 ± 13.5 years; 80% were female. BCVA showed no significant changes. OSDI scores improved from severe (>53) to moderate (approximately 35). Schirmer test increased from ~6.2 mm to >10 mm (p < 0.001). TBUT improved from approximately 6 to 10 s (p < 0.001), with significant differences after 6 months. CFS scores decreased from 3.4 to 2.05 (p < 0.001), indicating reduced corneal damage. IOP remained stable throughout the study period. Conclusions: Long-term use of 0.1% cyclosporine A cationic emulsion led to marked and sustained improvement in both subjective symptoms and objective ocular surface parameters in severe dry eye disease. The therapy was safe, well tolerated, and did not affect visual acuity or intraocular pressure, supporting its value as a long-term treatment option. Full article

Robotic mobile fulfillment systems have become an integral part of e-commerce warehouses. The pod repositioning problem, due to its interdependence with robot task allocation strategies, poses a significant challenge that constrains system performance. In this paper, we aim to jointly optimize the two interrelated problems of pod repositioning and task allocation. A multi-objective mixed-integer planning model is developed to minimize the maximum completion time of robots and the deviation between the pod position and the expected position. To tackle the challenges of decision coupling and a vast solution space, an adaptive genetic-neighborhood search algorithm guided by pod heat maps is designed. Additionally, to promptly correct expected layout deviations and avoid layout instability, a progressive storage mechanism is designed to update the expected layout. The numerical experiments show that compared to the staged optimization strategy, the joint optimization strategy proposed in this paper can reduce the maximum completion time by approximately 48%, and that the strategy reduces the maximum completion time by 9% to 16% compared to the nearest allocation strategy, which is commonly used and performs best in practice. Full article

In this study, molybdenum disulfide (MoS₂)-based water nanofluids were prepared and stabilized using two surfactants with opposite charges: the cationic cetyltrimethylammonium bromide (CTAB) and the anionic sodium lauryl sulfate (SLS). Different MoS₂:surfactant ratios (1:1, 1:2, and 1:3) were examined to identify the optimal formulation ensuring stable dispersion. Stability was evaluated through dynamic light scattering (DLS), zeta potential, and UV–Vis spectroscopy analyses. The results showed that the MoS₂:SLS (1:3) nanofluid achieved the highest stability, characterized by a zeta potential of −38 mV and a mean particle size of approximately 290 nm. Thermophysical properties were then investigated for nanoparticle concentrations of 0.05, 0.1, and 0.2 wt%. The 0.1 wt% nanofluid exhibited the best performance, showing a thermal conductivity enhancement of about 49% and an increased specific heat capacity compared with pure water. This improvement is attributed to uniform nanoparticle dispersion and enhanced phonon transport. Overall, the results demonstrate that the anionic SLS surfactant at a 1:3 ratio effectively enhances the stability as well as the thermal performance of MoS₂–water nanofluids, making them promising candidates for thermal management and energy systems applications. Full article

Additive manufacturing of polymer materials, also known as 3D printing, is becoming a key technology for the production of functional parts with the ability to customize the structure and properties according to the application requirements. Polylactide (PLA) is one of the most commonly used materials in this field due to its biodegradability, ease of processing, and adequate strength for lightweight functional components. An important factor that affects the resulting properties of parts is not only the filler structure and density but also the angle at which the material is deposited during the printing process. This article focuses on investigating the influence of the printing angle (0°, 30°, 60° and 90°) and the bulk density of the filler (20%, 40%, 60% and 80%) on the mechanical properties of PLA samples. Two series of samples were prepared—the first was subjected to direct mechanical tests, and the second series was first exposed to freezing conditions and then tested to evaluate the effect of freezing on the material behavior. The samples were tested for tensile strength according to ASTM D638 and for bending strength according to ASTM D790. The results showed that the highest values were achieved in tensile strength in the 60°/80% configuration with a strength of 39.27 MPa, which represents more than a twofold improvement over the weakest configuration (0°/20%–19.58 MPa). In the bending test, the best results were achieved by the 90°/80% sample with a strength of 58.89 MPa, approximately 18% higher than 0°/20%. Cryogenic treatment caused a deterioration of all monitored parameters, especially at low infill densities and at an angle of 0°, where the decrease in strength reached up to 10–13%. These results confirm that the combination of a higher printing angle and a higher infill density is key to optimizing the mechanical properties of PLA parts, while cryogenic treatment has a negative impact on their behavior. Full article

Combination drug treatment plays a central role in addressing complex diseases by enhancing the therapeutic benefit while mitigating adverse effects. However, determining optimal dose levels remains challenging due to additive drug effects, competing safety constraints, and the scarcity of reliable data in clinical and experimental settings. This paper develops a data-driven robust optimization framework for combination dose selection under uncertainty. The proposed approach integrates posterior sampling via Markov Chain Monte Carlo with convex hull-based and mean-based filtration methods to generate, evaluate, and refine candidate optimal solutions. By embedding uncertainty quantification into the optimization process, the framework systematically balances therapeutic efficacy against the risk of adverse effects, yielding risk-averse yet effective dose strategies. Numerical experiments using exponential dose–response models and the ED50 criterion demonstrate that convex hull-based methods consistently produce feasible solutions, while mean-based approaches are prone to infeasibility except in limited cases. Among hull methods, balance-oriented filtration (BOF) achieves the best balance between performance and conservativeness, closely approximating the benchmark solution under moderate levels of uncertainty for models with additive drug effects. These findings highlight the advantages of robust optimization for dose selection in settings where data are limited, variability is high, and risk management is essential. Full article

In numerical analysis, the Boole’s formula serves as a pivotal tool for approximating definite integrals. The approximation of the definite integrals has a big role in numerical methods for differential equations; in particular, in the finite volume method, we need to use the best approximation of the integrals to obtain better results. This paper presents a rigorous proof of integral inequalities for first-time differentiable s-convex functions in the second sense. This paper has two main goals. The first is that the use of s-convex function extends the results for convex functions which cover a large class of functions and the second is the best approximation. To prove the main inequalities, we drive integral identity for differentiable functions. Then, with the help of this identity, we prove the error bounds of Boole’s formula for differentiable s-convex functions in the second sense. Some new midpoint-type inequalities for generalized convex functions are also given which can help us in finding better error bounds for midpoint integration formulas compared to the existing ones. Moreover, we provide some applications to quadrature formulas and special means for the real numbers of these newly established inequalities. Furthermore, we present numerical examples and computational analysis that show that these newly established inequalities are numerically valid. Full article

The present study demonstrates the roller burnishing process of aluminum alloy 6061-T6 by using a combination of aluminum oxide and vegetable oil as a lubricant. Machining parameters were explored, varying speed (v) (range 100–300 rpm), feed (f) (range 0.1–0.3 mm), and number of passes (nop) (range 1 to 3). However, performance was measured in terms of surface roughness, microhardness, and roundness. According to the results obtained from experiments, it was found that lubrication had a significant impact on performance in terms of surface roughness, mmicrohardness and roundness. Under lubricated conditions, surface roughness ranged from 0.012 µm to 1.7 µm. However, an increase in mimicrohardnessrom 92 HV to 96 HV and an improvement in roundness from 0.07 mm up to 0.05 mm were observed. Additionally, the findings indicated that high speeds with low feed rates yielded the best results: for instance, at a feed of 0.1 mm/rev, speed (v) of 300 rpm, and number of passes of three, a surface roughness of about 0.8 µm, microhardness of approximately 94 HV, and roundness of about 0.02 mm were recorded when applying lubrication. This study demonstrates how minimal lubrication techniques can be used to improve the roller burnishing process, thereby achieving better mechanical properties and surface finishes while extending the lifespan of the burnishing tool. The study has brought about a conclusion that optimizing v and f during burnishing while including relevant lubricant helps manufacturers to realize significant product quality improvements and enhance production efficiency. Full article

Fiber Bragg Grating (FBG) sensors facilitate compact, multiplexed, and electromagnetic interference-immune monitoring in embedded and harsh environments. The removal of the polymer jacket, a measure taken to withstand elevated temperatures or facilitate integration, exposes the fragile glass. This underscores the necessity of functional coatings, which are critical for enhancing durability, calibrating sensitivity, and improving compatibility with host materials. This review methodically compares coating materials and deposition routes for FBGs, encompassing a range of techniques including top-down physical-vapor deposition (sputtering, thermal/e-beam evaporation, cathodic arc), bottom-up chemical vapor deposition (CVD)/atomic layer deposition (ALD), wet-chemical methods (sensitization/activation, electroless plating (EL), electrodeposition (ED)), fusion-based processes (casting and melt coating), and hybrid stacks (e.g., physical vapor deposition (PVD) seed → electrodeposition; gradient interlayers). The consolidation of surface-preparation best practices and quantitative trends reveals a comprehensive understanding of the interrelationships between coating material/stack, thickness/microstructure, adhesion, and sensitivity across a range of temperatures, extending from approximately 300 K to cryogenic regimes. Practical process windows and design rules are distilled to guide method selection and reliable operation across cryogenic and high-temperature regimes. Full article

The construction industry’s reliance on Portland cement (PC) significantly contributes to global CO₂ emissions, driving the search for sustainable binder alternatives. This study develops and evaluates novel mineral binder systems for wood wool acoustic panels with a reduced carbon footprint. Alternative binders, including calcium aluminate cement (CAC), magnesium oxychloride cement (MOC), and gypsum–cement–pozzolan (GCP) hybrids, were combined with additives such as metakaolin and liquid glass. Mechanical testing demonstrated that 20–30% metakaolin and liquid glass composites achieved flexural strengths of up to 2.65 MPa and densities above 490 kg/m³. The GCP system showed synergistic improvements in flexural and compressive strengths by nearly 50%, along with enhanced dimensional stability and water resistance. Life cycle assessment indicated substantial CO₂ emission increases, particularly for the MOC and CAC formulations, compared to conventional Portland cement-based panels. The carbon footprint of the binder system consisting of GCP is approximately 5.644 kg of CO₂ equivalent per functional unit compared to magnesium chloride binder systems, which reach up to 10.84 kg CO₂ eq., and white Portland cement systems, which are around 6.19 kg CO₂ eq. The three-component GCP binder system offers the best balance of mechanical performance and minimised environmental impact. Key raw material contributors to the ecological load are cement (various types), MgO, MgCl₂, and metakaolin, highlighting the importance of optimising binder formulations to reduce carbon emissions. The GCP system, in particular, demonstrates unprecedented synergistic improvements in flexural and compressive strengths, dimensional stability, and water resistance while minimising CO₂ emissions. Current work sets a new benchmark for sustainable building materials by offering an eco-innovative pathway towards low-carbon, high-performance wood wool acoustic panels, aligning with global decarbonisation goals. Full article

Artificial neural networks have been evaluated and compared for modeling extreme wave forces exerted on coastal bridges during hurricanes. Long Short-Term Memory (LSTM) is selected for deep learning neural networks. A feedforward neural network (FFNN) is employed to represent the shallow learning network for comparison purposes. The two case studies consist of an emerged bridge deck destroyed by Hurricane Ivan and a submerged bridge deck impaired in Hurricane Katrina. Datasets for model training and verifications consist of wave elevation and force time series resulting from previous validated numerical wave load modeling studies. Results indicate that both deep LSTM and shallow FFNNs are able to provide very good predictions of wave forces with correlation coefficients above 0.98 by comparing model simulations and data. Effects of training algorithms on network performance have been investigated. Among several training algorithms, the adaptive moment estimation (Adam) training optimizer leads to the best LSTM performance, while Levenberg–Marquardt (LM) optimized backpropagation is among the most effective training algorithms for FFNNs. In general, a shallow FFNN-LM network results in slightly higher correlation coefficients and lower error than those from an LSTM-Adam network. For sharp variation in nonlinear wave forces in the emerged bridge case study during Hurricane Ivan, FFNN-LM predictions of wave forces show better matching with the quick variations in nonlinear wave forces. FFNN-LM’s speed is approximately 4 times faster in model training but is about twice as slow in model verification and application than the LSTM-Adam network. Neural network simulations have shown substantially faster than CFD wave load modeling in our case studies. Full article

In recent years CO₂ reforming of methane has attracted great interest as it produces high CO/H₂ ratio syngas suitable for the synthesis of higher hydrocarbons and oxygenated derivatives since it is a way for disposing and recycling two greenhouse gases with high environmental impact, CH₄ and CO₂, and because it is regarded as a potential route to store and transmit energy due to its strong endothermic effect. Along with noble metals, all the group VIII metals except for osmium have been studied for catalytic CO₂ reforming of methane. It was found that the catalytic activity of Ni, though lower than those of Ru and Rh, was higher than the catalytic activities of Pt and Pd. Although noble metals have been proven to be insensitive to coke, the high cost and restricted availability limit their use in this process. It is therefore valuable to develop stable Ni-based catalysts. In this contribution, we show how their activity and coking resistivity are greatly related to the size and dispersion of Ni particles. Well-dispersed Ni nanoparticles were achieved by multistep impregnation on a mesoporous silica support, namely SBA-15, obtained through a sol-gel method, using acetate as a nickel precursor and keeping the Ni loading between 5% and 11%. Significant catalytic activity was obtained at temperatures as low as 450 °C, a temperature well below their deactivation temperature, i.e., 700 °C. For the pre-reduced samples, a CO₂ conversion higher than 99% was obtained at approximately 680 °C. As such, their deactivation by sintering and coke formation was prevented. To the best of our knowledge, no Ni-based catalysts with complete CO₂ conversion at temperatures lower than 800 °C have been reported so far. Full article

Procyanidins are oligomeric flavonoids with several bioactive properties. Their antidiabetic potential is related to their capacity to inhibit enzymes responsible for the absorption of dietary carbohydrates, such as pancreatic α-amylase. Procyanidins possess great structural diversity, including types of monomers and interflavanic bonds (A- or B-), and the degree of polymerization. However, there is a lack of evidence that systematically analyzes the effect of these structural features on their α-amylase inhibitory activity. In this paper, the activity of a mammalian pancreatic α-amylase was assessed using two different substrates, and the inhibitory activity of five commercially available procyanidins and three monomeric flavonoids was compared. The enzyme-binding sites of the eight compounds were predicted by in silico analysis to help explain the different enzyme-inhibitory activities. The inhibitory activity of procyanidins and monomeric flavonoids depended on the substrate used. A-type dimers presented the best activity against a polymeric substrate, while a B-type dimer was the best inhibitor for an oligomeric substrate. The predicted binding site for dimers and monomers was close to the active site. For the B-type trimer, the binding site was on the back side (approximately 180°) of the catalytic triad. In silico predictions suggested that dimeric procyanidins, especially A-type, could better enter the active site cavity, which could explain their superior inhibitory activity. We may conclude that inhibition of pancreatic α-amylase by procyanidins is mainly related to the type of interflavanic bond and the degree of polymerization. Dimers could be the most effective procyanidins to mildly inhibit this enzyme and present antidiabetic potential. Full article

Haemophilus influenzae type b (Hib) is a Gram-negative bacterium that causes severe infections in children under five, especially in developing countries. Although vaccination using capsular polysaccharide by Hib (linear polymer 5-D-ribitol-(1→1)-β-D-ribose-3-phosphate) conjugated to tetanus toxoid is effective, its production is complex and costly. This study aimed to develop a continuous production process for PRP to increase productivity, reduce batch numbers, and simplify manufacturing. Using a 1 L bioreactor, five dilution rates (0.13 to 0.32 h⁻¹) were tested, with the best performance observed at 0.23 h⁻¹, reaching a productivity of 167 mgL⁻¹·h⁻¹. Under optimized conditions, parameters such as free and immobilized PRP, glucose consumption, acetate formation, and biomass were monitored. The process yielded 874 mgL⁻¹ of PRP after 74.4 h, with 78% in the free form and a final productivity of 165 mgL⁻¹·h⁻¹, approximately six times higher than batch processes and twice as high as fed-batch processes. The continuous process proved more efficient and required less infrastructure to meet production demands. However, further optimization is needed to enhance product quality and assess overall feasibility. Full article

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article