Search Results (323)

Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware–software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder–Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 °C/min within 250–500 °C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization. Full article

Abnormalities were observed in the testes of common carp Cyprinus carpio collected from Willow Beach, Arizona, USA, a site on the lower Colorado River, downstream of Lake Mead and Hoover Dam. Testicular tissue collected from this site in 2003 exhibited numerous large, pigmented macrophage aggregates (MAs) and a novel, previously undescribed hypertrophy and proliferation of putative Sertoli cells. In testes samples collected in 2007, numerous testicular MA, testicular oocytes, and proliferations of Sertoli cells were observed. Three carp collected in 2007 also had raised nodules within the testes, and, microscopically, seminoma, spermatogenic seminoma, and mixed stromal cell–germ cell neoplasms were diagnosed. Several risk factors for these adverse effects were identified. Carp collected at this site in 2003 ranged in age from 35 to 54 years and had the oldest mean age of the thirteen sites sampled within the Colorado River basin. This site also has an unusual thermal regime when compared to other sites studied in Lake Mead and upstream sites, in that temperatures varied little over the seasons (amplitude around 1.5 °C) and barely reached 15 °C. Additionally, carp from this site had the highest total polychlorinated biphenyl (PCB) body burden. Hence, advanced age, low water temperature, and exposure to PCBs and other environmental contaminants may contribute to the observed abnormalities, highlighting the complex environmental factors initiating pre-neoplastic and neoplastic changes in wild carp. Full article

Precipitation hardening is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. In particular, predicting yield strength (YS) based on microstructure is experimentally complex and costly because its key variables, such as precipitate radius, spacing, and volume fraction (VF), are difficult to measure. Physics-based models have emerged to tackle these complications utilising advancements in simulation environments. Nevertheless, pure physics-based models require numerous free parameters and ongoing debates over governing equations. Conversely, purely data-driven models struggle with insufficient datasets and physical interpretability. Moreover, the complex dynamics between internal model variables has led both approaches to adopt heuristic optimisation methods, such as the Powell or Nelder–Mead methods, which fail to exploit valuable gradient information. To overcome these issues, we propose a gradient-based optimisation for the Kampmann–Wagner Numerical (KWN) model, incorporating CALPHAD (CALculation of PHAse Diagrams) and a strength model. Our modifications include facilitating differentiability via smoothed approximations of conditional logic, optimising non-linear combinations of free parameters, and reducing computational complexity through a single size-class assumption. Model calibration is guided by a mean squared error (MSE) loss function that aligns the YS predictions with interpolated experimental data using L2 regularisation for penalising deviations from a purely physics-based modelling structure. A comparison shows that the gradient-based adaptive moment estimation (ADAM) outperforms the gradient-free Powell and Nelder–Mead methods by converging faster, requiring fewer evaluations, and yielding more physically plausible parameters, highlighting the importance of calibration techniques in the modelling of 6xxx series precipitation hardening. Full article

Honey possesses unique properties, characterized by its high sugar concentration and the synergistic interaction among nectar, pollen, bees, and yeasts. These features render it an exceptional substrate for exploring microbial diversity for bioprospecting purposes. In this study, we characterized fermentative yeast populations from 19 honey samples collected in Northern Patagonia, Argentina. A total of 380 yeast isolates were obtained, identifying eight yeast species. Starmerella magnoliae emerged as the dominant species, found in 76% of samples and representing 63% of total isolates. Intraspecific diversity analysis, using mtDNA-RFLP and sequencing of nuclear genes (FSY1 and FFZ1), revealed the presence of two distinct phylogeographic populations. Phenotypic assays indicated that most S. magnoliae strains tolerate high sulfite and ethanol concentrations, alongside exhibiting broad temperature tolerance, with some strains thriving even at 37 °C. Despite the fact that none of the strains completed the fermentation, microfermentation trials confirmed the fructophilic nature of this species and highlighted intraspecific variability in glycerol and acetic acid production. These findings underscore S. magnoliae as a promising non-Saccharomyces yeast for the fermented beverage industry. Full article

The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. Full article

Audio-driven emotional talking face generation aims to generate talking face videos with rich facial expressions and temporal coherence. Current diffusion model-based approaches predominantly depend on either single-label emotion annotations or external video references, which often struggle to capture the complex relationships between modalities, resulting in less natural emotional expressions. To address these issues, we propose MF-ETalk, a multimodal feature-guided method for emotional talking face generation. Specifically, we design an emotion-aware multimodal feature disentanglement and fusion framework that leverages Action Units (AUs) to disentangle facial expressions and models the nonlinear relationships among AU features using a residual encoder. Furthermore, we introduce a hierarchical multimodal feature fusion module that enables dynamic interactions among audio, visual cues, AUs, and motion dynamics. This module is optimized through global motion modeling, lip synchronization, and expression subspace learning, enabling full-face dynamic generation. Finally, an emotion-consistency constraint module is employed to refine the generated results and ensure the naturalness of expressions. Extensive experiments on the MEAD and HDTF datasets demonstrate that MF-ETalk outperforms state-of-the-art methods in both expression naturalness and lip-sync accuracy. For example, it achieves an FID of 43.052 and E-FID of 2.403 on MEAD, along with strong synchronization performance (LSE-C of 6.781, LSE-D of 7.962), confirming the effectiveness of our approach in producing realistic and emotionally expressive talking face videos. Full article

Spectral flow cytometry has revolutionized traditional single-cell profiling to a new era of high-dimensional analysis, allowing for unprecedented deep phenotyping and more precise cell characterization, thereby significantly enhancing our multiplexing capability. The recent application of this technology in clinical settings has been redefining the landscape of clinical diagnostic panels and immune monitoring, particularly for hematologic malignancies, immunological disorders, and drug discovery. Emerging technologies like ghost cytometry, LASE, and imaging flow cytometry are advancing cytometry by improving sensitivity, throughput, and spatial resolution. In this review, we discuss the requirements, challenges, and considerations for spectral applications in clinical diagnostic laboratories and pharmaceutical/contract research organization (CRO) settings. We discuss how these recent innovations are set to push the boundaries of diagnostic accuracy and analytical power, heralding a new frontier in clinical cytometry with the potential to dramatically enhance patient care and treatment outcomes. Full article

This work investigated the 3D printing of fully compounded thermoset elastomers using a custom-designed printer capable of processing both thermoplastics and elastomers containing fillers and specific cure packages. The adhesion strength between selected thermoset elastomers and thermoplastic combinations was studied, and the influence of key process parameters on adhesion was evaluated. The results showed that interfacial bonding was favored by the proximity of solubility parameters, the amorphous morphology of the thermoplastic, and increased chain mobility at the processing temperature. Rubber processing parameters significantly influenced adhesion, showing that curing at a lower temperature for a longer duration yielded better results than shorter, higher-temperature cures. Elemental analysis revealed the presence of rubber-specific components on the thermoplastic surface, suggesting interfacial migration. These findings contribute to advancing multi-material 3D printing by enabling the integration of rubber-like materials with thermoplastics, expanding opportunities for applications in high-temperature and chemically demanding environments. Full article

This study investigates the in-plane shear behavior of solid brick masonry walls, both unreinforced and retrofitted using Reinforced Khorasan Jacketing (RHJ), a traditional pozzolanic mortar technique rooted in Iranian and Ottoman architecture. Six one-block-thick English bond masonry walls were tested in three configurations: unreinforced with Horasan plaster (Group I), reinforced with steel mesh aligned to wall edges (Group II), and reinforced with mesh aligned diagonally (Group III). All the walls were plastered with 3.5 cm of Horasan mortar and tested after 18 months using diagonal compression, with load-displacement data recorded. A detailed 3D micro-modeling approach was employed in finite element simulations, with bricks and mortar modeled separately. The Horasan mortar was represented using an elastoplastic Mohr-Coulomb model with a custom softening law (parabolic-to-exponential), calibrated via inverse parameter fitting using the Nelder-Mead algorithm. The numerical predictions closely matched the experimental data. Reinforcement improved the shear strength significantly: Group II showed a 1.8 times increase, and Group III up to 2.7 times. Ductility, measured as post-peak deformation capacity, increased by factors of two (parallel) and three (diagonal). These enhancements transformed the brittle failure mode into a more ductile, energy-absorbing behavior. RHJ is shown to be a compatible, effective retrofit solution for historic masonry structures. Full article

Background/Objectives: Autoimmune inner ear disease (AIED) causes sensorineural hearing loss that classically presents as fluctuating, asymmetric loss of hearing. Associated vestibular and other ear symptoms can be present in many patients. First-line treatment of AIED is high-dose corticosteroids. AIED can present either as a primary condition limited to ear involvement or secondary, as part of an underlying systemic autoimmune rheumatic disease, the most common of which include vasculitis and relapsing polychondritis. We described our cohort of primary AIED, including demographics, treatment, and outcomes. We excluded from this review sensorineural hearing loss in the context of vasculitis and relapsing polychondritis. Methods: We performed a chart review of patients with the diagnosis of AIED at Mayo Clinic and compared the cohort by sex. Results: Thirty-one patients met the inclusion criteria. The mean age was 48.5 years, and 17 were men. Patients were initially evaluated at the Department of Otorhinolaryngology or Internal Medicine, and 29 patients were subsequently referred to the Department of Rheumatology, with a mean of 12.2 weeks after the first evaluation. Treatment with corticosteroids showed improvement in hearing and vestibular symptoms during the first month but no further improvement by the end of the third month. Other immunosuppressive medications were used with various degrees of response. Methotrexate was the second most used therapy, with 11 of 17 patients reporting an improvement in symptoms. Conclusions: Corticosteroid therapy is an effective initial treatment for AIED and should be followed with corticosteroid-sparing agents to prevent further damage to the cochlea. Full article

Multi-thrust solid rocket motors are extensively used in tactical missiles. To effectively achieve the desired multi-thrust performance curve, firstly, the concept of modular grain is introduced. Star grain, slot grain, and end-burning grain are chosen as the fundamental templates, which can be flexibly combined to form an arbitrary multi-thrust performance curve. Secondly, a quadric approximation of the burning perimeter is derived, leading to the establishment of a governing equation for modular grain design. This equation ensures a close match between the resulting performance curve and the target one. Thirdly, the Nelder–Mead optimization algorithm is employed to maximize the propellant loading fraction and reduce the combustion chamber size. Finally, the method successfully produces single-thrust, dual-thrust, and triple-thrust grains. The results show that the relative maximum deviation between the designed and target pressure curves is less than 6.1%. Additionally, the best grain configuration is identified, which maximizes the propellant loading fraction while adhering to the throat-to-port ratio constraints. Consequently, the concept of modular grain offers a valuable approach for creating complex internal ballistic characteristics by combining simpler grain templates. This approach allows for fast, responsive motor conceptual design, prototyping, testing, and even production, thereby advancing the development of solid rocket motors in a more efficient and effective manner. Full article

In this paper, we introduce the Levy Flight-enhanced Whale Optimization Algorithm with Tabu Search elements (LWOATS), an innovative hybrid optimization approach that enhances the standard Whale Optimization Algorithm (WOA) with advanced local search techniques and elite solution management to improve performance on global optimization problems. Techniques from the Tabu Search algorithm are adopted to balance the exploration and exploitation phases, while an elite reintroduction strategy is implemented to retain and refine the best solutions. The efficient optimization of LWOATS is further aided by the utilization of Levy flights and local search based on the Nelder–Mead simplex method. An Orthogonal Experimental Design (OED) analysis was employed to fine-tune the algorithm’s parameters. LWOATS was tested against three different algorithm sets: fundamental algorithms, advanced Differential Evolution (DE) variants, and improved WOA variants. Wilcoxon tests demonstrate the promising performance of LWOATS, showing improvements in convergence speed, accuracy, and robustness compared to traditional WOA and other metaheuristic algorithms. After extensive testing against a challenging set of benchmark functions and engineering optimization problems, we conclude that our proposed method is well suited for tackling high-dimensional optimization tasks and constrained optimization problems, providing substantial computational efficiency gains and improved overall solution quality. Full article

To address grid variability caused by renewable energy integration and to maintain grid reliability and resilience, hydropower must quickly adjust its power generation over short time periods. This changing energy generation landscape requires advance technology integration and adaptive parameter optimization for hydropower systems via digital twin effort. However, this is difficult owing to the lack of characterization and modeling for the nonlinear nature of hydroturbines. To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. To assess the performance of the proposed optimal parametric tuning technique, operational data from a real-world Kaplan hydroturbine unit are collected and used to model the relationship between the gate opening and the generated power production. The findings show that the proposed technique can effectively and adaptively learn the unknown dynamics of the Kaplan hydroturbine while optimally tune the unknown coefficients to match the generated power output from the real hydroturbine unit with an inaccuracy of less than 5%. The method can be used to provides optimal tuning of parameters critical for controller design, operational optimization and daily maintenance for hydroturbines in general. Full article

The Upper Turi region in the Maranhão Amazon is a significant producer of honeybees, and mead production represents a cost-effective means of adding value to the honey production chain. This study investigates non-Saccharomyces yeasts isolated from honey as β-glucosidase producers to enhance the mead aroma. Sixty-five honey samples from the Upper Turi in Maranhão underwent yeast screening. Biochemical tests identified isolated yeasts, and β-glucosidase-producing strains were selected via esculin agar. Meads were produced using selected strains of Saccharomyces cerevisiae. Fermentation analyses included pH, °Brix, temperature, conductivity, dissolved oxygen, and volatile compounds (GC-MS). Thirty-six yeasts were isolated, with three identified as β-glucosidase producers. Strain 20 (Saccharomycopsis fibuligera) was selected for mead production due to its fermentative capacity, tolerance to pH and ethanol, and its ability to produce β-glucosidase, which hydrolyzes the glycosidic precursors in honey. During alcoholic fermentation, Saccharomycopsis fibuligera exhibited lower fermentative potential compared to Saccharomyces cerevisiae, reducing only 3.7% of the initial soluble solids (°Brix). The pH and temperature remained relatively stable throughout the fermentation for both yeast strains. The levels of dissolved oxygen and conductivity in the fermented mead were higher for S. cerevisiae than for Saccharomycopsis fibuligera. Specifically, S. cerevisiae showed reductions of 52.85% in dissolved oxygen and conductivity of 1115 µS/cm, while Saccharomycopsis fibuligera exhibited reductions of 33.0% in dissolved oxygen and conductivity of 511 µS/cm. Although the β-glucosidase-producing yeast yielded a mead with a low ethanol concentration, it contributes a unique fruity compound (ethyl hexanoate) and avoids high acetic acid production, providing a distinct aromatic profile that can be explored. Full article

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm²), MAE decreased by 31.43% (670.92→460.03 kg/hm²), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm²), MAE reduced by 26.37% (718.05→528.72 kg/hm²), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry. Full article