Search Results (3,572)

The growing awareness of celiac disease and gluten sensitivities has generated interest in gluten-free products. Whole sorghum (Sorghum bicolor) is an excellent source of nutrients and is gluten-free. However, the absence of gluten makes it technologically challenging to produce leavened products. This research aims to utilize a response surface methodology to optimize the specific loaf volume and crumb firmness of a whole sorghum-based gluten-free bread formulation, evaluating different levels of milk powder, egg white, yeast, sugar, psyllium husk powder, xanthan gum, and soy lecithin. The models fit achieved an $R^2\ge 80\%$. The optimized formulation increased the specific loaf volume from 1.7 to 2.8 cm³ g⁻¹ and decreased crumb firmness from 10.6 to 3.7 N compared to the initial gluten-free bread formulation (C1). Egg white, milk powder, and psyllium contribute to the formation of a gluten-like network, which enables gas retention, dough expansion, and volume increase. In addition, soy lecithin, among hydrocolloids, enhances dough stability and moisture retention, resulting in a softer crumb. Sensory evaluation indicated good consumer acceptability (average score of 7 on a 9-point hedonic scale), particularly for texture and flavor. These findings suggest that optimal formulation of sorghum achieves both technological and sensory properties, supporting its potential as a viable gluten-free bread alternative. Full article

Self-assembly has emerged as a powerful bottom-up strategy for the construction of multifunctional nanocomposites based on upconversion nanoparticles (UCNPs). In contrast to epitaxial shell growth, self-assembly enables the modular integration of UCNPs with a broad spectrum of other functional nanomaterials. This characteristic makes it particularly attractive for various practical applications. This review provides a comprehensive overview of self-assembly methodologies for UCNP-based nanocomposites, including electrostatic interactions, hydrophobic interactions, covalent coupling, and specific biorecognition. The resultant nanohybrids exhibit a wide range of morphologies and functionalities, making them suitable for various applications, including multimodal imaging, bioimaging, advanced biosensing, smart nanocarriers for controlled molecular delivery, and orthogonal photoactivation for programmable therapy. Key recent studies are highlighted to elucidate the structure–function relationships and technological potential of these materials. Additionally, the current challenges, such as stability, reproducibility, and functional integration, and proposed future directions for the development of UCNP-based nanocomposites are further discussed. Full article

Electricity prices are subject to constant changes, mainly owing to the increasing share of unstable renewable energy sources. The ability to predict short-term prices presents significant benefits to both energy consumers and producers. This is crucial for managing the energy in hybrid systems with energy storage. This study presents a methodology for predicting the electricity prices for three days with hourly resolution. The accuracy of the price prediction strongly depends on the stability and repeatability of the analysed energy market. The Polish market, characterised by a dynamically changing energy mix, where the selection of the training period and the training, validation, and test sets are crucial, is assessed. Two periods are analysed: 2019–2021, which is a period of stable prices, and 2022–2024, which is a period of high price variability. The multilayer perceptron (MLP) network and support vector machine (SVM) are trained using three sets of data: time, weather, and prices of various energy sources. The analysis indicates the correlation of data and their impact on the accuracy of the price forecast. Dedicated data processing, network model structures, and training techniques are used. The comparison between prediction accuracies shows the advantages of the SVM network, whose prediction error is lower by 45% for the period of stable prices and by 20% for the period of variable prices when compared with the MLP network. The results indicate a significant increase in accuracy when various types of training data, such as weather or energy prices, are considered. Full article

Sugar amino acids (SAAs) represent a privileged class of molecular chimeras that uniquely merge the structural rigidity of carbohydrates with the functional display of amino acids. These hybrid molecules have garnered significant attention as programmable conformational constraints, offering a powerful strategy to overcome the inherent limitations of peptide-based therapeutics, such as proteolytic instability and conformational ambiguity. The strategic incorporation of SAAs into peptide backbones, particularly within cyclic frameworks, allows for the rational design of peptidomimetics with pre-organized secondary structures, enhanced metabolic stability, and improved physicochemical properties. This review provides a comprehensive analysis of the synthetic methodologies developed to access the diverse structural landscape of SAAs, with a focus on modern, stereoselective strategies that yield versatile building blocks for peptide chemistry. A critical examination of the structural impact of SAA incorporation reveals their profound ability to induce and stabilize specific secondary structures, such as β- and γ-turns. Furthermore, a comparative analysis positions SAAs in the context of other widely used peptidomimetic scaffolds, highlighting their unique advantages in combining conformational control with tunable hydrophilicity. We surveyed the application of SAA-containing cyclic peptides as therapeutic agents, with a detailed case study on gramicidin S analogs that underscores the power of SAAs in elucidating complex structure–activity relationships. Finally, this review presents a forward-looking perspective on the challenges and future directions of the field, emphasizing the transformative potential of computational design, artificial intelligence, and advanced bioconjugation techniques to accelerate the development of next-generation SAA-based therapeutics. Full article

In response to the urgent need for performance predictions of damaged aerospace structures, this study undertakes a comprehensive investigation into the flutter characteristics of damaged variable-stiffness composite laminate (VSCL) plates. The governing boundary value problem for the dynamics of damaged VSCL plates is formulated using first-order shear deformation theory (FSDT). Additionally, the first-order piston theory is utilized to model the aerodynamic pressure in supersonic airflow. A novel coupling methodology is developed through the integration of penalty function methods and irregular mapping techniques, which effectively establishes the interaction between damaged and undamaged plate elements. The vibration characteristics and aeroelastic responses are systematically analyzed using the Chebyshev differential quadrature method (CDQM). The validity of the proposed model is thoroughly demonstrated through comparative analyses with the existing literature and finite element simulations, confirming its computational accuracy and broad applicability. A notable characteristic of this research is its ability to accommodate arbitrary geometric configurations within damaged regions. The numerical results unequivocally demonstrate that accurately predicting the flutter characteristics of damaged VSCL plates constitutes an effective strategy for mitigating structural stability degradation. This approach provides valuable insights for aerospace structural design and maintenance. Full article

This study considers a robust design methodology to reduce cogging torque in the EPS (Electric Power Steering) of an automotive system. Cogging torque reduction is the key design factor to improve steering feeling and drive stability in an EPS system. For this reason, an SPMSM (Surface Permanent Magnet Synchronous Motor) has been widely applied to drive a motor in an EPS system. Furthermore, two design methods, which are a magnet shape and step-skew design for rotor assembly, have been mainly used to reduce cogging torque in an SPMSM. In this paper, an SPMSM is selected as the drive motor and a robust design methodology is proposed to reduce cogging torque in an EPS system. Firstly, a cycloid curve is used for the magnet shape to reduce cogging torque. An evaluation index δq is also used to compare this with a conventional magnet shape design. Secondly, based on the results of the magnet shape design with the cycloid curve, a step-skew design for rotor assembly is also applied to reduce cogging torque. In order to validate the effectiveness of the robust design for the cycloid curve and conventional magnet shape with rotor step-skew, the results from FEM (Finite Element Method) analysis and prototype tests are compared. The cycloid curve magnet shape model with rotor step-skew was verified to reduce the cogging torque and enhance the robustness for cogging torque variation through the analysis and protype test results. The verified results for the proposed model will be extended to meet the required cogging torque variation for the various applications driven by SPMSM with the robust design model. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

A systematic fuzzy multi-criteria decision making (MCDM) framework is proposed to prioritize near-Earth asteroids (NEAs) for a boulder capture mission, addressing the requirement for rigorous prioritization of asteroid candidates under conditions of data uncertainty. Twenty-eight NEA candidates were first selected through filtering based on physical and orbital properties. Then, objective fuzzy weighting MCDM methods (statistical variance, CRITIC, and MEREC) were applied to determine the importance of criteria such as capture cost, synodic period, rotation rate, orbit determination accuracy, and similarity to other candidates. Subsequent fuzzy ranking MCDM techniques (WASPAS, TOPSIS, MARCOS) generated nine prioritization schemes whose coherence was assessed via correlation analysis. An innovative sensitivity analysis employing Dirichlet-distributed random sampling around reference weights quantified ranking robustness. All methodologies combinations consistently identified the same top four asteroids, with 2013 NJ ranked first in every scenario, and stability metrics confirmed resilience to plausible weight variations. The modular MCDM methodology proposed provides mission planners with a reliable, adaptable decision support tool for asteroid selection, demonstrably narrowing broad candidate pools to robust targets while accommodating future data updates. Full article

Despite the close linkage between carbon markets and fossil fuel markets, minimal research has investigated their co-movement dynamics during times of heightened geopolitical instability and public health crises, including the COVID-19 pandemic, Israeli–Palestinian conflict, and the Russia–Ukraine war. Most studies use conditional mean regression models for testing linear Granger causality, which falls short in assessing time-varying causal relationships. This paper employs a time-varying Granger causality framework to examine the dynamic linkages between fossil fuel markets and carbon markets across multiple time horizons. This methodology enables the evaluation of causal relationships that evolve over time, providing deeper insights into how the carbon market interacts with traditional fossil fuel markets. The study examines causal linkages among carbon, coal, and oil prices from 2 January 2018 to 11 July 2025, using data from Wind Database. The findings reveal a short-lived yet highly significant bidirectional causality between the carbon and fossil fuel markets during the COVID-19 period, whereas a sustained and highly significant bidirectional causal relationship emerges after the onset of the Russia–Ukraine war. During the outbreak of the Israeli–Palestinian conflict, this linkage continued without major disruptions or directional shifts. Furthermore, the recursive evolution approach, based on variable sub-window sizes, detects additional evidence of significant bidirectional causal relationships among carbon, coal, and oil prices. These discoveries can serve as valuable inputs for investors and policymakers, enabling them to make informed decisions that protect their interests and ensure market stability. Additionally, coal prices showed greater persistence than oil prices in these bidirectional causal links. Full article

Vertical alliances within agricultural supply chains serve as critical institutional vehicles for deepening triple-sector integration (primary–secondary–tertiary) in rural economies, driving agricultural modernization, and advancing rural revitalization. However, sustaining alliance stability constitutes a complex dynamic process wherein inadequate stakeholder engagement and collaborative failures frequently precipitate alliance instability or even dissolution. Existing scholarship exhibits limited systematic examination of the micro-mechanisms and regulatory pathways through which multi-agent strategic interactions affect alliance stability from a dynamic evolutionary perspective. To address this gap, this research focuses on China’s core agricultural innovation vehicle—the Agricultural Industrialization Consortium—and examines the tripartite structure of “Leading Enterprise–Family Farm–Integrated Agricultural Service Providers.” We construct a tripartite evolutionary game model to systematically analyze (1) the influence mechanisms governing cooperative strategy selection, and (2) the regulatory effects of key parameters on consortium stability through strategic stability analysis and multi-scenario simulations. Our key findings are as follows: Four strategic equilibrium scenarios emerge under specific conditions, with synergistic parameter optimization constituting the fundamental driver of alliance stability. Specific mechanisms are as follows: (i) compensation mechanisms effectively mobilize leading enterprises under widespread defection, though excessive penalties erode reciprocity principles; (ii) strategic reductions in benefit sharing ratios coupled with moderate factor value-added coefficients are critical for reversing leading enterprises’ defection; (iii) dual adjustment of cost sharing and benefit sharing coefficients is necessary to resolve bilateral defection dilemmas; and (iv) synchronized optimization of compensation, cost sharing, benefit sharing, and value-added parameters represents the sole pathway to achieving stable (1,1,1) full-cooperation equilibrium. Critical barriers include threshold effects in benefit sharing ratios (defection triggers when shared benefits > cooperative benefits) and the inherent trade-off between penalty intensity and alliance resilience. Consequently, policy interventions must balance immediate constraints with long-term cooperative sustainability. This study extends the application of evolutionary game theory in agricultural organization research by revealing the micro-level mechanisms underlying alliance stability and providing a novel analytical framework for addressing the ‘strategy–equilibrium’ paradox in multi-agent cooperation. Our work not only offers new theoretical perspectives and methodological support for understanding the dynamic stability mechanisms of agricultural vertical alliances but also establishes a substantive theoretical foundation for optimizing consortium governance and promoting long-term alliance stability. Full article

The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties. Full article

In 2024, 28% of the global population experienced moderate or severe food insecurity. The State of Food Security and Nutrition in the World (SOFI) 2024 report underscores that adequate and sustained financing is critical to achieving global food security and improved nutrition outcomes. Grounded in the entitlement theory, this study examines how financial inclusion can reinforce the relationship between entitlements and food security. The study conducts a systematic review research methodology to collect, interpret, and integrate 84 studies. The findings of the paper include a thematic map and a conceptual framework. The thematic map highlights the major themes of the research area. The conceptual framework illustrates how financial inclusion enhances key entitlements such as production, trade, labor, and aid, which help achieve the four dimensions of food security: availability, accessibility, utilization, and stability. The study contributes theoretically by extending both entitlement and capability theory, showing how financial services improve access to food and strengthen people’s capabilities. On the policy front, the study recommends enhancing digital infrastructure in rural areas, promoting sustainable agriculture, empowering women, and encouraging millet production through targeted subsidies and cash transfer schemes. The study also suggests future research directions to help address its limitations, such as the lack of empirical testing of the proposed relationships. Full article

This research seeks to reduce wind-blown sand hazards in saline deserts by introducing hyperbranched PVAc copolymer emulsion as a novel ecological sand-fixing material. The study began with the preparation of the emulsion, then evaluated its fundamental properties and the salt tolerance of latex films through FTIR, SEM, and mechanical strength assessments. The sand-fixing properties (compressive strength, anti-water erosion, anti-wind erosion, thermal aging, freeze–thaw stability, and water retention) were evaluated. In addition, their effects on increasing both the growth of microbes and plants in salty deserts have been evaluated by field experiments to understand their ecological effects. The experimental results showed that the hyperbranched PVAc copolymer emulsion has excellent salt resistance and can be used as an ecological sand-fixing material in salty deserts. The research findings demonstrate that the hyperbranched PVAc copolymer emulsion exhibits superior salt tolerance, rendering it an effective ecological sand-fixing material for saline deserts. Notable attributes encompass its capacity to significantly mitigate NaCl-induced aggregate damage to sand-fixing materials, thereby enhancing sand fixation performance; its robust thermal aging resistance, freeze–thaw stability, and salt tolerance, which enable it to withstand environmental temperature variations; and experimental assessments of sand-based plant and microbial growth confirming favorable ecological impacts. This study presents novel methodologies for designing ecological sand-fixing materials in saline deserts to combat desertification. Full article

Ecological concrete designed by empirical method does not consider the mesoscopic influence of aggregates, resulting in problems such as low strength, excessive porosity, and poor stability with different gradations, which severely restricts the development and application of ecological concrete. To achieve the refined design of ecological concrete, a mesoscopic specific surface area design method based on statistical analysis is proposed. First, the meso-aggregate model with sub-millimeter precision was established using a high-precision 3D scanner, and CloudCompare was used to calculate the specific surface area of the mesoscopic aggregate model, laying the foundation for the statistical analysis of specific surface area. Second, statistical analysis methods verified that the mean specific surface area of 20 aggregates from a single random sampling reliably estimates the mean of the overall aggregate population. Third, the optimal water–cement ratio was calculated considering the water absorption characteristics and the mortar-wrapping capacity of aggregates; standard cubic specimens were prepared using this optimal water–cement ratio, with aggregates evenly coated with mortar and no obvious mortar settlement. Fourth, the cubic compressive strength of specimens naturally cured for 7 days was tested; experimental results showed that the cubic compressive strength of specimens formed by this project’s design method increased by more than 30% compared to the empirical design method. The results indicate that using the average volume-specific surface area of 20 aggregates to assess the overall average volume-specific surface area of aggregates is both reliable and relatively efficient. Based on the reliable estimation of the overall average volume-specific surface area of aggregates derived from this method, measurements were taken of the thickness of water films adsorbed on dry aggregates and the thickness of mortar coatings on surface-dry aggregates. Further, the optimal water–cement ratio for eco-concrete was deduced, and a comprehensive set of feasible refined methods for eco-concrete mix proportion design was proposed. In contrast to the empirical method, concrete designed via the subject’s methodology exhibits a marked enhancement in compressive strength while retaining favorable pore characteristics—rendering it well-suited for deployment in the slope protection of reservoirs and ponds and thereby facilitating the realization of ecological slope protection functionality. Full article

Background. Food resilience is the ability of the food system to adapt to external and internal disturbances and maintain the outcome of food security. This paper focuses on shaping the concept of urban food resilience regarding the operation of urban food infrastructure and its capacity to provide food security. Methods. To achieve this, a methodology based on the pillars defined by the Food and Agriculture Organization (FAO) for food security, i.e., availability, accessibility, and stability, is used, operationalized from a spatial approach, and evaluated in terms of urban food resilience. Three simple indexes are built, i.e., diversity, redundancy, and short-term stability, and combined into a composite index: the Urban Food Resilience Index (UFRI). Results. The results are analysed from a spatial and quantitative perspective, linking scores with urban surface area, population, and density. The study examines the reality of Chilean intermediate cities distributed throughout the country, using the La Serena–Coquimbo Conurbation as a case study. Conclusions. The ultimate goal is to provide a straightforward methodology for assessing urban food resilience in countries with limited data access, thereby providing a foundation for informed urban planning decision-making. Full article