Search Results (323)

This study examines the associations between linearly scored phenotypic traits and auction sales prices of young event horses in Ireland, aiming to identify key traits influencing market value. Data from 307 horses sold at public auctions (2022–2023) were analysed using regression analysis, binary optimisation, and Principal Component Analysis (PCA). Regression identified Head–neck Connection, Quality of Legs, Walk length of Stride, and Scope as highly significant predictors of sales price (p < 0.001), with Length of Croup, Trot Elasticity, Trot Balance, and Take-off Direction also significant (p < 0.05). Optimised regression reduced the number of relevant traits from 37 to 8, streamlining evaluation. PCA highlighted eight principal traits, including Scope, Elasticity, and Canter Impulsion, explaining 61.19% of variance in the first four components. These results demonstrate that specific conformation, movement, and athleticism traits significantly affect auction outcomes. The findings provide actionable insights for breeders and stakeholders, suggesting that targeted selection for high-impact traits could accelerate genetic progress and improve market returns. Furthermore, these traits could underpin the development of economic or buyer indices to enhance valuation accuracy and transparency, with potential application across equestrian disciplines to align breeding objectives with market demands. Full article

This study presents a detailed and comprehensive investigation into the macro-scale performance, strength gain mechanisms, environment and economic performance of graphene oxide (GO)-enhanced low-emission concrete. A comprehensive experimental program evaluated fresh and hardened properties, including slump retention, bleeding, air content, compressive, flexural, and tensile strength, drying shrinkage, and elastic modulus. Scanning Electron Microscopy (SEM), energy-dispersive spectroscopy (EDS), Thermogravimetric analysis (TGA) and proton nuclear magnetic resonance (¹H-NMR) was employed to examine microstructural evolution and early age water retention, confirming GO’s role in accelerating cement hydration and promoting C-S-H formation. Optimal performance was achieved at 0.05% GO (by binder weight), resulting in a 25% increase in 28-day compressive strength without compromising workability. This outcome is attributed to a tailored, non-invasive mixing strategy, wherein GO was pre-dispersed during synthesis and subsequently blended without the use of invasive mixing methods such as high shear mixing or ultrasonication. Fourier-transform infrared (FTIR) spectroscopy further validated the chemical compatibility of GO and PCE and confirmed the compatibility and efficiency of the admixture. Sustainability metrics, including embodied carbon and strength-normalized cost indices (USD/MPa), indicated that, although GO increased material cost, the overall cost-performance ratio remained competitive at breakeven GO prices. Enhanced efficiency also led to lower net embodied CO₂ emissions. By integrating mechanical, microstructural, and environmental analyses, this study demonstrates GO’s multifunctional benefits and provides a robust basis for its industrial implementation in sustainable infrastructure. Full article

The international trade in agricultural products is complex and diverse. Global buyers must diversify their import sources, while sellers must explore new market opportunities. In the past, there has been no analysis on how second-tier exporters, with a smaller market share compared to dominant exporters, interact in the same target market and within an existing trade market and what factors affect trade prices and market forces. Based on Vollrath’s revealed competitive advantage index framework, this study analyzes the global tangerine trade (HS08052100) and means of production from 2008 to 2021, performs clustering, and estimates the residual demand elasticities of two main second-tier exporting countries—South Africa and Morocco—in four major importing countries for empirical analysis. The results show that South African tangerines have a lower market share than Moroccan tangerines in the Netherlands, the United States, and the United Kingdom. However, all data indicate that the residual demand elasticity for the country’s products in the target markets is negative, indicating that South African exporters have market influence in all three markets and significantly affect the prices of Moroccan products in these markets. Unlike other studies that have focused on the ranking analysis of export indices, the novelty of this study is that it provides an oligopolistic framework based on agricultural value chain analysis, which can be used for many countries with limited export scales. The method proposed in this study is expected to help citrus traders to effectively find export markets by evaluating the remaining market niches using key market data and the prices of similar competitors in the same category. Full article

Rising population, combined with declining home food production, in Arab nations has resulted in increased food imports that intensifies their dependence on international markets for vital food supplies. These nations face challenges in achieving food security because crude oil price volatility creates difficulties in managing the expenses of imported food products. This research calculates the income and price elasticities of imported food demand to understand consumer behavior changes in response to income and price variations, which helps to explain their impact on regional food security. To our knowledge, this research presents the first analysis of imported food consumption patterns across Arab countries according to their income brackets. This study employs the static Almost Ideal Demand System model to examine food import data spanning from 1961 to 2020. The majority of imported food categories demonstrate inelastic price and income demand, which means that their essential food consumption remains stable despite cost fluctuations. The need for imports makes Arab nations vulnerable to external price changes, which endangers their food security. This research demonstrates why governments must implement policies through subsidies and taxation to reduce price volatility risks while ensuring food stability, which will lead to sustained food security for these nations. Full article

The use of renewable plant resources for the formulation of adhesives is increasingly promising, thanks to their availability at an affordable price and their high content of biomolecules such as polyphenols. The study of tannins therefore remains an active and ongoing area of research. This article presents a recent characterization of tannins extracted from the barks of four types of tropical trees (Entandophragma candolei, Entandophragma cylindricum, Afzelia africana and Dacryodes klaineana) and their application in the development of bioresins. Tannin extraction with hot water yielded between 25% and 40%. Tannin from Entandophragma candolei produced the highest yield. Chemical analysis confirmed the high presence of condensed tannins, with the identification of several new monomers in each tannin type, underlining their uniqueness. The most chemically stable tannins, Dacryodes klaineana and Afzelia africana, demonstrated their ability to withstand temperatures of 525 °C and 375 °C, respectively, with carbon residues of 45.05% and 43.18%. As for the resins, Entandophragma candolei tannin resin stood out for its thermal properties, notably a degradation temperature of 500 °C and a carbon residue rate of 36.72%. As for E. cylindricum resin, it boasted the highest modulus of elasticity (5268 MPa). Characterized tannins can be exploited in the technological sector. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

In this paper, we study perpetual American put options with a generalized standard put payoff and establish sufficient conditions for the existence and uniqueness of the solution to the associated pricing problem. As a key tool, we express the Black–Scholes operator in terms of elasticity. This formulation enables us to demonstrate that the considered pricing problem admits a unique solution when the payoff function exhibits strictly decreasing elasticity with respect to the underlying asset. Furthermore, this approach allows us to derive closed-form solutions for option pricing. Full article

This article studies the location–price Hotelling game. Numerous studies have been conducted on the Hotelling game with simultaneous decisions; however, in real-life scenarios, decisions are frequently sequential. Unfortunately, studies on the sequential Hotelling (SHOT) game are quite scarce. This article contributes to the study of the SHOT game by considering the case in which the location of one of the players, either the leader or the follower, is externally fixed. The game is studied analytically and by numerical simulation to address scenarios where mathematical analysis is cumbersome due to the discontinuous nature of the game. Simulation is found to be particularly useful in evaluating the subgame perfect equilibrium (SPE) solution of these SHOT games, where the follower outperforms the leader as a very general rule, with very few exceptions. This article complements a previous study of the SHOT game where the two locations are parameterized and paves the way to address the analysis of more sophisticated formulations of the SHOT game, such as those with reservation cost and with elastic demand. Full article

Urban traffic management faces significant challenges in balancing parking supply with user demand. This study introduces a novel parking demand model that integrates simulation-based pricing with elasticity functions derived from revealed preference data, segmented across predefined user categories, such as short-term visitors (e.g., shoppers) and monthly subscribers (e.g., commuters). Unlike previous models, this approach does not rely on survey-based inputs and explicitly accounts for both natural and chaotic demand behaviors, thereby improving forecasting accuracy under oversaturated conditions. The model supports sustainable parking management by optimizing space availability, while simultaneously increasing occupancy and enhancing revenue generation. Full article

The intensive utilization of agricultural inputs is key to agricultural modernization. This study analyzed the elasticity of substitution among inputs in Chinese grain production (1991–2023) using a Translog production function, controlling for price disturbances. The key findings are as follows: (1) Complementary relationships exist between capital–fertilizer, capital–land, fertilizer–land, pesticide–land, and fertilizer–labor, while capital–pesticide, fertilizer–pesticide, pesticide–labor, and land–labor are substitutive. (2) The elasticity of substitution among agricultural inputs stabilizes over time, with substitutive and complementary relationships among most factors weakening after 2004. (3) Eastern and northeastern regions tend to substitute labor with capital more significantly, while central and western regions show a balanced interplay. (4) Nationwide trends in agricultural input shares indicate increasing mechanization, land-use efficiency, fertilizer use, and reduced labor input. These results provide insights for optimizing input allocation and enhancing food security. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

Decarbonizing the transportation sector is critical for sustainable development, particularly in rapidly urbanizing countries like Turkiye. This study analyzes fuel demand elasticities for diesel, gasoline, and LPG across 12 NUTS-1 regions of Turkiye in 2022, using a panel random effects SUR approach. The model accounts for regional variation and fuel interactions, producing robust estimates that uncover significant spatial and temporal differences in consumption patterns. Uniquely, diesel demand displays a significantly positive price elasticity, challenging the conventional assumption of inelasticity. Gasoline demand is moderately price-sensitive, while LPG appears relatively unresponsive. Strong cross-price elasticities—especially between diesel and gasoline—point to substitution effects that can inform more adaptive policy frameworks. Seasonal fluctuations and Istanbul’s outsized impact also shape national trends. These findings underscore the need for differentiated region- and fuel-specific strategies. While higher gasoline taxes may effectively reduce demand, lowering diesel and LPG use will require complementary measures such as infrastructure upgrades, behavioral incentives, and accelerated adoption of alternative fuels. The study advocates for regionally adjusted carbon pricing, removal of implicit subsidies, and targeted support for electric and hybrid vehicles. Aligning fiscal tools with actual demand behavior can enhance both the efficiency and equity of the transition to a low-carbon transportation system. Full article

Traditional time-of-use (TOU) pricing models ignore the delay characteristics of user behavior; consequently, the resulting load adjustments exhibit discrete patterns, whereas actual load variations follow gradual trajectories in reality. Hence, a dynamic process is to be considered when describing user behavior and designing pricing strategy, which will, however, add to the complexity of pricing. This paper proposes a TOU pricing strategy considering user response with delay. Firstly, based on the final state of user response, the time delay of the demand response is defined. Secondly, to describe the dynamic process of load transfer, a time-varying price elasticity matrix is proposed, and its parameters are newly identified by using the weighted least squares method. Finally, based on the elasticity matrix, a mixed-integer programming model is proposed with the multi-objective of minimizing the peak–valley difference of system load and maximizing user satisfaction. An improved non-dominated sorting genetic algorithm (NSGA-II) is applied to find the Pareto front solution and obtain the optimal price of the TOU. The simulation results based on a provincial load data in China show that the proposed optimization strategy to the TOU pricing can help the system reduce peak–valley load difference and effectively smooth the load curve. Full article

The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R² values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the requirements of specific age groups, address housing affordability and demographic shifts, and cater to prevailing market conditions. This research provides practical insights and guidance for urban planners, public housing agencies, residential developers, and residential contractors while offering a robust methodological framework for predictive modelling in the construction sector. Full article

This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a central model that connects key strands of the long run risk literature and recovers the stochastic discount factor (SDF) under the unit EIS assumption. I generate various economies based on Epstein–Zin preferences to simulate scenarios where the EIS deviates from unity. Then, I study the main estimation mechanism of the decomposition as well as the time discount factor and the risk aversion parameter estimation surface. The results demonstrate the robustness of estimating the average yield, change of measure, and preference parameters but also reveal an “absorption effect” arising from the unit EIS assumption. The findings highlight that asset pricing models assuming a unit EIS produce distorted parameter estimates, caution researchers about the potential under- or over-estimation of risk aversion, and provide insight into trends of misestimation when interpreting the results. I also identify an additional source of failure from a consumption component, which demonstrates a more general limit of the consumption-based capital asset pricing model and the structure used to estimate relevant preference parameters. Full article