Multiobjective optimization problems arise in numerous real-world applications where multiple conflicting objectives must be optimized simultaneously. A key challenge in solving such problems is efficiently computing Pareto critical points without imposing restrictive assumptions, such as convexity, on the objective functions. While conjugate gradient methods have been widely studied for single-objective optimization, their extension to multiobjective settings remains an active research area, particularly with nonmonotone line search techniques that enhance robustness. This study proposes a Polak-Ribi & egrave;re-Polyak conjugate gradient method for unconstrained multiobjective optimization, where the objective functions are continuously differentiable. The method employs an average-type nonmonotone Armijo-like line search to determine the step-size, improving flexibility and convergence behavior. Under mild assumptions (without convexity requirements), we establish the asymptotic convergence of the method, proving that every limit point of the generated iterate sequence is Pareto critical. To validate the method's effectiveness, we apply it to benchmark test problems and compare its performance with existing multiobjective conjugate gradient methods, such as the Hager-Zhang and Liu-Storey conjugate gradient approaches. Furthermore, to isolate and assess the specific contribution of the nonmonotone line search strategy, we provide an additional comparison between the proposed method and its monotone counterpart, which employs a standard Armijo-type line search. The quality of the Pareto front approximations generated by the considered methods is evaluated using the Hypervolume and Inverted Generational Distance performance indicators on a set of representative convex and nonconvex test problems. The numerical results demonstrate that the proposed nonmonotone Polak-Ribi & egrave;re-Polyak conjugate gradient method consistently outperforms the competing methods in terms of both computational efficiency and the quality of the Pareto front approximation.
This work examines how varying hydrogen addition levels-namely 0% (D100), 10% (D90H10), 20% (D80H20), and 30% (D70H30)-to a premixed diesel-air charge influence combustion behavior and emission formation when tested in a rapid compression machine (RCM). All tests were carried out under conditions of a 423.5 K chamber temperature and a unity equivalence ratio, with the fuel blend being prepared in a premixing chamber prior to its delivery into the combustion chamber. The results indicate that increasing the hydrogen fraction leads to higher in-cylinder pressure and temperature, accompanied by a pronounced shortening of ignition delay. Compared with D100, the D70H30 case shows a 94.85% reduction in ignition delay (1145.88 ms), while the in-cylinder pressure and temperature rise markedly by 108.51% (31.4 bar) and 69.33% (964.93 K), respectively. Regarding emissions, CO levels increased markedly, rising from 0.84 vol% for D100 to 4.35 vol% for D70H30, which corresponds to more than a fivefold increase. In contrast, CO2 shows a pronounced decline as hydrogen content increases, dropping by nearly three times from 9.46 vol% to 3.25 vol%. Meanwhile, unburned hydrocarbons (UHC) emissions also intensify with hydrogen enrichment, with D70H30 exhibiting an increase of 826 ppm, equivalent to a 74.41% rise compared with D100. With increasing hydrogen content, the combustion process accelerates markedly, as evidenced by the reduction in combustion duration from 190 ms for D100 to 77.5 ms for D70H30, representing a shortening of 112.5 ms or 59.21%. Based on the above results, it can be concluded that hydrogen addition has a significant influence on both combustion characteristics and exhaust emissions. These findings deepen the understanding of hydrogen-assisted combustion and establish a useful reference dataset for combustion kinetics and emission analysis in diesel-hydrogen systems, while also supporting the validation of existing kinetic models under RCM conditions. Moreover, the results highlight hydrogen as a practical near-term decarbonization option without requiring major engine design modifications and simultaneously improving ignition behavior. Collectively, this work lays a basis for dual-fuel engine development and for determining appropriate hydrogen blending levels in future applications.
The areca nut (Areca catechu L.) is a widely consumed masticatory product in Southeast Asian countries such as India, Indonesia, Taiwan, Thailand, and China owing to its cultural importance and potential medicinal value. The areca nut, which is the seed of the Areca catechu palm, is classified as a drupe and is traditionally consumed in both its unripe and ripe forms. In addition to the seeds, the leaves, roots, and inflorescences of the plant also present medicinal properties. Chemically, areca nut comprises approximately 0.5
Strategic autonomy, reflecting a firm’s autonomy in setting goals, enables the exploration and realization of business opportunities and may prove more critical in how a firm orchestrates its resources and capitalizes on them. However, the impact of strategic autonomy, as one of the entrepreneurial orientation (EO) dimensions, on firm performance remains understudied, especially during environmental shocks such as the COVID-19 pandemic. This study, therefore, utilizes resource orchestration and threat-rigidity theories to examine the relationship between strategic autonomy, potential slack, and environmental dynamism on publicly listed small and medium-sized enterprises’ (SMEs) performance in the COVID-19 pandemic. Using a longitudinal, computer-aided text analysis (CATA) of shareholder letters from 170 publicly listed SMEs in Taiwan from 2018 to 2023, generating 991 firm-year observations, we found that autonomy surprisingly and negatively impacted performance during the pandemic. However, the interaction between autonomy and potential slack positively affected performance. Conversely, the interaction between autonomy and environmental dynamism harmed performance. Our study highlights the complex interplay between autonomy and the internal and external factors shaping SME performance during turbulent times.
Coral reefs consist of diverse benthic habitats that influence seawater CO2 chemistry variability on multiple spatial and temporal scales. Understanding the present-day seawater CO2 chemistry variability across both habitat-specific and reef-wide scales is critical to accurately predict the effects of future environmental change. Here, we utilize autonomous sensors and discrete seawater samples across diverse habitats at multiple scales ranging from habitat-specific (inner lagoon, patch reefs and seagrass beds; 0.02–0.72km2) to reef-wide scales at Dongsha Atoll (250km2) and Taiping Island (20km2) to characterize seawater chemistry. Across all habitats, daily mean pH ranged from 7.79–8.60 with mean diel variability ranging from 0.19–0.91. Spatially, pH variability ranged from 0.08 (patch reef) to 1.29 (inner lagoon). Biogeochemical modification of seawater chemistry was dominated by organic carbon cycling at individual habitat scales, whereas inorganic carbon cycling dominated at the scale of Dongsha Atoll. The largest alkalinity depletion (net calcification) was associated with patch reef habitats, whereas the highest alkalinity repletion was associated with a semi-enclosed lagoon. Under two climate change scenarios (linear dissolved inorganic carbon increase derived from historical observations and the CMIP6 SSP5-8.5 pathway), pH and/or aragonite saturation state (ΩAr) observations across all habitats in this study are projected to be below proposed thresholds for net reef accretion (pH < 7.7: inner lagoon 10–13
