Wetland ecological health is assessed using an indicator system that comprehensively considers aquatic environments, soil conditions, biological communities, and socioeconomic factors. In this study, the assessment unit basins and baseline wetland sections were established, and a Pressure-State-Response (PSR) framework comprising twelve indicators was applied to evaluate the ecological health of riverine wetlands. The assessment units were delineated as small watersheds at a spatial scale appropriate for riverine wetland management. In addition, alluvial zones were introduced as a geomorphic reference to delineate baseline wetland sections, defined as the potential wetland space against which human-induced degradation was assessed. The indicator grades may vary depending on the study area, and the state indicators were analyzed using three to five indicators per basin. The assessment results of 80 unit basins in the upper Geum River Basin, South Korea, revealed that one unit basin rated as Grade 1 (Very healthy), 29 unit basins as Grade 2 (Healthy), 47 unit basins as Grade 3 (Moderate), three as Grade 4 (Vulnerable), and none as Grade 5 (Highly vulnerable). Unit basins with higher population density, urbanization rate, and cultivated land ratio generally showed lower wetland ecological health grades. Unit basins adjacent to the mainstream tended to be relatively healthy whereas some tributary unit basins with high urbanization rates and pollution loads showed degraded ecological conditions. This study provides a scientific foundation for prioritizing restoration efforts and establishing sustainable wetland management and conservation policies.
How organizations view “time” shapes how they coordinate and utilize operational resources. This study examines how proactive temporal framing, treating future contingencies as requiring present preparation and coordination, is associated with more efficient operational resource utilization. This study uses linguistic future-time reference (FTR) as a language-based proxy for this framing: weak-FTR languages express future events in present-tense forms, making the future feel psychologically closer. Using global airline data from 1994 to 2012, this study finds that airlines in weak-FTR contexts show higher efficiency in resource utilization. This association is stronger when more codeshare partners also come from weak-FTR contexts, although the moderation evidence is context-dependent. The study reframes the trade-off between future orientation and efficiency around operations and identifies temporal alignment as a boundary condition under temporal complexity.
Sintered Ag is a promising die-attach material for wide-bandgap power modules, but residual pores produce spatially non-uniform heat transport that is not captured by scalar effective thermal conductivity (ETC) alone. We develop a physics-corrected machine learning (ML)/deep learning (DL) framework for rapid prediction of vertical heat-flux fields from sintered-Ag cross-sections. A U-Net is trained on 691 of 867 real microstructures using a height-normalized target, q* = qH/ΔT, to remove the deterministic dependence of heat-flux magnitude on image height. An independent XGBoost model predicts ETC from normalized microstructural descriptors, and a row-wise correction enforces cross-sectional heat-flow conservation. The normalized U-Net retains high accuracy on 88 held-out real samples (field R² = 0.9898; ETC error = 1.20%). On 616 held-out aspect-ratio crops, normalization improves field R² from 0.0933 to 0.9852, reduces ETC error from 30.39% to 1.73%, and changes the geometry-dependent log-log slope from −0.9661 to −0.0077. On 320 unseen synthetic realizations, Raw / Normalized / + ML / + row-wise field R² values are −0.4291 / 0.7820 / 0.8117 / 0.8742, with ETC errors of 60.61 / 12.74 / 6.49 / 6.49%. The complete pipeline requires 176.4 ms per sample, approximately 130 times faster than finite element analysis (FEA). The resulting regime-level gating rule is explicit: use height normalization for geometry shift, apply ETC scaling only when the independent scalar predictor is more accurate than the ETC implied by the field, and use row-wise conservation to redistribute flux without altering the scalar ETC.
As the global transition toward carbon neutrality and a sustainable chemical economy accelerates, technologies converting biomass- and diverse carbon-source-derived C1–C4 lower alcohols into high-value fuels and chemical feedstocks play a pivotal role. Although conversion technologies for individual alcohols are well established, a unified framework for comparing catalytic valorization pathways across the C1–C4 spectrum has yet to be clearly defined. This review critically examines the impact of the carbon chain length and structural characteristics of feedstock alcohols on reaction mechanisms and catalyst design. For methanol (C1), which lacks C–C bonds, an indirect C–C bond-forming pathway via the Hydrocarbon Pool (HCP) mechanism within zeolite catalysts (e.g., H-ZSM-5, SAPO-34) predominates, where pore architecture governs product selectivity. In contrast, C2+ alcohols inherently possess C–C bonds and undergo direct chain growth via Guerbet condensation or dehydration-oligomerization pathways. Specifically for propanol (C3) and butanol (C4), steric hindrance and isomeric structures (iso- vs. n-) significantly determine pathway selectivity and catalyst deactivation. Furthermore, this review provides an in-depth analysis of universal catalyst deactivation issues, including coke deposition, hydrothermal degradation, and active site poisoning. To mitigate these challenges, advanced catalytic engineering strategies are presented, including hierarchical porous structures, nanocrystallization, precise modulation of active sites, and hybrid catalyst design. Conclusively, this review presents a unified catalytic overview by systematically contrasting the indirect HCP-mediated C1 pathways with the direct chain-growth mechanisms of C2+ alcohols, providing a strategic roadmap for engineering structure-optimized catalysts that overcome universal deactivation barriers in next-generation valorization technologies.
Materials that exhibit reversible field-tunable responses to electric and magnetic stimuli enable adaptive modulation of mechanical, dielectric, and magnetic properties. This review focuses on electro-magnetorheological (EMR) hybrid materials that concurrently exploit electric polarization and magnetization-induced structure formation, with particular emphasis on protocol-dependent, non-additive behavior under superimposed fields. The underlying electrorheological (ER) and magnetorheological (MR) response mechanisms are analyzed from classical dipolar models to advanced descriptions that incorporate interfacial polarization phenomena, effective (microstructure-mediated) magnetoelectric coupling, and nonlinear structural dynamics. Microscopic interaction mechanisms are consistently linked to the macroscopic rheological and functional behavior of EMR fluids, gels, and elastomers, with an emphasis on viscoelastic properties, electrical conductivity, and cross-field coupling effects. The impact of field orientation, application sequence, and driving frequency on emergent material behavior is critically evaluated. The design and synthesis concepts for bifunctional particles and hierarchical composites are summarized. Key challenges related to colloidal and structural stability, response reproducibility, scalable manufacturing routes, and environmental sustainability are also discussed. Emerging multiscale and data-driven modeling frameworks are highlighted as enabling tools for predictive material design and optimization of EMR materials. Standardization needs in measurement protocols and reporting practices are addressed. The focus is on synchronized in situ structural characterization combined with dielectric and electrical measurements. These methods are used to rigorously discriminate additive superposition of ER and MR responses from genuinely non-additive cross-field coupling. Finally, perspectives on applications and device-integration strategies for EMR-based components and systems are outlined.
