This study investigates the impact of blockchain integration within diversified distribution channels on supply chain performance through two primary mechanisms: enhancing consumer confidence in sustainable products and improving demand prediction accuracy. We first establish an analytical framework that illustrates the operational mechanisms by which blockchain adoption influences enterprise profitability, specifically by increasing net trust value and information traceability. The framework is subsequently expanded to identify the prerequisites for effective blockchain deployment in sustainable supply chains utilizing resale and marketplace models. The research systematically delineates parameterized thresholds for blockchain adoption tailored to each channel configuration while analyzing structural moderators that affect technological effectiveness. Several key findings emerge from this investigation: Positive generation of trust value necessitates exceeding critical implementation cost thresholds; Resale channels exhibit improvements in traceability enabled by blockchain only when sustainability investment efficiency surpasses identified critical levels, revealing counterintuitive performance dynamics; Marketplace systems demonstrate greater sensitivity to cost-sharing ratios compared to their resale counterparts, thereby requiring differentiated implementation strategies. This research contributes both theoretical and practical insights into blockchain implementation strategies applicable to resale and marketplace supply chains. It advances methodology through quantified adoption thresholds, clarifies channelspecific implementation dynamics, and develops coordination frameworks for manufacturers and retail platforms.
While agency theory has long dominated corporate governance research, we suggest that the common transplanting of the dyadic principal-agent problem into the corporate context has blurred key differences between principals and the firm as an entity. We redress this imbalance by advancing a conceptual framework of principal costs vis-a-vis the firm. We first show how principal costs can exist even in the single-principal corporate context, based on owner consumption and competence characteristics, which allows us to also distinguish principal costs from both agency costs and principal-principal expropriation costs. We then extend our principal costs theory to the multi-principal context, in which we highlight how principal costs, including private benefits of influence, can exist even in corporations with no controlling shareholder enjoying private benefits of control. In this latter context, we redirect the agency theoretic lens of incentive and informational concerns toward active minority shareholders whose actions generate principal costs vis-a-vis the firm, as well as passive shareholders who fail to constrain such principal costs. We conclude with a discussion of the broader implications of our theory for current and future corporate governance research, practice, and public policy.
Altermagnetic metals break time-reversal symmetry and feature spin-split Fermi surfaces generated by compensated N & eacute;el-ordered collinear magnetic moments. Being metallic, such altermagnets may undergo a further instability at low temperatures to a superconducting state, and it is an interesting open question what the salient features are of such altermagnetic superconductors. We address this question on the basis of realistic microscopic models that capture the altermagnetic sublattice degree of freedom. We find that the sublattice structure can strongly affect the superconducting gap structure in altermagnetic superconductors. In particular, it imposes nodes in the gap on the Brillouin zone edges for superconductors stabilized by momentum-independent bare attraction channels. We contrast this to the case of superconductivity generated by extended range interactions where pairing is allowed on the Brillouin zone edges and both spin-singlet and equal-spin-pairing triplet states can be stabilized. Equal-spin-pairing triplet superconductivity is generically favored in the limit of large altermagnetic spin splitting of the bands compared to the superconducting gap scale, and features characteristic nonunitary properties arising from the altermagnetic order.
Superconductivity in three dimensions is almost universally governed by Ginzburg-Landau mean field theory, with critical fluctuations typically confined to within a few percent of the transition temperature (T_ c). We report that the heavy-Fermion superconductor UTe_2 exhibits a fluctuation regime that extends over a temperature range as wide as T_ c itself – the largest observed for any three-dimensional superconductor. Through ultrasound measurements of the elastic moduli and sound attenuation, we find that UTe_2 transitions from a mean-field-like state at ambient pressure to a fluctuation dominated state at higher pressures. This regime is marked by elastic softening and an increase in sound attenuation that onsets well above T_ c, with the attenuation remaining anomalously high deep in the superconducting state. Our analysis shows that these features stem from an extremely low superfluid phase stiffness. This results in a kinetic inductance as high as that of granular aluminum, but achieved in the clean limit. We propose that this exotic state is driven by dominant inter-band pairing mediated by ferromagnetic fluctuations, leading to "local" cooper pairs with a coherence length of only a few lattice constants.
Self-reconfigurable modular robots (SRMRs) are engineered to adapt their morphology and behaviour to varying terrains. Despite their potential, most SRMRs face challenges in searching through a discrete space and interacting with various environments. We introduce an advanced Terrain-Aware Morphology Search (TAMS) algorithm to overcome these limitations. This innovative approach begins by establishing a set of module joint rules, enabling the random generation of feasible morphologies. It then employs a refined Grammar Variational Auto-encoder (GVAE) to transition these discrete morphologies into a continuous latent space. The core of TAMS lies in its unique hybrid morphology search method, which integrates Differential Evolution (DE) and Neural Network (NN). This combination is pivotal in identifying the most terrain-suited morphology within the continuous latent space. Additionally, TAMS leverages Model Predictive Path Integral (MPPI) control to swiftly derive appropriate behaviours for these optimized morphologies. The algorithm further enhances its efficiency by updating the DE population and NN weights based on the rewards obtained from environmental feedback. Comparative experimental results reveal that TAMS outperforms three benchmark algorithms - Grammar-guided Latent Space Optimization, Genetic Programming, and a Two-Stage Reconfiguration Algorithm. In terms of the best fitness and search speed, TAMS is positioned in the first echelon of the Pareto front in all tested terrains. In some complex terrains, the best morphology fitness and search speed achieved by TAMS far exceed those of other benchmark algorithms. Moreover, the morphologies obtained by the TAMS algorithm can achieve the fastest moving speed when traversing the terrain, and the output of its actuators is much smaller than that of the GP and GLSO algorithms.
