Steel fibers play a critical role in improving the mechanical performance of ultra-high-performance concrete (UHPC) and significantly affect its cost. This study investigates the effects of steel fiber shape and dosage on the mechanical properties and cost-efficiency of UHPC prepared using solid waste-based materials. Three types of steel fibers—straight, corrugated, and hooked-end—were incorporated at fiber volume fractions of Vf = 0, 0.3, 0.6, and 0.9
Epistemic Contrastivism is the thesis that, for any proposition A, one does not simply know that A. Rather, one knows A rather than some set of alternatives. Typically, arguments for Epistemic Contrastivism have relied on intuitions regarding knowledge ascriptions in ordinary language. In this article, I offer a novel argument for Epistemic Contrastivism (at least with respect to lottery propositions) from considerations of the Lottery Paradox. Recent treatments of the Lottery Paradox demonstrate that Doxastic Contrastivism, the thesis that rational belief is contrastive, provides powerful resources to resolve this paradox. Insofar as Doxastic Contrastivism entails Epistemic Contrastivism (assuming some contrastive beliefs constitute knowledge), these resources are only available if we accept Epistemic Contrastivism as well. The article has three sections. In the first, I give a brief overview of contrastivism in general, beforeexplaining Epistemic Contrastivism and Doxastic Contrastivism. In the second, I turn to the Lottery Paradox, and explain how recent work in formal epistemology supportsEpistemic Contrastivism. In the third, I show how these formal theories have resources to answer objections that have been raised against Epistemic Contrastivism and Doxastic Contrastivism.
The climate emergency has been reframed as a moral summons to mine. Across policy, finance, and corporate discourse, extraction now parades as ecological salvation rather than ecological debt. This paper argues that the critical-minerals agenda serves as a Trojan Horse within climate governance, smuggling extractivist logic beneath the rhetoric of decarbonization. Drawing on political ecology, post-extractivist, and degrowth traditions, we integrate a PESTLE-Force-Field analysis of ninety-five policy and industry texts (2019-2025) from Canada, the United States, the European Union, Australia, and Chile. Findings reveal a moral economy of speed in which urgency, techno-sovereignty, and ESG finance transform acceleration into virtue and restraint into failure. Under this logic, decarbonization becomes accumulation by decarbonization-an intensification of material throughput disguised as responsibility. Authentic transition requires embedding ecological limits in law, institutionalizing Indigenous co-governance, and redirecting finance toward sufficiency rather than expansion. Decarbonization cannot be mined into existence; it must be governed into balance.
Differing from AI and GenAI adoption, research on traditional systems emphasised extrinsic factors like utility, social influence and innovativeness as predictors of user behaviour. The role of proximal psychological factors like motivation, however, has been overlooked in this context, which becomes essential with this shift towards AI. In the educational sector, the students’ use of AI shows the possibility of intrinsic factors like motivation in shaping adoption behaviour. This study uses Self-Determination Theory (SDT) and its Organismic Integration Theory (OIT) extension to propose a conceptual map that examines the role of distinct motivational types in shaping students’ GenAI adoption behaviour. The adoption behaviour of 348 Indian students pursuing higher education was collected through a cross-sectional survey and analysed using structural equation modelling. Findings indicated that autonomous motivation, including intrinsic, identified, and integrated motivation, significantly predicts students’ intentions to use GenAI tools. The study further examined the moderating role of perceived compatibility, revealing that alignment between users’ lifestyles and GenAI usage strengthens the impact of controlled motivations. When students feel that AI fits well with their needs and learning requirements, showing high compatibility, external motivators have a stronger effect on their decision to adopt it. This makes compatibility an important new finding and provides additional insights into the motivational types of GenAI adoption in academic contexts. This study extends the body of knowledge by moving beyond the binary treatment of motivation and empirically distinguishing between specific types of motivation. It emphasises the importance of self-determined motivation while showing how the correlations between various motivation types and GenAI usage intentions are conditioned by perceived compatibility. The study also offers practical insights based on the significant results.
Next-generation (xG) wireless systems, including sixth-generation (6G) networks and beyond, are expected to deliver data rates on the order of terabits per second and sub-millisecond latency. Meeting these requirements increasingly relies on artificial intelligence (AI)-enabled radio access and physical-layer (PHY) processing. However, realizing such AI-driven PHY functionality with deep learning (DL) is challenging as deep neural networks (DNNs) are computationally intensive and memory hungry, often exceeding the capabilities of resource-constrained user equipment (UE) and edge hardware. This paper surveys model-compression techniques for efficient wireless intelligence, focusing on pruning, quantization, and knowledge distillation (KD), together with architectural and algorithmic optimizations. For each technique, we summarize theoretical foundations, practical implementation strategies, and wireless-specific considerations, and discuss how design choices translate into latency, energy, and memory outcomes on deployment hardware. We review applications across core PHY wireless tasks, including automatic modulation classification (AMC), channel state information (CSI) processing and feedback, beamforming (BF), recognition and identification, channel estimation and detection, and localization. Drawing on comparative analysis of more than 50 studies, we highlight trade-offs among model size, computational complexity, energy consumption, and task-level performance under wireless evaluation protocols. We further discuss hardware-software compatibility considerations for compressed model deployment and outline open challenges and future research directions for compression-aware deployment in xG wireless systems.
