Situational visual impairments (SVIs) hinder mobile readability, causing discomfort and limiting information access. Building on prior work in adaptive typography and accessibility, this paper presents SituFont, a context-aware and human-in-the-loop adaptive typography adjustment approach that enhances smartphone mobile readability by dynamically adjusting font parameters based on real-time contextual changes. Using smartphone sensors and a human-in-the-loop approach, SituFont personalizes text presentation to accommodate personal factors (e.g., fatigue, distraction) and environmental conditions (e.g., lighting, motion, location). To inform its design, we conducted formative interviews (N=15) to identify key SVI factors and controlled experiments (N=18) to quantify their impact on optimal text parameters. A comparative user study (N=12) across eight simulated SVI scenarios demonstrated SituFont’s effectiveness in improving smartphone mobile readability in terms of improved efficiency and reduced workload compared with a non-trivial manual adjustment baseline.

This study explores the factors influencing Chinese designers’ attitudes toward disclosing their use of artificial intelligence design tools (AIDTs). Using Partial Least Squares Structural Equation Modeling (PLS-SEM), 282 valid responses were analyzed. Based on technology readiness and self-determination theory, a structural equation model for the disclosure intention of using AIDTs was constructed. The results show that among the three basic needs of self-determination theory, autonomy and relatedness significantly and positively impact disclosure intention, whereas competence does not. Additionally, optimism and innovativeness from technology readiness do not directly influence disclosure intention but indirectly affect it through autonomy and relatedness. These results highlight the psychological mechanisms underlying disclosure behavior, identify a novel research path in AI design, and offer guidance for optimizing and promoting AIDTs. This contributes to the sustainable development of AI in the design industry.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The original optimization problem is prohibitively complicated when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, an im-proved K-means algorithm is proposed for user clustering, where the clustering metric considers both channel gain and channel correlation among users to reduce the intra- and inter-cluster interference. With this basis, the optimal cluster number and cluster centers are dynamically obtained by the semi-orthogonal user selection (SUS) algorithm. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved iteratively. Simulation results show that the proposed scheme achieves the near-optimal performance in terms of power consumption and energy efficiency with low computational complexity.

Future wireless communication systems have been imposed high requirement on power efficiency for operator's profitability as well as to alleviate information and communication technology (ICT) global carbon emission. To meet these challenges, the power consumption minimization problem for a generic multi-cell multiple input and single output non-orthogonal multiple access (MISO-NOMA) system is studied in this work. The associated joint user grouping, beamforming (BF) and power control problem is a mixed integer non-convex programming problem, which is tackled by an iterative distributed methodology. Towards this end, the near-optimal zero-forcing (ZF) BF is leveraged, wherein the semiorthogonal user selection (SUS) strategy is applied to select BF users. Based on these, the BF vectors and BF users are determined for each cell using only local information. Then, two distributed user grouping strategies are proposed. The first one, called channel condition based user clustering (CCUC), performs user grouping in each cell based on the channel conditions. This is conducted independently of the power control part and has low computational complexity. Another algorithm, called power consumption based user clustering (PCUC), uses both the channel conditions and inter-cell interference information to minimize each cell's power consumption. In contrary to CCUC, PCUC is optimized jointly with the power control. Finally, with the obtained user grouping and BF vectors, the resultant power allocation problem is optimally solved via an iterative algorithm, whose convergence is mathematically proven given that the problem is feasible. We perform Monte-Carlo simulation and numerical results show that the proposed resource management methods outperform various conventional MISO schemes and the non-clustered MISO-NOMA strategy in several aspects, including power consumption, outage probability, energy efficiency, and connectivity efficiency.