Gheorghe Asachi Technical University of Iași

We study general rotational surfaces in the pseudo-Euclidean 4-dimensional space with neutral metric and describe the behavior of geometric objects, such as Killing vector fields (and in particular homothetic vector fields), divergence-free vector fields, co-closed and harmonic one-forms, and also harmonic functions. We classify geodesic and parallel vector fields, geodesic curves, concircular vector fields and concircular functions, and also concurrent vector fields and functions whose gradient is concurrent. Our results are new, as they have not been obtained in the Euclidean and Minkowski framework. The tools here are taken from both differential geometry and partial and ordinary differential equations.

This study investigates the influence of the additive illite on the thermal, tribological, and energy efficiency characteristics of calcium grease (CG) at different concentrations (0.05 wt.%, 0.1 wt.%, 0.2 wt.%, 0.4 wt.%, 0.6 wt.%, and 0.8 wt.%). Thermo-gravimetric analysis under inert and oxidative atmospheres revealed that illite enhances thermal stability by increasing inorganic residue under N2, but promotes oxidative degradation under O2, limiting practical thermal use to around 400 °C. Grease with 0.1 wt.% illite (CGI2) performed well in tribological tests by reducing the coefficient of friction and wear scar diameter by 53% and 57%, respectively, compared to the base grease. Fleischer's energy-based wear model showed that all grease samples operated within the mixed friction regime, and CGI2 exhibited a 93% higher apparent frictional energy density and a substantially lower wear intensity that was 47% lower than the base grease, indicating improved energy dissipation and wear resistance. All samples had the same weld load (1568 N), but CGI2 had a 21% higher load-wear index than the base grease in the extreme-pressure test, indicating better load-carrying capacity. In the energy consumption test, a 6% reduction in current consumption was observed in CGI2 in comparison with the base grease. Overall, illite at an optimal concentration significantly enhances lubrication performance, wear protection, and energy efficiency.

The environmental impact of non-biodegradable lubricants has increased interest in eco-friendly alternatives. This study investigates the tribological properties of a calcium-based grease with biotite, a naturally occurring anti-wear additive, compared to molybdenum disulfide (MoS 2 ) which is not so environmentally friendly and commercial lithium grease. Biotite was added to calcium grease at various concentrations (0.2 wt.%, 0.4 wt.%, 0.6 wt.%, and 0.8 wt.%) along with castor oil and hydrogenated castor oil as other eco-friendly components. Thermogravimetric analysis showed that biotite-enhanced grease had superior thermal stability. Tribological testing revealed that 0.2 wt.% biotite and MoS 2 reduced wear scar diameter by 28.67% and 39.13%, and the coefficient of friction reduced by 10% and 14% as compared to the base calcium grease. The commercial lithium grease exhibited 15% and 19% higher frictional properties than our proposed calcium grease. The addition of biotite also improved the load wear index by 15.69%, demonstrating its superior anti-wear and load-carrying properties compared to MoS 2 . Additionally, the biotite-enhanced grease showed 13.79% lower current consumption than commercial lithium grease. The results were also supported by the statistical analysis, which indicated higher influence of the anti-wear properties of biotite than its anti-friction properties and vice-versa in the case of molybdenum disulfide. These findings suggest that biotite can be a sustainable, cost-effective additive for improving the tribological properties of bio-based greases.

This paper presents a comparative study of three major families of deep generative models—WGAN-GP, β-VAE, and diffusion models (DDPM)—applied to 3D shape generation. Using the standardized ModelNet40 dataset, we evaluated the architectures along four main axes: geometric fidelity (measured via FID3D), morphological diversity, computational efficiency (training and inference time, memory), and stability of convergence. The results show that diffusion models consistently achieve the best geometric fidelity, albeit with the highest computational cost. β-VAE provides the fastest training and inference with high diversity, but at the expense of reduced fidelity. WGAN-GP offers an intermediate compromise between fidelity, diversity, and computational load, representing a pragmatic option in resource-constrained scenarios. Ablation experiments conducted on DDPM highlight the influence of latent size, diffusion steps, β regularization, and voxel resolution on performance, revealing clear trade-offs between fidelity gains and computational overhead. Overall, the study confirms that no single architecture dominates across all criteria, and suggests that the optimal choice of model depends on the target application and the balance required between fidelity, diversity, and efficiency. AI-Driven 3D Shape Generation for Additive Manufacturing

The global trend of population aging presents urgent challenges to traditional healthcare systems, particularly in enabling older adults to live independently while maintaining quality of life. This paper presents SmartCare, a scalable and modular Ambient Assisted Living platform that integrates heterogeneous commercial off-the-shelf (COTS) IoT devices, real-time analytics, and user-friendly interfaces into a unified edge-cloud ecosystem. A real-world pilot deployment demonstrates the platform’s adaptability across diverse scenarios, including hypertensive and diabetic patients management. Evaluations include both technical validations and a usability study based on an extended Technology Acceptance Model, focusing on provisioning and configuration tasks. The results highlight the platform’s effectiveness in supporting safe, autonomous living while reducing caregiver burden and deployment complexity.