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The solution of ordinary differential equations using genetic programming with constant tuning
No full textIn this paper we report the solution of a benchmark set of ordinary differential equations (ODEs) using genetic programming (GP) within a collocation framework using tuning of the embedded tree constants. We report statistical comparison with a baseline GP approach without constant tuning that indicates that parameter tuning produces statistically superior results. We obtain highly accurate solutions for almost all the benchmark ODEs, but identify a hitherto unreported issue with GP finding trivial solutions. The characteristics of the individual ODEs appear to dictate whether or not solution is problematic
73 GHz pHEMT pumped transconductance RX Mixer with low local oscillator drive power
No full textFuture mmWave mobile architectures will benefit from low power RF circuits and systems. In this work we propose two pumped transconductance balanced mixers requiring low LO drive power and DC power. The mixers operate at circa 73 GHz RF and 70 GHz LO. A 90-degree hybrid is used on the input to simplify input matching. Off-chip IF matching is implemented on a test PCB. We observe a conversion gain of -17 dB for an LO power of -10 dBm, with a DC current of 1.3 mA and an IP1dB of - 3.3 dBm for one of the prototypes. LO powers down to -20 dBm are also potentially viable, if greater loss can be accepted
Pickering water-in-oil emulsions stabilized by beeswax crystals: Design and stability
No full textThe aim of this study was to understand how beeswax can be used to stabilize water-in-oil emulsions and understand the impact of droplet volume fraction on stability. Pickering water-in-oil (W/O) emulsions were successfully designed using beeswax oleogels (4 wt%) as the sole stabilizer via a facile homogenization approach. The effects of beeswax crystals on the formation, stability, and structural organization of W/O emulsions (containing up to 70% v/v water) were systematically investigated. Oleogelation reduced the interfacial tension between oil and water from 21 ± 1 mN/m to 14 ± 1 mN/m, enabling beeswax crystals to stabilize emulsions without the need for synthetic surfactants. X-ray diffraction analysis revealed that beeswax crystals in oleogels exhibited a stable β’ polymorph with orthorhombic packing, maintaining a reduced long-chain spacing from 7.8 nm in pure beeswax to 6.3 nm, which contributed to the stabilization of Pickering W/O emulsions. The emulsions ranged in size (D32) from 8 to 16 μm depending upon the droplet volume fraction and showed no significant change over storage period of 6 weeks. Microscopic observations of the interface demonstrated that emulsion stability was achieved through the synergistic effects of both the Pickering stabilization and bulk network stabilization. This dual stabilization mechanism was further confirmed by thermal cycling experiments and in situ crystallization analysis at the interface as well as rheological measurements showing gel formation at higher volume fractions of droplets. Overall, these findings highlight the potential of beeswax to stabilise W/O emulsions for applications in food and allied soft matter industries
An in-situ wear measurement instrumentation for thin metallic bearing coatings using high frequency ultrasound
No full textIn this paper, an in-situ measurement instrumentation for bearing wear status has been proposed, including the 22 MHz high frequency sensors, ultrasound multiplexer, data acquisition system and control algorithm. In-situ experiments have shown that it can measure the real-time thickness and wear depth of coating during the operation with a high accuracy. Additionally, the negative wear results have been discussed, which reveals the ultrasound reflection during the surface deformation stage, and explains the phenomenon of decreased wear depth during the wear progress. Generally, the research underpins the design of a prototype for an in-situ wear measurement instrument, which will be helpful for the further development of tribology equipment
Lubrication and delubrication behaviour of micron-sized whey protein microgels
No full textThis study aims to understand the tribological properties of whey protein microgels (WPM) by varying their sizes in the micron-scale and deformability. To decipher the lubrication mechanisms of WPM dispersions (3-48 vol %), we exploited two tribological systems using conventional smooth substrate and state of the art biomimetic tongue-like surfaces. We fabricated relatively hard WPM (G’ ≈ 350 kPa) of three distinct sizes (D4,3 ≈ 1-50 μm) and a soft WPM (G’ ≈ 85 kPa, D4,3 ≈ 50 μm) using water-in-oil emulsion templating. On smooth surfaces, dispersions of soft WPM delivered good lubrication in boundary and mixed regimes across the studied volume fractions (3-48 vol %), which was attributed to deformation-induced entrapment of WPM particles and the resulting surface separation. In contrast, hard WPM appeared to be entrapped between the contacting surfaces when their size is comparable to the theoretically derived film thickness (e.g. 1 μm). The incorporation of 1 μm hard WPM caused delubrication, which can be attributed to increased proportion of surface asperities which disrupt the aqueous lubrication. In a simulated tongue-palate interface (biomimetic tongue), microgel participation through the collision between WPM and papillated structure was found to be largely delubricating irrespective of size in the micron-scale which we postulate can be caused by jamming of WPM particles and/or even deformation of the papillae-like features by WPM. Our findings demonstrate that hard, micron-sized WPM exhibit delubrication when relatively soft tongue-like surfaces are used. These insights can inspire development of food products where an interplay between size and deformability of semi-solid particles is key to modulate mouthfeel
Energy efficiency support for software defined networks: a serverless computing approach
No full textAutomatic network management strategies have become paramount for meeting the needs of innovative real-time and data-intensive applications, such as those in the Internet of Things. However, the ever-growing and fluctuating demands for data and services in such applications require more than ever an efficient, scalable, and energy-aware network resource management. To address these challenges, this paper introduces a novel approach that leverages a modular architecture based on serverless functions within an energy-aware environment. By deploying SDN services as Functions as a Service (FaaS), the proposed approach enables dynamic, on-demand network function deployment, achieving significant cost and energy savings through fine-grained resource provisioning. Unlike previous monolithic SDN approaches, this work disaggregates SDN control plane into modular, serverless components, transforming tightly integrated functionalities into independent, on-demand services while ensuring performance, scalability, and energy efficiency. An analytical model is presented to approximate the service delivery time and power consumption, as well as an open source prototype implementation supported by an extensive experimental evaluation. Experimental results demonstrate significant improvement in energy efficiency compared to traditional approaches, highlighting the potential of this approach for sustainable network environments
The dimension of well approximable numbers
No full textIn this survey article, we explore a central theme in Diophantine approximation inspired by a celebrated result of Besicovitch on the Hausdorff dimension of well approximable real numbers. We outline some of the key developments stemming from Besicovitch's result, with a focus on the Mass Transference Principle, Ubiquity and Diophantine approximation on manifolds and fractals. We highlight the subtle yet profound connections between number theory and fractal geometry, and discuss several open problems at their intersection
Saying enough vs saying too much: lessons on optimizing project risk description for crowdfunding success in developing countries
No full textOnline crowdfunded technology projects offer opportunities for driving sustainable growth in developed and developing countries. However, entrepreneurs propose these projects within a context of information asymmetry and risk uncertainty which prospective backers must grapple with. Although Kickstarter has been requiring technology entrepreneurs to disclose project risk information since 2012, the value of this disclosure remains underexplored amidst controversies in the literature about whether project risk description enhances crowdfunding success. In addressing these issues, this research employs signaling theory and curvilinear analysis to examine what project risk description length and sentiment levels maximize crowdfunding success for technology projects, particularly those in developing countries. Statistical analyses of 1059 campaigns on Kickstarter partly support and challenge this study's theorizations. Risk description sentiment and length have U-shaped and inverted U-shaped effects on crowdfunding success respectively. Although these effects do not differ across projects in developed and developing countries, detailed risk descriptions generally yield better results for projects in developing countries. These insights advance and clarify the underdeveloped literature on the link between project risk description and crowdfunding outcomes and offer guidance on how developing country entrepreneurs can couch project risk statements to optimize crowdfunding success
Optimization under attack: Resilience, vulnerability, and the path to collapse
No full textOptimization is critical for improving the operations of large-scale socio-technical infrastructures such as those found in energy, mobility, and information systems. In particular, understanding the performance of multi-agent discrete-choice combinatorial optimization under distributed adversarial attacks is a compelling and underexplored problem. Multi-agent systems involve a large number of remote control variables that can influence the cost-effectiveness of distributed optimization heuristics. This paper unravels, for the first time, the trajectories of distributed optimization from resilience to vulnerability, and finally to collapse under varying adversarial influence. Using real-world and synthetic data to generate over 112 million multi-agent optimization scenarios, we systematically assess how the number of agents with varying levels of adversarial severity and network positioning influences optimization performance, with particular attention to the impact on Pareto optimality. With this large-scale dataset, made openly available as a benchmark, we disentangle how optimization systems remain resilient to adversaries and which adversary conditions make optimization vulnerable or cause collapse. These findings can support the design of self-healing strategies for fault tolerance and fault correction, addressing a critical gap in adversarial distributed optimization