RESUMEN
In the original publication [...].
RESUMEN
Downlink precoding is considered for multi-path multi-input single-output channels where the base station uses orthogonal frequency-division multiplexing and low-resolution signaling. A quantized coordinate minimization (QCM) algorithm is proposed and its performance is compared to other precoding algorithms including squared infinity-norm relaxation (SQUID), multi-antenna greedy iterative quantization (MAGIQ), and maximum safety margin precoding. MAGIQ and QCM achieve the highest information rates and QCM has the lowest complexity measured in the number of multiplications. The information rates are computed for pilot-aided channel estimation and a blind detector that performs joint data and channel estimation. Bit error rates for a 5G low-density parity-check code confirm the information-theoretic calculations. Simulations with imperfect channel knowledge at the transmitter show that the performance of QCM and SQUID degrades in a similar fashion as zero-forcing precoding with high resolution quantizers.
RESUMEN
We study the problem of compression for the purpose of similarity identification, where similarity is measured by the mean square Euclidean distance between vectors. While the asymptotical fundamental limits of the problem - the minimal compression rate and the error exponent - were found in a previous work, in this paper we focus on the nonasymptotic domain and on practical, implementable schemes. We first present a finite blocklength achievability bound based on shape-gain quantization: The gain (amplitude) of the vector is compressed via scalar quantization and the shape (the projection on the unit sphere) is quantized using a spherical code. The results are numerically evaluated and they converge to the asymptotic values as predicted by the error exponent. We then give a nonasymptotic lower bound on the performance of any compression scheme, and compare to the upper (achievability) bound. For a practical implementation of such a scheme, we use wrapped spherical codes, studied by Hamkins and Zeger, and use the Leech lattice as an example for an underlying lattice. As a side result, we obtain a bound on the covering angle of any wrapped spherical code, as a function of the covering radius of the underlying lattice.
RESUMEN
Control of the properties of nanoparticles (NPs), including size, is critical for their application in biomedicine and engineering. Polymeric NPs are commonly produced by nanoprecipitation, where a solvent containing a block copolymer is mixed rapidly with a nonsolvent, such as water. Empirical evidence suggests that the choice of solvent influences NP size; yet, the specific mechanism remains unclear. Here, we show that solvent controls NP size by limiting block copolymer assembly. In the initial stages of mixing, polymers assemble into dynamic aggregates that grow via polymer exchange. At later stages of mixing, further growth is prevented beyond a solvent-specific water fraction. Thus, the solvent sets NP size by controlling the extent of dynamic growth up to growth arrest. An a priori model based on spinodal decomposition corroborates our proposed mechanism, explaining how size scales with the solvent-dependent critical water fraction of growth arrest and enabling more efficient NP engineering.
RESUMEN
Polymeric nanoparticles (NPs) are increasingly used as therapeutics, diagnostics, and building blocks in (bio)materials science. Current barriers to translation are limited control over NP physicochemical properties and robust scale-up of their production. Flow-based devices have emerged for controlled production of polymeric NPs, both for rapid formulation screening (~µg min-1) and on-scale production (~mg min-1). While flow-based devices have improved NP production compared to traditional batch processes, automated processes are desired for robust NP production at scale. Therefore, we engineered an automated coaxial jet mixer (CJM), which controlled the mixing of an organic stream containing block copolymer and an aqueous stream, for the continuous nanoprecipitation of polymeric NPs. The CJM was operated stably under computer control for up to 24 h and automated control over the flow conditions tuned poly(ethylene glycol)-block-polylactide (PEG5K -b-PLA20K ) NP size between ≈56 nm and ≈79 nm. In addition, the automated CJM enabled production of NPs of similar size (D h ≈ 50 nm) from chemically diverse block copolymers, PEG5K -b-PLA20K , PEG-block-poly(lactide-co-glycolide) (PEG5K -b-PLGA20K ), and PEG-block-polycaprolactone (PEG5K -b-PCL20K ), by tuning the flow conditions for each block copolymer. Further, the automated CJM was used to produce model nanotherapeutics in a reproducible manner without user intervention. Finally, NPs produced with the automated CJM were used to scale the formation of injectable polymer-nanoparticle (PNP) hydrogels, without modifying the mechanical properties of the PNP gel. In conclusion, the automated CJM enabled stable, tunable, and continuous production of polymeric NPs, which are needed for the scale-up and translation of this important class of biomaterials.
RESUMEN
With increasing organizational and financial pressure on hospitals, each individual surgical treatment has to be reviewed and planned thoroughly. Apart from the expensive operating room facilities, proper staffing and planning of downstream units, like the wards or the intensive care units (ICUs), should be considered as well. In this article, we outline the relationship between a master surgery schedule (MSS), i.e., the assignment of surgical blocks to medical specialties, and the bed demand in the downstream units using an analytical model. By using historical data retrieved from the clinical information system and a patient flow model, we applied a recently developed algorithm for predicting bed demand based on the MSSs for patients of 3 surgical subspecialties of a hospital. Simulations with 3 different MSSs were performed. The impact on the required amount of beds in the downstream units was analyzed. We show the potential improvements of the current MSS considering 2 main goals: leveling workload among days and reduction of weekend utilization. We discuss 2 different MSSs, one decreasing the weekend ICU utilization by 20% and the other one reducing maximum ward bed demand by 7%. A test with 12 months of real-life data validates the results. The application of the algorithm provides detailed insights for the hospital into the impact of MSS designs on the bed demand in downstream units. It allowed creating MSSs that avoid peaks in bed demand and high weekend occupancy levels in the ICU and the ward.