Optimal low-complexity detection for space division multiple access wireless systems

A symbol detector for wireless systems using space division multiple access (SDMA) and orthogonal frequency division multiplexing (OFDM) is derived. The detector uses a sphere decoder (SD) and has much less computational complexity than the naive maximum likelihood (ML) detector. We also show how to detect non-constant modulus signals with constrained least squares (CLS) receiver, which is designed for constant modulus (unitary) signals. The new detector outperforms existing suboptimal detectors for both uncoded and coded systems

Time scale analysis of receptor enzyme activity : irreversible inhibition sometimes exhibits incubation-time independence

At early drug discovery, purified protein-based assays are often used to characterise compound potency. As far as dose response is concerned, it is often thought that a time-independent inhibitor is reversible and a time-dependent inhibitor is irreversible. Using a simple kinetics model, we investigate the legitimacy of this. Our model-based analytical analysis and numerical studies reveal that dose response of an irreversible inhibitor may appear time-independent under certain parametric conditions. Hence, time-independence cannot be used as evidence for inhibitor reversibility. Furthermore, we also analysed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell-based assay setting. Indeed, these processes may also influence dose response of an irreversible inhibitor in such a way that it appears time-independent under certain conditions. Hence, time-independent dose response in a cell assay also needs careful considerations. It is necessary to formulate a suitable model for analysis of protein-based assay and in vitro cell assay data to ensure a consistent understanding

Evolve the Model Universe of a System Universe

Uncertain, unpredictable, real time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash the potential of such systems and facilitate their wider adoption, ensuring the trustworthiness of their decision making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most updated states. Such representations are often in the form of prior knowledge based and machine learning models, together called model universe. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution

Investigating receptor enzyme activity using time-scale analysis

At early drug discovery, purified protein-based assays are often used to characterise compound potency. In the context of dose response, it is often perceived that a time-independent inhibitor is reversible and a time-dependent inhibitor is irreversible. The legitimacy of this argument is investigated using a simple kinetics model, where it is revealed by model-based analytical analysis and numerical studies that dose response of an irreversible inhibitor may appear time-independent under certain parametric conditions. Hence, the observation of time-independence cannot be used as sole evidence for identification of inhibitor reversibility. It has also been discussed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell-based assay setting. These processes may also influence dose response of an irreversible inhibitor in such a way that it appears time-independent under certain conditions. Furthermore, model-based steady-state analysis reveals the complexity nature of the drug-receptor process

What caused ozone pollution during the 2022 Shanghai lockdown? Insights from ground and satellite observations

Shanghai, one of China's most important economic centres, imposed a citywide lockdown in April and May 2022 to contain a resurgence in cases of the coronavirus disease in 2019. Compared with the 2020 lockdown, the 2022 lockdown occurred in a warm season and lasted much longer, thereby serving as a relevant real-world test of the response of ambient ozone (O3) concentrations to emission reductions in a high-O3 season. In this study, we analysed surface observations of O3 and nitrogen dioxide (NO2) concentrations and satellite-retrieved tropospheric NO2 and formaldehyde (HCHO) column concentrations in the first 5 months of 2022 with comparisons to the year 2021. During the 2-month 2022 lockdown, the maximum daily 8 h average (MDA8) O3 concentrations at 1 or more of the city's 19 sites exceeded China's air quality standard of 160 µg m−3 21 times, with the highest value being 200 µg m−3. The city-average MDA8 O3 concentration increased by 13 % in April–May 2022 year-on-year, despite sharp declines in NO2 surface and column concentrations (both by 49 %) and a 19 % decrease in the HCHO column concentration. These results show that the reductions in O3 precursors and other pollutants during the 2022 lockdown did not prevent ground-level O3 pollution. An analysis of meteorological data indicates that there were only small changes in the meteorological conditions, and there was little transport of O3 from the high-O3 inland regions during the 2022 lockdown, neither of which can account for the increased and high concentrations of O3 that were observed during this period. The mean HCHO/NO2 ratio in April–May increased from 1.11 in 2021 to 1.68 in 2022, and the correlation between surface O3 and NO2 concentrations changed from negative in 2021 to positive in 2022. These results indicate that the high O3 concentrations in 2022 were mainly due to large reductions in the emissions of NOx and that the decrease in the concentrations of volatile organic compounds (VOCs) could not overcome the NO titration effect. During the 2022 lockdown, Shanghai's urban centre remained VOC-sensitive despite drastic reductions in road transportation (73 %–85 %) and industrial activities (∼60 %), whereas its semi-rural areas transitioned from VOC-limited to VOC–NOx-co-limited regimes. Our findings suggest that future emission reductions similar to those that occurred during the lockdown, such as those that will result from electrifying transportation, will not be sufficient to eliminate O3 pollution in urban areas of Shanghai and possibly other VOC-limited metropoles without the imposition of additional VOC controls or more substantial decreases in NOx emissions.</p