University of Macau Advanced Research Institute in Hengqin

Abstract Decarbonizing power systems necessitates both integrating substantial renewable energy and exploring flexible resources to accommodate their inherent intermittency and variability. Building Heating, Ventilation, and Air Conditioning (HVAC) systems are emerging as valuable dispatchable resources, offering flexibility for power systems through demand response (DR) programs. As HVAC operation and occupant comfort are highly dependent on temperature and humidity (T&H), their flexibility potential exhibits significant regional disparities across China’s diverse climate. This research systematically investigates the flexibility across 5 representative city clusters (15 cities), to characterize these regional attributes and inform effective guidelines for harnessing this flexibility. We find that under 3 distinct weather conditions (sunny, cloudy, and rainy), the Maximum inter-regional flexibility exceeds the minimum by 61%, 113%, and 134%, respectively. Notably, intra-regional differences are also Substantial, reaching up to 51%, 89%, and 104% for the same weather conditions. Extending this analysis to a national scale, we evaluate the average flexibility per HVAC to be 45.1kW under extremely hot conditions and 16.1kW under hot-humid conditions. This research identifies regional patterns: northern cities exhibit significant diurnal flexibility variations, eastern regions demonstrate pronounced weather sensitivity, central areas show consistent weather response, western regions present distinct city-specific characteristics, and southern cities possess stable and high flexibility. These findings underscore the importance of considering such regional heterogeneity to prioritize efforts in leveraging urban HVAC systems, as well as help power systems optimize the development of renewable energy in China based on complementary HVAC flexibility.

Multidrug resistance (MDR) is a common and leading cause of treatment failure and mortality in cancer patient. While numerous biological processes contribute to drug resistance, recent studies have revealed that most anticancer drugs rapidly bind to and damage newly synthesized proteins upon entering cells. This process, termed acute drug protein damage (ADPD), occurs before the drugs act on their canonical targets. To evade the lethal effects of ADPD, cancer cells rapidly initiate a series of protective responses collectively termed the protein damage response (PDR). This cascade includes damage recognition via protein ubiquitination, damage clearance through the proteasome system, and subsequent mitophagy to remove damaged mitochondria caused by the co-import of drugs and damaged proteins. Here, we review the current understanding of multiple biological processes underlying drug resistance, with a focus on the mechanisms of ADPD induced by anticancer drugs, the pivotal role of PDR in driving MDR, and its potential applications in predicting and overcoming drug resistance.

Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique that probes microvascular blood flow in deep tissues. Here, we present and validate a new on-chip hardware correlator for high-speed DCS measurements. The correlator is embedded in a custom-built [Formula: see text] single-photon avalanche diode (SPAD) array named ATLAS, which computes intensity autocorrelation functions directly on-chip at a sampling rate of 116[Formula: see text]Hz — the fastest DCS acquisition reported to date. Unlike conventional DCS systems that suffer from low light throughput and therefore cannot resolve cardiac pulsations at source-detector separations ([Formula: see text]) beyond 30[Formula: see text]mm, our massively parallel on-chip architecture computes autocorrelations within each macropixel, eliminating the data-throughput bottleneck. This enables high-SNR, real-time detection of pulsatile blood flow even at [Formula: see text] [Formula: see text]mm on the human forehead. In phantom experiments at [Formula: see text] [Formula: see text]mm, ATLAS-DCS achieves a 12-fold improvement in signal-to-noise ratio over a conventional single-channel DCS instrument while operating at 116[Formula: see text]Hz. In human subjects, we resolve functional hyperemia during a mental arithmetic task at [Formula: see text] [Formula: see text]mm. Furthermore, we integrate ATLAS-DCS with a frequency-domain near-infrared spectroscopy (FD-NIRS) module, enabling simultaneous monitoring of blood flow and tissue oxygenation. With this combined system, we can concurrently resolve core hemodynamic parameters. The on-chip parallelized DCS design substantially improves detection speed, depth sensitivity, and real-time capability, paving the way for wearable, high-speed cerebral blood flow monitoring in both clinical and research settings.

Series-type hybrid railway power conditioners (S-HRPCs) have been proposed to compensate the negative sequence currents (NSCs) and reactive power in the railway power systems (RPSs) with reduced converter capacity. However, their practical application is limited by mismatched intermediate DC voltages. To address this issue, a novel thyristor-controlled LC-coupled (TCLC) hybrid cascaded active power quality conditioner (HC-APQC) is proposed in this article. Owing to its distributed DC capacitor configuration, the HC-APQC inherently eliminates the DC voltage mismatch issue. Furthermore, leveraging the adaptive impedance of the TCLC, the HC-APQC significantly reduces both the required operating DC voltage and the capacity of the cascaded converter. The operating principle of the TCLC is first analyzed, followed by an investigation of the DC voltage mismatch mechanism in S-HRPCs. Then, a two-stage power flow distribution strategy is developed to determine the HC-APQC reference currents for NSC and reactive power compensation under fluctuating traction loads and unbalanced grid conditions. Considering converter capacity constraints, the proposed strategy ensures real-time cascaded submodules (SMs) capacitor voltage equalization and current limitation within the rated range. Finally, experimental results are presented to validate the effectiveness of the proposed system and its control strategy, demonstrating that the HC-APQC achieves a 45% reduction in the required DC voltage compared with conventional APQCs.

In the Net Zero Emissions by 2050 Scenario, aviation electrification serves as a promising approach to improving energy efficiency and reducing greenhouse gas emissions. We present a data-driven protocol that forecasts the spatiotemporal distribution of energy-use flows across China's air transportation network. Using flight records aggregated monthly, we construct airport-level networks and generate scenario-specific energy-flow predictions with preference structures. We train and evaluate a multi-output machine learning framework of (1) classifiers for dual link prediction (addition, retention, and removal) and (2) regressors for weight (energy-demand) prediction, identifying suitable models for non-simultaneous, long-horizon forecasting. Results show that strengthened networks exhibit a bias toward rising energy demand, with hubs concentrating intensive flows, while non-hubs struggle to sustain links. This provides evidence for policies on electric plane deployment and airport-to-grid coordination.