The Hong Kong University of Science and Technology

Introducing nonlinearity into vortex-induced vibration (VIV) piezoelectric energy harvesters (PEHs) can enlarge bandwidths and improve energy harvesting efficiency. Through the analogy between mechanical and electrical domains, the mechanical model of the PEH can be equivalently represented by a circuit model, and the influences of the interface circuits on the energy harvester effect can be studied more conveniently. In this article, a magnetically coupled nonlinear VIVPEH prototype is first developed and tested in the wind tunnel. Second, the equivalent circuit model is established to study the performance of nonlinear VIVPEH. The simulation results are compared with the experimental ones for verification. Finally, the nonlinear VIVPEH is shunted to a simple ac circuit, a standard dc circuit, and SSHI interface circuits to investigate the effects of different interface circuits. The results show that the bistable nonlinear structure can increase the working bandwidth of the VIVPEH, indicating at least an 114.3% improvement over the monostable one. The P-SSHI circuit interface can effectively increase the average power output of the VIVPEH by 65.04% and 174.32% compared to the ac and dc circuits. The work in this article provides valuable insights and guidelines for designing efficient nonlinear VIVPEHs using magnetic coupling and advanced interface circuits.

Förster Resonance Energy Transfer (FRET) is a non‐radiative energy transfer process in a donor‐acceptor system and has applications in various fields, such as single‐molecule investigations, biosensor creation, and deoxyribonucleic acid (DNA) mechanics research. The investigation of FRET processes in metal halide perovskites has also attracted great attention from the community. The review aims to provide an up‐to‐date study of FRET in the context of perovskite systems. First, we discuss the fundamentals of FRET process, and then summarize the recent progress of FRET phenomenon in perovskite‐perovskite, perovskite‐inorganic fluorophores, perovskite‐organic fluorophores, and organic fluorophores‐perovskite systems. Finally, we speculate on the future prospects of roles of FRET in the implications for the overall performance of optoelectronic devices based on these systems, as well as the challenges in maximizing FRET efficiency.

Large shear deformations can induce structural changes within crystals, yet the microscopic kinetics underlying these transformations are difficult for experimental observation and theoretical understanding. Here, we drive shear-induced structural transitions from square lattices to triangular (△) lattices in thin-film colloidal crystals and directly observe the accompanying kinetics with single-particle resolution inside the bulk crystal. When the oscillatory shear strain amplitude 0.1 ≤ γm < 0.4, △-lattice nuclei are surrounded by a liquid layer throughout their growth due to localized shear strain at the interface. Such virtual melting at crystalline interface has been predicted in theory and simulation, but have not been observed in experiment. The mean liquid layer thickness is proportional to the shear which can be explained by the Lindemann melting criterion. This provides an alternative explanation on virtual melting.

Quantum information science has garnered significant attention due to its potential in solving problems that are beyond the capabilities of classical computations based on integrated circuits. At the heart of...

Objective Alzheimer's disease (AD) often coexists with cerebrovascular diseases. However, the impact of cerebrovascular diseases such as stroke on AD pathology remains poorly understood. Methods This study examines the correlation between cerebrovascular diseases and AD pathology. The research was carried out using clinical and neuropathological data collected from the National Alzheimer's Coordinating Center (NACC) database and an animal model in which bilateral common carotid artery stenosis surgery was performed, following the injection of tau seeds into the brains of wild‐type mice. Results Analysis of the NACC database suggests that clinical stroke history and lacunar infarcts are associated with lower neurofibrillary tangle pathology. An animal model demonstrates that chronic cerebral hypoperfusion reduces tau pathology, which was observed in not only neurons but also astrocytes, microglia, and oligodendrocytes. Furthermore, we found that astrocytes and microglia were activated in response to tau pathology and chronic cerebral hypoperfusion. Additionally, cerebral hypoperfusion increased a lysosomal enzyme, cathepsin D. Interpretation These data together indicate that cerebral hypoperfusion reduces tau accumulation likely through an increase in microglial phagocytic activity towards tau and an elevation in degradation through cathepsin D. This study contributes to understanding the relationship between tau pathology and cerebrovascular diseases in older people with multimorbidity.

In diabetes mellitus (DM), the prevalence of gastrointestinal (GI) complications, including constipation, diarrhoea, gastroparesis, and/or enteropathy, can be up to ~75%. In this study, we compared three zebrafish larvae models of DM and established an analytical protocol for GI motility. Larvae were fed with either a standard diet (SD; control), or one of three diets to induce a DM-like phenotype: excessive feeding of SD food (ED), a high-fat diet (HFD), or exposing SD-fed larvae to 30 mmol/L glucose (SDG). DM was confirmed using a body-mass index, assessment of adipose deposit areas, two glucose assays, and one insulin assay. An analytical technique, whereby GI motility was quantified using pixel differences to track displacement along the centreline of the anterior, middle, and posterior intestine (AI, MI, and PI, respectively), was developed. Our results indicated that clear DM-like traits were observed in the HFD and SGD models, but not the ED model. In the SD controls, the AI showed similar anterograde and retrograde contractions indicating normal GI mixing; the MI exhibited more prominent forward contractions, and the PI showed distinct rectal waves. Compared to the SD, the HFD and SDG models exhibited significantly increased and decreased contraction velocities and could be used as models of diarrhoea and constipation in DM, respectively, while the ED model showed comparatively little change in motility. Together, these data indicate that complex changes in GI motility are associated with diet and therapeutics used to alleviate GI complications in DM should take these into account. Ultimately, the HFD and SDG models can be used to investigate different aspects of GI motility in association to DM. Hence, zebrafish are a useful model for studying GI dysfunctions due to DM and/or DM medication side-effects.

Transformation models have been widely used in geotechnical engineering to relate data from lab or field tests (e.g., cone penetration tests, CPT) to design parameters required in geotechnical analysis and design. Proper selection of transformation models is crucial but challenging for accurate prediction of geotechnical responses (e.g., reclamation-induced settlement) in practice. This study proposes a general machine learning framework that accommodates a wide variety of existing CPT-based transformation models and uses field monitoring data (e.g., settlement data observed from a specific project) to select suitable transformation models for improving prediction of spatiotemporally varying reclamation-induced settlement. The proposed approach takes advantage of sparse dictionary learning (SDL) and achieves prediction of settlement by a linear weighted sum of dictionary atoms that are constructed using outputs from finite element models (FEM) of reclamation-induced consolidation. Input parameters of the FEM models are determined using existing transformation models in literature. A transformation model database that relates multiple soil consolidation parameters with CPT data is also compiled for consolidation analysis and dictionary construction in SDL. The proposed approach is illustrated using a real reclamation project in Hong Kong. Results show that the proposed approach provides an effective and transparent vehicle to leverage existing abundant transformation models, identify appropriate transformation models using field monitoring data, and improve prediction of spatiotemporally varying reclamation-induced settlement, with greatly reduced prediction uncertainty. The transformation model selection and settlement prediction are also improved continuously as more field monitoring data are obtained.

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.

Federated Learning (FL) has gained considerable attention recently, as it allows clients to cooperatively train a global machine learning model without sharing raw data. However, its performance can be compromised due to the high heterogeneity in clients' local data distributions, commonly known as Non-IID (non-independent and identically distributed). Moreover, collaboration among highly dissimilar clients exacerbates this performance degradation. Personalized FL seeks to mitigate this by enabling clients to collaborate primarily with others who have similar data characteristics, thereby producing personalized models. We noticed that existing methods for assessing model similarity often do not capture the genuine relevance of client domains. In response, our paper enhances personalized client collaboration in FL by introducing a metric for domain relevance between clients. Specifically, to facilitate optimal coalition formation, we measure the marginal contributions of client models using coalition game theory, providing a more accurate representation of potential client domain relevance within the FL privacy-preserving framework. Based on this metric, we then adjust each client's coalition membership and implement a personalized FL aggregation algorithm that is robust to Non-IID data domain. We provide a theoretical analysis of the algorithm's convergence and generalization capabilities. Our extensive evaluations on multiple datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, and under varying Non-IID data distributions (Pathological and Dirichlet), demonstrate that our personalized collaboration approach consistently outperforms contemporary benchmarks in terms of accuracy for individual clients.

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, researchers proposed solar-powered designs, equipped with rechargeable energy storage such as a supercapacitor. However, accurately monitoring the energy status - an essential step for device maintenance - has shown to be a major concern. Existing energy status monitoring methods, which are either crowd-assisted or require on-site data collection, suffer from severe losses of energy status information. This paper presents an energy status recovery framework with support vector regression (SVR) to address this issue. The proposed framework leverages recurrence training of SVR with lost energy status information to capture features from discharge behavior, achieving high accuracy while minimizing training and prediction time. Multiple real-life BLE beacon energy level records are evaluated to demonstrate that our proposed framework can recover the energy information with at least 98% accuracy under a data loss rate of up to 99%.