Electrochemical stereoselective synthesis of polysubstituted 1,4-dicarbonyl Z-alkenes via three-component coupling of sulfoxonium ylides and alkynes with water.

The first straightforward strategy for the synthesis of 1,4-dicarbonyl Z-alkenes has been developed via an electrochemical cross-coupling reaction of sulfoxonium ylides and alkynes with water. The metal-free protocol showed an easy-to-handle nature, good functional group tolerance, and high Z-stereoselectivity, which is rare in previous cases. The proposed reaction mechanism was convincingly established by carrying out a series of control experiments, cyclic voltammetry experiments, and density functional theory (DFT) studies.

Electrochemical Allylic C(sp3)-H Isothiocyanation via [3,3]-Sigmatropic Rearrangement.

The direct allylic C(sp3)-H functionalization provides a straightforward protocol for the synthesis of valuable molecules. We report herein the first chemo- and site-selective method for allylic C(sp3)-H isothiocyanation of various internal alkenes under mild electrochemical conditions. This method exhibits broad functional group tolerance and excellent selectivity and can be applied for late-stage isothiocyanation of bioactive molecules. Combined experimental and computational studies indicate that the reaction proceeds via an unexpected [3,3]-sigmatropic rearrangement.

Electrochemical Benzylic C-H Amination via N-Aminopyridinium.

An electrochemical protocol for benzylic C(sp3)-H aminopyridylation via direct C-H/N-H cross-coupling of alkylarenes with N-aminopyridinium triflate has been developed. This method features excellent site-selectivity, broad substrate scope, redox reagent-free and facile scalability. The generated benzylaminopyridiniums can be readily converted to benzylamines via electroreductive N-N bond cleavage.

Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture.

Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.

Review of Physical Layer Security in Molecular Internet of Nano-Things.

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts.

Drone's Objective Inference Using Policy Error Inverse Reinforcement Learning.

Drones are set to penetrate society across transport and smart living sectors. While many are amateur drones that pose no malicious intentions, some may carry deadly capability. It is crucial to infer the drone's objective to prevent risk and guarantee safety. In this article, a policy error inverse reinforcement learning (PEIRL) algorithm is proposed to uncover the hidden objective of drones from online data trajectories obtained from cooperative sensors. A set of error-based polynomial features are used to approximate both the value and policy functions. This set of features is consistent with current onboard storage memories in flight controllers. The real objective function is inferred using an objective constraint and an integral inverse reinforcement learning (IRL) batch least-squares (LS) rule. The convergence of the proposed method is assessed using Lyapunov recursions. Simulation studies using a quadcopter model are provided to demonstrate the benefits of the proposed approach.

All-inorganic perovskite solar cells featuring mixed group IVA cations.

All-inorganic perovskites are promising for solar cells owing to their potentially superior tolerance to environmental factors, as compared with their hybrid organic-inorganic counterparts. Over the past few years, all-inorganic perovskite solar cells (PSCs) have seen a dramatic improvement in certified power conversion efficiencies (PCEs), demonstrating their great potential for practical applications. Pb, Sn, and Ge are the most studied group IVA elements for perovskites. These group IVA cations share the same number of valence electrons and similarly exhibit the beneficial antibonding properties of lone-pair electrons when incorporated in the perovskite structure. Meanwhile, mixing these cations in all-inorganic perovskites provides opportunities for stabilizing the photoactive phase and tailoring the bandgap structure. In this mini-review, we analyze the structural and bandgap design principles for all-inorganic perovskites featuring mixed group IVA cations, discuss the updated progress in the corresponding PSCs, and finally provide perspectives on future research efforts faciliating the continued development of high-performance Pb-less and Pb-free all-inorganic PSCs.

A guide to organic electroreduction using sacrificial anodes.

Organic electrosynthesis is a green strategy for the synthesis of valuable molecules. Electrochemical reactions using sacrificial metal anodes enable new reactivity to be uncovered that could not be achieved with traditional non-electrochemical methods. Compared with reactions using metal powder as the reducing reagent, the mild electroreduction protocols usually exhibit diverse reactivity and excellent selectivity. The inexpensive metal anodes possess low oxidation potential, which could prevent undesired overoxidation of substrates, active intermediates and products. The in situ generated metal ions from sacrificial anodes could not only serve as Lewis acids to activate the reactants but also as a promoter or mediator. This tutorial review highlights the recent achievements in this rapidly growing area within the past five years. The sacrificial anode-enabled electroreductions are discussed according to the reaction type.

Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary polynomial chaos.

Complex systems derive sophisticated behavioral dynamics by connecting individual component dynamics via a complex network. The resilience of complex systems is a critical ability to regain desirable behavior after perturbations. In the past years, our understanding of large-scale networked resilience is largely confined to proprietary agent-based simulations or topological analysis of graphs. However, we know the dynamics and topology both matter and the impact of model uncertainty of the system remains unsolved, especially on individual nodes. In order to quantify the effect of uncertainty on resilience across the network resolutions (from macro-scale network statistics to individual node dynamics), we employ an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node. We test this using both a generic networked bi-stable system and also established ecological and work force commuter network dynamics to demonstrate applicability. This framework will aid practitioners to both understand macro-scale behavior and make micro-scale interventions.

Hippocampus experience inference for safety critical control of unknown multi-agent linear systems.

Risk mitigation is usually addressed in simulated environments for safety critical control. The migration of the final controller requires further adjustments due to the simulation assumptions and constraints. This paper presents the design of an experience inference algorithm for safety critical control of unknown multi-agent linear systems. The approach is inspired in the close relationship between three main areas of the brain cortex that enables transfer learning and decision making: the hippocampus, the neocortex, and the striatum. The hippocampus is modelled as a stable linear model that communicates to the striatum how the real-world system is expected to behave. The hippocampus model is controlled by an adaptive dynamic programming (ADP) algorithm to achieve an optimal desired performance. The neocortex and the striatum are designed simultaneously by an actor control policy algorithm that ensures experience inference to the real-world system. Experimental and simulations studies are carried out to verify the proposed approach.

A Closed-Loop Output Error Approach for Physics-Informed Trajectory Inference Using Online Data.

While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.

Sim2Real: Generative AI to Enhance Photorealism through Domain Transfer with GAN and Seven-Chanel-360°-Paired-Images Dataset.

This work aims at providing a solution to data scarcity by allowing end users to generate new images while carefully controlling building shapes and environments. While Generative Adversarial Networks (GANs) are the most common network type for image generation tasks, recent studies have only focused on RGB-to-RGB domain transfer tasks. This study utilises a state-of-the-art GAN network for domain transfer that effectively transforms a multi-channel image from a 3D scene into a photorealistic image. It relies on a custom dataset that pairs 360° images from a simulated domain with corresponding 360° street views. The simulated domain includes depth, segmentation map, and surface normal (stored in seven-channel images), while the target domain is composed of photos from Paris. Samples come in pairs thanks to careful virtual camera positioning. To enhance the simulated images into photorealistic views, the generator is designed to preserve semantic information throughout the layers. The study concludes with photorealistic-generated samples from the city of Paris, along with strategies to further refine model performance. The output samples are realistic enough to be used to train and improve future AI models.

Electrochemically Promoted [3 + 2] Annulation of Imidazo[1,2-a]pyridine with Alkynes.

The efficient electrochemically promoted [3 + 2] annulation of imidazo[1,2-a]pyridines with alkynes using traceless electrons as green reagents has been developed, leading to the synthesis of a large class of polycyclic heteroaromatics in good yields with a broad substrate scope under mild and green conditions. The scaled-up experiment, follow-up procedures, and potential biological applications show the practicability and feasibility of the electrochemical method.

Synthesis of pyrrole disulfides via umpolung of ß-ketothioamides.

A Na2CO3-promoted reaction of ß-ketothioamides (KTAs) and cyanoacetates was developed for the synthesis of pyrrole disulfides using air as a green oxidant. This protocol features a broad substrate scope and mild reaction conditions. Preliminary mechanistic studies indicate that the reaction involves a tandem unusual umpolung of KTAs, N-cyclization, tautomerization and oxidative coupling process.

Graph Layer Security: Encrypting Information via Common Networked Physics.

The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high computational power and is not suitable for low-power IoT scenarios. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose, for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for digital twins in adversarial radio environments.

Electrochemical Benzylic C(sp3)-H Isothiocyanation.

Selective C(sp3)-H isothiocyanation represents a significant strategy for the synthesis of isothiocyanate derivatives. We report herein an electrochemical benzylic isothiocyanation in a highly chemo- and site-selective manner under external oxidant-free conditions. The high chemoselectivity is attributed to the facile in situ isomerization of benzylic thiocyanates to isothiocyanates. Notably, the method exhibits high functional group compatibility and is suitable for late-stage functionalization of bioactive molecules.

Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks.

Trophic coherence, a measure of a graph's hierarchical organisation, has been shown to be linked to a graph's structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex's ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.

Visible light photoredox-catalysed remote C-H functionalisation enabled by 1,5-hydrogen atom transfer (1,5-HAT).

The generation of heteroatom-centred radicals (X˙), followed by intramolecular 1,5-hydrogen atom transfer (1,5-HAT) and the functionalisation of the translocated carbon-centred radicals, is the basic mechanism of the classic Hofmann-Löffler-Freytag (HLF) reaction and the Barton reaction. The chemoselectivity of the 1,5-HAT process is different from that of the transition metal-catalysed counterpart, providing, therefore, a complementary tool for remote C(sp3)-H bond functionalisation. There is a recent resurgence in this research field due to the emergence of visible light photocatalysis. This tutorial review summarises the recent progress in the remote functionalisation of C-H bonds featuring a key 1,5-HAT step with particular focus on photoredox-catalyzed remote C-H functionalisation.

Selective 1,2-Aminoisothiocyanation of 1,3-Dienes Under Visible-Light Photoredox Catalysis.

Selective three-component 1,2-diamination of 1,3-dienes with concurrent introduction of two orthogonally protected amino groups remains unknown despite its significant synthetic potential. We report herein that reaction of conjugated dienes with N-aminopyridinium salts and TMSNCS affords 1,2-aminoisothiocyanation products in a highly chemo- and regio-selective manner under mild photoredox catalytic conditions. Mechanistic studies indicate that the facile isomerization of allyl thiocyanates to allyl isothiocyanates under photocatalytic conditions is responsible for the selective formation of the observed products. The mild isomerization protocol is expected to find applications in the synthesis of allyl isothiocyanates in a broad sense.

Random sketch learning for deep neural networks in edge computing.

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50-90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications.