Here, we illustrate what happens inside the catalytic cleft of an enzyme when substrate or ligand binds on single-millisecond timescales. The initial phase of the enzymatic cycle is observed with near-atomic resolution using the most advanced X-ray source currently available: the European XFEL (EuXFEL). The high repetition rate of the EuXFEL combined with our mix-and-inject technology enables the initial phase of ceftriaxone binding to the Mycobacterium tuberculosis β-lactamase to be followed using time-resolved crystallography in real time. It is shown how a diffusion coefficient in enzyme crystals can be derived directly from the X-ray data, enabling the determination of ligand and enzyme–ligand concentrations at any position in the crystal volume as a function of time. In addition, the structure of the irreversible inhibitor sulbactam bound to the enzyme at a 66 ms time delay after mixing is described. This demonstrates that the EuXFEL can be used as an important tool for biomedically relevant research.

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.

As the next wave of productivity, Industry 4.0 aims to enhance the competitiveness and efficiency of manufacturers by bridging the gap between industrial manufacturing and information technology. Through digitalization, it provides the advantage of enabling the real-time/near-real-time monitoring of manufacturing. This digital information allows monitoring tools such as Value stream mapping (VSM) to help the decision makers efficiently capture the non-value-adding processes on the factory floor. However, the application of VSM into small and medium sized enterprises (SMEs), including diverse manufacturing environments, is not an easy task. It is even more challenging especially when the product processing is more complicated and requires improvements to labour management and facility utilization. Conventional VSM is not competent to handle the contemporary rapid dynamic manufacturing environment, complex material flow or efficiency of machine and labour performance. These three are the most important resources on the shop floor to bring transparency to the decision maker. We present a multi-agent system composed of several cost effective embedded Arduino systems as agents and a Raspberry-Pi ® as a core agent. Equipped with Cyber-Physical System (CPS) technology, these agents, placed on or near the station, could reflect the non-linear material value flow without modelling the process or using RFID tags. Moreover, through the sensor node installed in each machine and by knowing the staff ID, the agents could send the relevant information in the form of dynamic value stream mapping (DVSM) in near-real-time for storage, analysis and visualization. We present a suitable visualization tool based in Node-RED ® to carry out DVSM.