Synthetic cells (SynCells) are artificial constructs designed to mimic cellular functions, offering insights into fundamental biology, as well as promising impact in the fields of medicine, biotechnology, and bioengineering. Achieving a functional SynCell from the bottom up, i.e. by assembling it from molecular components, requires a global collaboration to overcome the many challenges of engineering and assembling life-like modules while addressing biosafety, equity, and ethical concerns in order to guide responsible innovation. Here, we highlight major scientific hurdles, such as the integration of functional modules by ensuring compatibility across diverse synthetic subsystems, and we propose strategies to advance the field.

Large data centers are currently the mainstream infrastructures for big data processing. As one of the most fundamental tasks in these environments, the efficient execution of distributed data operators (e.g., join and aggregation) are still challenging current data systems, and one of the key performance issues is network communication time. State-of-the-art methods trying to improve that problem focus on either application-layer data locality optimization to reduce network traffic or on network-layer data flow optimization to increase bandwidth utilization. However, the techniques in the two layers are totally independent from each other, and performance gains from a joint optimization perspective have not yet been explored. In this article, we propose a novel approach called NEAL (NEtwork-Aware Locality scheduling) to bridge this gap, and consequently to further reduce communication time for distributed big data operators. We present the detailed design and implementation of NEAL, and our experimental results demonstrate that NEAL always performs better than current approaches for different workloads and network bandwidth configurations.

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

With urban population rapidly growing, traffic congestion is becoming a major problem. In this paper, a framework is proposed for identifying the spatial congested partitions in a dynamic urban road network and for monitoring the temporal changes in their locations and structure. To that end, a given road network is transformed into a suitable graph representation, an initial partitioning based on a spectral clustering approach is performed, and then the partitions continue to be updated incrementally on the basis of the newly obtained traffic data at each new time point. The congested partitions are then identified on the basis of traffic measures (e.g., volume and green time utilization) available from the traffic signal control system. Experiments with the proposed method are conducted with real historical traffic data collected from the 493 signalized traffic sites in Melbourne, Victoria, Australia, with a total of 1,444 road segments and 581 intersection points. Experimental results show that large-scale urban traffic networks undergo many rapid but regular and frequent traffic patterns, which often go unnoticed by the traffic network operators. Tracking these kinds of changes in real time by means of the proposed framework can improve the reaction time of the traffic management team and result in less congestion.

The evolution of the microstructure and mechanical properties in a low-density, low-alloy steel containing 6.57Al-3.34Mn-0.18C (wt.%) has been investigated as a function of processing. The steel was designed to have a duplex microstructure with ferrite as the major phase and austenite as the minor phase within the temperature range 800-1350 °C. The steel was processed to sheet form, which was thermomechanically treated and characterised by a variety of techniques such as optical metallography, scanning electron microscopy, electron backscatter diffraction, electron probe microanalysis, X-ray diffraction, transmission electron microscopy, tensile testing and density and elastic modulus measurements. The amount of austenite was found to decrease with an increase in the annealing temperature. While C and Mn partitioned into the austenite phase, Al partitioned into the ferrite. The tensile elongation increased with an increase in the amount of austenite in the microstructure. However, the austenite showed only a small transformation induced plasticity effect during tensile deformation due to its high stability. After annealing, mostly κ-carbide precipitates with a (Fe + Mn)/Al ratio of 3.6 appeared in the ferrite matrix, along with some cementite and complex carbides. These precipitates became coarser (330 nm) during a short time overageing treatment at 400 °C, causing a significant increase in elongation. The κ-carbides were found to have a close to Nishiyama-Wasserman type orientation relationship with the ferrite matrix. Although the Young's modulus of the steel dropped due to the large amount of Al added, the reduced density was found to be beneficial for automotive applications, overriding the effect of a drop in Young's modulus.

Fe-Al solid-solution alloys, with interstitial-free matrix to avoid detrimental κ-carbides, have been investigated in the context of low-density steels for automotive applications. The mechanical properties of the 6.8 wt.% Al-containing alloy were found to be comparable to those of dual-phase steel but with the benefit of reduced density and better formability. Future work on these alloys should concentrate on improving the Young's modulus and the deep drawability.