Xylan cinnamoylation for reinforcing poly (butylene adipate-co-terephthalate): Molecule design and interaction optimization.

PBAT composites with biomass fillers have gained considerable attention as alternatives to non-biodegradable plastics. This work employed xylan derivatives as fillers for PBAT composites. Xylan was modified by introducing cinnamoyl side groups which limit the hydrogen bonding and construct π-π stacking interactions with PBAT chains. The resultant xylan cinnamates (XCi) show degree of substitution (DS) of 0.55-1.89, glass-transition temperatures (Tg) of 146.5-175.0 °C and increased hydrophobicity, which can be simply controlled by varying the molar ratio of reactants. NMR results demonstrate that the C3-OH of xylopyranosyl unit is more accessible to cinnamoylation. XCi fillers (30-50 wt%) were incorporated into PBAT through melt compounding. The filler with a DS of 0.97 exhibited the optimal reinforcing effect, showing superior tensile strength (19.4 MPa) and elongation at break (330.9 %) at a high filling content (40 wt%), which is even beyond the neat PBAT. SEM and molecular dynamics simulation suggest improved compatibility and strengthened molecular interaction between XCi and PBAT, which explains the suppressed melting/crystallization behavior, the substantial increase in Tg (-34.5 â†’ -1.8 °C) and the superior mechanical properties of the composites. This research provides valuable insights into the preparation of high-performance composites by designing the molecular architecture of xylan and optimizing the associated interactions.

Many-Objective Evolutionary Algorithm With Reference Point-Based Fuzzy Correlation Entropy for Energy-Efficient Job Shop Scheduling With Limited Workers.

Because of COVID-19, factories are facing many difficulties, such as shortage of workers and social alienation. How to improve production performance under limited labor resources is an urgent problem for global manufacturing factories. This work studies an energy-efficient job-shop scheduling problem with limited workers. Those workers can have multiskills. A many-objective model with five objectives, that is: 1) makespan; 2) total tardiness; 3) total idle time; 4) total worker cost; and 5) total energy, is built. To solve this many-objective optimization problem (MaOP), a novel fitness evaluation mechanism (FEM) based on fuzzy correlation entropy (FCE) is adopted. Two construction methods for reference points are proposed to build the bridge between MaOP and a fuzzy set. Based on FCE and cluster methods, an environmental selection mechanism (ESM) is proposed to achieve a balance between solution convergence and diversity. With the proposed FEM and ESM, two many-objective evolutionary algorithms are proposed to solve MaOP. The effect of FCE-based FEM and ESM on the performance of algorithms is verified via experiments. The proposed algorithms are compared with four well-known peers to test their performance. The extensive experimental results show that they are very competitive for the considered many-objective scheduling problem.

Use of multiple light sources to enhance the resolution of point light source displays.

The point light sources (PLSs) of integral imaging displays have a wide depth range; however, the resolution is very low. We developed resolution-enhanced PLS displays using multiple light sources that create extra PLSs in the PLS plane. Given aberrations in the lens arrays, the PLSs initially appeared on planes and at distances that differed from the theoretical values. We thus determined the distances between adjacent light sources that compensated for the aberrations. Experimentally, our method enhanced the resolution fourfold compared to that of a conventional PLS display in both vertical and horizontal directions. Our approach allows facile compensation of lens array aberrations and is applicable to 3D displays.

Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status.

Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.

An Information Theory Inspired Real-Time Self-Adaptive Scheduling for Production-Logistics Resources: Framework, Principle, and Implementation.

The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent factory is random, such as the arrival of customers' jobs, and fuzzy, such as the processing time of Production-Logistics Resources. Besides, the coordination of production and logistic resources in a flexible workshop is also a hot issue. The availability of this information will enhance the quality of making scheduling decisions. However, when and how to use this information to realize the adaptive collaboration of Production-Logistics Resources are vital issues. Therefore, this paper studies the above problems by establishing a real-time reaction scheduling framework of Production-Logistics Resources dynamic cooperation. Firstly, a real-time task triggering strategy to maximize information utilization is proposed to explore when to use real-time information. Secondly, a collaborative method for Production-Logistics Resources is studied to explore how to use real-time information. Thirdly, a real-time self-adaptive scheduling algorithm based on information entropy is utilized to obtain a stable and feasible solution. Finally, the effectiveness and advancement of the proposed method are verified by a practical case.

"Signal-on" SERS sensing platform for highly sensitive and selective Pb2+ detection based on catalytic hairpin assembly.

In this work, a surface-enhanced Raman scattering (SERS) sensing strategy was proposed for the analysis of lead ion (Pb2+) with high sensitivity and specificity based on the high specificity of the catalytic nucleic acids (DNAzymes) to Pb2+ and the catalytic hairpin assembly (CHA) amplification. First, the Pb2+-DNAzyme initiated the reaction by target Pb2+ and released a linear DNA (rS1). Second, the hairpin DNA 1 (H1) was immobilized on the SERS substrate surface via Ag-S bond. Then, the rS1 could cyclically trigger the allosteric effects of CHA amplification and the H1 was opened and then the R6G-labeled hairpin probe 2 (H2) hybridized with H1 to form H1-H2 double-stranded and the released rS1 could initiate the next cycle of CHA reaction. This process made the Raman tag of R6G close to the surface of SERS substrate, and the intensity of SERS signal from R6G labels increase with the increase of concentration of target Pb2+. Benefiting from outstanding performances of the Pb2+-specific DNAzymes and enzyme-free CHA amplification system, this biosensor exhibits good sensitivity for Pb2+ with a limit of detection of 0.42 pM. More importantly, this developed detection platform could be employed for reliable analysis of Pb2+ in real environment system.

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Hybrid III-V/silicon laser with laterally coupled Bragg grating.

In this paper, we demonstrate a compact electrically pumped distributed-feedback hybrid III-V/silicon laser with laterally coupled Bragg grating for the first time to the best of our knowledge. The hybrid laser structure consists of AlGaInAs/InP multi-quantum-well gain layers on top of a laterally corrugated silicon waveguide patterned on a silicon on insulator (SOI) substrate. A pair of surface couplers is integrated at the two ends of the silicon waveguide for the optical coupling and characterization of the ouput light. Single wavelength emission of ~1.55µm with a side-mode-suppression- ratio larger than 20dB and low threshold current density of 1.54kA/cm(2) were achieved for the device under pulsed operation at 20 °C.

Passively mode-locked III-V/silicon laser with continuous-wave optical injection.

We demonstrate electrically pumped two-section mode locked quantum well lasers emitting at the L-band of telecommunication wavelength on silicon utilizing die to wafer bonding techniques. The mode locked lasers generate pulses at a repetition frequency of 30 GHz with signal to noise ratio above 30 dB and 1 mW average output power per facet. Optical injection-locking scheme was used to improve the noise properties of the pulse trains of passively mode-locked laser. The phases of the mode-locked frequency comb are shown to be coherent with that of the master continuous-wave (CW) laser. The radio-frequency (RF)-line-width is reduced from 7.6 MHz to 150 kHz under CW optical injection. The corresponding pulse-to-pulse jitter and integrated RMS jitter are 29.7 fs/cycle and 1.0 ps, respectively. The experimental results demonstrate that optical injection can reduce the noise properties of the passively mode locked III-V/Si laser in terms of frequency linewidth and timing jitter, which makes the devices attractive for photonic analog-to-digital converters and clock generation and recovery.

Link Clustering to Explore Brain Dynamics Using Resting State Functional MRI.

Quantitative network analysis of brain networks has been an important tool for characterizing brain function. Cognitive abilities emerge from coordinated activity of distributed brain regions that may participate in multiple networks at different times. However, neuroimaging has few available tools to model and quantify networks with spatially overlapping nodes that are active at different times. The dynamics of network reconfiguration may yield important insight into networks that are damaged with neurodegenerative disease. We describe here an approach that uses a graph analytic technique called link clustering, which identifies communities that have overlapping functional nodes, demonstrating its ability to highlight differences in the dynamic reorganization of networks between subjects with Alzheimer's dementia and normal controls.

High-brightness 1.3 µm InAs/GaAs quantum dot tapered laser with high temperature stability.

We demonstrate high-brightness 1.3 µm tapered lasers with high temperature stability by using p-doped InAs/GaAs quantum dots (QDs) as the active region. It is found that the beam quality factor M(2) for the devices is almost unchanged as the light power and temperature increase. The almost constant M(2) results from the p-doped QD active region.