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Semiconductor nanostructures with low dimensionality like quantum dots and quantum dashes are one of the best attractive and heuristic solutions for achieving high performance photonic devices. When one or more spatial dimensions of the nanocrystal approach the de Broglie wavelength, nanoscale size effects create a spatial quantization of carriers leading to a complete discretization of energy levels along with additional quantum phenomena like entangled-photon generation or squeezed states of light among others. This article reviews our recent findings and prospects on nanostructure based light emitters where active region is made with quantum-dot and quantum-dash nanostructures. Many applications ranging from silicon-based integrated technologies to quantum information systems rely on the utilization of such laser sources. Here, we link the material and fundamental properties with the device physics. For this purpose, spectral linewidth, polarization anisotropy, optical nonlinearities as well as microwave, dynamic and nonlinear properties are closely examined. The paper focuses on photonic devices grown on native substrates (InP and GaAs) as well as those heterogeneously and epitaxially grown on silicon substrate. This research pipelines the most exciting recent innovation developed around light emitters using nanostructures as gain media and highlights the importance of nanotechnologies on industry and society especially for shaping the future information and communication society., Quantum dot are one of the best practical examples of nanotechnologies. Owing to the discrete energy levels, quantum dot lasers output unique features like thermal stability, feedback insensitivity and spectral purity.

Transition metal catalysts for replacing noble metals have been extensively studied, but the deficiencies in intrinsic activity and stability limit their application in electrocatalysis. Here, we present CoNi alloy nanoparticles loaded on Ti

Speech emotion recognition is a challenging task in natural language processing. It relies heavily on the effectiveness of speech features and acoustic models. However, existing acoustic models may not handle speech emotion recognition efficiently for their built-in limitations. In this work, a novel deep-learning acoustic model called attention-based skip convolution bi-directional long short-term memory, abbreviated as SCBAMM, is proposed to recognize speech emotion. It has eight hidden layers, namely, two dense layers, convolutional layer, skip layer, mask layer, Bi-LSTM layer, attention layer, and pooling layer. SCBAMM makes better use of spatiotemporal information and captures emotion-related features more effectively. In addition, it solves the problems of gradient exploding and gradient vanishing in deep learning to some extent. On the databases EMO-DB and CASIA, the proposed model SCBAMM achieves an accuracy rate of 94.58% and 72.50%, respectively. As far as we know, compared with peer models, this is the best accuracy rate.

Targeted therapy showed broad application prospects in the treatment of various types of cancer. Through carriers such as aptamers, antibodies, proteins and peptides, targeted therapy can selectively deliver drugs into tumor cells. Compared with traditional treatment methods such as chemo- and radiotherapy, targeted drug delivery systems can reduce the toxic effects of drugs on normal cells and avoid adverse reactions. Herein, an aptamer-cyclometalated iridium(III) complex conjugate (ApIrC) has been designed and developed as a targeted anticancer agent. Owing to the targeting ability of aptamers, ApIrC specifically bound to nucleolin over-expressed on the surface of cancer cells and showed strong fluorescence signal for tumor imaging and diagnosis. ApIrC had more substantial cellular uptake in cancer cells than the iridium complex alone and exhibited favorable low toxicity to normal cells. After uptake by cells through endocytosis, ApIrC can selectively accumulated in mitochondria and induced caspase-3/7-dependent cell death. Remarkably, ApIrC can also specifically target 3D multicellular spheroids (MCSs) and show excellent tumor permeability. So, it can effectively reach the interior of MCSs and cause cell damage. To our knowledge, this is the first report of the aptamer-cyclometalated iridium(III) complex conjugate which studied for cancer targeted therapy. The developed conjugate has great potential to be developed as novel therapeutics for effective and low-toxic cancer treatment.

Photonic integrated circuits (PICs) have enabled numerous high performance, energy efficient, and compact technologies for optical communications, sensing, and metrology. One of the biggest challenges in scaling PICs comes from the parasitic reflections that feed light back into the laser source. These reflections increase noise and may cause laser destabilization. To avoid parasitic reflections, expensive and bulky optical isolators have been placed between the laser and the rest of the PIC leading to large increases in device footprint for on-chip integration schemes and significant increases in packaging complexity and cost for lasers co-packaged with passive PICs. This review article reports new findings on epitaxial quantum dot lasers on silicon and studies both theoretically and experimentally the connection between the material properties and the ultra-low reflection sensitivity that is achieved. Our results show that such quantum dot lasers on silicon exhibit much lower linewidth enhancement factors than any quantum well lasers. Together with the large damping factor, we show that the quantum dot gain medium is fundamentally dependent on dot uniformity, but through careful optimization, even epitaxial lasers on silicon can operate without an optical isolator, which is of paramount importance for the future high-speed silicon photonic systems.

57Fe-ZSM-5 catalysts were prepared by solid-state ion exchange of 57FeCl3 into H-ZSM-5, the exchange capacity of which had been blocked by Na+ or Ca2+ to ≈50, ≈25, and 0%. Their Fe site structure w...

To schedule wind power efficiently and to mitigate the adverse effects caused by wind's intermittency and variability, an advanced wind power prediction model is proposed in this paper. This model is a combined model via switching different data-driven chaotic time series models. First, inputs of this model come from the reconstructed data based on the chaotic characteristics of wind power time series. Second, three different data mining algorithms are used to construct wind power prediction models individually. To obtain a regime for switching optimal models, a Markov chain is trained. Then, weights of different data-driven modes are calculated by the Markov chain switching regime, and used in the final combined model for wind power prediction. The industrial data from actual wind farms is studied. Results of the proposed model are compared with that of non-reconstructed input data, traditional data-driven models and two typical combined models. These results validate the superiority of proposed model on improving wind power prediction accuracy.

This paper investigates the intensity noise and pulse oscillation characteristics of an InAs/GaAs quantum dot laser epitaxially grown on germanium. We show that the relative intensity noise of the free-running laser generally decreases with increasing pump current, and the minimum value reaches down to about $-$ 126 dB/Hz. The intensity noise is hardly affected by the optical feedback, unless there is a resonance or pulse oscillation in the noise spectrum. The laser pumped at a high current is more sensitive to the optical feedback. Interestingly, it is found that the free-running Ge-based quantum dot laser generates self-sustained pulse oscillations with one period or two periods upon the pump current, without incorporating saturable absorbers. This behavior is valuable for both self-generation of photonic microwaves and for understanding nonlinear dynamics of semiconductor lasers.

To mitigate the threat to power system caused by ramp events – large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. This approach contains two successive stages of work, including wind power forecasting and ramp detection. Considering high-performance ramp prediction requires long-term and accurate wind power prediction results; this study also proposes a hybrid prediction model at the first stage. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long-term trend prediction. Then, a multivariate model is built with a data-mining algorithm to correct system errors of the primary prediction, which is addressed to improve long-term prediction performance. At the second stage, a modified swinging door algorithm is applied for ramp detection. Performance of both the proposed long-term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach.

The nonlinear features of both semiconductor optical amplifiers (SOAs) and semiconductor lasers, which are made from the same InAs/GaAs quantum dot (QD) wafers, are investigated in detail. By employing pump-probe driven four-wave mixing as an experimental tool, the wave conversion process shows notably different profiles for the two types of devices. Due to the contributions of ultrafast, sub-picosecond mechanisms, such as carrier heating and spectral hole burning, the pump-probe frequency can be easily tuned to the THz range. SOAs generally benefit more from sub-picosecond carrier dynamics, hence exhibiting a higher conversion efficiency (CE) in the THz range, compared to their laser diode counterparts. The discrepancy even exceeds 10 dB. In addition, laser experiments yield some differences from the amplifier ones, hence leading to a higher nonlinear CE at small detuning ranges. These results strongly improve our insight into the fundamental nonlinear properties of InAs/GaAs QD material, and contribute to the conception of novel devices for future on-chip applications in all-optical communication networks, such as signal wavelength conversion, mode-locking, and optical frequency comb generation.

Nonalcoholic steatohepatitis (NASH) is a common chronic liver disease that is increasingly prevalent worldwide. Liver inflammation is an important contributor to disease progression from nonalcoholic fatty liver (NAFL) to NASH, but there is a lack of efficient therapies. In the current study we evaluated the therapeutic potential of givinostat, a histone deacetylase (HDAC) inhibitor, in the treatment of NASH in vivo and in vitro. Liver inflammation was induced in mice by feeding a methionine- and choline-deficient diet (MCD) or a fructose, palmitate, cholesterol diet (FPC). The mice were treated with givoinostat (10 mg·kg(−1)·d(−1), ip) for 8 or 10 weeks. At the end of the experiment, the livers were harvested for analysis. We showed that givoinostat administration significantly alleviated inflammation and attenuated hepatic fibrosis in MCD-induced NASH mice. RNA-seq analysis of liver tissues form MCD-fed mice revealed that givinostat potently blocked expression of inflammation-related genes and regulated a broad set of lipid metabolism-related genes. In human hepatocellular carcinoma cell line HepG2 and human derived fetal hepatocyte cell line L02, givinostat significantly decreased palmitic acid-induced intracellular lipid accumulation. The benefit of givinostat was further confirmed in FPC-induced NASH mice. Givinostat administration significantly attenuated hepatic steatosis, inflammation as well as liver injury in this mouse model. In conclusion, givinostat is efficacious in reversing diet-induced NASH, and may serve as a therapeutic agent for the treatment of human NASH.

Condition monitoring plays an important role in wind turbine maintenance, wherein monitoring turbines’ unhealthy statuses, mainly those not-runnable statuses causing turbines stop working, could be beneficial in both maintenance cost and wind power generation. A data-driven approach based on support vector data description (SVDD) and extreme learning machine (ELM) algorithms is proposed in this paper to realize effective monitoring on wind turbines’ unhealthy status. The SVDD algorithm is applied to separate data of unhealthy statuses from the healthy one. The ELM algorithm is used to construct a classifier for monitoring unhealthy (not-runnable) statuses of wind turbines. Industrial data from real wind farms are studied. Numerical results illustrate the feasibility of the proposed approach, and comparison studies with six monitoring algorithms validate that the proposed two-phase model could obtain better performance than other models.