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We report that an ErAs quantum-dot array in a GaAs matrix under 1550 nm pulsed excitation produces cooperative spontaneous emission—Dicke superradiance—in the terahertz frequency region at room temperature. Two key points pertain to the experimental evidence: (i) the pulsed THz emission power is much greater than the continuous wave (CW) photomixing power; and (ii) the ultrafast time-domain waveform displays ringing cycles. A record of ~117 μW pulsed THz power was obtained, with a 1550 nm-to-THz power conversion efficiency of ~0.2%.

Quantum networking and computing technologies demand scalable hardware with high-speed control for large systems of quantum devices. Solid-state platforms have emerged as promising candidates, offering scalable fabrication for a wide range of qubits. Architectures based on spin-photon interfaces allow for highly-connected quantum networks over photonic links, enabling entanglement distribution for quantum networking and distributed quantum computing protocols. With the potential to address these demands, optically-active spin defects in silicon are one proposed platform for building quantum technologies. Here, we electrically excite the silicon T centre in integrated optoelectronic devices that combine nanophotonic waveguides and cavities with p-i-n diodes. We observe single-photon electroluminescence from a cavity-coupled T centre with \\(g^{(2)}(0)=0.05(2)\\). Further, we use the electrically-triggered emission to herald the electron spin state, initializing it with \\(92(8)\\%\\) fidelity. This shows, for the first time, electrically-injected single-photon emission from a silicon colour centre and a new method of electrically-triggered spin initialization. These findings present a new telecommunications band light source for silicon and a highly parallel control method for T centre quantum processors, advancing the T centre as a versatile defect for scalable quantum technologies.

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform.

RF sputtered ZnO and Al:ZnO films are attractive transparent conductive oxides for fabrication of opto-electronic devices. In this paper we present efforts to control carrier concentration and mobility of ZnO/Al:ZnO thin films by controlling deposition parameters (RF power, pressure and substrate temperature. Al:ZnO thin film with resistivity as low as \\(\\rho\\) = \\(3.8\\times 10^{-4}\\) \\(\\Omega\\).cm at deposition temperature of 250{\\deg}C has been achieved. Zinc oxide thin film with low resistivity of \\(\\rho\\) = \\(3.7\\times 10^{-2}\\) \\(\\Omega\\).cm and high electron mobility of \\(30\\) \\(\\mathrm{cm^{-2}V^{-1}s^{-1}}\\) at deposition temperature of 250{\\deg}C with acceptable electronic parameters stability has been obtained.Light transmission of Al:ZnO and ZnO samples deposited on glass at different substrate temperature has been studied. Investigation were made to assess the effect of deposition temperature on the photoluminescence spectra (PL) of ZnO/Al:ZnO sputtered on silicon and glass substrate. The evolution of near band edge (NBE) and deep level emission (DLE) photoluminescence peaks with deposition temperature in ZnO/Al:ZnO sputtered on Silicon and glass substrate have been studied.

Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.

Tight carbonate reservoirs appear to be heterogeneous due to the patchy production of various digenetic properties. Consequently, the permeability calculation of tight rocks is costly, and only a finite number of core plugs in any single reservoir can be estimated. Hence, in the present study, a novel hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), i.e., IHHO, and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables. The proposed IHHO employs a mutation mechanism to avoid trapping in local optima by increasing the search capabilities. Subsequently, ELM-IHHO, a novel metaheuristic ELM-based algorithm, was developed to predict the permeability of tight carbonates. Experimental results show that the proposed ELM-IHHO attained the most accurate prediction with R2 = 0.9254 and RMSE = 0.0619 in the testing phase. The result of the proposed model is significantly better than those obtained from other ELM-based hybrid models developed with particle swarm optimisation, genetic algorithm, and slime mould algorithm. The results also illustrate that the proposed ELM-IHHO model outperforms the other benchmark model, such as back-propagation neural nets, support vector regression, random forest, and group method of data handling in predicting the permeability of tight carbonates.

Measurement of anti-Müllerian hormone (AMH) is applied to diagnose polycystic ovary syndrome in human. A 7-year-old female Persian cat was presented for persistent expression of behavioural oestrus. In response to examination of spines, the queen exhibited treading movements of hind legs. Trans-abdominal ultrasonography revealed a multicystic appearance of both ovaries. Oestradiol in the queen with prolonged oestrus (145 pg/ml) was higher than that in normal oestrous cats (50.8 ± 7.41 pg/ml). However, AMH concentration in the cat with prolonged oestrus (1.8 ng/ml) was within the range of AMH concentration in normal oestrous queens (3.5 ± 0.81 ng/ml; range, 1.2–5.8 ng/ml). The queen was diagnosed with cystic ovarian disease and was subjected to ovariohysterectomy. Excision and histopathology confirmed cystic ovarian disease as well as uterine hyperplasia. In conclusion, it seems that AMH could not serve as a diagnostic indicator of cystic ovarian disease in cat.