Rare-earth free sensitizer in NaLuCrF4:Er upconversion material

Upconversion phosphors are known as a material system that can convert near-infrared light into visible/ultraviolet emissions by sequentially absorbing multiple photons. The studies on upconversion materials often use two rare earth (RE) ions as a sensitizer-activator pair. We investigated the influences on luminescence intensity depending on Cr-doping content (x) of hexagonal NaLu_0.98–xCr_xF₄Er_0.02 (x = 0–0.9) upconversion material by substituting Lu³⁺ ions with Cr³⁺in the absence of Gd³⁺. The change in upconversion luminescence intensity appears with saddle-like shape. We suggest that Cr³⁺ ions play the dual role as a constituent in host lattice and a sensitizer in the upconversion process. Optimal conditions for gaining the strongest upconversion emission correspond to x = 0.3–0.5, where there are effective energy transfers between Cr³⁺ and Er³⁺ ions and CrEr dimers. Apart from these values, the emission intensity decreases rapidly which can be ascribed to the absence of multiple-photon absorption for the case of low Cr³⁺ contents, and to the coupling between Cr³⁺ and/or Er³⁺ ions for the case of high Cr³⁺ contents. Magnetization and electron-spin-resonant measurements were performed to understand the correlation between the optical and magnetic properties.     

Complications of chronic otitis media

In summary, recent literature indicates that the complications of chronic otitis media have been decreasing. However, even with the advent of modern and more powerful antimicrobials and aggressive surgical eradication of disease, the morbidity and mortality are still high. Some complications may initially be quite obvious and some complications may be quite subtle. Therefore, the most important tools in making early diagnosis are careful history and physical examination, and a high index of suspicion for impending complications.     

Intelligent control of the feeding of aluminum electrolytic cells using neural networks

To be efficient, the control of alumina feeding of the electrolytic cell must be based on cell resistance, alumina concentration, and cell state. Most control schemes now in use are based on cell resistance only, and, thus, constitute an open-loop control that lacks robustness because their decision criteria are not explicitly tied to concentration nor to cell state. This results in the cell operating at nonoptimal concentrations, and cell efficiency is diminished. An optimal operation requires a knowledge of concentration and an adjustment of the decision criteria as a function of concentration. A learning vector quantization (LVQ) type of neural network was built and trained to recognize the cell state. Knowing the state of the cell and its resistance, concentration can be estimated using predetermined regression functions. The decision criteria for the control logic are then consequently adapted. A closed-loop control scheme is thus obtained. Results show that, with its control so structured, the cell can operate at or near optimal concentrations independently of its state. This flexible and intelligent character of the neural control can provide a considerable advantage as compared to the standard control.     

Unique ESD failure mechanisms during negative to Vcc HBM tests

HBM ESD tests on two types of 0.6 μm DRAM devices showed that internal circuit or output driver failures would occur after the input or I/O pins were ESD stressed negative with respect to Vcc at ground. These failures occurred at lower than expected ESD stress voltages due to power-up circuit interactions that either turned-on unique internal parasitic ESD current paths or disrupted the normal operation of the output pin’s ESD protection circuit. ESD analysis found there exists a set of power-up sensitive circuits and if placed near a Vcc bond pad can result in low voltage ESD failures.     

High-reliability GaAs-AlGaAs HBTs by MBE with Be base doping andInGaAs emitter contacts

The authors have developed a modified MBE growth process to produce high-gain n-p-n GaAs-AlGaAs heterojunction bipolar transistors (HBTs) with a mean time to failure (MTTF) of 1.5×10⁸ h at 125°C. Beryllium incorporation and diffusion are controlled through a combination of reduced substrate temperature and increased As/Ga flux ratio during MBE growth, resulting in extremely stable HBT profiles. The authors also demonstrate graded InGaAs surface layers with nonalloyed refractory metal contacts that significantly improve ohmic reliability compared to alloyed AuGe contacts. The ability to produce robust HBTs by MBE is critically important to this technology     

Broadband monolithic passive baluns and monolithic double-balancedmixer

The design and fabrication of four broadband monolithic passive baluns including CPW Marchand, multilayer MS Marchand, planar-transformer and broadside-coupled line baluns are presented. Operational frequencies range from 1.5 GHz to 24 GHz. Maximum relative bandwidths in excess of 3:1 are achieved. Simulated performances using full wave electromagnetic analysis are shown to agree with the measured results. Two accurate equivalent circuit models constructed from either electromagnetic simulated or measured S-parameters are developed for the MS Marchand and transformer baluns making the optimization of baluns and circuit design using the baluns much more efficient. The design of monolithic double-balanced diode mixer using two planar-transformer baluns is also presented. Without DC bias, the mixer shows a minimum conversion loss of 6 dB with the RF at 5 GHz and a LO drive of 15 dBm at 4 GHz. The measured input IP₃ of this mixer is better than 15 dBm over the 4 to 5.75 GHz frequency band     

Coupling techniques in boundary-combined methods for solving elliptic problems with singularities

Three coupling strategies in matching the Ritz-Galerkin method and the finite element method are introduced for general elliptic equations, and useful numerical techniques are provided. Numerical experiments have been carried out for solving the typical, singular Motz problem, which shows that optimal convergence rates of numerical solutions can be achieved by using the combined methods and techniques provided in this paper.     

Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.