Wrinkled 2D Materials: A Versatile Platform for Low‐Threshold Stretchable Random Lasers

A stretchable, flexible, and bendable random laser system capable of lasing in a wide range of spectrum will have many potential applications in next‐ generation technologies, such as visible‐spectrum communication, superbright solid‐state lighting, biomedical studies, fluorescence, etc. However, producing an appropriate cavity for such a wide spectral range remains a challenge owing to the rigidity of the resonator for the generation of coherent loops. 2D materials with wrinkled structures exhibit superior advantages of high stretchability and a suitable matrix for photon trapping in between the hill and valley geometries compared to their flat counterparts. Here, the intriguing functionalities of wrinkled reduced graphene oxide, single‐layer graphene, and few‐layer hexagonal boron nitride, respectively, are utilized to design highly stretchable and wearable random laser devices with ultralow threshold. Using methyl‐ammonium lead bromide perovskite nanocrystals (PNC) to illustrate the working principle, the lasing threshold is found to be ≈10 µJ cm^?2, about two times less than the lowest value ever reported. In addition to PNC, it is demonstrated that the output lasing wavelength can be tuned using different active materials such as semiconductor quantum dots. Thus, this study is very useful for the future development of high‐performance wearable optoelectronic devices.     

A Generalized Quasi-MMSE Controller for Run-to-Run Dynamic Models

This study proposes a generalized quasi-minimum mean square error (qMMSE) controller for implementing a run-to-run process control where the process input–output relationship follows a general-order dynamical model with added noise. The expression of the process output, the long-term stability conditions and the optimal discount factor of this controller are derived analytically. Furthermore, we use the proposed second-order dynamical model to illustrate the effects of mis-identification of the process I-O model on the process total mean square error (TMSE). Via a comprehensive simulation study, the model demonstrates that the TMSE may inflate by more than 150% if a second-order dynamical model with moderately large carryover effects is wrongly identified as that of a first-order model. This means that the effects of mis-identification of the process I-O model on the process total mean square error (TMSE) is not negligible for implementing a dynamic run-to-run (RTR) process control. Supplementary materials for this article are available online.     

Using big data to make better decisions in the digital economy

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health.     

Solid–liquid interdiffusion bonding of Cu/In/Ni microjoints

Low-temperature solder is needed for temperature-sensitive components, step soldering and wearable devices. Low-temperature bonding is effective for reducing temperature and manufacturing costs. Indium has a low melting point, low resistance and good anticorrosion properties. Indium plays important roles in transparent conductors, the aerospace industry and flexible displays. Solid–liquid interdiffusion (SLID) bonding is one of the most reliable 3D integration technologies. The microstructure of the Cu/In/Ni bonding interface was investigated in this study. A low temperature of 180 °C was used for SLID bonding. Cu-In compounds, Cu₁₁In₉ and CuIn₂, were formed after SLID bonding at 180 °C and storing at room temperature. The reaction between Cu and In is fast, even at room temperature. The low-temperature CuIn₂ phase is undoubtedly worth investigating for solder joints.

     

Minimization of Ion–Solvent Clusters in Gel Electrolytes Containing Graphene Oxide Quantum Dots for Lithium‐Ion Batteries

This study uses graphene oxide quantum dots (GOQDs) to enhance the Li⁺‐ion mobility of a gel polymer electrolyte (GPE) for lithium‐ion batteries (LIBs). The GPE comprises a framework of poly(acrylonitrile‐co‐vinylacetate) blended with poly(methyl methacrylate) and a salt LiPF₆ solvated in carbonate solvents. The GOQDs, which function as acceptors, are small (3?11 nm) and well dispersed in the polymer framework. The GOQDs suppress the formation of ion?solvent clusters and immobilize anions, affording the GPE a high ionic conductivity and a high Li⁺‐ion transference number (0.77). When assembled into Li|electrolyte|LiFePO₄ batteries, the GPEs containing GOQDs preserve the battery capacity at high rates (up to 20 C) and exhibit 100% capacity retention after 500 charge?discharge cycles. Smaller GOQDs are more effective in GPE performance enhancement because of the higher dispersion of QDs. The minimization of both the ion?solvent clusters and degree of Li⁺‐ion solvation in the GPEs with GOQDs results in even plating and stripping of the Li‐metal anode; therefore, Li dendrite formation is suppressed during battery operation. This study demonstrates a strategy of using small GOQDs with tunable properties to effectively modulate ion?solvent coordination in GPEs and thus improve the performance and lifespan of LIBs.     

Pressure Welding of Silver Nanowires Networks at Room Temperature as Transparent Electrodes for Efficient Organic Light‐Emitting Diodes

In this work, polymethylmethacrylate (PMMA) as a superior mediate for the pressure welding of silver nanowires (Ag NWs) networks as transparent electrodes without any thermal treatment is demonstrated. After a pressing of 200 kg cm^?2, not only the sheet resistance but also the surface roughness of the PMMA‐mediated Ag NWs networks decreases from 2.6 kΩ sq^?1 to 34.3 Ω sq^?1 and from 76.1 to 12.6 nm, respectively. On the other hand, high transparency of an average transmittance in the visible wavelengths of 93.5% together with a low haze value of 2.58% can be achieved. In terms of optoelectronic applications, the promising potential of the PMMA‐mediated pressure‐welded Ag NWs networks used as a transparent electrode in a green organic light‐emitting diode (OLED) device is also demonstrated. In comparison with the OLED based on commercial tin‐doped indium oxide electrode, the increments of power efficiency and external quantum efficiency (EQE) from 80.1 to 85.9 lm w^?1 and 19.2% to 19.9% are demonstrated. In addition, the PMMA‐mediated pressure welding succeeds in transferring Ag NWs networks to flexible polyethylene naphthalate and polyimide substrates with the sheet resistance of 42 and 91 Ω sq^?1 after 10 000 times of bending, respectively.     

Programmable Hydrogel Ionic Circuits for Biologically Matched Electronic Interfaces

The increased need for wearable and implantable medical devices has driven the demand for electronics that interface with living systems. Current bioelectronic systems have not fully resolved mismatches between engineered circuits and biological systems, including the resulting pain and damage to biological tissues. Here, salt/poly(ethylene glycol) (PEG) aqueous two‐phase systems are utilized to generate programmable hydrogel ionic circuits. High‐conductivity salt‐solution patterns are stably encapsulated within PEG hydrogel matrices using salt/PEG phase separation, which route ionic current with high resolution and enable localized delivery of electrical stimulation. This strategy allows designer electronics that match biological systems, including transparency, stretchability, complete aqueous‐based connective interface, distribution of ionic electrical signals between engineered and biological systems, and avoidance of tissue damage from electrical stimulation. The potential of such systems is demonstrated by generating light‐emitting diode (LED)‐based displays, skin‐mounted electronics, and stimulators that deliver localized current to in vitro neuron cultures and muscles in vivo with reduced adverse effects. Such electronic platforms may form the basis of future biointegrated electronic systems.     

On-line coupling of capillary electrochromatography, capillary electrophoresis, and capillary HPLC with nuclear magnetic resonance spectroscopy

A novel capillary NMR coupling configuration, which offers the possibility of combining capillary zone electrophoresis (CZE), capillary HPLC (CHPLC), and for the first time capillary electrochromatography (CEC) with nuclear magnetic resonance (NMR), has been developed. The hyphenated technique has a great potential for the analysis of chemical, pharmaceutical, biological, and environmental samples. The versatile system allows facile changes between these three different separation methods. A special NMR capillary containing an enlarged detection cell suitable for on-line NMR detection and measurements under high voltage has been designed. The acquisition of 1D and 2D NMR spectra in stopped-flow experiments is also possible. CHPLC NMR has been performed with samples of hop bitter acids. The identification and structure elucidation of humulones and isohumulones by on-line and stopped-flow spectra has been demonstrated. The suitability of the configuration for electrophoretic methods has been investigated by the application of CZE and CEC NMR to model systems.     

Recommendation system based on rule-space model of two-phase blue-red tree and optimized learning path with multimedia learning and cognitive assessment evaluation

Among the various indicators used by the Ministry of Education to assess the learning performance and competencies in Taiwan, a highly emphasized one for students in the vocational education system is the numbers of professional certification they have, which is also an important factor for vocational students to gain suitable job opportunities and to enhance their working competitiveness. As a result, the importance of obtaining professional certifications can never be over emphasized. Specifically, the numbers of certifications they obtained is highly related with the numbers of job opportunities they can expect. In this research, we propose a RS (Recommendation System) based solution. The proposed solution combines two-phase Blue-Red trees of Rule-Space Model and the optimized learning path, and is focused on remedying and analyzing the learning status of MTA courses with the goal of enhancing students’ pass rate of MTA certifications. In phase one, we then identify three SGs (Skill Groups) based on course information from the Certiport of Microsoft certification center, and the three SGs can be utilized for producing both concept maps and Blue-Red trees. In phase two, we classified ten chapters of MTA course into three SGs identified in phase one based on the similarities observed in the ten chapters and the three SGs. The three SGs will then be used for generating the needed concept maps and groups of Blue-Red trees. In this research, we generated three of each. The analysis is based on Rule-Space Mode for all learning objects in each skill group of phase two. For each pair of learning objects, we define the RW (Relation Weight) of them. From all learning paths, we calculate the Confidence Level values of each adjacent pairs of learning objects. Finally, we obtain the optimized learning path through the adoption of the inferred optimized learning path derivation algorithm from the combination of RW (Relation Weight) and CL (Confidence Level). It can be used in OCMLS (Online Course Multimedia Learning System) that recommended the optimized learning path of learning objects for learners to online self-learning, or to RS (Recommendation System) that provides the basis of self-learning remedies for RFRC (Recommended Form of Remedial Course). By adopting this recommendation system for giving guidance for students in preparing for the MTA (Microsoft Technology Associate) certification, we have observed good results in learning performance and pass rate.     

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.