High‐Precision Temperature‐Controllable Metal‐Coated Polymeric Molds for Programmable,Hierarchical Patterning

Nanofabrication is an indispensable process in nanoscience and nanotechnology. Unconventional lithographic techniques are often used for fabrication as alternatives to photolithography because they are faster, more cost‐effective, and simpler to use. However, these techniques are limited in scalability and utility because of the collapse of preprinted structures during step‐and‐repeat processes. This study proposes a new class of temperature‐controllable polymeric molds that are coated with a metal such that any site‐specific patterning can be accomplished in a programmable manner using selective contact‐dewetting lithography. The lithography allows sub‐100 nm patterning, step‐and‐repeat processing, and hierarchical structure fabrication. The programmable feature of the lithography can be utilized for the structural coloring and shaping of objects. Large‐area programmable patterning, semiconductor device manufacturing, and the fabrication of iridescent security devices would benefit from the unique features of the proposed strategy.     

Interpretation and diagnosis of fouling progress in membrane bioreactor plants using a periodic pattern recognition method

Fouling is a principal constraint of membrane bioreactors (MBRs). It blocks the wide use of MBRs and aggravates the ability of MBRs. Trans-membrane pressure (TMP) is measured simply from MBRs and is a useful factor for evaluating fouling phenomena such as fouling mechanisms. Fouling mechanism diagnosis based on a measured TMP was used to evaluate MBRs operation conditions. However, diagnosis of MBR conditions is difficult due to the dynamic conditions of MBRs. Therefore, we used differential calculus, exponential weighted moving average (EWMA) and fast Fourier transform (FFT) to determine a periodic pattern for diagnosing fouling mechanisms in the dynamic operating conditions of MBRs. The periodic pattern was reflected in the operating conditions of MBRs, based on the fouling mechanism using TMP. We used two data sets obtained from pilot-scale MBR to suggest a periodic pattern and validated the proposed method using a lab-scale MBR experiment. Consequently, the suggested periodic pattern can diagnose fouling mechanisms using the proposed method, because the methods can be adjusted under the dynamic conditions of MBRs.     

Steam reforming of liquefied natural gas (LNG) for hydrogen production over nickel–boron–alumina xerogel catalyst

A series of mesoporous nickel–boron–alumina xerogel (x-NBA) catalysts with different boron/nickel molar ratio (x = 0–1) were prepared by an epoxide-driven sol–gel method. The effect of boron/nickel molar ratio on the catalytic activities and physicochemical properties of nickel–boron–alumina xerogel catalysts was investigated in the steam reforming of liquefied natural gas (LNG). All the mesoporous x-NBA catalysts showed similar surface area. Introduction of boron increased interaction between nickel and support. In addition, introduction of boron into x-NBA catalysts reduced methane activation energy and increased nickel surface area. Promotion of boron had a positive effect on the catalytic activity due to the increase of adsorbed methane and nickel surface area. The amount of adsorbed methane and nickel surface area exhibited volcano-shaped trends with respect to boron/nickel molar ratio. LNG conversion and hydrogen yield increased with increasing the amount of adsorbed methane and with increasing nickel surface area. Among the catalysts, 0.3-NBA, which retained the largest amount of adsorbed methane and the highest nickel surface area, showed the best catalytic performance. It was also revealed that x-NBA catalysts showed strong coke resistance during the steam reforming reaction.     

Rapid and efficient deoxygenation of sulfoxides to sulfides with tantalum(V) chloride/sodium iodide system

TaCl₅/NaI system converts a wide range of sulfoxides to the corresponding sulfides in high yields with short reaction times, under mild conditions. It is worth mentioning that this protocol is chemoselective and tolerates various functional groups (such as –Br, –Cl, –OCH₃, –CHO, and –NO₂) and double bond.     

Commercial process for mass production of synthetic natural gas through the adiabatic reactors: operational characteristics of a 50‐kW pilot‐plant,influence of steam,and CO2

In synthetic natural gas (SNG) reaction process, the water gas shift (WGS) reaction and methanation reaction take place simultaneously, and an insufficient supply of steam might deactivate the catalyst. In this study, the characteristics of the methanation reaction with a commercial catalyst and using a low [H₂]/[CO] mole ratio in SNG synthesis are evaluated. The reaction characteristics at various possible process parameters are evaluated varying different process parameters such as the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, flow of different % CO₂, and reaction temperature. Temperature profiles on catalyst bed are monitored as a function of the [H₂O]/[CO] mole ratio, [H₂]/[CO] mole ratio, and flow of different % CO₂. Through a lab‐scale optimization process, suitable optimum conditions are selected and in the same condition a 50‐kW pilot‐scale SNG production process through adiabatic reactors is carried out. The pilot scale SNG reaction is stable through overnight and the CO conversion efficiency and CH₄ selectivity are 100% and 97.3%, respectively, while the maximum CH₄ productivity is 0.654 m³/kg_cat · h. Copyright © 2016 John Wiley & Sons, Ltd.     

Deep Q-network-based auto scaling for service in a multi-access edge computing environment

In 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi-access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto-scaling method using deep Q-networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open-source software.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Activation of the Complement System on Human Endothelial Cells by Urban Particulate Matter Triggers Inflammation-Related Protein Production

Exposure to particulate matter (PM) is becoming a major global health issue. The amount and time of exposure to PM are known to be closely associated with cardiovascular diseases. However, the mechanism through which PM affects the vascular system is still not clear. Endothelial cells line the interior surface of blood vessels and actively interact with plasma proteins, including the complement system. Unregulated complement activation caused by invaders, such as pollutants, may promote endothelial inflammation. In the present study, we sought to investigate whether urban PM (UPM) acts on the endothelial environment via the complement system. UPM-treated human endothelial cells with normal human serum showed the deposition of membrane attack complexes (MACs) on the cell surface via the alternative pathway of the complement system. Despite the formation of MACs, cell death was not observed, and cell proliferation was increased in UPM-mediated complement activation. Furthermore, complement activation on endothelial cells stimulated the production of inflammation-related proteins. Our results revealed that UPM could activate the complement system in human endothelial cells and that complement activation regulated inflammatory reaction in microenvironment. These findings provide clues with regard to the role of the complement system in pathophysiologic events of vascular disease elicited by air pollution.     

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.