National Taiwan University of Science and Technology

In 2008 we published the fi rst set of guidelines for standardiz- ing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the fi eld. Our knowledge base and relevant new tech- nologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in differ- ent organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between meas urements that monitor the num- bers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the com- plete process including the amoun t and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accu mulation must be differentiated from stimuli that increas e autophagic activity, de fi ned as increased autophagy induction coupled with in creased delivery to, and degra- dation within, lysosomes (in most higher eukaryotes and some pro- tists such as Dictyostelium ) or the vacuole (in plants and fungi). In otherwords,itisespeciallyimportantthatinvestigatorsnewtothe fi eld understand that the appearance of more autophagosomes does not necessarily equate with mo re autophagy. In fact, in many cases, autophagosomes accumu late because of a block in traf fi cking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may re fl ect a reductionindegradativeactivity .Itisworthemphasizingherethat lysosomal digestion is a stage of au tophagy and evaluating its com- petence is a crucial part of the evaluation of autophagic fl ux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appro- priate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no indi- vidual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy- related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a speci fi c marker for an autophagic process. In these guidelines, we con- sider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the fi eld

Limnophila aromatica is commonly used as a spice and a medicinal herb in Southeast Asia. In this study, water and various concentrations (50%, 75%, and 100%) of methanol, ethanol, and acetone in water were used as solvent in the extraction of L. aromatica. The antioxidant activity, total phenolic content, and total flavonoid content of the freeze-dried L. aromatica extracts were investigated using various in vitro assays. The extract obtained by 100% ethanol showed the highest total antioxidant activity, reducing power and DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity. The same extract also exhibited the highest phenolic content (40.5 mg gallic acid equivalent/g of defatted L. aromatica) and the highest flavonoid content (31.11 mg quercetin equivalent/g of defatted L. aromatica). The highest extraction yield was obtained by using 50% aqueous acetone. These results indicate that L. aromatica can be used in dietary applications with a potential to reduce oxidative stress.

What are the bottlenecks for organometal halide perovskite solar cells to achieve the stability required for commercialization?

An effective method to control the rate of perovskite crystallization by incorporating rationally chosen additives into the perovskite precursor solutions is demonstrated. The processing additives simultaneously facilitate nucleation and modulate the kinetics of crystal growth during crystallization, leading to much smoother perovskite morphology with improved coverage area and crystal uniformity. As a result, it enables very high PCE (∼12%) planar-heterojunction solar cells to be fabricated through the low-temperature solution processes (under 150 °C). This study opens up a new direction for optimizing perovskite active layer properties to expand device performance ceilings.

The explosion of Internet usage and the huge funding initiatives in electronic banking have drawn the attention of researchers towards Internet banking. In the past, the conventional focus of Internet banking research has been on technological development, but this is now shifting to user-focused research. Although millions of dollars have been spent on building Internet banking systems, reports have shown that potential users may not use the systems in spite of their availability. This points out the need for research to identify the factors that determine acceptance of Internet banking by the users. According to the technology acceptance model (TAM), perceived ease of use and perceived usefulness constructs are believed to be fundamental in determining the acceptance and use of various IT. These beliefs may however not fully explain the user's behavior toward newly emerging IT, such as Internet banking. Using the technology acceptance model (TAM) as a theoretical framework, this study introduces “perceived credibility” as a new factor that reflects the user's security and privacy concerns in the acceptance of Internet banking. It also examines the effect of computer self-efficacy on the intention to use Internet banking. Based on a sample of 123 users from a telephone interview, the results strongly support the extended TAM in predicting the intention of users to adopt Internet banking. It also demonstrates the significant effect of computer self-efficacy on behavioral intention through perceived ease of use, perceived usefulness, and perceived credibility.

Purpose This paper develops a research model to examine the relationship among e‐service quality dimensions and overall service quality, customer satisfaction and purchase intentions. Design/methodology/approach Data from a survey of 297 online consumers were used to test the research model. Confirmatory factor analysis was conducted to examine the reliability and validity of the measurement model, and the structural equation modelling technique was used to test the research model. Findings The analytical results showed that the dimensions of web site design, reliability, responsiveness, and trust affect overall service quality and customer satisfaction. Moreover, the latter in turn are significantly related to customer purchase intentions. However, the personalization dimension is not significantly related to overall service quality and customer satisfaction. Research limitations/implications Future research can use different methodologies, such as longitudinal studies, focus groups and interviews, to examine the relationship between service quality and customer purchase behaviour in online shopping contexts. Practical implications This study suggests that to enhance customer purchase intentions, online stores should develop marketing strategies to better address the trustworthiness, reliability, and responsiveness of web‐based services. Online stores can devote valuable corporate resources to the important e‐service quality attributes identified by this study. Originality/value This study developed the instrument dimensions of e‐service quality by modifying the SERVQUAL model to consider online shopping context. Moreover, the results of this study provide a valuable reference for managers of online stores, as well as for researchers interested in internet marketing.

The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.

Previous work on service performance has focused on either organization- or individual-level analysis. This multilevel study of 257 employees, 44 managers, and 1,993 customers from 25 restaurants d...

High gravimetric energy density, earth-abundance, and environmental friendliness of hydrogen sources have inspired the utilization of hydrogen fuel as a clean alternative to fossil fuels. Hydrogen evolution reaction (HER), a half reaction of water splitting, is crucial to the low-cost production of pure H 2 fuels but necessitates the use of electrocatalysts to expedite reaction kinetics. Owing to the availability of low-cost oxygen evolution reaction (OER) catalysts for the counter electrode in alkaline media and the lack of low-cost OER catalysts in acidic media, researchers have focused on developing HER catalysts in alkaline media with high activity and stability. Nickel is well-known as an HER catalyst and continuous efforts have been undertaken to improve Ni-based catalysts as alkaline electrolyzers. In this review, we summarize earlier studies of HER activity and mechanism on Ni surfaces, along with recent progress in the optimization of the Ni-based catalysts using various modern techniques. Recently developed Ni-based HER catalysts are categorized according to their chemical nature, and the advantages as well as limitations of each category are discussed. Among all Ni-based catalysts, Ni-based alloys and Ni-based hetero-structure exhibit the most promising electrocatalytic activity and stability owing to the fine-tuning of their surface adsorption properties via a synergistic nearby element or domain. Finally, selected applications of the developed Ni-based HER catalysts are highlighted, such as water splitting, the chloralkali process, and microbial electrolysis cell.

Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners’ answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners’ during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.

In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.

In this paper, we study a probabilistically robust transmit optimization problem under imperfect channel state information (CSI) at the transmitter and under the multiuser multiple-input single-output (MISO) downlink scenario. The main issue is to keep the probability of each user's achievable rate outage as caused by CSI uncertainties below a given threshold. As is well known, such rate outage constraints present a significant analytical and computational challenge. Indeed, they do not admit simple closed-form expressions and are unlikely to be efficiently computable in general. Assuming Gaussian CSI uncertainties, we first review a traditional robust optimization-based method for approximating the rate outage constraints, and then develop two novel approximation methods using probabilistic techniques. Interestingly, these three methods can be viewed as implementing different tractable analytic upper bounds on the tail probability of a complex Gaussian quadratic form, and they provide convex restrictions, or safe tractable approximations, of the original rate outage constraints. In particular, a feasible solution from any one of these methods will automatically satisfy the rate outage constraints, and all three methods involve convex conic programs that can be solved efficiently using off-the-shelf solvers. We then proceed to study the performance-complexity tradeoffs of these methods through computational complexity and comparative approximation performance analyses. Finally, simulation results are provided to benchmark the three convex restriction methods against the state of the art in the literature. The results show that all three methods offer significantly improved solution quality and much lower complexity.

The need for lighter, thinner, and smaller products makes lithium ion batteries popular power sources for applications such as mobile phones, laptop computers, digital cameras, electric vehicles, and hybrid electric vehicles. For high power applications, the development of high capacity and high voltage electrode materials is in progress. Battery performance and safety issues are also related to the properties of the electrolytes used. To improve the properties of the electrolytes, small amounts of other components, known as electrolyte additives, are incorporated. This paper reviews the recent progress in electrolyte additives used to improve performance and other properties, such as safety. This review classifies the additives based on their functions and their effects on specific electrode materials focusing on electrodes under current development. From anodes: carbonaceous electrodes, silicon, tin and Li4Ti5O12; from layered cathodes: LiCoO2, Li-rich and LiNiyMnyCo1−2yO2 (NMC); from spinel: LiMn2O4, and from olivine: LiFePO4 are selected. We believe that this approach will help readers easily identify and understand the additives suitable for their target materials.

Separating molecules or ions with sub-Angstrom scale precision is important but technically challenging. Achieving such a precise separation using membranes requires Angstrom scale pores with a high level of pore size uniformity. Herein, we demonstrate that precise solute-solute separation can be achieved using polyamide membranes formed via surfactant-assembly regulated interfacial polymerization (SARIP). The dynamic, self-assembled network of surfactants facilitates faster and more homogeneous diffusion of amine monomers across the water/hexane interface during interfacial polymerization, thereby forming a polyamide active layer with more uniform sub-nanometre pores compared to those formed via conventional interfacial polymerization. The polyamide membrane formed by SARIP exhibits highly size-dependent sieving of solutes, yielding a step-wise transition from low rejection to near-perfect rejection over a solute size range smaller than half Angstrom. SARIP represents an approach for the scalable fabrication of ultra-selective membranes with uniform nanopores for precise separation of ions and small solutes.

Hydrogen evolution reaction (HER) from water through electrocatalysis using cost-effective materials to replace precious Pt catalysts holds great promise for clean energy technologies. In this work we developed a highly active and stable catalyst containing Co doped earth abundant iron pyrite FeS(2) nanosheets hybridized with carbon nanotubes (Fe(1-x)CoxS(2)/CNT hybrid catalysts) for HER in acidic solutions. The pyrite phase of Fe(1-x)CoxS(2)/CNT was characterized by powder X-ray diffraction and absorption spectroscopy. Electrochemical measurements showed a low overpotential of ∼0.12 V at 20 mA/cm(2), small Tafel slope of ∼46 mV/decade, and long-term durability over 40 h of HER operation using bulk quantities of Fe(0.9)Co(0.1)S(2)/CNT hybrid catalysts at high loadings (∼7 mg/cm(2)). Density functional theory calculation revealed that the origin of high catalytic activity stemmed from a large reduction of the kinetic energy barrier of H atom adsorption on FeS(2) surface upon Co doping in the iron pyrite structure. It is also found that the high HER catalytic activity of Fe(0.9)Co(0.1)S(2) hinges on the hybridization with CNTs to impart strong heteroatomic interactions between CNT and Fe(0.9)Co(0.1)S(2). This work produces the most active HER catalyst based on iron pyrite, suggesting a scalable, low cost, and highly efficient catalyst for hydrogen generation.

In this paper, an image authentication technique by embedding digital "watermarks" into images is proposed. Watermarking is a technique for labeling digital pictures by hiding secret information into the images. Sophisticated watermark embedding is a potential method to discourage unauthorized copying or attest the origin of the images. In our approach, we embed the watermarks with visually recognizable patterns into the images by selectively modifying the middle-frequency parts of the image. Several variations of the proposed method are addressed. The experimental results show that the proposed technique successfully survives image processing operations, image cropping, and the Joint Photographic Experts Group (JPEG) lossy compression.

This paper presents a model for designing adaptive wormhole routing algorithms for meshes without virtual channels. The model restricts the locations where some turns can be taken so that deadlock is avoided. In comparison with previous methods, the degree of routing adaptiveness provided by the model is more even for different source-destination pairs. The mesh network may benefit from this feature in terms of communication efficiency. Simulation results show that the even adaptiveness provided by the odd-even turn model makes message routing less vulnerable to nonuniform factors such as hot spot traffic. In addition, this property results in a smaller fluctuation of the network performance with respect to different traffic patterns.

Wearable electronics fabricated on lightweight and flexible substrate are believed to have great potential for portable devices, but their applications are limited by the life span of their batteries. We propose a hybridized self-charging power textile system with the aim of simultaneously collecting outdoor sunshine and random body motion energies and then storing them in an energy storage unit. Both of the harvested energies can be easily converted into electricity by using fiber-shaped dye-sensitized solar cells (for solar energy) and fiber-shaped triboelectric nanogenerators (for random body motion energy) and then further stored as chemical energy in fiber-shaped supercapacitors. Because of the all-fiber-shaped structure of the entire system, our proposed hybridized self-charging textile system can be easily woven into electronic textiles to fabricate smart clothes to sustainably operate mobile or wearable electronics.

This paper presents a newly developed simulation-based approach for Bayesian model updating, model class selection, and model averaging called the transitional Markov chain Monte Carlo (TMCMC) approach. The idea behind TMCMC is to avoid the problem of sampling from difficult target probability density functions (PDFs) but sampling from a series of intermediate PDFs that converge to the target PDF and are easier to sample. The TMCMC approach is motivated by the adaptive Metropolis–Hastings method developed by Beck and Au in 2002 and is based on Markov chain Monte Carlo. It is shown that TMCMC is able to draw samples from some difficult PDFs (e.g., multimodal PDFs, very peaked PDFs, and PDFs with flat manifold). The TMCMC approach can also estimate evidence of the chosen probabilistic model class conditioning on the measured data, a key component for Bayesian model class selection and model averaging. Three examples are used to demonstrate the effectiveness of the TMCMC approach in Bayesian model updating, model class selection, and model averaging.

) based photoanodes offer promise for such applications, due to their high chemical stability, great abundance and low cost. Despite these promising properties, progress towards the manufacture of practical water splitting devices has been limited. This review is intended to highlight recent advancements and the limitations that still hamper the full utilization of hematite electrodes in PEC water splitting systems. We review recent progress in manipulating hematite for PEC water splitting through various approaches, focused on e.g. enhancing light absorption, water oxidation kinetics, and charge carrier collection efficiency. As the morphology affects various properties, progress in morphological characterization from thicker planar films to recent ultrathin nanophotonic morphologies is also examined. Special emphasis has been given to various ultrathin films and nanophotonic structures which have not been given much attention in previous review articles.