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Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020.Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts.Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33–0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07–0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23–2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20–76.70) or unverified (OR = 5.03; 95% CI, 1.66–15.24).Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online “infodemics” to inform public health responses.

Background Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature. Objective Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers’ training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms. Methods Windowed timeline data of patients with a diagnosis of schizophrenia spectrum disorder before a known hospitalization event and healthy controls were gathered from 3 platforms: Facebook (254/268, 94.8% of participants), Twitter (51/268, 19% of participants), and Instagram (134/268, 50% of participants). We then used a 3 × 3 combinatorial binary classification design to train machine learning classifiers and evaluate their performance on testing data from all available platforms. We further compared results from models in intraplatform experiments (ie, training and testing data belonging to the same platform) to those from models in interplatform experiments (ie, training and testing data belonging to different platforms). Finally, we used Shapley Additive Explanation values to extract the top predictive features to explain and compare the underlying constructs that predict hospitalization on each platform. Results We found that models in intraplatform experiments on average achieved an F1-score of 0.72 (SD 0.07) in predicting a psychiatric hospitalization because of schizophrenia spectrum disorder, which is 68% higher than the average of models in interplatform experiments at an F1-score of 0.428 (SD 0.11). When investigating the key drivers for divergence in construct validities between models, an analysis of top features for the intraplatform models showed both low predictive feature overlap between the platforms and low pairwise rank correlation ( Conclusions We demonstrated that models built on one platform’s data to predict critical mental health treatment outcomes such as hospitalization do not generalize to another platform. In our case, this is because different social media platforms consistently reflect different segments of participants’ identities. With the changing ecosystem of social media use among different demographic groups and as web-based identities continue to become fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required.

BACKGROUND Previous research has shown the feasibility of utilizing social media data from a singular platform (e.g., Facebook or Twitter) in distinguishing individuals with a diagnosis of mental illness or experiencing an adverse outcome from healthy volunteers. However, the performance of these models on data from other social media platforms unseen in the training data (e.g., Instagram, TikTok) have not been investigated. OBJECTIVE This study aims to explore if online identities fragmented across social media platforms, models would have better testing performance on data from already seen social media platforms, in comparison to unseen social media platforms. It also aims to explain such discrepancies in performances if they are found. METHODS Windowed timeline data from three platforms with clinically-verified labels of hospitalization among patients with a diagnosis of schizophrenia was gathered: Facebook (N = 254), Twitter (N = 54), and Instagram (N = 124). Then, we utilized a 3 x 3 combinatorial binary classification design to test model’s performance on testing data from all available platforms. We further compared results from models within intra-platform experiments (i.e., training and testing data belongs to the same platform) to models within inter-platform experiments (i.e., training and testing data belongs to the different platforms). Finally, we utilized SHapley Additive exPlanations (SHAP) to extract top predictive features to explain the underlying constructs that predict hospitalization on each platform. RESULTS We found that models within intra-platform experiments on average achieved an F1-score of 0.72 in detection a psychiatric hospitalization due to schizophrenia, which is 68% higher compared to the average of models within inter-platform experiments at an F1-score of 0.428. We also found that by combining training data of all three platforms, a slight improvement of 0.5% was observed on the testing sets on average, compared to original intra-platform models. An analysis of top features for the intra-platform models shows low predictive feature overlap between the platforms, with ‘anger’ being an unique top feature for Facebook while ‘sad’ being an unique top feature for Instagram. CONCLUSIONS We demonstrated models built on one platform’s data to predict critical mental health treatment outcomes, such as a hospitalization, may not generalize to another, because each platform offers different construct validity. However, combining data from multiple platforms together may offer a more comprehensive view of a patient’s state and situation, and therefore fare better in relapse prediction. With the changing ecosystem of social media use among different demographic groups and as online identities continue to get fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required.

Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster–Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties. To evaluate the eRSM, we used two material datasets, including 3[Formula: see text] transition metal–4[Formula: see text] rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively. The results show that the eRSM can adequately measure the similarities and dissimilarities between materials in these datasets with respect to mechanisms of the target properties.

Smart power outlets equipped with appliance classification function can play an important role in smart homes. With the help of smart power outlets, users can know exactly the appliance plugged into an outlet and monitor or turn on/turn off the appliance. In this paper, we propose the design of low-cost smart power outlets, which can detect appliance-on state for triggering the sampling of electric current and extract frequency feature of electric current based on Fast Fourier Transform (FFT). The frequency feature of appliances is then used to classify appliances with the help of Feedforward neural networks (FNN). As results, our proposed method can achieve high classification accuracy with small traffic cost and hardware cost.

We present a data-driven approach to explore high-entropy alloys (HEAs). To overcome the challenges with numerous element-combination candidates, selecting appropriate descriptors, and the limitations and biased of existing data, we apply the evidence theory to develop a descriptor-free evidence-based recommender system (ERS) for recommending HEAs. The proposed system measures the similarities between element combinations and utilizes it to recommend potential HEAs. To evaluate the ERS, we compare its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known data sets, including binary and ternary alloys. The results of experiments using k-fold cross-validation on the data sets show that the ERS outperforms all competitors. Furthermore, the ERS shows excellent extrapolation capabilities in experiments of recommending quaternary and quinary HEAs. We experimentally validate the most strongly recommended Fe-Co-based magnetic HEA, viz. FeCoMnNi, and confirm that it shows a body-centered cubic structure and is stable at high temperatures.

Background Trends in the public perception and awareness of COVID-19 over time are poorly understood. We conducted a longitudinal study to analyze characteristics and trends of online information during a major COVID-19 outbreak in Da Nang province, Vietnam in July-August 2020 to understand public awareness and perceptions during an epidemic. Methods We collected online information on COVID-19 incidence and mortality from online platforms in Vietnam between 1 July and 15 September, 2020, and assessed their trends over time against the epidemic curve. We explored the associations between engagement, sentiment polarity, and other characteristics of online information with different outbreak phases using Poisson regression and multinomial logistic regression analysis. We assessed the frequency of keywords over time, and conducted a semantic analysis of keywords using word segmentation. Results We found a close association between collected online information and the evolution of the COVID-19 situation in Vietnam. Online information generated higher engagements during compared to before the outbreak. There was a close relationship between sentiment polarity and posts’ topics: the emotional tendencies about COVID-19 mortality were significantly more negative, and more neutral or positive about COVID-19 incidence. Online newspaper reported significantly more information in negative or positive sentiment than online forums or social media. Most topics of public concern followed closely the progression of the COVID-19 situation during the outbreak: development of the global pandemic and vaccination; the unfolding outbreak in Vietnam; and the subsiding of the outbreak after two months. Conclusion This study shows how online information can reflect a public health threat in real time, and provides important insights about public awareness and perception during different outbreak phases. Our findings can help public health decision makers in Vietnam and other low and middle income countries with high internet penetration rates to design more effective communication strategies during critical phases of an epidemic.

The thesis demonstrates for the first time coexistence of write once read many memory (WORM) with resistive random access memory (RRAM). This is the first time such coexistence phenomena are reported in organic materials system. Furthermore, better understanding of working mechanism in resistive switching phenomena in organic materials particularly conjugated polymer has been achieved. Two materials systems are chosen, namely, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT PSS) and Polyvinyl alcohol (PVA) where PEDOT PSS is a conducting polymer and PVA is an insulator polymer. For a device with 5 μm thick PEDOT PSS sandwiched between two Au electrodes, the coexistence of WORM memory and RRAM memory can be achieved (Chapter 4). The WORM result was attributed to dedoping of the conducting PEDOT + PSS- segments to insulating PEDOT 0 segments. The RRAM was explained due to Proton H+ conductive properties of PSS acid domains inside the film after dedoping. This points to a general feature: resistive switching behavior of a whole device is the result of additive/summative resistive switching characteristic of its components. Based on this, we reveal the coexistence of RRAM memory and WORM memory demonstrated in PEDOT PSS blend with PVA (Chapter 5). The WORM memory is again attributed to PEDOT PSS while RRAM memory is attributed to the charge trapping characteristic of PVA. The device performance is improved significantly as compared to the earlier PEFOT PSS system, with more than 1000 cycling stability and much more than 10^4 s retention for RRAM mode. Additional I-V characteristic of this device is further studied in chapter 6 which reveals a new type of WORM memory. This type of WORM memory is driven by metallic migration of Au electrodes. Doctor of Philosophy (MSE)

In this paper, we introduce an ECHONET Lite based IoT platform which allows households to set up smart home environment with ease as well as allows service providers to deploy their IoT applications easily and securely. Our proposed platform is fully implemented with three main features: a simple process for integrating new IoT devices into smart home environment, a seamless data transfer mechanism between IoT devices and service providers, and a RESTful API for verifying and supporting service providers to deploy their IoT applications into our platform. The experimental results verified the practicability of our proposed IoT platform.

The study assesses the impact of different types of land use rights certificates (LURC) on individual and household welfare, expanding on the previous research of Menon, Rodgers, and Kennedy (2016), which assessed the effects of LURCs on agricultural land on household welfare. This study considers more recent data from the Vietnam Household Living Standards Survey (VHLSS) of 2014 in addition to VHLSS data from 2002–08 and includes an analysis of LURCs for agricultural as well as residential land. The study findings are presented below in three sections. The first is an analysis of land use and LURC distribution trends based on the VHLSS data. The second draws on a group of impact evaluations that compares the effects of having different types of LURCs. We regress individuals' employment and health care outcomes on whether their name was included on a LURC. The treatment variables are whether one has been issued an agricultural or residential LURC, with controls for age, education, ethnicity, urban residence, household consumption, land area, and district. We also regress household-level outcomes—expenditures, credit levels, and incomes—on whether the household's LURC is singly or jointly titled. Notably, the distribution of LURCs is not randomized, making it difficult to estimate the causal effects of LURC status and thereby limiting the conclusions that can be drawn. The third section, using 2014 demographic data and LURC distributions, offers a cost-benefit analysis of efforts to convert and reissue all remaining singly titled LURCs to jointly titled ones. We estimate the benefit as the impact difference between single- and jointly titled LURCs as calculated in the impact evaluation section and estimate costs as those of reissuing a LURC.

Molybdenum trioxide is an interesting inorganic system in which the empty 4d states have potential to hold extra electrons and therefore can change states from insulating opaque (MoO3) to colored semimetallic (HxMoO3). Here, we characterize the local electrogeneration and charge transfer of the synthetic layered two-dimensional 2D MoO3-II (a polymorph of MoO3 and analogous to α-MoO3) in response to two different redox couples, i.e., [Ru(NH3)6]3+ and [Fe(CN)6]3- by scanning electrochemical microscopy (SECM). We identify the reduction of [Ru(NH3)6]3+ to [Ru(NH3)6]2+ at the microelectrode that leads to the reduction of MoO3-II to conducting blue-colored molybdenum bronze HxMoO3. It is recognized that the dominant conduction of the charges occurred preferentially at the edges active sites of the sheets, as edges of the sheets are found to be more conducting. This yields positive feedback current when approaching the microelectrode toward 2D MoO3-II-coated electrode. In contrast, the [Fe(CN)6]4-, which is reduced from [Fe(CN)6]3-, is found unfavorable to reduce MoO3-II due to its higher redox potential, thus showing a negative feedback current. The charge transfer on MoO3-II is further studied as a function of applied potential. The results shed light on the charge transfer behavior on the surface of MoO3-II coatings and opens the possibility of locally tuning of their oxidation states. National Research Foundation (NRF) We acknowledge financial support from the National Research Foundation Investigatorship Award, NRF-NRFI-2016-05. Part of this work was also supported by the Nanomaterials for Energy and Water Management Programme under the Campus for Research Excellence and Technological Enterprise (CREATE) that is supported by the National Research Foundation, Prime Minister’s Office, Singapore.

We investigate determinants of individual migration decisions in Vietnam, a country with increasingly high levels of geographical labour mobility. Using data from the Vietnam Household Living Standards Survey 2012 (VHLSS2012), we find that the probability of migration is strongly associated with individual, household and community-level characteristics. The probability of migration is higher for young people and those with post-secondary education. Migrants are more likely to be from households with better-educated household heads, female-headed households, and households with higher youth dependency ratios. Members of ethnic minority groups are much less likely to migrate, other things being equal. Using multinomial logit methods, we distinguish migration by broad destination, and find that those moving to Ho Chi Minh City or Hanoi have broadly similar characteristics and drivers of migration as those moving to other destinations. We also use the VHLSS2012 together with the VHLSS2010, which allows us to focus on a narrow cohort of recent migrants—those present in the household in 2010, but who had moved away by 2012. This yields much tighter results. For education below upper secondary school, the evidence on positive selection by education is much stronger. However, the ethnic minority “penalty” on spatial labor mobility remains strong and significant, even after controlling for specific characteristics of households and communes. This lack of mobility is a leading candidate to explain the distinctive persistence of poverty among Vietnam’s ethnic minority populations, even as national poverty has sharply diminished.

Transparent nonvolatile memory has great potential in integrated transparent electronics. Here, we present highly transparent resistive switching memory using stoichiometric WO3 film produced by cathodic electrodeposition with indium tin oxide electrodes. The memory device demonstrates good optical transmittance, excellent operative uniformity, low operating voltages (+0.25 V/–0.42 V), and long retention time (>104 s). Conductive atomic force microscopy, ex situ transmission electron microscopy, and X-ray photoelectron spectroscopy experiments directly confirm that the resistive switching effects occur due to the electric field-induced formation and annihilation of the tungsten-rich conductive channel between two electrodes. Information on the physical and chemical nature of conductive filaments offers insightful design strategies for resistive switching memories with excellent performances. Moreover, we demonstrate the promising applicability of the cathodic electrodeposition method for future resistive ...

Self-powered flexible graphene thermistor device is successfully fabricated by modifying the graphene layers with ferroelectric polarization. Chemical vapor deposited graphene monolayer was transferred to the surface of a ferroelectric polymer poly[(vinylidenefluoride- co -trifluoroethylene)] P(VDF-TrFE) thin film. By utilizing alternating polarization charge, the graphene surface contains polarization induced hole-rich, intrinsic and electron-rich regions (p-i-n regions). The potential difference created in the graphene layer leads to the drift of the extra carriers created in the graphene monolayer with increasing temperature and gives rise to an exemplary self-powered or autonomous thermistor.

Hexagonal boron nitride (hBN), which is a 2D layered dielectric material, sometimes referred as “white graphene” due to its structural similarity with graphene, has attracted much attention due to its fascinating physical properties. Here, for the first time the use of chemical vapor deposition -grown hBN films to fabricate ultrathin (≈3 nm) flexible hBN-based resistive switching memory device is reported, and the switching mechanism through conductive atomic force microscopy and ex situ transmission electron microscopy is studied. The hBN-based resistive memory exhibits reproducible switching endurance, long retention time, and the capability to operate under extreme bending conditions. Contrary to the conventional electrochemical metallization theory, the conductive filament is found to commence its growth from the anode to cathode. This work provides an important step for broadening and deepening the understanding on the switching mechanism in filament-based resistive memories and propels the 2D material application in the resistive memory in future computing systems.

Miniaturization of electronic devices to gain speed and reduce cost is no longer the only criterion for emerging technological needs. The devices with advanced functionalities such as flexibility, stretchability, transparency, and environmental robustness have significant advantages and added value in different areas for future electronics' applications. Data storage devices are one of the critical and fundamental components in fully integrated electronics. The functional resistive switching random access memory (RRAM) device, which is one of the most promising candidates for the next generation of non-volatile memory for data storage, has attracted immense attention because of its speed, ease of fabrication and scalability. In this paper, the recent progress, materials' selection, device architecture, and the emerging multi-functional RRAM devices are reviewed.

In this paper, a nanocrystalline porous silicon-based propulsion system is designed, and its explosion impulse is tuned by varying the propulsion design parameters and etching duration. The porous silicon is ignited by an electrical current passed through 100-nm-thick aluminum film deposited on the unpolished side of the wafer and the ignited porous silicon caused strong explosion, which destroys the chip into tiny fragments. The explosion impulse in the system can reach about 0.14 N·s at optimal conditions, which is two orders stronger than the impulse produced by conventional propellants (ZakarE., “Technology Challenges in Solid Energetic Materials for Micro Propulsion Applications” U.S. Army Research Lab. Rept. ARL-TR-5035, Nov. 2009). It is also shown that, by varying the etching time, which is an important factor that determines the porous layer thickness and the volume of nanocrystallite, the strength of the impulse can be tuned. Furthermore, a linear increasing trend of the explosion impulse with ...

The integration of photonics and electronics on a converged silicon CMOS platform is a long pursuit goal for both academe and industry. We have been developing technologies that can integrate III-V compound semiconductors and CMOS circuits on 200 mm silicon wafers. As an example we present our work on the integration of InGaP light-emitting diodes (LEDs) with CMOS. The InGaP LEDs were epitaxially grown on high-quality GaAs and Ge buffers on 200 mm (100) silicon wafers in a MOCVD reactor. Strain engineering was applied to control the wafer bow that is induced by the mismatch of coefficients of thermal expansion between III-V films and silicon substrate. Wafer bonding was used to transfer the foundry-made silicon CMOS wafers to the InGaP LED wafers. Process trenches were opened on the CMOS layer to expose the underneath III-V device layers for LED processing. We show the issues encountered in the 200 mm processing and the methods we have been developing to overcome the problems. Published version

Electronics with multifunctionalities such as transparency, portability, and flexibility are anticipated for future circuitry development. Flexible memory is one of the indispensable elements in a hybrid electronic integrated circuit as the information storage device. Herein, we demonstrate a transparent, flexible, and transferable hexagonal boron nitride (hBN)-based resistive switching memory with indium tin oxide (ITO) and graphene electrodes on soft polydimethylsiloxane (PDMS) substrate. The ITO/hBN/graphene/PDMS memory device not only exhibits excellent performance in terms of optical transmittance (∼85% in the visible wavelength), ON/OFF ratio (∼480), retention time (∼5 × 104 s) but also shows robust flexibility under bending conditions and stable operation on arbitrary substrates. More importantly, direct observation of indium filaments in an ITO/hBN/graphene device is found via ex situ transmission electron microscopy, which provides critical insight on the complex resistive switching mechanisms.

This study reports a novel strategy to obtain a layered metastable phase of molybdenum trioxide (MoO3). The metastable phase in the layered framework, i.e., MoO3-II, was a result of the topotactic phase transformation of hexagonal-MoO3 (h-MoO3) into MoO3-II. This transformation was realized through the removal of the water and ammonium molecules from the framework of h-MoO3. Next, a facile exfoliation strategy was adopted to prepare MoO3-II nanosheets, consisting of double-layers of MoO6 octahedra. The as-exfoliated MoO3-II nanosheets had measured thicknesses of 1.4, 2.4, and 11.2 nm, as quantified using atomic force microscopy (AFM), equivalent to 2, 3, and 16 double-layers of the MoO6 octahedra in MoO3-II, respectively, after taking into account their respective van der Waals gaps (thickness of a MoO3-II double-layer ∼0.709 nm). This work is the first attempt to synthesize MoO3-II nanosheets and shed light on their formation mechanism.

The Curie temperature (T C) of RT binary compounds consisting of 3d transition-metal (T ) and 4f rare-earth elements (R) is analyzed systematically by a developed machine learning technique called kernel regression-based model evaluation. Twenty-one descriptive variables were designed assuming completely obtained information of the T C. Multiple kernel regression analyses with different kernel types: cosine, linear, Gaussian, polynomial, and Laplacian kernels were implemented and examined. All possible descriptive variable combinations were generated to construct the corresponding prediction models. As a result, by appropriate combinations between descriptive variable sets and kernel formulations, we demonstrate that a number of kernel regression models can accurately reproduce the T C of the RT compounds. The relevance of descriptive variables for predicting T C are systematically investigated. The results indicate that the rare-earth concentration is the most relevant variable in the T C phenomenon. We demonstrate that the regression-based model selection technique can be applied to learn the relationship between the descriptive variables and the actuation mechanism of the corresponding physical phenomenon, i.e., T C in the present case.

We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture models. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature ( T C ) of binary 3d transition metal- 4f rare-earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

We study resistive switching memory phenomena in conducting polymer PEDOT PSS. In the same film, there are two types of memory behavior coexisting; namely, the switchable diode effect and write once read many memory. This is the first report on switchable diode phenomenon based on conducting organic materials. The effect was explained as charge trapping of PEDOT PSS film and movement of proton. The same PEDOT PSS device also exhibits write once read many memory (WORM) phenomenon which arises due to redox reaction that reduces PEDOT PSS and renders it non-conducting. The revelation of these two types of memory phenomena in PEDOT PSS highlights the remarkable versatility of this conducting conjugated polymer. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

The Poisson's ratio of perovskites with a high degree of anisotropy can change from negative to positive and become much larger than the normally observed range of 0.2–0.4 and even greater than the isotropic upper limit 0.5 along certain crystallographic orientations. These results pave the way for a better understanding of elastic-mediated physical properties of perovskites by engineering the orientation-dependent Poisson's ratios.

In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.

Gel-derived TiO2–SiO2 and N–TiO2–SiO2 mixed oxides with the TiO2 : SiO2 weight ratio of 95 : 5 are synthesised and employed for phenol removal under UVA light and natural sunlight in this study. Both SiO2 and N are interestingly found to improve the specific surface area of resulting catalysts up to 169 m2 g−1. Meanwhile, only N is observed to shift significantly the light absorption of derived catalyst to visible range. Optimisation between specific surface area and crystallinity in TiO2–SiO2 mixed oxide is found to give rise to the superior photoactivity of TiO2–SiO2 catalyst in comparison with TiO2 counterpart. Under natural sunlight in Hochiminh City on October 2007, N–TiO2–SiO2 presents the outstanding photoactivity towards decomposition of 10 ppm phenol aqueous solution with the efficiency up to 100% as compared to those of 65 and 48% for bare TiO2–SiO2 and bare TiO2, respectively.

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn^Mn^_3 single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn^ and Mn^ ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained resutls.

Amorphous Si (a-Si) based memory devices that offer significant improvement in switching performance are fabricated by inserting a gold nanoparticles (Au NPs) monolayer between the amorphous Si and inert bottom electrodes. It is demonstrated that the Au NPs-interlayer amorphous Si memory devices deliver great improvements in lowering the electroforming voltage as well as enhancing the ON/OFF ratio, as compared to a pure amorphous Si memory structure. The switching mechanism is due to the modified electric field distribution with the insertion of Au NPs, which can directly affect the dynamic transport of ions and lead to the change of filament growth. Transmission electron microscopy (TEM) is used to directly capture the filaments growth process in these two types of memory devices to verify the evolution of conductive filament (CF). In addition, it is also demonstrated that the Au NPs-interlay amorphous Si memory fabricated on a flexible substrate can maintain low electroforming voltage and high ON/OFF ratio. This fabrication approach offers a versatile structural platform for next-generation memory applications with enhancement of the switching properties.

We investigate the emission of electromagnetic waves from biaxial subwavelength metamaterials. For anisotropic structures that exhibit a vanishing dielectric response along the longitudinal axis and possess a tunable transverse dielectric response, we find remarkable variation in the launch angles of energy associated with the emission of leaky wave radiation. We write closed-form expressions for the energy transport velocity and corresponding radiation angle $\ensuremath{\phi}$, defining the cone of radiation emission, as functions both of frequency and of material and geometrical parameters. Full wave simulations exemplify the broad range of directivity that can be achieved in these structures.

Text segmentation is a fundamental problem in natural language processing, which has application in information retrieval, question answering, and text summarization. Almost previous works on unsupervised text segmentation are based on the assumption of lexical cohesion, which is indicated by relations between words in the two units of text. However, they only take into account the reiteration, which is a category of lexical cohesion, such as word repetition, synonym or superordinate. In this research, we investigate the non-systematic semantic relation, which is classified as collocation in lexical cohesion. This relation holds between two words or phrases in a discourse when they pertain to a particular theme or topic. This relation has been recognized via a topic model, which is, in turn, acquired from a large collection of texts. The experimental results on the public dataset show the advantages of our approach in comparison to the available unsupervised approaches.

In this Letter, we theoretically investigate microwave transmission through a zero-index metamaterial loaded with dielectric defects. The metamaterial is impedance matched to free space, with the permittivity and permeability tending towards zero over a given frequency range. By simply varying the radii and permittivities of the defects, total transmission or reflection of the impinging electromagnetic wave can be achieved. The proposed defect structure can offer advances in shielding or cloaking technologies without restricting the object’s viewpoint. Active control of the observed exotic transmission and reflection signatures can occur by incorporating tunable refractive index materials such as liquid crystals and BaSr TiO3. Accepted version

Along with the growing software market in recent years, business requirements are changing more rapidly and the complexity of enterprise applications is growing continuously. An ideal goal is to encapsulate these requirements into a high-level abstraction, which can be used to drive large-scale adaptation of the underlying software implementation. Model Driven Engineering (MDE) is one of the enabling techniques that support this objective. One of the important parts in this process is the creation of Platform Independent Model (PIM). There are several approaches on this matter. However there is still the absence of clear documentation on the methods especially when the purpose is aimed for generic PIMs in a specific domain. This article introduces an approach to developing PIMs by using UML, OCL and promotes agility by applying agile modeling for this purpose. A practical example will be demonstrated.

Adapting a software system to the changing requirements and demands of the users is a big challenge in the area of software evolution. Representing these requirements in a high-level abstraction seems to be an optimal target. Model Driven Engineering (MDE) is one of the techniques that support this objective. Model transformation (automated and semi-automated), being the core mechanism in Model Driven Architecture (MDA), is considered to be the key factor in producing various software artifacts from all levels of modeling. Recently, the application of MDA to Web services has received considerable attention. This article introduces an approach of developing Web services by using model driven techniques, combined with the tools available on the market. We also present a view from a higher level of abstraction, describing particularly meta-model transformation using Kermeta.

This paper measures impacts of production of crops, forestry, livestock and aquaculture on household welfare, poverty and inequality in rural Vietnam using fixed-effects regressions. Data used in this paper are from Vietnam Household Living Standard Surveys 2002 and 2004. It is found that impact estimates of the production of crops and forestry on per capita income and consumption expenditure are not statistically significant. Impact estimates of the livestock production are positive and statistically significant for per capita income, but not statistically significant for per capita expenditure. However, the aquacultural production has positive and statistically significant impacts on both income and expenditure. As a result, the aquacultural production helps the producing households reduce the poverty incidence by 4.3 percentage points. It also decreases the poverty gap and poverty severity indexes of the producing households by around 13 percent and 15 percent, respectively. The aquacultural production also reduces the rural expenditure inequality, albeit at an extremely small magnitude.

Meta material has been demonstrated and applied in fabricating novel structures such as super lens, invisible cloaking and perfect absorber. The existence of a meta material layer in a stack consists of positive index material can result in a zero refractive index stack, which is also interesting in both physics and technology points of view. The stacking of negative index material and positive index material, where the impedances of negative index slab and positive index slab are matched, was manipulated so that the favorable outcome will be a zero equivalent index material with equivalent impedance different from zero. The reflection coefficient was zero at 90 degree incident angle, hence no reflection, and the calculated transmission is unity. Simulation demonstrates that there is no phase change and the wave propagates smoothly through the stack and the interfaces between slabs. Another kind of perfect absorber is also proposed by composing the 3-slab structure into the diffraction multi slits. Simulations show there are no back-scattering wave and transmitted wave.

Difference-in-differences with matching is a popular method to measure the impact of an intervention in health as well as social sciences. This method requires baseline data, i.e., data before interventions, which are not always available in reality. Instead, panel data with two time periods are often collected after interventions begin. In this paper, a simple matching method is proposed to measure the impact of an intervention using two-period panel data after the intervention. The method is illustrated by the measurement of the effect of health insurance in Vietnam using household panel data.

A development of new energetic materials are critical for the success propellants, micro-actuation, and power sources for next generation microsystems. If silicon (Si) can be utilized in the nanoenergetic field, a host of applications will soon arise because the standard silicon CMOS technology is well developed and widely used in the production of modern integrated circuits. Moreover, it is inexpensive, well characterized, and best adopted for microelectromechanical systems (MEMS). Porous silicon impregnated with solid state oxidants have demonstrated the combustion and explosion processes [1-4]. These processes can be used for various microactuators such as ignition for airbag systems [5] and self-destructed Si-chips [4]. Other applications may be in the field of MEMS, where nano-explosive devices can be used to propel miniature mechanical devices, for example, mini rockets [6]. In this paper we present mechanical impulse measurements for nanostructured porous silicon impregnated with solid state oxidants which can be utilized for jet-propulsion microsystem.

Farm workers are exposed to high risk of heat-related illness, especially when their jobs require working outside at a fast pace during hot days. Climate change has increased the number of days with high temperatures, and thereby the amount of time that farm workers are likely exposed to extreme heat. To better understand how high heat exposure affects farm workers, this study investigates how crop workers respond to heat exposure and estimates the effects of different pay and work arrangements on workers’ responses to heat exposure. We explore, specifically, whether piece-rate arrangements increase workers’ effort during periods with high heat exposure compared to workers paid by hourly wages. We use observational data from detailed measurements of localized heat exposure and individual workers’ effort in the field. First, these results show workers adjust their effort in response to heat exposure when the heat exposure level changes. Second, piece-rate arrangements increase workers’ effort during work shifts. Third, piece-rate arrangements allow workers to modify their effort more easily during different heat exposure levels. When facing low levels of heat exposure, workers who were paid by piece-rate arrangements exert a higher effort than workers paid by hourly wages, up until WBGT is 26.6˚C. When facing high levels of heat exposure (with WBGT exceeding 29.6˚C), workers paid by piece-rate arrangements lower their effort compared to workers paid by hourly wage arrangements.