Explore the vast range of titles available.

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

Construction of hybrid three-dimensional (3D) hierarchical nanostructures via self-assembly of organic and inorganic compounds have recently attracted immense interest from scientists due to their unique properties and promise in a large range of applications. In this article, hybrid flower structures were successfully constructed by self-assembly an antibiotic, kanamycin, with Cu 2+ . The flower-like morphology was observed by scanning electron microscopy, to be approximately 4 µm in diameter and about 10 nm in thickness. FTIR spectroscopy and X-ray diffraction confirmed the antibiotic-inorganic hybrid structure was uniform composition, and showed crystallinity due to ordered self-assembly. The hybrid flowers showed high photocatalytic activity towards degradation of methyl blue during 240 minutes under visible light irradiation. A possible mechanism of photocatalytic activity was also proposed, that exposes the inherent advantages in using antibiotic-inorganic hybrid flowers as photocatalysts, where self-assembly can be used to generate active, high surface area structures for photodegradation of pollutants.

The strong confinement of surface plasmons’ optical fields at metal surfaces makes them highly sensitive to the structural shape and refractive index change of target biological1,2, chemical3,4 or atomic species5. This has made surface plasmon resonance a widely applicable sensing technique. Plasmonic metrology is primarily based on the spectral shift of the scattering intensity spectrum. Although broadband phase spectra are known to provide richer information on target samples as opposed to intensity spectra, direct acquisition of broadband phase spectra in plasmonics has been made difficult by the lack of highly stabilized light sources. Here, we demonstrate that frequency-comb-referenced phase spectroscopy provides high speed, high resolution, and high linearity with respect to plasmonic rulers, with direct traceability to a time standard. As a demonstration, we measure the 1.94 Å dynamic motion of a pair of nanoholes with a resolution of 1.67 pm. The interaction through the propagation of the plasmonic field is enhanced by a factor of 155 compared to the physical sample length. Our realization of a fast and robust plasmonic ruler with picometre resolution makes it possible to obtain high-precision plasmonic phase spectroscopy for in-depth analysis of the dynamics of samples in nanoscopic volumes.

The concept of three-dimensional stacking of device layers has attracted significant attention with the increasing difficulty in scaling down devices. Monolithic 3D (M3D) integration provides a notable benefit in achieving a higher connection density between upper and lower device layers than through-via-silicon. Nevertheless, the practical implementation of M3D integration into commercial production faces several technological challenges. Developing an upper active channel layer for device fabrication is the primary challenge in M3D integration. The difficulty arises from the thermal budget limitation for the upper channel process because a high thermal budget process may degrade the device layers below. This paper provides an overview of the potential technologies for forming active channel layers in the upper device layers of M3D integration, particularly for complementary metal-oxide-semiconductor devices and digital circuits. Techniques are for polysilicon, single crystal silicon, and alternative channels, which can solve the temperature issue for the top layer process.

Surface plasmon polaritons (SPPs) have found their key applications in high-sensitivity biomolecular detection and integrated photonic devices for optical communication via light manipulation at nanostructures. Despite their broad utility, SPPs are known to be accompanied by other complex near-field propagation modes, such as quasi-cylindrical waves (QCWs) and composite diffracted evanescent waves (CDEWs), whose electromagnetic and quantum propagation effects have not been comprehensively understood especially regarding their mutual interaction with SPPs. In this study, we addressed this complexity by employing a nano groove structure and a high-stability broadband femtosecond laser as a light source, the spatial phase distribution around the nano slit edge was measured with relative stability of a 4.6 × 10 at an averaging time of 0.01 s. Through this spatial phase spectrum, we precisely measured the nonlinear distribution of effective refractive index changes with an amplitude of 10 refractive index units at the edge of the nano slit–groove structure. These results reveal that the near-field effects on local electron concentration induced by nanostructure’s discontinuity can be quantitatively measured, which can contribute to a deeper understanding of SPP phenomena in nanostructures for the optimal design and utilization of the SPP effects in diverse nano-plasmonic applications.

In the digital age and technological advancements, short video platforms have become essential tools for online sales and marketing. In addition, shopping through short video marketing has gained significant attention, especially among Generation Z, as it brings unique and novel shopping experiences. The primary goal of this study is to explore the factors of short video marketing that influence the purchase intentions of Generation Z consumers in Vietnam. To conduct this study, a quantitative approach was employed, utilizing a 5-point Likert scale questionnaire administered online through a non-probability sampling method. The sample comprised 350 respondents aged between 16 and 26 from Vietnam, representing Generation Z, who made purchases through short video marketing. The relationships among various variables were analyzed using Structural Equation Modeling (SEM). The study’s results demonstrated a positive, significant, and direct relationship between all factors of short video marketing, including interesting content, perceived usefulness, scenario-based experience, user interaction, perceived enjoyment, and involvement of celebrities and consumer brand attitude. Among these factors, perceived usefulness is the most influential factor on customer brand attitude. In addition, the study revealed that consumer brand attitude, acting as a mediating variable, had a positive and significant impact on consumers’ purchase intentions. Based on the findings, the study suggested strategies for businesses to enhance the quality and content on short video platforms, thereby improving the effectiveness of their marketing strategies. AcknowledgmentThe authors express a sincere gratitude to all the participants who generously took part in this research study.

Surface plasmon resonance (SPR) sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces, which makes them highly sensitive to biological or chemical molecular bindings to functional metallic surfaces. Metal nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit, thus strengthening photon-sample interactions. However, plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental noises. Here, we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase spectroscopy. The photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement effects. By utilizing a sharp plasmonic phase slope and a high heterodyne information carrier, a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole (in 10 –11 RIU) in Allan deviation at 0.2 s; a faster motion up to 200 Hz was clearly resolved. This fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical, chemical, and physical events and interactions.

Objectives: This study aimed to evaluate the incidence of adverse reactions to the COVID-19 vaccine among pregnant and breastfeeding women and identify associated demographic and clinical factors. Methods: A cross-sectional study was conducted at a hospital in Hanoi, Vietnam, from November 2021 to March 2022. A total of 1204 participants, including 991 pregnant women beyond 13 weeks of gestation and 213 breastfeeding women, were recruited through convenience sampling. Data were collected using a self-administered questionnaire designed to capture demographic information and adverse reactions occurring within seven to 28 days post-vaccination. Statistical analyses, including chi-square tests, Fisher’s exact tests, and logistic regression, were performed using Stata 16.0, with the significance set at p < 0.05. Results: The most common adverse reactions were localized pain at the injection site (26.2%), dizziness and fatigue (19.2%), and fever below 39 °C (29.1%). Severe adverse reactions, such as a tight throat, coma, and premature birth, were rare. A multivariate analysis identified the significant factors associated with the adverse reactions, including age (aOR = 2.04 for participants aged 36–40 years), occupation (lower odds for farmers and business professionals), urban residency (aOR = 0.64), and a history of allergies (aOR = 1.59). Education level, number of children, and gestational age were not significantly associated with adverse events. Conclusions: The findings support the safety of the COVID-19 vaccine in pregnant and breastfeeding women, with most of the adverse reactions being mild and self-limiting.

Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context. This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately. Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model's performance. We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F -score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases. The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.

The effects of the grain size of Pt bottom electrodes on the ferroelectricity of hafnium zirconium oxide (HZO) were studied in terms of the orthorhombic phase transformation. HZO thin films were deposited by chemical solution deposition on the Pt bottom electrodes with various grain sizes which had been deposited by direct current sputtering. All the samples were crystallized by rapid thermal annealing at 700 °C to allow a phase transformation. The crystallographic phases were determined by grazing incidence X-ray diffraction, which showed that the bottom electrode with smaller Pt grains resulted in a larger orthorhombic phase composition in the HZO film. As a result, capacitors with smaller Pt grains for the bottom electrode showed greater ferroelectric polarization. The smaller grains produced larger in-plane stress which led to more orthorhombic phase transformation and higher ferroelectric polarization.