Assessing the Fit between Mobile Technology on Self-Service and Individual Difference in the Exhibition Industry

The concept of self-service is getting popular. Many firms and users adopt the SST (Self-service Technology) due to its lower cost and saving time. Mobile technology is an emerging technology and because of its characteristics such as identity and location-sensitivity that it is quite suitable for Self-Service in terms of people can easily obtain information tailored for them. Individual difference is one the factors that affect the user adoption. This research is to find out what kind of mobile self-service fits with the certain individual characteristic that generates the better task performance. The result can guide service providers to develop attractive self-service through mobile technology

Optimized detector tomography for photon-number resolving detectors with hundreds of pixels

Photon-number resolving detectors with hundreds of pixels are now readily available, while the characterization of these detectors using detector tomography is computationally intensive. Here, we present a modified detector tomography model that reduces the number of variables that need optimization. To evaluate the effectiveness and accuracy of our model, we reconstruct the photon number distribution of optical coherent and thermal states using the expectation-maximization-entropy algorithm. Our results indicate that the fidelity of the reconstructed states remains above 99%, and the second and third-order correlations agree well with the theoretical values for a mean number of photons up to 100. We also investigate the computational resources required for detector tomography and find out that our approach reduces the solving time by around a half compared to the standard detector tomography approach, and the required memory resources are the main obstacle for detector tomography of a large number of pixels. Our results suggest that detector tomography is viable on a supercomputer with 1~TB RAM for detectors with up to 340 pixels

Electrically-controllable RKKY interaction in semiconductor quantum wires

We demonstrate in theory that it is possible to all-electrically manipulate the RKKY interaction in a quasi-one-dimensional electron gas embedded in a semiconductor heterostructure, in the presence of Rashba and Dresselhaus spin-orbit interaction. In an undoped semiconductor quantum wire where intermediate excitations are gapped, the interaction becomes the short-ranged Bloembergen-Rowland super-exchange interaction. Owing to the interplay of different types of spin-orbit interaction, the interaction can be controlled to realize various spin models, e.g., isotropic and anisotropic Heisenberg-like models, Ising-like models with additional Dzyaloshinsky-Moriya terms, by tuning the external electric field and designing the crystallographic directions. Such controllable interaction forms a basis for quantum computing with localized spins and quantum matters in spin lattices.Comment: 5 pages, 1 figur

Records of volcanic events since AD 1800 in the East Rongbuk ice core from Mt. Qomolangma

Continuous Bi profile of the East Rongbuk (ER) ice core near Mt. Qomolangma reveals nine major volcanic events since AD 1800. Compared with Volcanic Explosivity Index (VEI), it shows that the concentrations of Bi in the ER ice core can reflect the major volcanic events within the key areas. This provides a good horizon layer for ice core dating, as well as a basis for reconstructing a long sequence of volcanic records from the Qinghai-Xizang (Tibet) Plateau ice cores

Seq’ and ARMS shall find: DNA (meta)barcoding of Autonomous Reef Monitoring Structures across the tree of life uncovers hidden cryptobiome of tropical urban coral reefs

Coral reefs are among the richest marine ecosystems on Earth, but there remains much diversity hidden within cavities of complex reef structures awaiting discovery. While the abundance of corals and other macroinvertebrates are known to influence the diversity of other reef-associated organisms, much remains unknown on the drivers of cryptobenthic diversity. A combination of standardized sampling with 12 units of the Autonomous Reef Monitoring Structure (ARMS) and high-throughput sequencing was utilized to uncover reef cryptobiome diversity across the equatorial reefs in Singapore. DNA barcoding and metabarcoding of mitochondrial cytochrome c oxidase subunit I, nuclear 18S and bacterial 16S rRNA genes revealed the taxonomic composition of the reef cryptobiome, comprising 15,356 microbial ASVs from over 50 bacterial phyla, and 971 MOTUs across 15 metazoan and 19 non-metazoan eukaryote phyla. Environmental factors across different sites were tested for relationships with ARMS diversity. Differences among reefs in diversity patterns of metazoans and other eukaryotes, but not microbial communities, were associated with biotic (coral cover) and abiotic (distance, temperature and sediment) environmental variables. In particular, ARMS deployed at reefs with higher coral cover had greater metazoan diversity and encrusting plate cover, with larger-sized non-coral invertebrates influencing spatial patterns among sites. Our study showed that DNA barcoding and metabarcoding of ARMS constitute a valuable tool for quantifying cryptobenthic diversity patterns and can provide critical information for the effective management of coral reef ecosystems

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

IntroductionEfficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.MethodsThese features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.ResultsThe best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.DiscussionCompared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition

Seq’ and ARMS shall find: DNA (meta)barcoding of Autonomous Reef Monitoring Structures across the tree of life uncovers hidden cryptobiome of tropical urban coral reefs

Coral reefs are among the richest marine ecosystems on Earth, but there remains much diversity hidden within cavities of complex reef structures awaiting discovery. While the abundance of corals and other macroinvertebrates are known to influence the diversity of other reef-associated organisms, much remains unknown on the drivers of cryptobenthic diversity. A combination of standardized sampling with 12 units of the Autonomous Reef Monitoring Structure (ARMS) and high-throughput sequencing was utilized to uncover reef cryptobiome diversity across the equatorial reefs in Singapore. DNA barcoding and metabarcoding of mitochondrial cytochrome c oxidase subunit I, nuclear 18S and bacterial 16S rRNA genes revealed the taxonomic composition of the reef cryptobiome, comprising 15,356 microbial ASVs from over 50 bacterial phyla, and 971 MOTUs across 15 metazoan and 19 non-metazoan eukaryote phyla. Environmental factors across different sites were tested for relationships with ARMS diversity. Differences among reefs in diversity patterns of metazoans and other eukaryotes, but not microbial communities, were associated with biotic (coral cover) and abiotic (distance, temperature and sediment) environmental variables. In particular, ARMS deployed at reefs with higher coral cover had greater metazoan diversity and encrusting plate cover, with larger-sized non-coral invertebrates influencing spatial patterns among sites. Our study showed that DNA barcoding and metabarcoding of ARMS constitute a valuable tool for quantifying cryptobenthic diversity patterns and can provide critical information for the effective management of coral reef ecosystems

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.

Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition