Advanced Metering Infrastructure Based on Smart Meters in Smart Grid

Due to lack of situational awareness, automated analysis, poor visibility, and mechanical switches, today\u27s electric power grid has been aging and ill‐suited to the demand for electricity, which has gradually increased, in the twenty‐first century. Besides, the global climate change and the greenhouse gas emissions on the Earth caused by the electricity industries, the growing population, one‐way communication, equipment failures, energy storage problems, the capacity limitations of electricity generation, decrease in fossil fuels, and resilience problems put more stress on the existing power grid. Consequently, the smart grid (SG) has emerged to address these challenges. To realize the SG, an advanced metering infrastructure (AMI) based on smart meters is the most important key

New cognitive deep-learning CAPTCHA

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), or HIP (Human Interactive Proof), has long been utilized to avoid bots manipulating web services. Over the years, various CAPTCHAs have been presented, primarily to enhance security and usability against new bots and cybercriminals carrying out destructive actions. Nevertheless, automated attacks supported by ML (Machine Learning), CNN (Convolutional Neural Network), and DNN (Deep Neural Network) have successfully broken all common conventional schemes, including text- and image-based CAPTCHAs. CNN/DNN have recently been shown to be extremely vulnerable to adversarial examples, which can consistently deceive neural networks by introducing noise that humans are incapable of detecting. In this study, the authors improve the security for CAPTCHA design by combining text-based, image-based, and cognitive CAPTCHA characteristics and applying adversarial examples and neural style transfer. Comprehend usability and security assessments are performed to evaluate the efficacy of the improvement in CAPTCHA. The results show that the proposed CAPTCHA outperforms standard CAPTCHAs in terms of security while remaining usable. Our work makes two major contributions: first, we show that the combination of deep learning and cognition can significantly improve the security of image-based and text-based CAPTCHAs; and second, we suggest a promising direction for designing CAPTCHAs with the concept of the proposed CAPTCHA.Web of Science234art. no. 233

Isolation and identification of some . Strain from traditional fermented foods

Recent publications showed that Lactic acid bacteria (LAB) are used extensively to inhibit growth of spoilage and pathogenic bacterial strains. That is being applied in food processing such as tradition fermented food or dairy, beverage and meat products. Lactic acid bacteria can produce a variety of antibacterial agents including bacteriocin, diacetyl, etc. Thus, the isolation, identification and taxonomical characterization of each new Lactobacillus sp. strain is being more and more required. However, the large number of species in the genus Lactobacillus almost have their high phenotypic and physiological similarity which easily leads to misidentification. The present study was aimed for isolation and reliable identification of Lactobacillus sp. strains from some traditional fermented foods by on the basis of phenotypic analysis and combination of PCR and sequencing of target sequences base on 16S-23S rRNA gene. Eight strains of LAB were isolated and characterized through morpholigical, physiological, biochemical and carbohydrate fermentation tests. All of them were determined as Lactobacillus sp. Moreover, the nucleotides sequences of 16S-23S rDNA of them was compared and phylogenetic analysis to those of Lactosbacillus species in GenBank and the results confirm that four strains: L1, L3, L4 and L7 belong to Lactobacillus platarum and four strains: L2, L5, L6 and L8 belong to species L. rhamnosu

Managing egress of crowd during infrastructure disruption

National Research Foundation (NRF) Singapore under Corp Lab @ University schem

Improvement of traditional shrimp culture in the Mekong Delta

Improvements to traditional brackishwater shrimp culture in the Mekong Delta, Vietnam are discussed. A technical support program has been implemented based on a so-called improved extensive shrimp culture method, as previously developed and tested by the Artermia and Shrimp Research and Development Center (ASRDC). The program focuses on: 1) the use of hatchery-produced postlarvae (of Penaeus monodon and P. merguinensis) nursed for three to four weeks, and 2) the application of low-cost pond management practices including predator control, supplementary feeding and frequent water renewal. A credit program, managed as a revolving fund was made available. A dialogue among participating farmers was encouraged through the organization of group meetings before and after each production cycle.Shrimp culture, Aquaculture development, Brackishwater aquaculture, Mekong Delta, Viet Nam,

First record of Cantharellus minor from Vietnam with identification support from a combination of nrLSU and nrSSU phylogenetic analysis

Background: A previously identified sample XC02, which was collected from a pine forest (Pinus kesiya Royle ex Gordon), in Xuan Tho Commune, Da Lat, Lam Dong Province, Vietnam, was identified as Cantharellus minor based on morphology and nrLSU phylogeny analysis. Sequence analysis of multiple genes are becoming more and more common for phylogenetic analysis of mushrooms.Method: Total DNA was isolated from sample XC02. The primer NS1, NS4 were applied to amplify the target gene the nuclear ribosomal small subunit DNA (nrSSU). For phylogenetic analysis, individual and concatenated datasets (nrSSU and nrLSU-nrSSU) were constructed. Phylogenetic tree was constructed with MEGA 6.0 with a 1000 replicate bootstrap based on the neighbor joining, maximum likelihood, maximum parsimony method.  Results: A concatenated dataset containing a total of 14 sequences from Cantharellus, Craterellus (Cantharellaceae, Canthraellales) and Hydnum (Hydnaceae, Cantharellales) were constructed. For the specimen XC02, the phylogenies based on the first, second, and third datasets (nrLSU, nrSSU, and nrLSU-nrSSU) and the morphological analysis, reported in our previous study, strongly confirmed the identity of XC02 as Cantharellus minor.Conclusion: The combination between the morphological analysis and phylogenetic analysis is confirmed as the best approach for the identification of Cantharellus and other mushroom species that we collected in the Central Highlands, Vietnam.Keywords: nrLSU; Cantharellus, Cantharellus minor; nrSSU; nrLSU; phylogeny analysis; Vietna

Gain and Frequency-Selectivity Enhancement of Dual-Polarized Filtering IBFD Antenna Using PRS

A dual-polarized filtering Fabry–Perot antenna (FPA) with high selectivity and high isolation is proposed for in-band full-duplex (IBFD) applications. The proposed antenna utilizes a square patch as the feeding element, which is fed by a double differential-fed scheme for dual-polarized radiation with high isolation. The patch is loaded with a symmetrical cross-slot and four shorting pins for a broad passband filtering feature. To enhance broadside gain across a wide frequency range, the patch is incorporated with a partially reflecting surface (PRS), which is composed of two complementary cross-slot and patch arrays. Moreover, the frequency selectivity of PRS is exploited to improve the filtering characteristic. The double differential feeds are realized based on out-of-phase power dividers, which are combined with simple low-pass filters to further improve the out-of-band suppression. The final design was fabricated and measured. The measurement results show excellent results with a 10-dB return loss bandwidth of 21.5% (4.91–6.09 GHz), isolation of greater than 40 dB, peak gain of 13.7 dBi, out-of-band suppression level of better than 27 dB, and a cross-polarization level of less than −27 dB

Upping the game of taxi driving in the age of Uber

National Research Foundation (NRF) Singapore under its Corp Lab @ University scheme; Fujitsu Limite

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573