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In the field of computer vision, fine-grained visual categorization has attracted a lot of attention and made great progress due to convolutional neural networks and a large number of publicly available datasets. With next-generation sensing technology, RGB-D cameras can provide high-quality synchronized RGB and depth images for solving many computer vision problems. Although RGB-D cameras have been used in the context of multi-view object category detection and scene understanding, they have not been widely used in fine-grained classification. In this paper, we introduce a multiview RGB-D dataset RGBD-FG for fine-grained categorization. Currently, the dataset contains 93,051 RGB-D images covering 19 super-categories and 50 sub-categories of common vegetables and fruit, and is organized in a hierarchical manner. We provide extensive experimental results to establish state-of-the-art benchmarks for our dataset, illustrating its diversity and scope for improvement through future work. We also propose a novel modality-specific multimodal network called FS-Multimodal network, which can solve two limitations of multimodal networks trained based on fine-tuning techniques: over-fitting and lack of effective depth-specific features. We hope that our study lays the foundations for fine-grained categorization of RGB-D data.

Human civilization is experiencing a critical situation that presents itself for a new coronavirus disease 2019 (COVID-19). This virus emerged in late December 2019 in Wuhan city, Hubei, China. The grim fact of COVID-19 is, it is highly contagious in nature, therefore, spreads rapidly all over the world and causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Responding to the severity of COVID-19 research community directs the attention to the analysis of COVID-19, to diminish its antagonistic impact towards society. Numerous studies claim that the subcontinent, i.e., Bangladesh, India, and Pakistan, could remain in the worst affected region by the COVID-19. In order to prevent the spread of COVID-19, it is important to predict the trend of COVID-19 beforehand the planning of effective control strategies. Fundamentally, the idea is to dependably estimate the reproduction number to judge the spread rate of COVID-19 in a particular region. Consequently, this paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers. More specifically, we use various models (for example, susceptible infection recovery (SIR), exponential growth (EG), sequential Bayesian (SB), maximum likelihood (ML) and time dependent (TD)) to estimate the reproduction numbers and observe the model fitness in the corresponding data set. Experimental results show that the reproduction numbers produced by these models are greater than 1.2 (approximately) indicates that COVID-19 is gradually spreading in the subcontinent.

With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state-of-the-art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state-of-the-art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state-of-the-art methods on the popular public datasets.

The objective of the present study was to elucidate the toxic effects of imidacloprid on the embryonic and larval stages of common carp (Cyprinus carpio). Each of the 1000 fertilized eggs and larvae were exposed to six different concentrations (0, 10, 30, 100, 300, and 1000 µg/L) of imidacloprid in 30 plastic bowls. Each of the control and treatment was maintained in five replicates. Results showed that hatching rate of fertilized eggs and number of dead embryos and larvae significantly decreased with increasing imidacloprid concentrations. The 24-, 48-, and 72-h LC50 value of imidacloprid for common carp embryos were estimated to be 12,708.8, 611, and 274.9 µg/L, respectively and the 24-, 48-, 72-, and 96-h LC50 of imidacloprid for common carp larvae were estimated to be 93719, 21691, 2352.7, and 1292.6 µg/L, respectively. Furthermore, results showed several malformations of common carp embryos and larvae induced by the toxicity of imidacloprid when exposed to 300 and 1000 µg/L. The results suggest that the minimum concentration of 10 µg/L imidacloprid in the aquatic environment may have adverse effects on the embryonic and larval stages of common carp.

In general, past studies have estimated the average effect of public smoking bans on youth, ignoring differences across sub-populations. We extend the literature by considering Indigenous youth, who are a vulnerable and previously unexamined group (however, our analysis excludes First Nations youth who live on reserve). We also consider previously unexamined outcomes among youth: self-assessed health and subjective well-being. Our difference-in-differences estimates from Canada indicate that public bans reduced youth smoking and second-hand exposure in public places, on average. There was no displacement on the extensive margin, but the bans increased the number of people who smoke in the homes of youth, conditional on the presence of smokers in the household. Beyond average effects, however, we find that public bans reduced second-hand exposure in the homes of Indigenous youth (particularly Metis youth), on the extensive and intensive margins. The same youth experienced concurrent improvements in self-assessed health and life satisfaction. We conclude that public bans mitigate disparities in health and well-being between Indigenous and non-Indigenous youth, but the extent varies across Indigenous sub-populations, even within a particular country.

This paper reveals a THz leading nodeless antiresonant slotted hollow core fiber (NARS-HCF) with low transmission loss and broad band flat near zero dispersion. We have achieved better results in a simpler structure as far as we know: a minimal confinement loss in the order of around 10−4 dBm-1 and a very low effective material loss of 0.0248 dBm-1, resulting in a lowest total transmission loss of 0.0254 dBm-1 at 1.27 THz and offering a 0.88 THz (0.8 THz – 1.68 THz) low loss transmission bandwidth with the loss of less than 0.0811 dBm-1. Additionally, we achieved a 0.0614 ± 0.0468 ps/THz/cm near zero flat dispersion over the widest bandwidth of 1.02 THz (0.80 THz to 1.82 THz). Moreover, our proposed structure can also effectively perform as single mode fiber with excellent bending loss performance, hence, can be useful for efficient THz transmission systems.

This study estimates racial/ethnic differences in the association between 100% smoke-free state laws and smoking, as well as self-reported health, to facilitate policy aimed at reducing disparities. Data pertain to adults aged 18 years and older, obtained from the public-use Behavioral Risk Factor Surveillance System (2002-2014). The authors exploit variation in the timing of 100% smoke-free state laws using a difference-in-differences model. Examining heterogeneity across racial/ethnic minority groups, the authors consider the association between smoke-free laws and the probability of being: a daily smoker (versus occasional); an occasional smoker (versus former); and at the top of the self-reported health scale (versus the bottom). The authors find that 100% smoke-free state laws were not correlated with smoking among women. Moreover, racial/ethnic minority men who smoked occasionally were less likely to quit than white men, and results suggest that smoke-free laws did not reduce these disparities. However, there is evidence that smoke-free laws reduced the probability of being a daily smoker for Asian and Hispanic/Latinx men, but not the probability of quitting or being at the top of the self-reported health scale. More generally, smoke-free laws were not associated with self-reported health, except that there may have been an improvement among nonsmoking American Indian/Alaska Native women. These findings underscore the importance of looking beyond average effects to consider how 100% smoke-free state laws impact racial/ethnic minorities. There is evidence that they reduced smoking and improved self-reported health for some groups, but a suite of tobacco control policies is necessary to reduce racial/ethnic disparities more broadly.

Fine-grained visual categorization (FGVC) has received a great deal of attention in recent years. Currently, several public datasets are available for FGVC. However, all these datasets were created with RGB images. RGB-D sensors can provide high-quality synchronized video in terms of both color and depth. In this paper, we introduce a multi-view, large-scale RGB-D dataset called RGBD-FG to establish a novel benchmark for FGVC in RGB-D images. RGBD-FG contains RGB data and the corresponding depth data of vegetables and fruits. Our dataset was captured by a depth sensor and contained 50 categories and a total of 93,051 RGBD images with labels, and organized in a hierarchical manner. Additionally, we used several strategies on our dataset for FGVC, including a multi-modal deep CNN. We present extensive experimental results to create state-of-the-art baselines for the dataset. We hope that this dataset can fill the gap of FGVC among the RGB-D datasets.

Antimicrobial resistance gene mcr-1 has been disseminated globally since its first discovery in Southern China in late 2015. However, the mcr-1 gene had not been identified previously in Salmonella isolates from poultry in Bangladesh. Here, we aimed to explore antimicrobial resistance gene mcr-1 in Salmonella isolates. Eighty two Salmonella isolates were isolated and characterized from suspected poultry specimens received from different zones of the country. A phenotypic disc diffusion assay with 15 antimicrobial agents was performed following CLSI standard. The disk diffusion assay showed that, all of the isolates presented high resistance to colistin (92.68%), oxytetracycline (86.59%), co-trimoxazole (76.83%), ciprofloxacin (73.17%) and enrofloxacin (65.85%). Further, randomly selected 10 Salmonella isolates were analyzed by polymerase chain reaction (PCR) targeting genus-specific invA and antimicrobial (colistin) resistance mcr-1 genes. Five were confirmed for the presence of the mcr-1 gene belonging to Salmonella spp. Further, sequencing followed by phylogenetic analysis revealed divergent evolutionary relation between the LptA and MCR proteins rendering them resistant to colistin. Three-dimensional homology structures of MCR-1 proteins were constructed and verified using different bioinformatics tools. Moreover, molecular docking interactions suggested that, MCR-1 and LptA share a similar substrate binding cavity which could be validated for the functional analysis. The results represent here is the first molecular and in silico analysis of colistin resistance mcr-1 gene of Salmonella in poultry in Bangladesh, which may emphasize the importance of the study on antibiotic resistance genes requiring for national monitoring and strategic surveillance in the country.

Background Diarrhea remains a major public health problem and characterization of its etiology is needed to prioritize interventions. However, most data are from single-site studies of children. We tested samples from participants of any age from 11 geographically diverse hospitals in Bangladesh to describe pathogen-specific burdens of diarrhea. Methods We utilized 2 existing diarrhea surveillance systems: a Nationwide network at 10 sentinel hospitals and at the icddr,b hospital. We tested stools from enrolled participants and nondiarrheal controls for enteropathogens using quantitative polymerase chain reaction and calculated pathogen-specific attributable fractions (AFs) of diarrhea. Results We analyzed 5516 patients with diarrhea and 735 controls. Overall, rotavirus had the highest attributable burden of diarrhea (Nationwide AF, 17.7%; 95% confidence interval [CI], 14.3–20.9%; icddr,b AF, 39.9%; 38.0–41.8%), followed by adenovirus 40/41 (Nationwide AF, 17.9%; 95% CI: 13.9–21.9%; icddr,b AF, 16.6%; 95% CI, 14.4–19.4%) and Vibrio cholerae (Nationwide AF, 10.2%; 95% CI, 9.1–11.3%; icddr,b AF, 13.3%; 95% CI: 11.9–15.1%). Rotavirus was the leading pathogen in children 5 years old, but was more geographically variable (coefficient of variation = 71.5%). Other attributable pathogens included astrovirus, norovirus, Shigella, Salmonella, ETEC, sapovirus, and typical EPEC. Conclusions Rotavirus, adenovirus 40/41, and V. cholerae were the leading etiologies of infectious diarrhea requiring hospitalization in Bangladesh. Other pathogens were important in certain age groups or sites., Patients of all ages were enrolled in the sentinel surveillance from 11 Bangladesh hospitals. Rotavirus, adenovirus 40/41, and Vibrio cholerae were the leading causes of diarrhea across age and geography with variations in frequency. Other pathogens were important in certain ages or sites.

Mesoporous magnetic silica particles bearing a stimuli-responsive polymer valve were prepared and their performance as a microcapsule was evaluated. In this study, first, mesoporous magnetic iron oxide (Fe3O4) particles were prepared by a solvothermal method. Then, the magnetic particles were coated with silica and functionalized with vinyl groups using 3-(trimethoxysilyl)-propyl methacrylate (MPS). Subsequently, the Fe3O4/SiO2 composite particles grafted with MPS were used to carry out the seeded precipitation copolymerization of N-isopropylacrylamide (NIPAM) and 2,2-dimethylaminoethyl methacrylate (DMA). Here N,N'-methylenebisacrylamide (MBA) was used as a cross-linker. Brunauer-Emmett-Teller (BET) surface analysis suggested that the mesoporous structure was retained in the final Fe3O4/SiO2/P(NIPAM-DMA-MBA) composite hydrogel particles. The prepared Fe3O4/SiO2/P(NIPAM-DMA-MBA) composite hydrogel microspheres exhibited a pH-dependent volume phase transition. At lower pH values (

With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state-of-the-art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state-of-the-art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state-of-the-art methods on the popular public datasets.

Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multi-class object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Single-stage Multi-class Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multi-class objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multi-scale, multi-level feature map and get effective attention representation for multi-class remote sensing object detection. SMOD can be trained from scratch without pre-trained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfrom-scratch method can obtain better performance compared with some state-of-art algorithms on public multi-class remote sensing dataset AIIA2018-6.

Magnetic iron oxide (Fe3O4) microparticles prepared by solvothermal route are modified with SiO2 layer and then functionalized with vinyl groups. Finally, polyelectrolyte layer composed of isobornyl methacrylate (iBMA) and (3-acrylamidopropyl)trimethylammonium chloride (APTMACl) is grafted onto the surface of magnetic SiO2 particles via free radical polymerization to produce composite particles abbreviated as Fe3O4/SiO2/P(iBMA-APTMACl). The size distribution and structural composition of Fe3O4/SiO2/P(iBMA-APTMACl) composite particles are measured by electron microscopy and different spectroscopy techniques. Fe3O4/SiO2/P(iBMA-APTMACl) composite particles possessed moderate paramagnetic property. The mesoporous surface structure of composite particles is confirmed from average pore diameter (3.8 nm), measured by Barrett–Joyner–Halenda (BJH) method. The composite particles exhibited pH dependent phase transition near 5.8. The pH dependent adsorption and release study of anionic drug (aspirin) through off- and on-capping of polyelectrolyte valve/gate confirmed the ion exchange property of Fe3O4/SiO2/P(iBMA-APTMACl) composite particles. Compared to Fe3O4/SiO2 particles, the loading capacity of biomolecules in polyelectrolyte grafted composite particles (∼ 30 mg g−1) is relatively larger at the respective isoelectric point. In presence of 11.2 kU mg−1 conjugated streptokinase (SK), Fe3O4/SiO2/P(iBMA-APTMACl) composite particles exhibited significant amount of thrombolytic activity (56%). A brief study of hemolysis suggested that composite particles are hemocompatible up to 0.18 mg mL−1. The higher inhibition percentage of protein denaturation also indicated the anti-inflammatory property of bare composite particles. The prepared Fe3O4/SiO2/P(iBMA-APTMACl) composite particles can therefore be applied in targeted thrombolysis, drug/gene carrier, anti-inflammatory agent and hyperthermia.

Electric current responsive magnetic composite particles are prepared in three steps. In the first step, spherical and mesoporous submicrometer-sized magnetic iron oxide (Fe3O4) core particles are prepared by solvothermal method. Then magnetic Fe3O4 particles are functionalized with amine groups using glycine, where the COO- group of glycine formed a bridging bidentate interaction with the hydroxyl groups on Fe3O4 particle surface. Finally polyaniline (PAni) is grafted onto the functional Fe3O4 surface via in situ seeded chemical oxidative polymerization of aniline. The average size of Fe3O4/PAni composite particles is 418.82 nm with mesoporous surface structure. Electron microscopic images confirmed that PAni pockets are localized on the surface of Fe3O4 core particles. The surface composition is further confirmed by Fourier transform IR (FTIR), X-ray photoelectron spectroscopy (XPS) and thermogravimety (TG) analyses. Fe3O4/PAni composite particles possessed strong paramagnetic property (47.77 emu g−1). The electrical conductivity of Fe3O4/PAni composite particles (1.46 × 10-4 S cm−1) remained in the same order of magnitude as that of reference PAni particles (3.42 × 10-4 S cm−1). The release behavior of biologically active molecules such as trypsin (TR), albumin (AL) and p-acetamido phenol (pAP) is studied using low intensity electric current as stimuli to prevent degradation. Depending on the nature, up to ˜ 33–88% of adsorbed biomolecules/drug are released from the electromagnetic Fe3O4/PAni composite particles. Compared to Fe3O4 particles, Fe3O4/PAni composite particles exhibited moderate antibacterial property.

The visually impaired community suffers from an immense disadvantage in academic sector. They are also not up to speed with the staggering development of technology and portable devices. To solve this issue, the area of braille technology has been widely explored since its inception. Refreshable braille display is a small step towards the advent of smart braille devices. In this paper a single cell refreshable braille display model has been proposed that is based on micro servos, as opposed to solenoids, lead screw actuation etc., to formulate different patterns of braille symbols, each representing an alphanumerical value or a contracted word. Many existing refreshable braille displays perform outstandingly, however, it is the portability, simplicity and the cost efficiency of the proposed model that contribute to the novelty of this study. A prototype of the proposed model has been built to conduct an experiment to showcase the efficiency, cost analysis and portability analysis of the system. The results show that the presented model can be a better choice in simplified and cost effective refreshable braille display implementations.

Automatic text summarization process has been significantly explored throughout the years to cope with the staggering increase of virtual data. Text summarization process is commonly divided into two areas-Extractive and Abstractive. Extractive summarization processes largely depend on sentence extraction techniques- implementing graph models or sentence-based models. In this paper, a sentence-based model has been proposed where the sentence ranking procedure adopts fuzzy C-Means (FCM) clustering, an unsupervised classification method, for sentence extraction purpose. The sentence scoring task relies on five key features, including Topic Sentence which is the first novelty of the proposed model. Furthermore, C-Means clustering is a soft-computing technique that is usually used for pattern recognition tasks but can be improved significantly by hard clustering the membership of the elements which has not been regarded in similar processes in any of the previous works, adding to the novelty of the presented model. Standard summary evaluation techniques have been used to gauge the precision, recall and f-measure of the proposed FCM model and have been compared with different summarizers from different perspectives. The outcome shows that the FCM model surpasses the previous approaches significantly.

In many underdeveloped and developing countries, the custom and laws of crossing the roads are not very strict, in which case an automated road crossing system is much needed. Besides, the amount of risk being taken every other day around us, only makes the case for an automated zebra crossing system stronger. This paper presents a cost effective and easy solution to this problem using a simple system based on Arduino-UNO. The system is entirely autonomous and can effectively measure the parameters expected of an Automated Zebra Crossing system. Measurements of the parameters required in the system has been done by ultrasonic sensors, and the experiments done on the system show that it has a chance of being outsmarted 1 out of 200 times, which is an efficiency rate close to 99.5%.

Nowadays, Wireless Sensor Networks (WSNs) are widely used in many areas, especially in environment applications, military applications, queue tracking, etc. WSNs are vulnerable to different types of security attacks due to various constraints such as broadcasted nature of transmission medium, deployment in open or hostile environment where they are not physically protected, less memory, and limited battery power. So, security system is the crucial requirements of these networks. One of the most notably routing attacks is the sinkhole attack where an adversary captures or insert nodes in the sensor field that advertise high quality routes to the base station. In this paper, a mechanism is proposed against sinkhole attacks which detect malicious nodes using hop counting. The main advantage of the proposed technique is that, a node can detects malicious nodes only collaborating with the neighbor nodes without requiring any negotiation with the base station. Simulation result shows that, the proposed technique successfully detects the sinkhole nodes for large sensor field.