Metasurface supporting quasi-BIC for optical trapping and Raman-spectroscopy of biological nanoparticles

Optical trapping combined with Raman spectroscopy have opened new possibilities for analyzing biological nanoparticles. Conventional optical tweezers have proven successful for trapping of a single or a few particles. However, the method is slow and cannot be used for the smallest particles. Thus, it is not adapted to analyze a large number of nanoparticles, which is necessary to get statistically valid data. Here, we propose quasi-bound states in the continuum (quasi-BICs) in a silicon nitride (Si3N4) metasurface to trap smaller particles and many simultaneously. The quasi-BIC metasurface contains multiple zones with high field-enhancement (‘hotspots’) at a wavelength of 785 nm, where a single nanoparticle can be trapped at each hotspot. We numerically investigate the optical trapping of a type of biological nanoparticles, namely extracellular vesicles (EVs), and study how their presence influences the resonance behavior of the quasi-BIC. It is found that perturbation theory and a semi-analytical expression give good estimates for the resonance wavelength and minimum of the potential well, as a function of the particle radius. This wavelength is slightly shifted relative to the resonance of the metasurface without trapped particles. The simulations show that the Q-factor can be increased by using a thin metasurface. The thickness of the layer and the asymmetry of the unit cell can thus be used to get a high Q-factor. Our findings show the tight fabrication tolerances necessary to make the metasurface. If these can be overcome, the proposed metasurface can be used for a lab-on-a-chip for mass-analysis of biological nanoparticles

Impact of Moral and Ethical Degradation on Poverty in Bangladesh: A Sustainable Solution from Islamic Perspective

The paper aimed to study the impact of degradation of moral and ethical values in some cases since this degradation had become a worried matter for our society In this study we also tried to mention some interior causes which have an exquisite interrelation with the poverty nature of Bangladesh Researchers followed the analytical method to complete this study The research shows there is a mentionable impact of educational political cultural and economic moral degradation of poverty Hence a critical proper sustainable solution from the Islamic perspective is needed to protect this degradation It is also proven that Islam as a comprehensive way of life encompasses a complete moral and ethical ground that is amplify in human social culture and their lifestyle So abide by the precept of Islamic views it is possible to build a sustainable social development in completing with moral ethical and Islamic perception with collectivel

Estimation of population structure, growth and condition of Parastromateus niger in the Bay of Bengal : suggestions for catchable sizes

Black pomfret (Parastromateus niger) is one of the major commercial species of pomfret fishery in Bangladesh. This study illustrates the population structure (Length Frequency Distribution, LFD), relationship between length and weight (LWR), relationships between length and length (LLRs), Fulton’s condition factor (KF) and relative weight (WR) of P. niger in the Bay of Bengal (BoB). A total of 225 P. niger were collected from the four locations during January to December 2020. LFD analysis indicates three length classes 21-27 cm, 30-32 cm and 35-37 cm, respectively. This species showed isometric growth pattern (b = 2.981) that indicates that the size and weight increases proportionally and the surrounding habitat provides favourable environment for the growth. LWR between TL and BW were highly correlated (r² = 0.951). LLRs also showed significant correlation between TL and SL (r² = 0.845) and TL and FL (r² = 0.861). The mean value of KF was found as 1.60 which indicates that the BoB provides healthy environment for this species. The mean value of WR (101.09) indicates that the relationship between prey and predator was in balanced condition. This study suggests optimum catchable length for P. niger at 27 cm. Therefore, these findings could provide important information to design effective conservation and management planning for this species

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community’s research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.publishedVersio

Predictive Health Analysis in Industry 5.0: A Scientometric and Systematic Review of Motion Capture in Construction

In an era of rapid technological advancement, the rise of Industry 4.0 has prompted industries to pursue innovative improvements in their processes. As we advance towards Industry 5.0, which focuses more on collaboration between humans and intelligent systems, there is a growing requirement for better sensing technologies for healthcare and safety purposes. Consequently, Motion Capture (MoCap) systems have emerged as critical enablers in this technological evolution by providing unmatched precision and versatility in various workplaces, including construction. As the construction workplace requires physically demanding tasks, leading to work-related musculoskeletal disorders (WMSDs) and health issues, the study explores the increasing relevance of MoCap systems within the concept of Industry 4.0 and 5.0. Despite the growing significance, there needs to be more comprehensive research, a scientometric review that quantitatively assesses the role of MoCap systems in construction. Our study combines bibliometric, scientometric, and systematic review approaches to address this gap, analyzing articles sourced from the Scopus database. A total of 52 papers were carefully selected from a pool of 962 papers for a quantitative study using a scientometric approach and a qualitative, indepth examination. Results showed that MoCap systems are employed to improve worker health and safety and reduce occupational hazards.The in-depth study also finds the most tested construction tasks are masonry, lifting, training, and climbing, with a clear preference for markerless systems

Aiming to Minimize Alcohol-Impaired Road Fatalities: Utilizing Fairness-Aware and Domain Knowledge-Infused Artificial Intelligence

Approximately 30% of all traffic fatalities in the United States are attributed to alcohol-impaired driving. This means that, despite stringent laws against this offense in every state, the frequency of drunk driving accidents is alarming, resulting in approximately one person being killed every 45 minutes. The process of charging individuals with Driving Under the Influence (DUI) is intricate and can sometimes be subjective, involving multiple stages such as observing the vehicle in motion, interacting with the driver, and conducting Standardized Field Sobriety Tests (SFSTs). Biases have been observed through racial profiling, leading to some groups and geographical areas facing fewer DUI tests, resulting in many actual DUI incidents going undetected, ultimately leading to a higher number of fatalities. To tackle this issue, our research introduces an Artificial Intelligence-based predictor that is both fairness-aware and incorporates domain knowledge to analyze DUI-related fatalities in different geographic locations. Through this model, we gain intriguing insights into the interplay between various demographic groups, including age, race, and income. By utilizing the provided information to allocate policing resources in a more equitable and efficient manner, there is potential to reduce DUI-related fatalities and have a significant impact on road safety.Comment: IEEE Big Data 202

Germination and early seedling growth of a medicinal plant giant milkweed (Calotropis gigantea) under salinity stress

Giant milkweed (Calotropis gigantea L.) is a salinity and drought-resistant xerophyte that is widespread around the world and serves significant ecological and medicinal purposes. The research aimed to evaluate the influence of saline stress on germination characteristics and prompt growth attributes of seedlings of C. gigantea. Seeds were germinated under five salinity levels viz. 0, 6, 8, 10 and 12 dS m-1 and allowed to grow for 30 days for traits assessment. Germination percentage (GP), germination rate index (GRI), co-efficient of the velocity of germination (CVG), mean germination time (MGT), Timson germination index (TGI), shoot length (SL), root length (RL), seedling dry weight (SDW) and healthy seedling number at 30 days were found lower in the salt solution compared to the control condition. Mean germination time was expanded with the increment of salinity levels. TGI of C. gigantea sustained a significant positive linear regression with GP (r = 0.9881), GRI (r = 0.9923) and CVG (r = 0.7887) at P < 0.001, but MGT (r = 0.7855) at P = 0.005. The correlation coefficient among the germination traits revealed insignificant between RL and other germination traits (GP, CVG, MGT, and TGI) except GRI (r = 0.499*) and SL (r = 0.541*).  It is recommended that, as an emerging medicinal and fiber resource plant, C. gigantea can be cultured productively in coastal saline areas.  Int. J. Agril. Res. Innov. Tech. 14(1): 10-17, June 202

Ligand based sustainable composite material for sensitive nickel(II) capturing in aqueous media

© 2019 Elsevier Ltd. Organic ligand based sustainable composite material was prepared for the detection and removal of nickel (Ni(II)) ion from contaminated water. The ligand was anchored based on the building-block approach. The carrier silica and ligand embedded composite material were characterized systematically. The detection and removal of Ni(II) ion operation was evaluated according to the solution pH, reaction time, detection limit, initial Ni(II) concentration and diverse co-existing metal ions. The detection limit of Ni(II) ion by the proposed composite material was 0.41 μg L-1. The detection and removal of Ni(II) ion was significantly influenced by the solution pH. However, the neutral pH 7.0 was chosen for sensitive and selective detection and removal of Ni(II) ion. The co-existing diverse metal ions were not interfered during the detection and removal of Ni(II) ion because of the high affinity of Ni(II) ion to composite material at the optimum experimental conditions. The Langmuir adsorption isotherm model was selected based on the materials morphology and applied to validate the adsorption isotherms according to the homogeneous ordered frameworks. The adsorption capacity was 199.19 mg g-1 as expected due to the high surface area of material. The adsorbed Ni(II) ion was completely eluted from the composite material with the eluent of 0.50 M HCl and the regenerated material was used in several cycles without deterioration in its initial performances. Therefore, it is expected to that the Facile composite material may hold huge potentials in applications and may be scaled up for commercial applications, including environmental detection and removal of Ni(II) ion