Reliability-based semi-analytical solution for ground improvement by PVDs incorporating inherent (spatial) variability of soil

The design of soil consolidation via prefabricated vertical drains (PVDs) has been traditionally carried out deterministically and thus can be misleading due to the ignorance of the uncertainty associated with the inherent (spatial) variation of soil properties. To treat such uncertainty in the design process of soil consolidation by PVDs, stochastic approaches that combine the finite element method with the Monte Carlo technique (FEMC) have been usually used. However, such approaches are complex, computationally intensive and time consuming. In this paper, a simpler reliability-based semi-analytical (RBSA) method is proposed as an alternative tool to the complex FEMC approach for soil consolidation by PVDs, considering soil spatial variability. The RBSA method is found to give similar results to those obtained from the FEMC approach and can thus be used with confidence in practice

Three-dimensional finite element analysis of spatially variable PVD improved ground

A stochastic approach that investigates the effects of soil spatial variability on stabilisation of soft clay via prefabricated vertical drains (PVDs) is presented and discussed. The approach integrates the local average subdivision of random field theory with the Monte Carlo finite element (FE) technique. A special feature of the current study is the investigation of impact of spatial variability of soil permeability and volume compressibility in the smear zone as compared to that of the undisturbed zone, in conjunction with uncoupled three-dimensional FE analysis. A sensitivity analysis is also performed to identify the random variable that has the major contribution to the uncertainty of the degree of consolidation achieved via PVDs. The results of this study indicate that the spatial variability of soil properties has a significant impact on soil consolidation by PVDs; however, the spatial variability of soil properties in the smear zone has a dominating impact on soil consolidation by PVDs over that of the undisturbed zone. It is also found that soil volume compressibility has insignificant contribution to the degree of consolidation estimated by uncoupled stochastic analysis

USAGE OF SMARTPHONE BY HIGH SCHOOL STUDENTS: A STUDY ON JHIKARGACHA SUB-DISTRICT OF BANGLADESH

Students now have more access to online courses thanks to the COVID-19 pandemic. The current study included high school students and aimed to comprehend the drivers behind smartphone use as well as the consequences of smartphone consumption. The school-based work in the sub-district area is not seen in that way, despite the fact that there are numerous forms of research on the use of mobile phones in the context of the city, college, or university. Bangladesh's upazila (subdistricts), in particular, do not have a lot of school-related work. The study uses a quantitative approach. In order to choose Jhikargacha Upazila of Jessore District, which is situated in the Southwest of Bangladesh, purposive sampling is utilized. Additionally, three schools were purposefully selected for data collection. A further offline survey was used to collect data, and SPSS version 24 was used to analyze the findings. In addition, the facts and context of smartphone usage are analyzed using the uses and gratification theory. Most participants claimed to use their smartphones for a range of purposes and to devote varying amounts of time to each. When the corona pandemic forces all educational activities to be conducted online, they are using cellphones. Additionally, certain changes in their daily routine have been brought about by using a smartphone. Some of them think it affects their day-to-day activities by making them feel detached from reality, stay up late, experience worry and melancholy, etc.  Article visualizations

Phytochemical and Elemental Screening on Different Extracts of Leaf, Flower, Stem& Seed of Cassia Sophera Linn: An Important Medicinal Plant of Bangladesh

Qualitative analysis of Cassia Sophera Linn plant confirms the presence of various phytochemicals like alkaloids, flavonoids, terpenoids, saponins, steroids, carbohydrates, anthaquinone glycosides etc in different extracts of its leaves flowers, stems and seeds. Some minerals have also been identified in the leaves, flowers, stems and seed part of the plant by Atomic absorption spectroscopic techniques

Multiplex-PCR protocol development for rapid screening of white spot syndrome virus (WSSV) in shrimp

This study was aimed to develop a faster single step multiplex PCR protocol for the simultaneous detection of white spot syndrome virus (WSSV) with its host (i.e. shrimp) as internal positive control. To do so, four combinations of primer were tested (I. 16S rRNA+Lo F1R1; II. 16S rRNA+Lo F2R2; III. 16S rRNA+Lo F1R2; IV. 16S rRNA+Lo F2R1) which were selected based on two pairs of WSSV specific primer (Lo F1R1 and Lo F2R2) and one pair of shrimp specific primer (16S rRNA). DNA extracted from WSSV infected shrimp were amplified by PCR in a single tube using each of the primer combinations and the thermal cycling conditions as well as reagent compositions were optimized. All the primer combinations yielded their expected band sizes with stronger band resolution intensity that indicated the development of four multiplex PCR protocols. The developed multiplex protocols reduced the chance of cross contamination and these were found to be faster, single step and unique with less effort and resource use. Considering sensitivity and specificity, among the protocols, we suggested the protocols based on 16S rRNA+Lo F1R1 and/or 16S rRNA+Lo F2R2 primer combinations for rapid and routine screening of WSSV in shrimp PL, juvenile and adult

Active Learning on Medical Image

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Growth and yield performance of hybrid rice varieties under varying zinc levels

Zinc deficiency in soil is currently a widespread problem in Bangladesh that significantly reduces the yield of a variety of crops, including rice. Despite the fact that many farmers started applying zinc fertilizer, many are unaware of the right amount and application technique. For this reason, to determine the impact of variety and zinc treatment on the performance of hybrid rice, a field experiment was carried out between November 2019 and May 2020 at the Sher-e-Bangla Agricultural University's experimental field in Sher-e-Bangla Nagar, Dhaka-1207. The experiment consisted of two factors as variety (3 types) viz., V1 – BRRI hybrid dhan2, V2 – BRRI hybrid dhan3 and V3 – BRRI hybrid dhan5 and, Zinc management (4 levels) viz., Zn0 – 0 kg ha-1 (control), Zn1 – 2 kg ha-1, Zn2 – 4 kg ha-1 and Zn3 – 6 kg ha-1. The experiment was laid out in a Randomized Complete Block Design (Factorial) with three replications. Data on different growth and yield parameter of rice were recorded and significant variation was found for different treatments. Regarding varietal performance, the maximum panicle number hill-1 (17.10), panicle length (28.03 cm), grain number panicle-1 (109.45), 1000-grain weight (26.50 g), grain yield ha-1 (6.94 t), straw yield ha-1 (8.58 t), biological yield ha-1 (15.51 t) and harvest index (44.62%) were found from the variety BRRI hybrid dhan5. Considering Zn effect, the maximum panicle number hill-1 (16.33), panicle length (27.14 cm), grain number panicle-1 (108.11), 1000-grain weight (25.38 g), grain yield ha-1 (6.81 t), straw yield ha-1 (8.34 t), biological yield ha-1 (15.15 t) and harvest index (44.88%) were found from 6 kg Zn ha-1. In the case of treatment combination of variety and zinc, the maximum panicle number hill-1 (20.17), panicle length (29.45 cm), number of grains panicle-1 (117.74), 1000-seed weight (27.43 g), grain yield (7.80 t ha-1), straw yield (9.20 t ha-1), biological yield (17.00 t ha-1) and harvest index (45.78%) were found from BRRI hybrid dhan5 along with 6 kg Zn ha-1. Therefore, the hybrid rice variety BRRI hybrid dhan5 with a Zn application of 6 kg ha-1 yielded considerably more grain than the other treatment combinations under evaluation

Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Comparative Analysis of Compression Molded Products of Recycled Waste Poly(Vinyl Chloride) and Virgin Poly(Vinyl Chloride) Fill Material

Waste poly(vinyl chloride) fill material from the cooling tower of a power plant was used for mechanical recycling. Mechanical recycling is the processing of plastic waste without changing the original chemical structure of the plastic. The waste rigid poly(vinyl chloride) fill material was cleaned, grinded and compression molded at optimized conditions of time, temperature and pressure using a compression molding machine. Virgin poly(vinyl chloride) was purchased and compression molded by the same compression molding machine. The compression molded sheets of waste poly(vinyl chloride) and virgin poly(vinyl chloride) were characterized by attenuated total reflection Fourier transform infrared analyses, tensile properties analyses, and thermal properties analyses. The results revealed that waste rigid poly(vinyl chloride) fill material is mechanically recyclable into new products such as pipes, profiles, furniture and other related products

Comparative Analysis of Compression Molded Products of Recycled Waste Poly(Vinyl Chloride) and Virgin Poly(Vinyl Chloride) Fill Material

Waste poly(vinyl chloride) fill material from the cooling tower of a power plant was used for mechanical recycling. Mechanical recycling is the processing of plastic waste without changing the original chemical structure of the plastic. The waste rigid poly(vinyl chloride) fill material was cleaned, grinded and compression molded at optimized conditions of time, temperature and pressure using a compression molding machine. Virgin poly(vinyl chloride) was purchased and compression molded by the same compression molding machine. The compression molded sheets of waste poly(vinyl chloride) and virgin poly(vinyl chloride) were characterized by attenuated total reflection Fourier transform infrared analyses, tensile properties analyses, and thermal properties analyses. The results revealed that waste rigid poly(vinyl chloride) fill material is mechanically recyclable into new products such as pipes, profiles, furniture and other related products