Explore the vast range of titles available.

This study describes how, as part of the administrative reform of Bangladesh, most of the urban local governments have set up some public service center like Pourasava Digital Center (PDC), where ICT (Information and Communication Technology) has been commonly applied to make e-services more convenient, efficient and transparent. The current study measures the Service Quality Satisfaction and Continuous Use Intention to use Pourasava Digital Center (PDC) in Bangladesh by adopting citizen participation as a moderator. Theoretically, this study has used the DeLone & McLean Information Systems (D&M IS) Success Model and Zhang’s two-dimensional satisfaction model. However, most of the existing studies in Bangladesh are qualitative, and the relationship between service quality and citizen satisfaction has not been tested. A survey was conducted based on a structured questionnaire method and data collected from 332 respondents from 05 PDC and applying structural equation modelling in AMOS software while analyzing the data. The empirical results showed that the data fit the model. The finding of this study is that information quality affects specific satisfaction but not accumulative satisfaction, and specific satisfaction might not lead to accumulative satisfaction. One of the worthy findings of this study is that citizen satisfaction is highly dependent on system quality and service quality rather than information quality. The continuous use intention of the citizen is not based on specific satisfaction but significantly depends on accumulative satisfaction. To ensure the improvement of PDC’s service quality, all dimensions related to the quality of service should be modified, and the administrative system and citizens should be encouraged to participate in all aspects of services.

We experimentally investigate the breakup mechanisms and probability of Hinze-scale bubbles in turbulence. The Hinze scale is defined as the critical bubble size based on the critical mean Weber number, across which the bubble breakup probability was believed to have an abrupt transition from being dominated by turbulence stresses to being suppressed completely by the surface tension. In this work, to quantify the breakup probability of bubbles with sizes close to the Hinze scale and to examine different breakup mechanisms, both bubbles and their surrounding tracer particles were simultaneously tracked. From the experimental results, two Weber numbers, one calculated from the slip velocity between the two phases and the other acquired from local velocity gradients, are separated and fitted with models that can be linked back to turbulence characteristics. Moreover, we also provide an empirical model to link bubble deformation to the two Weber numbers by extending the relationship obtained from potential flow theory. The proposed relationship between bubble aspect ratio and the Weber numbers seems to work consistently well for a range of bubble sizes. Furthermore, the time traces of bubble aspect ratio and the two Weber numbers are connected using the linear forced oscillator model. Finally, having access to the distributions of these two Weber numbers provides a unique way to extract the breakup probability of bubbles with sizes close to the Hinze scale.

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high F1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state-of-the-art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine-tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.

Traditional medicines have been an accessible, affordable, and culturally acceptable model of healthcare that is trusted acrossthe globe in terms of disease management, symptoms relief and patient satisfaction. The natural products derived from plant sources arethe fundamental ingredients of traditional and folk medicine and serve as lead compounds or pharmacophore for most effective drugs inmodern pharmacology for treatment of infectious and chronic diseases. Ochradenus baccatus, which belongs to family Resedaceae, is aperennial shrub, widely distributed in the arid regions of Arabian Peninsula and finds mention in the traditional medicine for reproductivehealth, inflammation and infection. Results: As a proof-of-concept, this study has investigated the various bioactive properties of the plantvis-à-vis anti-oxidative, anti-inflammatory and inhibition of xanthine oxidase using standard in vitro experimental models. The bioactivemolecules found in the extract included flavonoids, phenolics, tannins, saponins and alkaloids in varying quantities, which are known tobe associated with a multitude of pharmacological properties. Our results show that the extract caused a progressive inhibition of DPPHradical and neutralizes superoxide anions in a dose-dependent manner. The extract demonstrated its redox-active potential by means of itsability to reduce Cu (II) to Cu (I) that is detected using bathocuproine, a Cu (I)-specific sequestering agent. Progressive doses of the extractsinhibited the denaturation of protein (a factor associated with inflammation), with maximum inhibition of 70.6% observed at concentrationof 600 μg/ml, after which the effect plateaued for further doses. Moreover, the extract displayed a progressive inhibitory property againstthe enzymatic activity of xanthine oxidase with a maximum inhibition of 66% observed at a dose of 300 μg/ml. Conclusion: Inhibition ofxanthine oxidase reduces the hyperuricemia in patients with gout and nephrolithiasis as it alleviates the tendency of salt accumulation inthe joints and kidney. The interest in the natural products pharmacology is escalating and exploring novel bioactive properties in plantsof interest is a significant stride from the traditional knowledge to evidence-based practice

This study investigates the paraxial approximation of the M-fractional paraxial wave equation with Kerr law nonlinearity. The paraxial wave equation is most important to describe the propagation of waves under the paraxial approximation. This approximation assumes that the wavefronts are nearly parallel to the axis of propagation, allowing for simplifications that make the equation particularly useful in studying beam-like structures such as laser beams and optical solitons. The paraxial wave equation balances linear dispersion and nonlinear effects, capturing the essential dynamics of wave evolution in various media. It plays a crucial role in understanding phenomena like diffraction, focusing, and self-phase modulation in optical fibers. It substantially contributes to our comprehension of the special characteristics of optical soliton solutions and the dynamics of soliton in a variety of optical systems. We create a range of wave structures using the powerful extended Jacobian elliptic function expansion (EJEFE) method, including periodic waves, lump-periodic waves, periodic breather waves, kink-bell waves, kinky-periodic waves, anti-kinky-periodic waves, double-periodic waves, etc. These solutions have applications in wave dynamics in different optical systems and optical fibre. Furthermore, we investigate chaotic phenomena by analyzing the model qualitatively. We analyze phase portraits in detail for a range of parameter values to provide insights into the behavior of the system. We also investigate the sensitivity analysis for diverse parametric values of the perturbated coefficient. We may use various strategies, including time series and 3D and 2D phase patterns, to identify chaotic and quasi-periodic phenomena by providing an external periodic strength. The above discussion of the suggested method demonstrates adaptability and usefulness in resolving a broad spectrum of mathematics and physical difficulties, indicating its potential for generating such optical solutions.

This article discusses the (2 + 1)-dimensional Hirota-Maccari (HM) model, a particular type of Schrödinger equation that addresses various nonlinear phenomena in physics, optics, fluid dynamics, plasma physics, and other scientific areas. It uses a variable relation to transform the system into an ordinary form and builds different soliton solutions using the -expansion and generalized -expansion approaches. We create double periodic waves, dark solitons, bright solitons, anti-compacton solitons, bright dark breather waves, periodic multiple waves, multiple dark-bright breather waves, compactons, and -shaped periodic waves using the above methods plus a soft computing package. We numerically simulate some results in 3D with density, 2D, and contour formats. Additionally, we converted the dynamic planner structure of the governing model using the Galilean transformation. We then studied the chaotic properties of this model using various chaos-detecting tools, including fractal dimensions, basins of attraction, recurrence maps, strange attractors, multistability, and return maps. The significance of this study lies in its ability to bridge the theoretical understanding of the governing model with potential applications in diverse nonlinear physical systems. To our knowledge, these two methods have not yet yielded any solutions to the underlying model.

Gaining insight into the mechanisms of zoonotic disease transmission in both animal and human populations is essential for implementing effective measures to control the disease spread and mitigate its impact. This paper employs an evolutionary game theory framework to analyze the intricate dynamics of Monkeypox (mpox) epidemics across diverse networks, including scale-free and random regular networks with four network settings (BA-BA, ER-ER, BA-ER, and ER-BA) in both humans and animals. We investigate how individual behaviors and interactions influence the spread of diseases in different populations by combining network structures with evolutionary game dynamics. The results of our research reveal complex patterns, including the emergence of super-spreaders who transmit the disease to numerous others and the impact of the network structure on the disease’s persistence and transmission. Furthermore, we demonstrate the practicality of this method in clarifying crucial elements that drive the spatial and temporal expansion of mpox, providing a valuable understanding of the efficacy of focused intervention strategies. Our work emphasizes the importance of multidisciplinary approaches in understanding the complex dynamics of infectious diseases and informing public health responses.

Historically, alloy development with better radiation performance has been focused on traditional alloys with one or two principal element(s) and minor alloying elements, where enhanced radiation resistance depends on microstructural or nanoscale features to mitigate displacement damage. In sharp contrast to traditional alloys, recent advances of single-phase concentrated solid solution alloys (SP-CSAs) have opened up new frontiers in materials research. In these alloys, a random arrangement of multiple elemental species on a crystalline lattice results in disordered local chemical environments and unique site-to-site lattice distortions. Based on closely integrated computational and experimental studies using a novel set of SP-CSAs in a face-centered cubic structure, we have explicitly demonstrated that increasing chemical disorder can lead to a substantial reduction in electron mean free paths, as well as electrical and thermal conductivity, which results in slower heat dissipation in SP-CSAs. The chemical disorder also has a significant impact on defect evolution under ion irradiation. Considerable improvement in radiation resistance is observed with increasing chemical disorder at electronic and atomic levels. The insights into defect dynamics may provide a basis for understanding elemental effects on evolution of radiation damage in irradiated materials and may inspire new design principles of radiation-tolerant structural alloys for advanced energy systems.

We experimentally investigate the rise velocity of finite-sized bubbles in turbulence with a high energy dissipation rate of$\\unicode[STIX]{x1D716}\\gtrsim 0.5~\\text{m}^{2}~\\text{s}^{-3}$. In contrast to a 30–40 % reduction in rise velocity previously reported in weak turbulence (the Weber number ($We$) is much smaller than the Eötvös number ($Eo$);$We\\ll 1

Journal Article

Share this book

Add to My Shelf