Hybrid renewable energy systems are becoming more predominant because of climate change and the overconsumption of natural fuels. Proper utilization of renewable resources can uplift energy-deprived regions while also contributing to a nation's economic growth. However, effective system planning and resource assessment are essential for effective utilization. In that regard, the study proposes a hybrid microgrid design for a remote island in Bangladesh. The proposed system comprises solar photovoltaics, wind turbines, and lithium-ion battery storage which is coupled to the utility grid. For modeling and simulation of the optimal system design of the residential load in Urir char, Hybrid Optimization Multiple Energy Resources (HOMER) pro was utilized. The load profile for the system was created employing fuzzy logic and random probability, as well as meteorological data for the chosen location. Several instances with reliability factors such as short-term and long-term interruptions are also taken into consideration in the design. Additionally, the paper discussed a comparison between the proposed system and other considered scenarios as well as the utility grid. The proposed system is a viable approach for providing cleaner energy for the selected area in regards to energy cost (0.035$/kWh), a renewable fraction (90 % ), emission reduction (78%), and reliability.
Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising 74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks. According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic target replacement, alteration and antibiotic efflux pump processes. They exhibit resistance to several antibiotics, such as isoniazid, ethionamide, cycloserine, fosfomycin, triclosan, etc. Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant interactors in the interaction network and are therefore regarded as the hub nodes. These genes can be exploited to create novel medications by serving as possible therapeutic targets. Finally, we believe that our findings could be useful to advance knowledge of the AMR system present in M. catarrhalis.
Co0.2Ni0.5Zn0.3EuxFe2-xO4 (CNZEFO) composites were synthesized by the conventional double sintering technique, where x = 0.00, 0.02, 0.04, and 0.08. Rietveld refined x-ray diffraction indicates the spinel cubic structure (Fd-3 m space group) of the samples. The refined XRD pattern also mentioned the Fe2O3 secondary peak for x = 0.04 and 0.08 samples. The structural lattice constant was initially increased, but after that it decreased with the Eu concentration. The bulk density of the samples was always lower than the x-ray density, where the densities were increased with the enhancement of Eu concentration. FTIR analysis confirmed the metal–oxygen bonds in ferrite with spinel cubic structure. FESEM micrographs provide the average grain size of the samples, which varies between 105.1 nm and 169.9 nm. EDX analysis was used to carry out the compositional verification and confirm that the elements were present in the required ratios. Magnetic hysteresis loop measurements were studied at room temperature, where the magnetization demonstrates the decline with the substitution of Eu ions. The addition of Eu concentration also changed other fundamental properties of the ferrites, such as coercivity (Hc), retentivity (Mr), anisotropy constant (K), and magnetic moment (nB). The permeability measurements show that the real permeability of the samples was decreasing at lower frequency region further they became high at higher frequency region. The magnetic loss tangent (tanδM) and dielectric loss tangent (tanδD) were reduced with the addition of Eu concentration, where the Eu doped samples show higher dielectric and magnetic quality factor than the pure sample. The enhancement of resistivity and impedance were also noticed for the addition of Eu content in Co0.3Ni0.2Zn0.5Fe2O4 sample. These europium doped cobalt–nickel-zinc ferrites may be strong candidates for potential high frequency applications.
Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass-slide specimen under a microscope by an expert. Whole slide image (WSI) is the digital specimen produced from the glass-slide. WSI enabled specimen to be observed on a computer-screen and led to computational pathology where computer-vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, entire WSI can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the WSI is affected by tissue artifacts such as tissue fold or air bubble depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact-affected regions from analysis. This process is time-consuming, exhausting and undermines the goal of automated analysis or removal of artifacts without evaluating their severity, which could result in the loss of diagnostically important data. Therefore, it is necessary to detect artifacts and then assess their severity automatically. In this paper, we propose a system that incorporates severity evaluation with the artifact detection utilizing convolutional neural networks (CNN). The proposed system uses DoubleUNet to segment artifacts and an ensemble network of six fine-tuned CNN models to determine severity. This method outperformed current state-of-the-art in accuracy by 9% for artifact segmentation and achieved a strong correlation of 97% with pathologist's evaluation for severity assessment. The robustness of the system was demonstrated using our proposed heterogeneous dataset and practical usability was ensured by integrating it with an automated analysis system. INDEX TERMS Tissue artifact, artifact detection, quality evaluation, whole slide image, digital pathology, automated image analysis
E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students’ expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student’s emotional state. As a result, this research detects that emotions’ mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing.
This study investigates the augmentation of students' engagement in the online learning process using Zoom platform. To engage students more in the online classes we have conducted a survey on four universities students in the four dimensions. To investigate effective online class, we have gone through descriptive statistics followed by principal component analysis (PCA) and factor regression model to identify predicted factors that engage students more in the Zoom online classes. The results of PCA confirmed that questions answer session, instructor asks question to them, break during the class, topic related examples, experience sharing scope, case studies, using Google classroom, screen share, screen annotation, video contents share, class recording, raise hand and reactions to topics can enhance students engagement in the Zoom online classes. The regression results validate all four dimensions have significant influence on effective zoom online class that enhance students learning process. Thus, findings of this study recommend educating course instructors for ensuring all the applications of online learning process while conducting online classes. We strongly believe this course of action will engage students in the online class to enhance learning activities using Zoom platform in Bangladesh.
Since the COVID-19 pandemic, international higher education and student mobility have faced tremendous pressure and challenges. To address COVID-induced challenges and stress, higher education institutions and host governments undertook responses. This article has humanistically looked into the institutional responses of host universities and governments to international higher education and student mobilities during the COVID-19 pandemic. Informed by a systematic literature review of publications released between 2020 and 2021 in a wide range of academic sources, we argue that many of these responses were problematic and did not adequately maintain student well-being and fairness; instead, international students were treated to some extent with poor services in the host countries. To situate our comprehensive overview and propose ideas for forward-thinking conceptualisation, policy, and practice in higher education in the context of the ongoing pandemic, we engage with the literature on ethical and humanistic internationalisation of higher education and (international) student mobilities.
This paper studied the impact of education investment on regional poverty alleviation of Yunnan’s poor counties, especially the dynamic constraints and marginal benefits of education input. This paper takes 30 poor counties in Yunnan province from 2007 to 2020 as the research object. A double fixed effect model, a systematic GMM model, and a quantile regression model are used to study the effect of education investment on regional poverty alleviation from static and dynamic levels. The results show that education investment has a significant positive effect on regional poverty alleviation at both static and dynamic levels. At the same time, under different poverty levels, the effect of education investment on poverty alleviation shows a law of diminishing marginal effect. As for the current situation of poor counties in Yunnan Province, the effect of education investment on poverty alleviation is increasing at a decreasing rate. The research object of this paper has achieved the goal of getting rid of absolute poverty, so the conclusion of this paper has more reference value.
Elemental contamination in cosmetics is a serious health concern as it can pose a cumulative effect on the user’s body over a long period. The prime motive of the study was to assess the concentration of 10 concerning chemical elements (Pb, Cd, Cr, As, Co, Ni, Cu, Zn, Fe, and Mn) in imported and local lipsticks and eye pencil samples collected from retail outlets in central Bangladesh (Dhaka city) and to assess their dynamic health risks for users. A total of 18 lipsticks and 24 eye pencils were studied and concentrations of chemical elements were examined with atomic absorption spectrophotometer. The health risk assessment was performed for dermal and ingestion routes of the contaminants. The results reveal that the concentrations of the examined elements vary with colors, brands, and origins. Pb and As concentrations were found below the permissible limit but Cr concentration in some samples exceeded the allowable limit in cosmetics. Cd was not detected in any samples; however, other examined elements such as Co, Ni, Cu, Mn, Zn, and Fe were detected in considerable concentrations. Elements like Mn, Zn, and Fe were found at high levels. In the case of lipstick samples, elemental concentrations followed the order of Fe > Zn > Mn > Ni > Cr > Cu > Pb > Co > As, while the order was Fe > Cu > Mn > Zn > Ni > Cr > Co > Pb > As for eye pencil samples. Results of the hazards quotient (HQ) indicate that there were no non-carcinogenic or carcinogenic risks of elements in samples for dermal exposure. But the cancer risk values of Cr (HQ > 1 for ingestion) in brown color lipsticks and Ni indicate that lipsticks have some carcinogenic effects if they enter the user’s body. Dermal cancer risk for eye pencils has also been calculated and for Pb, Cr, Ni, and As; the values were found within the acceptable ranges of 10−6–10−4. It is suggested that the allowable limit of all toxic elements in cosmetics must be established. Furthermore, continuous monitoring is urgently needed for personal care products like lipsticks and eye pencils commonly available in the local markets in the country like Bangladesh.
In this paper, a waveguide-fed hybrid graphene plasmonic nanoantenna operating in the optical frequency spectrum for the point-to-point wireless nanolink has been studied, modeled, and simulated. For the improvement of certain radiation characteristics of the proposed nanoantenna at 193.5 THz, an electrostatic gate bias potential of 0.7 eV has been set at every graphene layer—which in turn allows the transverse electromagnetic surface plasmon polariton to resonate. Gain, directivity, and efficiency of 12.4 dB, 13.3 dBi, and 93.23%, respectively, have been obtained when combining the graphene, aluminum, and SiO2 layers in the all-around W-shape slotted radiators fed by a waveguide port. Side lobes of −18.7 dB and reflection coefficient of −28.22 dB have been attained with a unidirectional radiation pattern. Contingent upon the performance of the proposed nanoantenna, it can be integrated into the inter- or intra-on-chips for safe optical data transmission at the resonant frequency of 193.5 THz.
Though in a relatively infant stage, the soft drink industry of Bangladesh has been thriving. Coupled with high-tech machinery, a stable production process (i.e., aseptic plant) and maintaining international standards have led some companies to export soft drinks to approximately 110 countries. However, despite these achievements, the companies still deal with wavering consumer perception, cultural differences and barriers to enter foreign markets (i.e., the Middle East). Focusing on Akij Food and Beverage Ltd., this case mainly sheds light on how local companies are adapting their strategies to tackle these hurdles: dealing with the fast and fickle consumers, scepticism about quality, changing production lines and even the brand name of products to enter the Middle East markets. The case revisits classical international business topics, such as expansion strategies, culture, communications, global strategic rivalry theory, global supply chain management and distribution networks.
Background Sucrose non-fermenting-1 (SNF1)-related protein kinase 2 (SnRK2), a plant-specific serine/threonine kinase family, is associated with metabolic responses, including abscisic acid signaling under biotic and abiotic stresses. So far, no information on a genome-wide investigation and stress-mediated expression profiling of jute SnRK2 is available. Recent whole-genome sequencing of two Corchorus species prompted to identify and characterize this SnRK2 gene family. Result We identified seven SnRK2 genes of each of Corchorus olitorius ( Co ) and C. capsularis ( Cc ) genomes, with similar physico-molecular properties and sub-group patterns of other models and related crops. In both species, the SnRK2 gene family showed an evolutionarily distinct trend . Highly variable C-terminal and conserved N-terminal regions were observed. Co- and CcSnRK2.3, Co- and CcSnRk2.5, Co- and CcSnRk2.7, and Co- and CcSnRK2.8 were upregulated in response to drought and salinity stresses. In waterlogging conditions, Co- and CcSnRk2.6 and Co- and CcSnRK2.8 showed higher activity when exposed to hypoxic conditions. Expression analysis in different plant parts showed that SnRK2.5 in both Corchorus species is highly expressed in fiber cells providing evidence of the role of fiber formation. Conclusion This is the first comprehensive study of SnRK2 genes in both Corchorus species. All seven genes identified in this study showed an almost similar pattern of gene structures and molecular properties. Gene expression patterns of these genes varied depending on the plant parts and in response to abiotic stresses.
We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck blocks contain depthwise, standard, and linear convolution layers. A single-lens reflex digital image camera was used to collect 1320 images of tea leaves from the North Bengal region of India for preparing the tea grey blight disease dataset. The nongrey blight diseased tea leaf images in the dataset were categorized into two subclasses, such as healthy and other diseased leaves. Image transformation techniques such as principal component analysis (PCA) color, random rotations, random shifts, random flips, resizing, and rescaling were used to generate augmented images of tea leaves. The augmentation techniques enhanced the dataset size from 1320 images to 5280 images. The proposed DCNN model was trained and validated on 5016 images of healthy, grey blight infected, and other diseased tea leaves. The classification performance of the proposed and existing state-of-the-art techniques were tested using 264 tea leaf images. Classification accuracy, precision, recall, F measure, and misclassification rates of the proposed DCNN are 98.99%, 98.51%, 98.48%, 98.49%, and 1.01%, respectively, on test data. The test results show that the proposed DCNN model performed superior to the existing techniques for tea grey blight disease detection.
The history of the medical robot is not very far from the first experiment in the 1980s. Nowadays robot in the medical sector plays a vital role in monitoring patient's health condition from distance. This paper aimed at developing an auxiliary medical solution that could provide a wide range of non-invasive diagnoses carried out by an automated robot whose motion can also be controlled manually using either a mobile application or voice command. The authors also incorporate modern features of video conferences and automated patient data management systems using the Internet of Things (IoT) which eventually facilitate medical practitioners in proper investigation from distance. The results of the clinical trial among 6 persons indicated that the robot could measure different health parameters properly using the proposed non-invasive method. The non-invasive results are verified by standard testing equipment and conventional clinical investigation and are also presented in this paper. The developed medical robot having a wide range of functionality could play a significant role in reducing human workload and ensuring timely medical assistance during a challenging crisis pandemic period like COVID-19.
The decrease in fossil fuel reserves has prompted a global move toward distributed energy resources. For this reason, solar PV power generation has recently gained much attention as a feasible renewable energy source. However, large-scale generation is challenging if there are anomalies in individual solar PV panels. This will reduce the efficiency of the PV system and create a potential fire hazard. In this perspective, the anomaly detection technique discloses system anomalies accurately and effectively. Identified anomalies will localize the event for an improved generation. This paper addresses the performance analysis of using the isolation forest technique to identify anomalies in the PV system and the rule-based fault localization technique to identify defective panel events. In the developed model, the isolation forest technique found around 453 anomalies in 45,740 observations, and approximately six panels indicated a fault in the system. The accuracy score is found to be approximately 0.9886. The proposed fault detection method will help detect the faults in solar power systems.
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