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This study refers to the general reproductive biology of Osteochillus waandersii, and provides information about the sex ratio, gonad development stages, gonado-somatic index (GSI), length at maturity, fecundity, and growth pattern. The fish samples were collected monthly, between March and August 2018, from the Landak River, Kalimantan Province, Indonesia. A total of 234 specimens were collected using gillnets and dipnets, 136 males and 98 females, resulting in a 1.39:1 sex ratio, which is significantly different from the expected ratio of 1:1 (P<0.05). Males reached sexual maturity at a lower size than females, with a length at first maturity estimated at 137.9 mm for males and 140.3 mm for females. Analysis of the macroscopic and histological observations showed that testicles could be classified into four stages: immature, maturing, mature and spent. The GSI ranged between 1% and 9.54% and 1% and 11.67% for males and females, respectively. The ovaries were classified into five stages: immature or resting, maturing, mature, ripe, and spawned-recovering. The absolute fecundity varied from a minimum of 2130 to a maximum of 13640 eggs and a mean of 7165 eggs, corresponding to fish with total length between 145 to 177 mm and a weight between 30 to 67 g.

In this study, we explored the role of biochar (BC) and/or urease inhibitor (UI) in mitigating ammonia (NH 3 ) and nitrous oxide (N 2 O) discharge from urea fertilized wheat cultivated fields in Pakistan (34.01°N, 71.71°E). The experiment included five treatments [control, urea (150 kg N ha −1 ), BC (10 Mg ha −1 ), urea + BC and urea + BC + UI (1 L ton −1 )], which were all repeated four times and were carried out in a randomized complete block design. Urea supplementation along with BC and BC + UI reduced soil NH 3 emissions by 27% and 69%, respectively, compared to sole urea application. Nitrous oxide emissions from urea fertilized plots were also reduced by 24% and 53% applying BC and BC + UI, respectively, compared to urea alone. Application of BC with urea improved the grain yield, shoot biomass, and total N uptake of wheat by 13%, 24%, and 12%, respectively, compared to urea alone. Moreover, UI further promoted biomass and grain yield, and N assimilation in wheat by 38%, 22% and 27%, respectively, over sole urea application. In conclusion, application of BC and/or UI can mitigate NH 3 and N 2 O emissions from urea fertilized soil, improve N use efficiency (NUE) and overall crop productivity.

Nanotechnology explores a variety of promising approaches in the area of material sciences on a molecular level, and silver nanoparticles (AgNPs) are of leading interest in the present scenario. This review is a comprehensive contribution in the field of green synthesis, characterization, and biological activities of AgNPs using different biological sources. Biosynthesis of AgNPs can be accomplished by physical, chemical, and green synthesis; however, synthesis via biological precursors has shown remarkable outcomes. In available reported data, these entities are used as reducing agents where the synthesized NPs are characterized by ultraviolet-visible and Fourier-transform infrared spectra and X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Modulation of metals to a nanoscale drastically changes their chemical, physical, and optical properties, and is exploited further via antibacterial, antifungal, anticancer, antioxidant, and cardioprotective activities. Results showed excellent growth inhibition of the microorganism. Novel outcomes of green synthesis in the field of nanotechnology are appreciable where the synthesis and design of NPs have proven potential outcomes in diverse fields. The study of green synthesis can be extended to conduct the in silco and in vitro research to confirm these findings.

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works.

This study aimed to investigate the miRNA expression patterns in granulosa cells of subordinate (SF) and dominant follicle (DF) during the early luteal phase of the bovine estrous cycle. For this, miRNA enriched total RNA isolated from granulosa cells of SF and DF obtained from heifers slaughtered at day 3 and day 7 of the estrous cycle was used for miRNAs deep sequencing. The results revealed that including 17 candidate novel miRNAs, several known miRNAs (n = 291-318) were detected in SF and DF at days 3 and 7 of the estrous cycle of which 244 miRNAs were common to all follicle groups. The let-7 families, bta-miR-10b, bta-miR-26a, bta-miR-99b and bta-miR-27b were among abundantly expressed miRNAs in both SF and DF at both days of the estrous cycle. Further analysis revealed that the expression patterns of 16 miRNAs including bta-miR-449a, bta-miR-449c and bta-miR-222 were differentially expressed between the granulosa cells of SF and DF at day 3 of the estrous cycle. However, at day 7 of the estrous cycle, 108 miRNAs including bta-miR-409a, bta-miR-383 and bta-miR-184 were differentially expressed between the two groups of granulosa cell revealing the presence of distinct miRNA expression profile changes between the two follicular stages at day 7 than day 3 of the estrous cycle. In addition, unlike the SF, marked temporal miRNA expression dynamics was observed in DF groups between day 3 and 7 of the estrous cycle. Target gene prediction and pathway analysis revealed that major signaling associated with follicular development including Wnt signaling, TGF-beta signaling, oocyte meiosis and GnRH signaling were affected by differentially expressed miRNAs. Thus, this study highlights the miRNA expression patterns of granulosa cells in subordinate and dominant follicles that could be associated with follicular recruitment, selection and dominance during the early luteal phase of the bovine estrous cycle.

New results relating to the maximum and minimum degree spectral radii of generalized splitting and shadow graphs have been constructed on the basis of any regular graph, referred as base graph. In particular, we establish the relations of extreme degree spectral radii of generalized splitting and shadow graphs of any regular graph.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

This study investigates mechanical properties, durability performance, non-destructive testing (NDT) characteristics, environmental impact evaluation, and advanced machine learning (ML) modelling techniques employed in the analysis of high-strength self-compacting concrete (HSSCC) incorporating varying supplementary cementitious materials (SCMs) to develop sustainable building construction. The findings from the fresh characteristics test indicate that mixes’ optimal flowability and passing qualities can be achieved using different concentrations of marble powder (MP) alongside a consistent amount of silica fume (SF) and fly ash (FA). Moreover, the incorporation of 10% MP along with 10% FA and 20% SF in HSSCC significantly improved the compressive strength by 14.68%, while the splitting tensile strength increased by 15.59% compared to the reference mix at 56 days. While random forest (RF), gradient boosting (GB), and their ensemble models exhibit strong coefficient correlation (R2) values, the GB model demonstrates more precision, indicating reliable predicted outcomes of the mechanical properties. Following subsequent testing, it has been demonstrated that incorporating SCMs improves the NDT properties of HSSCC and enhances its durability. The finer MP, SF, and FA particles enhanced microstructural performance by minimizing voids and cracks while improving the C–H–S bond. As noticed by its lower CO2-eq per MPa for SCMs, the HSSCC mix with up to 15% MP inclusion increased mechanical strength while reducing the environmental footprint, making it an eco-friendly concrete alternative.