Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

The study evaluated 33 accessions of groundnut in the field, consisting of 23 landraces from Nasarawa communities in Nigeria and 10 inbred lines. Assessment entailed the determination of plant survivorship, yield related parameters and pathological indices while genetic diversity study was undertaken using SSR and RAPD molecular markers. Data analysis was done on the Minitab 17.0 software. Significant variability was noted in all traits except in pod sizes, seed sizes and % infected seeds. About 33.3% of the accessions had a survival rate of ≥ 70.0% where 9/10 Inbred lines were found with overall yield (kg/ha) ranging from 4.0 ± 1.6 in Akwashiki-Doma to 516.8 ± 46.9 kg/ha in Samnut 24 × ICGV–91328. Five accessions (15.5%) had pathological indices of zero indicating no traces of any disease of any type and they included one landrace called Agric-Dazhogwa and four Inbred lines: Samnut 25 × ICGV–91317, Samnut 26 × ICGV–19324, Samnut 26 × ICGV–91328 and Samnut 26 × ICGV–91319. Coefficients of yield determination R2 by survivorship and pathological index were 50.6% and 15%, respectively. A fit model was established (Yield in kg/ha = –146 − 7.94 × Pi + 5.88 × S). Susceptibility to diseases depends on the type of variety (χ2(32) = 127.67, P = 0.00). Yield was significantly affected by BNR@30 (F = 5.47, P = 0.025, P < 0.05) and DSV@60*RUST@60 interaction effect (F = 4.39, P = 0.044, P < 0.05). The similarity coefficient ranged from 28.57 to 100 in plant morphology. Four varieties had no amplified bands with SSR primers whereas amplified bands were present only in four landraces accessions using the RAPD primer. The dendrogram generated by molecular data gave three groups where genetic similarity ranged from 41.4 to 100.0. The Inbred lines were noted for their high survivorship, good yield and disease resistance. Samnut 24 × ICGV–91328, an inbred line, had the highest yield but was susceptible to diseases. Among the landraces, Agric-Musha, Bomboyi-Dugu and Agric-Dazhogwa were selected for high survivorship and disease resistance. The selected inbred lines and landraces are valuable genetic resources that may harbour useful traits for breeding and they should be presented to the growers based on their unique agronomic values. The highest yielding inbred lines should be improved for resistance to late leaf spot diseases while the outstanding landraces should be improved for yield.

This paper presents a numerical method for solving a nonlinear age-structured population model based on a set of piecewise constant orthogonal functions. The block-pulse functions (BPFs) method is applied to determine the numerical solution of a non-classic type of partial differential equation with an integral boundary condition. BPFs duo to the simple structure can efficiently approximate the solution of systems with local or non-local boundary conditions. Numerical results reveal the accuracy of the proposed method even for the long term simulations.

The effective drainage radius of coal seam is an important basis for the spacing of pre-drainage gas boreholes. To quickly and accurately determine the effective drainage radius, a new method was proposed. For the coal face where the desorbable gas content before mining has met the standard, the compliance of mine gas drainage rate was used as the basis to determine the effective drainage radius. The fluid-structure interaction model was constructed, numerical simulation of coal seam gas drainage was carried out by using COMSOL software, and the model was validated by combining the field test results. The results show that the new method has the advantage of short cycle. With the extension of drainage time, the increase of effective drainage radius gradually decreases, and finally reaches a relatively stable limit value, which conforms to the Langmuir function. The average error between numerical simulation and field test values of effective drainage radius is 4.9%, which proves that the model is reliable. This model can accurately predict the effective drainage radius under different coal seam gas contents and drainage times. The research results provide a new mean for determining the effective drainage radius of coal seam and the layout of gas drainage boreholes.

The persistence of coastal ecosystems is jeopardized by deforestation, conversion, and climate change, despite their capacity to store more carbon than terrestrial vegetation. The study’s objectives were to investigate how spatiotemporal changes impacted blue carbon storage and sequestration in the Satkhira coastal region of Bangladesh over the past three decades and, additionally to assess the monetary consequences of changing blue carbon sequestration. For analyzing the landscape change (LSC) patterns of the last three decades, considering 1992, 2007, and 2022, the LSC transformations were evaluated in the research area. Landsat 5 of 1992 and 2007, and Landsat 8 OLI-TIRS multitemporal satellite images of 2022 were acquired and the Geographical Information System (GIS), Remote Sensing (RS) techniques were applied for spatiotemporal analysis, interpreting and mapping the output. The spatiotemporal dynamics of carbon storage and sequestration of 1992, 2007, and 2022 were evaluated by the InVEST carbon model based on the present research years. The significant finding demonstrated that anthropogenic activity diminished vegetation cover, vegetation land decreased by 7.73% over the last three decades, and agriculture land converted to mariculture. 21.74% of mariculture land increased over the last 30 years, and agriculture land decreased by 12.71%. From 1992 to 2022, this constant LSC transformation significantly changed carbon storage, which went from 11,706.12 Mega gram (Mg) to 9168.03 Mg. In the past 30 years, 2538.09 Mg of carbon has been emitted into the atmosphere, with a combined market worth of almost 0.86 million USD. The findings may guide policymakers in establishing a coastal management strategy that will be beneficial for carbon storage and sequestration to balance socioeconomic growth and preserve numerous environmental services.