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Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

An arboreal ant species by nature, the Asian weaver ant Oecophylla smaragdina F., (Hymenoptera: Formicidae) colony’s social structure composition was investigated in depth. Brood and barrack nests were collected from the African oil palm ( Elaeis guineensis ) canopies and Limau kasturi ( Citrus microcarpa ) orchards, and dissected. All caste’s morphological traits were examined stereo-microscopically. The workers’ width and length measurements of the separately dissected head, thorax, frontal view, abdomen, and body full side view sizes were recorded. All colonies comprise a founding queen laying thousands of eggs stored in a protective yellowish, unknown sticky substance (shining reflection), with reproductive winged green and newly emerged yellow queens, adult drone males, and wingless workers along their immature pupae and larvae arranged in woven, solid silken chambers (brood nests exclusively). Besides the traditionally known caste of minor and major workers, five polymorphic individuals comprising two unidentified novel sub-castes of intermediate workers and one sub-caste of major workers were described. The full body and abdomen lengths are proposed as dominant predicting factors differentiating among the five sub-castes. The discovery of a multimodal size frequency distribution model contrasts with the classical archetypical bimodal systems in ants. Intermediate workers foraging outside the nest revealed reconnaissance autonomy and aggressive behaviors that aided larger workers in securing the territorial perimeter. Bigger workers occupied the first defensive layers of the colony’s territorial frontier, while the intermediate workers maintained their stance at a closer nest distance. Major workers systematically acted as leaders-supervisors by removing individuals of smaller size during overheating exposure. Due to their short lifespan and segregated nests, it is difficult to collect adult males in wide plantations. A stable and average mature three-year-old colony produces several reproductive individuals monthly. The mean number of emerging queens is higher in older colonies (scarcity of males) and lower for younger colonies (queens-males averages are correlated). The queen production increases with higher rainfall and relative humidity. This study identified three novel worker sub-castes: one major intermediate, two intermediate in size. These findings contribute to a better understanding of the overall worker’s functional activity. The Asian weaver ant demonstrates adaptive measures in response to challenging abiotic factors (temperature), defying classical labor division rules.

Coastal erosion and sediment transport dynamics in Iraq’s shoreline are increasingly affected by extreme climate conditions, including rising sea levels and intensified storms. This study introduces a novel fractional-order sediment transport model, incorporating a modified gamma function-based differential operator to accurately describe erosion rates and stabilization effects. The proposed model evaluates two key stabilization approaches: artificial stabilization (breakwaters and artificial reefs) and bio-engineering solutions (coral reefs, sea-grass, and salt marshes). Numerical simulations reveal that the proposed structures provide moderate sediment retention but degrade over time, leading to diminishing effectiveness. In contrast, bio-engineering solutions demonstrate higher long-term resilience, as natural ecosystems self-repair and adapt to changing environmental conditions. Under extreme climate scenarios, enhanced bio-engineering retains 55% more sediment than no intervention, compared to 35% retention with artificial stabilization.The findings highlight the potential of hybrid coastal protection strategies combining artificial and bio-based stabilization. Future work includes optimizing intervention designs, incorporating localized field data from Iraq’s coastal zones, and assessing cost-effectiveness for large-scale implementation.

In order to solve fractional differential equations on quantum domains, this work provides a spectral approach based on higher‐order ( q , τ )‐Bernoulli functions and polynomials. We build a robust basis for approximation in ( q , τ )‐weighted Hilbert spaces by using the orthogonality properties of these extended polynomials and the Sheffer‐type generating function. Prototype equations of the form D q , τ u ( x ) = f ( x ) are numerically solved using the ( q , τ )‐Lagrange interpolation approach modified to represent arbitrary functions in terms of Bernoulli bases. Spectral expansion is used to recreate the solution, and a thorough example is given. The technique shows spectral convergence and shows how well higher‐order ( q , τ )‐Bernoulli systems capture the global structure and local behavior of fractional quantum calculus solutions.

PurposeIn this study, the authors introduce a solvability of special type of Langevin differential equations (LDEs) in virtue of geometric function theory. The analytic solutions of the LDEs are considered by utilizing the Caratheodory functions joining the subordination concept. A class of Caratheodory functions involving special functions gives the upper bound solution.Design/methodology/approachThe methodology is based on the geometric function theory.FindingsThe authors present a new analytic function for a class of complex LDEs.Originality/valueThe authors introduced a new class of complex differential equation, presented a new technique to indicate the analytic solution and used some special functions.

Cell blebbing is a fundamental morphodynamic process governed by the interplay of cytoplasmic pressure, cortical contractility, and membrane tension. Classical geometric flow models capture instantaneous mechanical effects but fail to represent hereditary and viscoelastic memory inherent to living cells. In this work, we propose a (q,τ) \\left{(}{q}{,}τ \\right) ‐fractional geometric flow framework for bleb morphogenesis, where the parameters q q and τ τ quantify deformation memory and stress‐relaxation tempering, respectively. Fluorescence microscopy frames from the WRAP dataset and synthetic simulations are segmented to extract time series of bleb height, effective radius, and fractional mean curvature. These observables are fitted using a predictor‐corrector numerical scheme for a (q,τ) \\left{(}{q}{,}τ \\right) ‐fractional evolution equation subject to an energy dissipation law. The numerical solver and segmentation pipeline are validated on synthetic and experimental data. The proposed model accurately reproduces both the rapid expansion and slow relaxation phases of bleb evolution, with residual errors below 10−3 10⁻³ and fitted parameters in biologically plausible ranges. Moreover, the total fractional energy exhibits monotonic decay, consistent with thermodynamic dissipation. The results demonstrate that (q,τ) \\left{(}{q}{,}τ \\right) ‐fractional geometric flows provide a unified and physically interpretable framework for coupling image‐based quantification with nonlocal curvature‐driven dynamics in cellular morphogenesis. We propose a (q,τ) \\left{(}{q}{,}τ \\right) ‐fractional geometric flow model for cell blebbing that incorporates hereditary memory and viscoelastic effects in curvature‐driven membrane dynamics. Image‐based measurements of bleb geometry are coupled with fractional evolution equations and validated numerically. The model accurately reproduces rapid expansion and slow relaxation phases of bleb morphogenesis, providing a quantitative link between fractional calculus and cellular mechanobiology.

The second iteration of the optimal homotopy asymptotic technique (OHAM-2) has been protracted to fractional order partial differential equations in this work for the first time (FPDEs). Without any transformation, the suggested approach can be used to solve fractional-order nonlinear Zakharov–Kuznetsov equations. The Caputo notion of the fractional-order derivative, whose values fall within the closed interval [0, 1], has been taken into consideration. The method's appeal is that it provides an approximate solution after just one iteration. The suggested method's numerical findings have been contrasted with those of the variational iteration method, residual power series method, and perturbation iteration method. Through tables and graphs, the proposed method's effectiveness and dependability are demonstrated.