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The goal of this work is to further improve our knowledge of the nonlinear radiative second-grade nano fluid flow boundary layer phenomena which is associated with an Arrhenius activation energy, a sinusoidal magnetic field, and a stretched peripheral with a heat source. The unsteady governing equations are transformed into a proper dimensionless arrangement, and then the explicit finite difference (EFD) method is applied to numerically calculate the equations. However, precise stability and convergence criteria have been developed to make the solution convergent. Along with the typical profile of other flow fields, the oscillatory forms of the velocity are shown. Tabular research has even demonstrated a relationship between the Nusselt number and other parameters, and graphical depiction has been used for regression and data prediction. The novel conclusions drawn from this research indicate that, in comparison to linear patterns, nonlinear radiative heat flux significantly raises (30.35 %) flow profiles with second-grade characteristics. Moreover, the heat transfer rates of second-grade Nano fluids are seen to be significantly influenced (35.14 %) by the sinusoidal magnetic component. When considering nonlinear thermal radiation, activation energy principles cause a major change (34.19 % more) in mass transmission, as high-temperature processes become an essential part of chemical reactions. [ABSTRACT FROM AUTHOR]

Swift identification of leaf diseases is crucial for sustainable rice farming, a staple grain consumed globally. The high costs and inefficiencies of manual identification underline the requirement of prompt disease detection. Traditional approaches for identifying leaf diseases in crops, particularly rice, are laborious, and and often ineffective. Given the significant impact of leaf diseases (such as Rice Blast, Brown Spot, and Rice Turgor) on rice quality and yield, computer-assisted detection can be an effective method of ensuring the long-term sustainability of rice production. This study utilizes advanced artificial intelligence (AI) as the optimized bidirectional encoder representations from the transformers for images(BEiT) model along with pre-trained CNNs (Convolutional Neural Networks), to build a comprehensive study for detecting rice leaf diseases. We train and validate two extensive datasets, featuring healthy and various types of unhealthy plant and rice leaf images respectively.Our optimized model demonstrates high accuracy, outperforming other deep learning and transformer-based models such as ViT, Xception, InceptionV3, DenseNet169, VGG16, and ResNet50. The proposed model achieves a precision of 0.97, a recall of 0.96, and an F1-score of 0.97.The explainability of our proposed model is achieved through the use of segmentation techniques in conjunction with the Local Interpretable Model-agnostic Explanations (LIME) method. • Efficient low-parameter vision transformer for accurate rice leaf disease detection • With an accuracy of 98.1%, the BEiT model outperforms other state-of-the-art models. • SLIC-based segmentation improves transparency in AI diagnostics. • Multi-model approach ensures accurate classification and interpretability for early diagnosis [ABSTRACT FROM AUTHOR]

This study focused on the effects of alternating current (AC) and radio frequency (RF) power in the formation of plasma polymerized 3,4-ethylenedioxythiophene (PPEDOT) thin films onto glass substrates. Field emission scanning electron microscopy revealed that thin films produced both in AC and RF conditions exhibited a pristine surface morphology without any pinholes. Fourier transform infrared spectroscopy indicated that the chemical compositions of the PPEDOT films differed from that of the 3,4-ethylenedioxythiophene (EDOT) monomer due to structural rearrangements. The energy band gap values determined from the UV–Vis spectra were approximately 3.68 eV for the samples produced under AC power, while those for the samples produced under RF power were relatively higher, around 3.84 eV. Furthermore, the thin films synthesized using RF power and AC power exhibited significant differences in optical properties, including Urbach energy, extinction coefficient, steepness parameter, refractive index, optical conductivity, and skin depth. The EDOT monomer, PPEDOT and computed PEDOT via Density Functional Theory have been explicitly modeled with the intend to scrutinize the interplay between polymer formation and the modulation of molecular orbital energy levels, calculating band gaps, and molecular vibrations. [ABSTRACT FROM AUTHOR]

Giant hepatic haemangioma (GHH) is defined as a hepatic haemangioma (HH) of >10 cm in diameter. Its association with thrombocytopenia and consumption coagulopathy is quite rare. Here, we present a case of a 39-year-old man with a rapidly enlarging 25-cm GHH arising from the entire left hemiliver. Laboratory findings suggested pancytopenia but normal liver and renal functions. He was diagnosed with Kasabach-Merritt syndrome (KMS). After three units of aphaeretic platelet transfusion, the patient underwent left hepatectomy. Postoperative recovery was uneventful, and his regular follow-up revealed no recurrence even after two years. HH predominantly affects females, but males can also be affected, as seen in this case. With observation, it can grow over time, particularly in patients under 50 years of age. Surgical management should be considered when HH causes symptoms or is larger than 10 cm. The evolving understanding of GHH and the critical role of surgery are important, particularly when they complicate haematological or coagulation profiles and lead to thrombocytopenia. Our case report highlights the significance of surgical intervention in GHH, and a disease-free outcome can be expected for patients with this condition in the future. To our knowledge, this is the first such case report from Bangladesh. • Role of surgical intervention in the management of giant hepatic haemangioma (GHH) is essential, especially when it is symptomatic and presents with pancytopenia. • The growth potential for GHH, particularly in patients under 50 years of age, necessitates careful observation and timely surgical intervention. • Kasabach-Merritt syndrome (KMS) reverses rapidly after surgery. [ABSTRACT FROM AUTHOR]

This study intends to elaborate the heat and mass transfer analysis of Casson nanofluid flow past a stretching sheet together with magnetohydrodynamics (MHD), thermal radiation and chemical reaction effects. The boundary layer approximations established the governing equations, i.e., time-subservient momentum, energy and diffusion balance equations. An explicit finite difference scheme was implemented as a numerical technique where Compaq Visual Fortran 6.6.a programming code is also developed for simulating the fluid flow system. In order to accurateness of the numerical technique, a stability and convergence analysis was carried out where the system was found converged at Prandtl number, Pr ≥ 0.062 and Lewis number, Le ≥ 0.025 when τ = 0.0005, ΔX = 0.8 and ΔY = 0.2. The non-dimensional outcomes are apprehended here which rely on various physical parameters. The impression of these various physical parameters on momentum and thermal boundary layers along with concentration profiles are discussed and displayed graphically. In addition, the impact of system parameters on Cf, Nu and Sh profiles with streamlines and isothermal lines are also discussed. [ABSTRACT FROM AUTHOR]

Stomatal traits of leaves are critical for regulating the exchange of gases between plant tissues and the atmosphere, and thus play a crucial role in the physiological activities of plants. The hypothesis of this study is that distinct stomatal features among different species grown in diverse habitats can serve as a potential marker for species identification. Leaf samples were collected from the mangrove forests of Sundarbans and the freshwater swamp forests of Ratargul in Bangladesh. In total, we examined 11 species from eight different families. We used deep convolutional neural network (DCNN) to automatically identify tree species from microscopic stomatal imprints, as there is currently no established protocol for this task. For model training, 80% (866 images) of the data was used for training the models. Our study observed significant variations in stomatal attributes such as length, width, and density among different species, families, and habitats. These variations could help in accurate species identification by machine learning approaches used in the present study. An empirical comparison was conducted among EfficientNetV2, Xception, VGG16, VGG19, MobileNetV2, ResNet50V2, Resnet152, DenseNet201, and NasNetLarge. We propose a novel approach called the "Normalized Leverage Factor" that utilizes accuracy, precision, recall, and f1-score to select the optimal model. This approach eliminates the non-uniformity of the scores. Although MobileNetV2 achieved an accuracy of 99.06%, our findings indicate that EfficientNetV2 is the optimal model for species identification. This is due to its higher normalized leverage factor (1.92) compared to MobileNetV2 (1.88). The findings demonstrate that plants of diverse habitats show a unique footprint of stomata that offers an innovative method of species identification using DCNN. The study would help to develop a stomatal image-based user interface to identify species even without expert taxonomic knowledge and could be particularly useful in fields such as pharmacology, conservation biology, forestry, and environmental science. [Display omitted] • Stomata bears a unique footprint for the species identifications. • Deep learning techniques are used for automated species identifications. • We studied 11 species under eight families from mangrove and freshwater swamp forest. • A new approach formulated to evaluate the performance weight of models. • EfficientNetV2 outperforms the other models while showing no overfitting or performance degradation. [ABSTRACT FROM AUTHOR]