The N-Queens problem is a classic combinatorial challenge in artificial intelligence, where the objective is to place n queens on an n×n chessboard such that no two queens threaten each other. As n increases, the search space expands factorially, demanding robust and scalable optimisation strategies. This study presents a comparative analysis of four algorithmic approaches: Depth-First Search (DFS), Hill Climbing, Simulated Annealing, and Genetic Algorithms. Each method was implemented in Python and tested across varying board sizes (n = 10, 30, 50, 100, 200) to evaluate solution accuracy, execution time, memory usage, and scalability.

DFS proved reliable for smaller instances but became computationally infeasible as n grew. Hill Climbing showed efficiency for mid-sized boards but often stagnated in local optima beyond n = 100. Simulated Annealing consistently delivered high-quality solutions with excellent time and memory efficiency across all test cases. The Genetic Algorithm, while slower and more memory-intensive, achieved solutions for all tested n values after extensive tuning of hyperparameters.

The results highlight trade-offs between deterministic and stochastic approaches, emphasizing that no single algorithm outperforms across all dimensions. Simulated Annealing emerged as the most balanced method in terms of scalability and efficiency. This work contributes a practical benchmark for researchers evaluating optimisation techniques on NP-complete problems and provides a foundation for developing hybrid or parallelised methods for large-scale constraint satisfaction challenges.

Spiro-OMeTAD, currently the most widely used hole transport material, has high hole mobility. However, its poor thermal durability and low long-term stability when applied to photovoltaic devices are due to the moisture absorption and ion diffusion of the dopant. The objectives of this research are to fabricate and evaluate perovskite solar cells consisting of various perovskite compositions and stacked layered structures using decaphenylcyclopentasilane (DPPS), and to clarify the effects of DPPS on the perovskite layers. The novelty of this study is that bilayer stacked structures were fabricated using different types of perovskites using DPPS as a protective and hole transport layer, and to propose the design principles for highly reliable photovoltaic devices. Due to the high-temperature durability and chemical inertness of DPPS, high-temperature annealing of devices is possible, which is expected to improve the thermal stability and long-term stability of perovskite solar cells. Current–voltage measurements showed that devices using DPPS and heat-treated at higher temperatures than the ordinary annealing temperatures maintained their photoconversion efficiency even after one year. Thermal stability tests also showed that the devices maintainedefficiencies higher than 85% of their initial efficiencies after 3600 s at elevated temperatures. In addition, microstructure analysis through XRD measurements revealed that DPPS crystallized after the heat treatment. The results of optical absorption measurements showed that the crystallization of DPPS suppressed the desorption of MA⁺ from the perovskite, thereby increasing the optical absorption intensity, which indicated contribution to the improvement of the photoelectric conversion efficiencies of the devices.

Pneumonia remains a leading cause of mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. This study presents a comparative evaluation of three deep learning models—Custom Convolutional Neural Network (CNN), ResNet50, and EfficientNet-B0—for automated pneumonia detection using chest X-ray images. The analysis is conducted on the publicly available Kaggle Chest X-ray Pneumonia dataset, comprising 5,863 pediatric images, preprocessed and augmented to enhance model generalization.

Each model was assessed based on classification accuracy, AUC-ROC scores, training time, and diagnostic sensitivity. The custom CNN was designed and trained from scratch, while ResNet50 and EfficientNet-B0 utilized transfer learning with pre-trained ImageNet weights and customized classification heads. Experiments were executed in a PyTorch environment with GPU acceleration and early stopping to prevent overfitting.

Among the three, ResNet50 demonstrated superior performance with 85.42% accuracy and an AUC of 0.946, achieving the best trade-off between diagnostic precision and computational efficiency (10.2 minutes training time). EfficientNet-B0 achieved moderate accuracy (78.21%) and AUC (0.891) but required longer training time. The custom CNN, while competitive in training speed (12.4 minutes), achieved lower accuracy (71.15%) and was more prone to overfitting.

These results confirm the advantage of transfer learning in medical imaging, particularly for limited datasets. ResNet50 emerges as a robust candidate for clinical screening applications in resource-constrained settings. Future work should focus on domain-specific fine-tuning, multiclass classification, and external validation across diverse populations and imaging protocols.

Formamidinium lead triiodide (FAPbI₃) is one of the candidate materials for stable perovskite solar cells. There exists an optically active cubic α-FAPbI₃ phase, an optically inactive hexagonal δ-FAPbI₃ phase, and a one-dimensional phase. Since the δ-phase is thermodynamically stable, the structural phase transition from the α-phase to the δ-phase causes a serious problem on the photovoltaic efficiencies. The aim of this study is to investigate the effects of copper (Cu) or germanium (Ge) additions on the formation of FAPbI₃. Cu or Ge were added in the present study, as a method for stabilizing the α-phase, to suppress formation of the δ-phase and one-dimensional phase. When Cu was added at the lead site, diffraction peaks of the α-phase increased. Ge addition also increased the diffraction intensity of the α-phase and decreased the diffraction intensity of PbI₂. The possibility of stabilization of FAPbI₃ by Cu introduction at the FA site was also demonstrated. The first-principles band calculation on the Cu-doped FAPbI₃ at the FA site indicated that the total energy value of the crystal decreased. From the calculated partial density of states, the valence band and conduction band are dominated by I-p orbitals and Pb-p orbitals, respectively, and the energy level of the Cu-d orbital is formed at a position slightly lower than the valence band maximum. The effectiveness of Cu introduction in stabilizing the formation of α-FAPbI₃ was also demonstrated in the synthesized FAPbI₃ crystal.

Geopolymer cement has garnered increasing attention as a low-carbon alternative to ordinary Portland cement (OPC) for wellbore cementing applications, particularly in CO₂-rich subsurface environments where OPC is prone to chemical degradation. The study investigates the mechanical and structural effects of incorporating styrene-butadiene latex—a colloidal polymer dispersion commonly employed for fluid loss mitigation—into geopolymer formulations subjected to elevated temperature and pressure conditions.

Geopolymer cement slurries were prepared using Class F fly ash, activated by sodium silicate and sodium hydroxide solutions, and modified with varying latex dosages ranging from 0 to 10 wt.% relative to fly ash mass. The specimens were cured at 100 °C and 3,000 psi for 48 hours, followed by evaluations of compressive strength, microstructure, and elemental distribution. The optimal latex concentration of 4 wt.% yielded a maximum compressive strength of 2,937 psi, marginally outperforming the control sample. Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDX) revealed that latex incorporation facilitated silica-rich film formation, needle-like structure, and enhanced microstructural connectivity. In contrast, excessive latex concentrations produced flocculated domains and phase irregularities, resulting in mechanical impairment.

The preliminary findings establish styrene-butadiene latex as a promising fluid loss control agent capable of contributing to early strength gain and structural refinement in geopolymer cement systems under elevated conditions. Future studies should explore co-additive synergies, long-term durability and phase stability to optimize cement integrity for demanding wellbore environments.

Pansharpening consists of fusing a multispectral (MS) image and a panchromatic (PAN) image to generate a high quality MS image. Several pansharpening techniques have been developed to enhance the spatial quality of MS data. The estimation of objects in the scene, such as cars, trees or buildings, require accurate pixels synthesis during fusion process. In this paper, we proposed to estimate the pixel reflectance of fused multispectral images using a generalized UIQI band-wise metric. This criteria is validated on pansharpened results at reduced-scale using Wald protocol. In this context, we presented two comparative studies. In the first case, we compared statistically the proposed criteria to the pixel correlation and the Euclidian distance. The proposed criteria presented promising results quantitatively. Concerning the second case study, we considered the assessment of deep learning-based fusion methods versus the state-of-the-art. Indeed, the pansharpening based on Neural Networks (PNN), Convolutional Neural Networks (CNN) and the adaptive PNN with fine tuning have been trained and tested. The state-of-the art pansharpening methods include the Generalized Laplacian Pyramids (GLP), Additive Wavelet Luminance Proportion (AWLP), Gram-Schmidt adaptive (GSA), Total variation (TV), Model-based fusion using principle component and wavelets (PWMBF) and Filter estimation based on a semi-blind deconvolution framework (FE). The experimental results have been performed on two remote sensing data sets captured by GeoEye-1 and Worldview-3 satellites. The comparative study allows a better understanding of the displacement of objects or the misregistration of PAN and MS images.

The chemical treatment of seeds is a fundamental practice that ensures protection against pests and diseases, thus promoting robust plant establishment. However, this process is susceptible to failures, particularly in the form of inadequate coating. As such, the precise assessment of treatment quality emerges as a critical factor in securing high-performance crop yields. In this work, we present an approach based on image processing and convolutional neural networks (CNNs) to segment and predict the quality of chemical coverage on seeds from RGB images. The seeds were arranged on a homogeneous surface and labeled in six categories (C1 to C6), according to the level of chemical coating, with C1 corresponding to no treatment and C6 to adequate treatment, totaling 1,165 seed images, with half of the images captured under natural light and the other half under artificial lighting. For segmentation, granulometric analysis and morphological segmentation techniques were applied, allowing the individual isolation of each seed. For classification, a CNN based on the MobileNetV2 architecture was used, with fine-tuning and data augmentation techniques. The model achieved an average F1 score of 0.96, performing well in all classes. The results demonstrate that the proposed approach is capable of identifying subtle variations in color and uniformity of coverage with excellence, indicating its potential for embedded automated screening applications. The proposal contributes to the standardization and automation of seed evaluation, with direct applicability in the agribusiness sector.

Atypical Teratoid Rhabdoid Tumor (ATRT) is a highly aggressive pediatric brain tumor, which poses substantial clinical challenges due to its rapid proliferation and complex morphological diversity. Precise segmentation of ATRT in Magnetic Resonance Imaging (MRI) is essential for accurate diagnosis, effective treatment planning, and thorough outcome evaluation. However, manual segmentation is time-consuming and susceptible to errors, particularly given the tumor's intricate structure. To overcome these limitations, deep learning-based automated segmentation techniques have attracted significant attention, with UNet architectures emerging as a leading solution in medical image analysis. This study introduces an advanced segmentation approach, utilizing a Fork of the Tumor-Segmentation-UNet+ model integrated with residual networks, namely ResNext and ResNet. These architectures enhance the model's ability to delineate complex tumor boundaries and account for the high degree of heterogeneity seen in ATRT. The inclusion of residual blocks from ResNext and ResNet facilitates more efficient feature extraction while also mitigating common issues in deep neural networks, such as vanishing gradients. The proposed model was trained and validated on a dataset comprising ATRT-specific MRI scans and compared against conventional segmentation approaches. Performance metrics, including the Dice coefficient, Intersection over Union (IoU), and sensitivity, were used to measure segmentation accuracy. The results indicate that the UNet+ model enhanced with ResNext and ResNet significantly outperforms standard UNet configurations, delivering more accurate and reliable segmentation of ATRT tumors.

Thyroid cancer is the most common type of endocrine cancer. Most cases are called differentiated thyroid cancer (DTC), which includes papillary, follicular, and Hurthle cell types. DTC usually grows slowly and has a good prognosis, especially when found early and treated with surgery, radioactive iodine, and thyroid hormone therapy. However, cancer can come back sometimes even years after treatment. This recurrence can appear as abnormal blood tests or as lumps in the neck or other parts of the body. Being able to predict and detect these recurrences early is important for improving patient care and planning follow-up treatment. In this view, this research explores the different machine learning algorithms and neural networks to effectively predict DTC recurrence. A total of 16 machine learning algorithms were utilized for the experiment, namely, logistic regression, random forest, k-nearest neighbors, Gaussian naïve Bayes, multi-layered perceptron, extreme gradient boosting, adaptive boosting, gradient boosting classifier, extra tree classifier (ETC), light gradient boosting machine, categorical boosting, Bernoulli naïve Bayes, complement naïve Bayes, multinomial naïve Bayes, histogram-based gradient boosting, and nearest centroid, followed by building an artificial neural network. Among the classifiers, ETC performed best with 98.7% accuracy, 99.99% precision, 95.45% recall, 99.99% specificity, 97.67% F1 score, and 99.5% AUROC. To improve model interpretability, Shapley Additive Explanations (SHAP) was also used to explain the contribution of each clinical feature to the model's predictions, allowing for transparent, patient-specific insights into which factors were most important for predicting recurrence, thereby supporting the proposed model’s clinical relevance.

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behaviour of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making it as promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behaviour of the solid polymer electrolytes.