GBM is a highly aggressive primary malignancy in adults, necessitating personalized therapeutic strategies due to its inherent molecular heterogeneity. MGMT promoter methylation is a pivotal prognostic biomarker for anticipating response to temozolomide-based chemotherapy. Although various AI frameworks have been developed for non-invasive MGMT prediction, spatial heterogeneity of methylation status and the high-dimensional and correlated nature of MRI data frequently constrain discriminative feature learning and generalizability of classical models. To circumvent these limitations, a specialized IA-QCNN architecture is proposed, based on the principles of quantum mechanics, including superposition and entanglement, and enabling more efficient representation learning in high-dimensional Hilbert space. The framework establishes a methodological bridge between GBM radiogenomics and quantum deep learning by integrating energy-based slice selection, importance-aware weighting, ring-topology quantum convolution, and folding-based pooling layers. When the model predicts MGMT promoter methylation status using both mpMRI and T1Gd images, experimental results demonstrate that the IA-QCNN achieves high accuracy despite its low number of trainable parameters while effectively minimizing the overfitting problem observed in classical models. Quantitative analyses reveal that the T1Gd modality possesses higher discriminative power than mpMRI, establishing a clinically significant sequence preference. Furthermore, the model exhibits exceptional robustness in hybrid noise environments, effectively utilizing noise as a regularization mechanism to enhance predictive performance. Consequently, the specialized IA-QCNN architecture provides a robust and computationally efficient alternative to classical approaches in the analysis of heterogeneous radiogenomic data.

With increasing life expectancy, AD has become a major global health concern. While classical AI-based methods have been developed for early diagnosis and stage classification of AD, growing data volumes and limited computational resources necessitate faster, more efficient approaches. Quantum-based AI methods, which leverage superposition and entanglement principles along with high-dimensional Hilbert space, can surpass classical approaches' limitations and offer higher accuracy for high-dimensional, heterogeneous, and noisy data. In this study, a Quantum-Based Parallel Model (QBPM) architecture is proposed for the efficient classification of AD stages using MRI datasets, inspired by the principles of classical model parallelism. The proposed model leverages quantum advantages by employing two distinct quantum circuits, each incorporating rotational and entanglement blocks, running in parallel on the same quantum simulator. The classification performance of the model was evaluated on two different datasets to assess its overall robustness and generalization capability. The proposed model demonstrated high classification accuracy across both datasets, highlighting its overall robustness and generalization capability. Results obtained under high-level Gaussian noise, simulating real-world conditions, further provided experimental evidence for the model's applicability not only in theoretical but also in practical scenarios. Moreover, compared with five different classical transfer learning methods, the proposed model demonstrated its efficiency as an alternative to classical approaches by achieving higher classification accuracy and comparable execution time while utilizing fewer circuit parameters. The results indicate that the proposed QBPM architecture represents an innovative and powerful approach for the classification of stages in complex diseases such as Alzheimer's.

In this study, we conducted an exploration of the optimization of various parameters of a photodetector using SCAPS-1D simulation to enhance its overall performance. The photodetector structure was modified based on the structure proposed by N.I.M. Ibrahim et al. (AMPC, 14(04), 55–65 (2024) by changing the order of the hole transport layer (HTL) and electron transport layer (ETL). Through the optimization of layer thicknesses and doping concentrations, we significantly improved the photovoltaic parameters of our optimized structure (FTO/PFN/PBDB-T-2F/PEDOT/Ag). The optimized device exhibited VOC of 1.02V, JSC of 35.20 mA/cm², FF of 84.61%, and an overall efficiency of 30.40%. Additionally, the device demonstrated a high quantum efficiency (EQ) of over 99% and responsivity peaking at 0.65 A/W, covering a broad spectral region from 300 nm to 900 nm. The results indicate the critical role of meticulous optimization in developing high-performance photodetectors, providing valuable insights into the design and fabrication of devices with superior performance characteristics.

DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of the molecular mechanisms underlying complex diseases such as brain tumors and the identification of diagnostic genetic signatures. To derive meaningful biological insights from the high-dimensional and complex gene features obtained through this technology and to analyze gene properties in detail, classical AI-based approaches such as machine learning and deep learning are widely employed. However, these methods face various limitations in managing high-dimensional vector spaces and modeling the intricate relationships among genes. In particular, challenges such as hyperparameter tuning, computational costs, and high processing power requirements can hinder their efficiency. To overcome these limitations, quantum computing and quantum AI approaches are gaining increasing attention. Leveraging quantum properties such as superposition and entanglement, quantum methods enable more efficient parallel processing of high-dimensional data and offer faster and more effective solutions to problems that are computationally demanding for classical methods. In this study, a novel model called "Deep VQC" is proposed, based on the Variational Quantum Classifier approach. Developed using microarray data containing 54,676 gene features, the model successfully classified four different types of brain tumors-ependymoma, glioblastoma, medulloblastoma, and pilocytic astrocytoma-alongside healthy samples with high accuracy. Furthermore, compared to classical ML algorithms, our model demonstrated either superior or comparable classification performance. These results highlight the potential of quantum AI methods as an effective and promising approach for the analysis and classification of complex structures such as brain tumors based on gene expression features.

Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This study introduces a novel hybrid classical and quantum computing model to distinguish LGGs from HGGs using data from The Cancer Genome Atlas (TCGA). In the classical part, an ensemble feature selection method was employed to identify the most informative molecular markers and clinical features in the TCGA. In the quantum component, six variational quantum classifier (VQC) models with varying hyperparameters were evaluated. These classifiers utilize selected features to differentiate LGGs from HGGs. Among these, the VQC-1 model, which employs [Formula: see text] and CX gates in the feature map and [Formula: see text] [Formula: see text] and CY gates in the parameterized quantum circuit, achieved the highest classification accuracy of 0.74 using the AQCD optimization method. Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. This study provides a novel perspective on glioma classification by integrating classical and quantum computing, offering valuable insights into hybrid computational approaches.

Early detection and accurate classification of brain tumors using MRI scans are crucial for effective diagnosis and treatment planning. However, with the growing patient population and the increasing volume of MRI data, as well as limitations like noise in image data and poor resolution, accurate and rapid diagnosis becomes challenging. To address these issues, AI systems are needed to support radiologists by offering a second opinion. Recent advancements in deep learning (DL) have significantly improved MRI-based brain tumor diagnosis. Despite these improvements, challenges such as the need for higher computational power, difficulty processing large and high-resolution datasets, and limitations of classical vector space. However, quantum computing and quantum computing-based AI methods, by leveraging properties such as superposition and entanglement, have the potential to process data in parallel, handle higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with, more quickly and efficiently. In this study, we proposed four different hybrid quantum–classical integrated neural network (HQCINN) models featuring various multilayer parameterized quantum circuit architectures, which we refer to as “shallow and deep circuits,” designed based on properties such as “entanglement capability, circuit loss, and the number of trainable parameters.” These models aim to distinguish between glioma, meningioma, pituitary and non-tumor classes. The performance of these models was compared to classical DL models, revealing that quantum models provide higher accuracy and lower loss values with fewer parameters. Additionally, when the HQCINN model with the best performance was applied to a brain tumor dataset consisting of CT images, it demonstrated consistent performance across different patient data distributions and imaging modalities, thereby showing strong generalization capability. These results suggest that HQCINN approaches could provide significant advantages in medical imaging tasks, particularly in complex datasets like brain tumor classification.

The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance.

This study investigates the optimization of organic photodetectors (OPDs) using SCAPS-1D simulation, focusing on the effects of layer thickness, doping density, temperature, external quantum efficiency (EQE), and responsivity on key performance metrics. The device structure includes PBDB-T/ITIC as the active layer and graphene oxide (GO) as the hole transport layer (HTL). By systematically varying the thickness of the PBDB-T/ITIC active layer and the GO hole transport layer, as well as adjusting the donor and acceptor densities, we analyze their impact on open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), power conversion efficiency (η), EQE, and responsivity. The simulation results reveal that an optimal active layer thickness of 800 nm for PBDB-T/ITIC and a GO layer thickness of 50 nm maximize device performance. Additionally, a donor density of \({9\times 10}^{19}{cm}^{-3}\) for PFN and an acceptor density of \({10}^{20}{cm}^{-3}\) for GO significantly enhance efficiency. The photodetector demonstrates a high current under illumination, peaking responsivity around 920 nm, and excellent performance in the visible spectrum. Temperature variations show optimal performance around 330 K. These findings highlight the critical role of precise material and structural optimization in achieving high-efficiency OPDs, providing valuable insights for future research and development in this field.

Wind energy holds a significant position among renewable energy sources. Wind turbines generate electricity by harnessing wind power, and wind farms, typically consisting of several turbines, are commonly employed. The location of wind farms can greatly influence the efficiency of the energy produced. Offshore wind farms, in particular, offer various advantages over onshore installations. In a well-optimized energy production process, the in-tegrity of the grid structure enhances the operational efficiency of wind energy. Therefore, accurately pre-dicting the energy output of wind farms is critical. While classical machine learning (ML) approaches, such as regression and deep learning, are widely used for wind power forecasting, these methods often require large datasets and substantial computational power. Although parallel computing methods can help improve performance, they are typically limited in scope. In contrast, quantum computing represents a new computational paradigm, offering advantages in natural parallelization and efficient data processing. This paper proposes a hybrid quantum-classical model for power forecasting of offshore wind farms. In the proposed model, a quantum neural network is employed as a feature extractor, while support vector regression performs the forecasting task. This study marks one of the initial steps in exploring the opportunities offered by quantum computing beyond classical methods in wind power forecast. The results demonstrate the applicability of quantum-classical hybrid models to computationally intensive problems such as energy production.

Organic electronics have great potential due to their flexible structure, high performance, and their ability to build effective and low-cost photodetectors. We investigated the parameters of the P3HT and PCBM layers for device performance and optimization. SCAPS-1D simulations were employed to optimize the thicknesses of the P3HT and PCBM layers, investigate the effects of shallow doping in the P3HT layer, and assess the influence of the back contact electrode's work function on device performance. Furthermore, this study explored the impact of interface defect layer density on the characteristics of the device. Through systematic analyses, the optimal parameters for enhancing device responsivity were identified. The findings indicate that a P3HT layer thickness of 1200 nm, a PCBM layer thickness of 20 nm, and a back contact electrode with a work function of 4.9 eV achieve the highest responsivity. Notably, at a bias of -0.5 V, the responsivity exceeds 0.4 A/W within the wavelength range of 450 nm to 630 nm. These optimized parameters underscore the significant potential of the developed device as an organic photodetector, particularly for visible light detection.

Abstract Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.

In this research, the aluminum (Al2024) matrix composites are reinforced with nano magnesium oxide (MgO) and multi-walled carbon nanotubes (MWCNTs). The aim of Al2024 alloy reinforced with MgO and MWCNTs is to reveal the effects of the reinforcement particle ratio on the microstructure and mechanical properties of the hybrid composites, to find the optimum hybrid ratio, and to form a stronger hybrid composite. The composites with the different hybrid ratios are produced via stir casting method. The theoretical and measured densities and porosity content of the composites are studied. The microstructure and fracture surface of the composites are examined by optical microscopy, scanning electron microscopy (SEM), and electron dispersive spectrum (EDS). The hardness, compression, and tensile properties of the composites are studied in this work. Results indicated that the hardness of the aluminum (Al) matrix composites (AMCs) is significantly improved after the reinforcement with MgO and carbon nanotube (CNT), and also heat treatment of T6. The stress–strain curve of the composites is tested by a material testing machine. The maximum tensile and compression strengths are obtained at Al2024-0.2 wt.% (MgO 50% + CNT 50%) the composite as 226 MPa and 684 MPa, respectively .The hardness, compression, and tensile strengths of the hybrid composites are higher than 1.51, 1.39, and 1.31 than that of the base metal. After heat treatment of T6, the maximum harness, compression, and tensile strengths are obtained by 102 HB, 730 MPa, and 277 MPa, respectively, which are 1.82, 1.49, and 1.61 times higher hardness than that that of the base Al2024 alloy. The main strengthening mechanism of nano-MgO particles and MWCNTs-reinforced Al2024/MgO/CNTs composites is observed by the precipitation strengthening mechanism.

Knowledge of optical properties of blood is very important to solve problems presented by biomedical optics. The development of predictive models of blood is a challenging task due to the inherent complexity of biological systems. In this paper, we investigated the optical parameter responsible for the appearance attributes of whole blood. Blood, an extraordinary fluid, makes life possible. The circulation of blood through the body results to function and fights diseases or infections. This work focuses on generative computational simulations, modelling, and deep learning techniques to gain data-driven insights about the valuable fluid of whole blood and its properties. The analysis of optical properties of blood is essential to interpret its interaction with light to accurately diagnose illnesses. In recent years, the studies of the association between blood and previously known symptoms have attracted increasing attention. The bibliometric analysis showed the trends of whole blood and its parameters in an increasing pattern. In this study, we proposed an interpretable deep learning strategy incorporating the neural network to examine the refractive index of blood data. The new proposed method fully employed raw data and allowed for implementation of a build-up and a validation model. The developed model was used to successfully diagnose, detect and define the diseases in a manner that is noninvasive, simple, accurate and completely cost-effective. These intelligent models can play an important role in future biomedical applications in design and improvements to be made on the performance of optical devices.

Water is conceivably the most important material in the universe and most essential to the functioning of all the known life-forms. A simple expression for the real part of refractive index of water was investigated and proposed a new equation as a function of temperature between 0 oC and 100 oC and also wavelength in the range of 200 to 1100 nm. Water is transparent in the visible light and has a complex optical absorption property in the infrared and ultraviolet ranges. The refractive index highly depends on wavelength and temperature. The expression for refractive index is useful for different applications in biomedical optics. The proposed sample has accurate expression, has good agreement, as well as demonstrates increased performance with experimental measurements for calculations of knowledge of the refractive index of water at given ranges.

The aim of the study is to research and compare the influences of the confirmed cases, test number and time range on the death and recovery rates in the United State of America, China, and Turkey, and to find out the effect of the epidemic in the near future of Turkey. The modelling and prediction of effects of the day, case and test numbers of COVID-19 infection in the USA, China and Turkey are carried out using the artificial neural network approach (ANN). The system are trained and tested with the different numbers of neurons, hidden layers and activation functions to increase the reliability and accuracy of model. The proposed models have a high R2 value for China and Turkey. We can say according to the results that the measures taken by the USA are inadequate. The formulation is applied to predict the effect of Covid-19 infection in Turkey. The test number that is an important factor in detecting the cases should be increased. The results show a good fit between the observed data and those obtained by the ANN model. If the precautions are strictly followed, the case number will be decreased significantly after 160 days for Turkey according to result of the proposed model but due to the uncontrolled variables, this time may result in between 200 and 250 days.

The analysis of blood parameters is main procedure for defining the patient's condition. The refractive index of blood was calculated using the experimental data for a medical application and diagnostic purposes. There is small change in the refractive indices with changing with the personal conditions, illness, parasitization, temperature and others. Theoretical simulations of the refractive index of blood are difficult, and it is unpredictable in different conditions. We proposed a new formula for the refractive index of blood as a function of wavelength, concentration, and temperature by using the genetic programming method. Input parameters were temperature (°C), concentration (g/L), and wavelength (nm). The refractive index values of blood were the output parameters. A total of 492 training and testing sets were selected in the spectral range of 436 to 1550 nm, in the temperature range of 20-45 °C and the concentration of 0-200 g/L HbC. The model proposes the refractive index formula blood for all the input parameters given in the range without need of extra parameters. The results are good agreement with experimental measurements in the literature and compared to Sellmeier equation.

The growing attention regarding aluminum alloy matrix composites within the aerospace, automotive, defense, and transportation industries make the development of new engineering materials with the improved mechanical properties. Currently, materials are selected because of their abilities to satisfy engineering demands high for strength-to-weight ratio, tensile strength, corrosion resistance, and workability. These properties make aluminum alloys and aluminum matrix composites (AMCs) an excellent option for various industrial applications. Soft computing methods such as the artificial neural network (ANN), adaptive-neuro fuzzy inference systems (ANFIS), and Taguchi with ANOVA are the most important approaches to solve the details of the mechanism and structure of materials. The optimal selection of variables has important effects on the final properties of the alloys and composites. The chapter presents original research papers from our works and taken from literature studies dealing with the theory of ANN, ANFIS, and Taguchi, and their applications in engineering design and manufacturing of aluminum alloys and AMCs. Also, the chapter identifies the strengths and limitations of the techniques. The ANFIS and ANN approaches stand out with wide properties, optimization, and prediction, and to solving the complex problems while the Taguchi experimental design technique provides the optimum results with fewer experiments.

The multi-walled carbon nanotubes (MWCNTs) have drawn great attention due to their exceptional mechanical, physical, thermal and electrical properties. The MWCNTs as the reinforcements significantly improved the properties of materials. However, the major challenges in composites containing CNTs are the poor wettability and poor interfacial bonding between matrix and CNTs. In this study, the used MWCNTs have a diameter of 8–10 nm and 1.5 μm in length. MWCNTs are purified in HNO3: H2SO4, sensitized in Sn solution and activated in Pd solution at 90 °C, and coated with the Nickel and Cobalt elements using an electroless coating method. The holding time in the bath is 15, 30 and 60 min, and the bath concentration is also changed. The coatings are characterized by Scanning Electron Microscopy (SEM) equipped with electron dispersive spectrum (EDS), elemental mapping, x-ray diffraction (XRD), Raman spectroscopy, Transmission Electron Microscopy (TEM) and Fourier Transform Infrared (FTIR) spectrometer. The results showed that the Ni and Co coating layers are successfully formed on the surface of MWCNTs. The deposition rate is affected by the holding time and the bath concentration. The optimal results are obtained at the holding time of 60 min in the C concentration sample.

Received/Geliş: 31.07.2020 Accepted/Kabul: 04.09.2020 Abstract: The research on dilute bismuth containing III-V semiconductor alloys and its applications are studied. These alloys are obtained by incorporating a small amount of Bi in the host semiconductor. The presence of Bi reduced the energy bandgap of the alloys. The bandgap and optical properties of InAs1− Bi , InP1-xBix, and InSb1− Bi alloy systems are investigated for optoelectronic devices. The optical properties of semiconductors are important to change the properties of device performance. The refractive index strongly depends on the direct bandgap of the semiconductor alloys. The bandgap of the In-V-Bi semiconductor layer can be engineered by means of adding bismuth into InAs, InP, InSb. In this work, the refractive indices and the optical parameters of the In-V-Bi alloys are investigated.