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Prostate cancer (PC) is a prevalent and potentially fatal form of cancer that affects men globally. However, the existing diagnostic methods, such as biopsies or digital rectal examination (DRE), have limitations in terms of invasiveness, cost, and accuracy. This study proposes a novel machine learning approach for the diagnosis of PC by leveraging clinical biomarkers and personalized questionnaires. In our research, we explore various machine learning methods, including traditional, tree-based, and advanced tabular deep learning methods, to analyze tabular data related to PC. Additionally, we introduce the novel utilization of convolutional neural networks (CNNs) and transfer learning, which have been predominantly applied in image-related tasks, for handling tabular data after being transformed to proper graphical representations via our proposed Tab2Visual modeling framework. Furthermore, we investigate leveraging the prediction accuracy further by constructing ensemble models. An experimental evaluation of our proposed approach demonstrates its effectiveness in achieving superior performance attaining an F1-score of 0.907 and an AUC of 0.911. This offers promising potential for the accurate detection of PC without the reliance on invasive and high-cost procedures. [ABSTRACT FROM AUTHOR]

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs. [ABSTRACT FROM AUTHOR]

Zinc‐bromine batteries (ZBBs) have emerged as a compelling solution for large‐scale energy storage, yet they confront significant technical challenges impeding widespread commercialization. The electrochemical processes within ZBBs rely on a stoichiometric mechanism, where the bromine reaction at the cathode drives the zinc plating reaction on the anode. However, the sluggish electrochemical kinetics of Br2/Br− redox reactions lead to substantial electrochemical polarization, resulting in interruptions in zinc plating and significant voltage losses in ZBBs. This study introduces a new solution to address these challenges by leveraging carbon nanofiber decorated with tin nanoparticles as an efficient catalyst. The catalyst serves to enhance the Br2/Br– redox reaction, effectively reducing voltage losses in ZBBs. When implemented in static ZBB configurations, the Sn/CNF catalysts demonstrate exceptional long‐term stability, achieving an impressive 3000 cycles with minimal voltage loss. In contrast, ZBBs utilizing SnO2 based catalysts experience a substantially higher voltage loss of 736 mV, along with limited and unstable cycling performance. These findings signify a promising approach for the development of catalytic electrodes, paving the way for high‐performance ZBBs with improved efficiency and cycling durability. [ABSTRACT FROM AUTHOR]