Application of artificial intelligence in the diagnosis and treatment of urinary tumors

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI’s application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI’s role in clinical decisions, providing insights for future AI applications in urological surgery

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications

Bridging Sustainable Development Goals and Land Administration: The Role of the ISO 19152 Land Administration Domain Model in SDG Indicator Formalization

This study illustrates the linkages between the ISO’s Land Administration Domain Model (LADM) and the UN’s sustainable development goals (SDGs), highlighting the role of the LADM in promoting effective land administration suitable for efficient computation of land/water (space)-related SDG indicators. The main contribution of this study is the formalization of SDG indicators by using the ISO standard LADM. This paper proposes several SDG-indicator-related extensions to the multi-part LADM standard that is currently under revision. These extensions encompass the introduction of new procedures for calculating indicators, the integration of blueprints for external classes to fulfil additional information needs and the design of interface classes for presenting indicator values across specific countries and reporting years. In an innovative approach, this paper introduces the Four-Step Method—a powerful framework designed to formalize SDG indicators within the LADM framework. Detailed attention is devoted to specific indicators, including 1.4.2 (secure land rights), 5.a.1 (women’s agricultural land rights), 14.5.1 (protected marine areas) and 11.5.2 (valuation as a basis for direct economic loss). In short, the Four-Step Method is pivotal in eliminating ambiguities, enhancing the efficiency of indicator computation and securing more accurate indicator values that more truly reflect the progress towards SDG realization. This approach is also expected to work with other (ISO) standards for other SDG indicators

Numerical Analysis and Strength Evaluation of an Exposed River Crossing Pipeline with Casing Under Flood Load

Pipelines in service always experience complicated loadings induced by operational and environmental conditions. Flood is one of the common natural hazard threats for buried steel pipelines. One exposed river crossing X70 gas pipeline induced by flood erosion was used as a prototype for this study. A mechanical model was established considering the field loading conditions. Morison equations were adopted to calculate distributional hydrodynamic loads on spanning pipe caused by flood flow. Nonlinear soil constraint on pipe was considered using discrete nonlinear soil springs. An explicit solution of bending stiffness for pipe segment with casing was derived and applied to the numerical model. The von Mises yield criterion was used as failure criteria of the X70 pipe. Stress behavior of the pipe were analyzed by a rigorous finite element model established by the general-purpose Finite-Element package ABAQUS, with 3D pipe elements and pipe-soil interaction elements simulating pipe and soil constraints on pipe, respectively. Results show that, the pipe is safe at present, as the maximum von Mises stress in pipe with the field parameters is 185.57 MPa. The critical flow velocity of the pipe is 5.8 m/s with the present spanning length. The critical spanning length of the pipe is 467 m with the present flow velocity. The failure pipe sections locate at the connection point of the bare pipe and the pipe with casing or the supporting point of the bare pipe on riverbed

Bayesian model averaging for nuclear symmetry energy from effective proton-neutron chemical potential difference of neutron-rich nuclei

The data-driven Bayesian model averaging is a rigorous statistical approach to combining multiple models for a unified prediction. Compared with the individual model, it provides more reliable information, especially for problems involving apparent model dependence. In this work, within both the non-relativistic Skyrme energy density functional and the nonlinear relativistic mean field model, the effective proton-neutron chemical potential difference $\Delta \mu^*_{\rm{pn}}$ of neutron-rich nuclei is found to be strongly sensitive to the symmetry energy $E_{\rm{sym}}(\rho)$ around $2\rho_0/3$, with $\rho_0$ being the nuclear saturation density. Given discrepancies on the $\Delta \mu^*_{\rm{pn}}$-$E_{\rm{sym}}(2\rho_0/3)$ correlations between the two models, we carry out a Bayesian model averaging analysis based on Gaussian process emulators to extract the symmetry energy around $2\rho_0/3$ from the measured $\Delta \mu^*_{\rm{pn}}$ of 5 doubly magic nuclei $^{48}$Ca, $^{68}$Ni, $^{88}$Sr, $^{132}$Sn and $^{208}$Pb. Specifically, the $E_{\mathrm{sym}}(2\rho_0/3)$ is inferred to be $E_{\mathrm{sym}}(2\rho_0/3) = 25.6_{-1.3}^{+1.4}\,\mathrm{MeV}$ at $1\sigma$ confidence level. The obtained constraints on the $E_{\mathrm{sym}}(\rho)$ around $2\rho_0/3$ agree well with microscopic predictions and results from other isovector indicators.Comment: 6 pages, 4 figures; published versio

Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach

3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving. Despite the demonstrated improvements in accuracy and velocity estimation compared to perspective view methods, the deployment of BEV-based techniques in real-world autonomous vehicles remains challenging. This is primarily due to their reliance on vision-transformer (ViT) based architectures, which introduce quadratic complexity with respect to the input resolution. To address this issue, we propose an efficient BEV-based 3D detection framework called BEVENet, which leverages a convolutional-only architectural design to circumvent the limitations of ViT models while maintaining the effectiveness of BEV-based methods. Our experiments show that BEVENet is 3$\times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge, achieving a mean average precision (mAP) of 0.456 and a nuScenes detection score (NDS) of 0.555 on the NuScenes validation dataset, with an inference speed of 47.6 frames per second. To the best of our knowledge, this study stands as the first to achieve such significant efficiency improvements for BEV-based methods, highlighting their enhanced feasibility for real-world autonomous driving applications

The role of calcium channels in osteoporosis and their therapeutic potential

Osteoporosis, a systemic skeletal disorder marked by diminished bone mass and compromised bone microarchitecture, is becoming increasingly prevalent due to an aging population. The underlying pathophysiology of osteoporosis is attributed to an imbalance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation. Osteoclasts play a crucial role in the development of osteoporosis through various molecular pathways, including the RANK/RANKL/OPG signaling axis, cytokines, and integrins. Notably, the calcium signaling pathway is pivotal in regulating osteoclast activation and function, influencing bone resorption activity. Disruption in calcium signaling can lead to increased osteoclast-mediated bone resorption, contributing to the progression of osteoporosis. Emerging research indicates that calcium-permeable channels on the cellular membrane play a critical role in bone metabolism by modulating these intracellular calcium pathways. Here, we provide an overview of current literature on the regulation of plasma membrane calcium channels in relation to bone metabolism with particular emphasis on their dysregulation during the progression of osteoporosis. Targeting these calcium channels may represent a potential therapeutic strategy for treating osteoporosis