Ryan Daigle¹, Ilene Staff², Joseph Tortora², Tara McLaughlin Proto^3,*, Kevin Pinto³, Rosa Negron², Jonathan Earle⁴, Joseph Wagner³

Canadian Journal of Urology, DOI:10.32604/cju.2025.063773

Abstract Introduction: We examined the pathology and safety outcomes associated with the extent of pelvic lymph node dissection in patients with high-risk prostate cancer undergoing radical prostatectomy.
Materials and Methods: We retrospectively identified men with prostate cancer who underwent robot-assisted radical prostatectomy with pelvic lymph node dissection between May 2016 and September 2021. Cases were categorized using Current Procedural Terminology (CPT) codes (38571) for extended lymph node dissection and super-extended lymph node dissection (38572). Using logistic regression, we compared the groups on a number of factors, including recurrence.
Results: Super-extended lymph node dissection had significantly higher median prostate-specific antigen… More >

Tianshuo Liu^1,2, Xiaoyu Cui¹, Lihao Lu^2,3, Kongkuai Ying^2,*, Yang Wang^2,3, Kangjun Liu², Zilong Wang¹, Zhenhua Jiang^2,3, Shaoshuai Liu^2,3,*

Frontiers in Heat and Mass Transfer, DOI:10.32604/fhmt.2025.064532

Abstract Helium sorption cooler technology is a key means to realize highly reliable low-vibration very low-temperature environments, which have important applications in fields such as quantum computing and space exploration. The laboratory designed a superfluid suppression small hole and a multi-ribbed condenser, developed a reliable-performance helium sorption cooler (HSC), and conducted experimental studies. Experimental results show that the prototype can achieve the lowest cooling temperature of 873 mK without load by filling 6 MPa helium at room temperature. The low-temperature hold time is 26 h, and the temperature fluctuation is within 0.8 mK. The cooling power More >

Jiran Zhu¹, Silin He¹, Chun Chen^2,*, Li Zhou², Hongqing Li¹, Di Zhang¹, Fenglin Hua¹, Tianhao Zhu²

Energy Engineering, DOI:10.32604/ee.2025.065453

Abstract In the islanded operation of distribution networks, due to the mismatch of line impedance at the inverter output, conventional droop control leads to inaccurate power sharing according to capacity, resulting in voltage and frequency fluctuations under minor external disturbances. To address this issue, this paper introduces an enhanced scheme for power sharing and voltage-frequency control. First, to solve the power distribution problem, we propose an adaptive virtual impedance control based on multi-agent consensus, which allows for precise active and reactive power allocation without requiring feeder impedance knowledge. Moreover, a novel consensus-based voltage and frequency control More >

Wensheng Zhou^1,2, Chen Liu^1,2, Deqiang Wang^1,2, Bin Wang³, Yaguang Qu^4,*

Energy Engineering, DOI:10.32604/ee.2025.062530

Abstract The J oilfield in the Bohai has a long development history and has undergone comprehensive adjustment measures, including water injection and polymer injection. Following these adjustments, the injection and production well network now features coexistence of both polymer injection wells and water injection wells, which has negatively impacted production dynamics. Firstly, based on the adjusted reservoir well network in the J oilfield, a representative water-polymer co-injection well network was established. Subsequently, a numerical simulation model of this typical reservoir unit was developed using reservoir numerical simulation methods to confirm the interference issues associated with water-polymer More >

Seham M. AL-Mekhlafi^1,*, Kamal R. Raslan², Khalid K. Ali², Sadam. H. Alssad^2,3, Nehaya R. Alsenaideh⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063971

Abstract This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals. The model incorporates three key fractional derivatives: the Caputo-Fabrizio fractional derivative with a non-singular kernel, the Caputo proportional constant fractional derivative with a singular kernel, and the Atangana-Baleanu fractional derivative with a non-singular kernel. We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model. To achieve this, the approximation of Caputo proportional constant fractional… More >

Stephan Rosswog^1,2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062063

Abstract We present and explore a new shock-capturing particle hydrodynamics approach. Our starting point is a commonly used discretization of smoothed particle hydrodynamics. We enhance this discretization with Roe’s approximate Riemann solver, we identify its dissipative terms, and in these terms, we use slope-limited linear reconstruction. All gradients needed for our method are calculated with linearly reproducing kernels that are constructed to enforce the two lowest-order consistency relations. We scrutinize our reproducing kernel implementation carefully on a “glass-like” particle distribution, and we find that constant and linear functions are recovered to machine precision. We probe our More >

Mukesh Dalal^1,*, Payal Mittal²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.066056

Abstract Deep learning-based object detection has revolutionized various fields, including agriculture. This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years, highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time. The review emphasizes the integration of advanced models, such as You Only Look Once (YOLO) v9, v10, EfficientDet, Transformer-based models, and hybrid frameworks that improve the precision, accuracy, and scalability for crop monitoring and More >

Yiwei Zhang, Xin Cui^*, Qinghui Zhao

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.065430

Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >

Jie He, Dongmei Zhao, Tao Liu^*, Qingfeng Zou, Jian’an Xie

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.065205

Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >

Mengke Tang¹, Yong Gan^2,*, Yifan Zhang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064541

Abstract Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the… More >