Robotic approach together with an enhanced recovery programme improve the perioperative outcomes for complex hepatectomy

ObjectiveRobotic surgery has more advantages than traditional surgical approaches to complex liver resection; however, the robotic approach is invariably associated with increased cost. Enhanced recovery after surgery (ERAS) protocols are beneficial in conventional surgeries.MethodsThe present study investigated the effects of robotic surgery combined with an ERAS protocol on perioperative outcomes and hospitalization costs of patients undergoing complex hepatectomy. Clinical data from consecutive robotic and open liver resections (RLR and OLR, respectively) performed in our unit in the pre-ERAS (January 2019–June 2020) and ERAS (July 2020–December 2021) periods were collected. Multivariate logistic regression analysis was performed to determine the impact of ERAS and surgical approaches—alone or in combination—on LOS and costs.ResultsA total of 171 consecutive complex liver resections were analyzed. ERAS patients had a shorter median LOS and decreased total hospitalization cost, without a significant difference in the complication rate compared with the pre-ERAS cohort. RLR patients had a shorter median LOS and decreased major complications, but with increased total hospitalization cost, compared with OLR patients. Comparing the four combinations of perioperative management and surgical approaches, ERAS + RLR had the shortest LOS and the fewest major complications, whereas pre-ERAS + RLR had the highest hospitalization costs. Multivariate analysis found that the robotic approach was protective against prolonged LOS, whereas the ERAS pathway was protective against high costs.ConclusionsThe ERAS + RLR approach optimized postoperative complex liver resection outcomes and hospitalization costs compared with other combinations. The robotic approach combined with ERAS synergistically optimized outcome and overall cost compared with other strategies, and may be the best combination for optimizing perioperative outcomes for complex RLR

Improved GPU implementations of the Pair-HMM forward algorithm for DNA sequence alignment

With the rise of Next-Generation Sequencing (NGS), clinical sequencing services have become more accessible but also facing new challenges. As we discovered the closed connection between key DeoxyriboNucleic Acid (DNA) mutation spots and major diseases or conditions, the need for computational genomics has increased significantly. The surging demand motivates developments of more efficient algorithms for genome assembly, error correction, k-mer counting etc. In this thesis, we focus on DNA sequencing analysis, one of the fastest-growing markets in NGS, and its related alignment problems. In recent years, many new hardware technologies and algorithms have been researched for their potential applications in massive parallel sequencing. The emerging hardware includes GPU, FPGA and other ASICs providing parallel processing resources. In this thesis, we choose GPU as our computation platform for its massive parallel processing capabilities. The Forward Algorithm (FA) still remains one of the most commonly used methods in solving sequences alignment problems modeled as Pair-Hidden Markov Model (HMM). The Pair-HMM Forward Algorithm (FA) is not only a computation but data intensive algorithm. Multiple previous works have been done in efforts to accelerate the computation of the FA by applying massive parallelization on the workload, and in this thesis, we bring more optimizations not only by improving the computation concurrency of both initialization process and Pair-HMM FA but also by tackling the communications overhead between the host and devices. We will discuss the general principles of optimizing the Forward Algorithm on GPU and present an improved implementation of the Pair-HMM FA with native CUDA C++. Our design has shown a speedup of 25.10x over the C++ baseline on the GATK HaplotypeCaller Pair-HMM workload with a portion of the real dataset from human genome database, NA12878. This is a major improvement that beats the state-of-the-art implementation with a margin of 60%.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Design of Small MEMS Microphone Array Systems for Direction Finding of Outdoors Moving Vehicles

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise

Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace

Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality

Study on the Constitutive Equation and Mechanical Properties of Natural Snow under Step Loading

Snow, as an important component of the cryosphere, holds a crucial role in the construction of polar infrastructure. However, the current research on the mechanical properties of snow is not comprehensive. To contribute to our understanding of the mechanical behaviors of snow in cold regions, uniaxial compression tests under step loading were performed on the snow. With the Maxwell model as the basis, different temperatures, densities, and loading rates were set to establish constitutive equations of snow. The changes in the elastic modulus and viscosity coefficient of snow with respect to three variables were investigated. The results show that the loading rate has no obvious effect on the elastic modulus and viscosity coefficient of snow. Both the elastic modulus and viscosity coefficient of snow follow an exponential function with respect to density, with an increase in density, resulting in a higher value. As temperature decreases, the elastic modulus and viscosity coefficient initially decrease and then increase, whereas no specific functional relationship between them was observed. Additionally, a new constitutive equation considering snow density is derived based on the Maxwell model

Ice Velocity in Upstream of Heilongjiang Based on UAV Low-Altitude Remote Sensing and the SIFT Algorithm

In river management, it is important to obtain ice velocity quickly and accurately during ice flood periods. However, traditional ice velocity monitoring methods require buoys, which are costly and inefficient to distribute. It was found that UAV remote sensing images combined with machine vision technology yielded obvious practical advantages in ice velocity monitoring. Current research has mainly monitored sea ice velocity through GPS or satellite remote sensing technology, with few reports available on river ice velocity monitoring. Moreover, traditional river ice velocity monitoring methods are subjective. To solve the problems of existing time-consuming and inaccurate ice velocity monitoring methods, a new ice velocity extraction method based on UAV remote sensing technology is proposed in this article. In this study, the Mohe River section in Heilongjiang Province was chosen as the research area. High-resolution orthoimages were obtained with a UAV during the ice flood period, and feature points in drift ice images were then extracted with the scale-invariant feature transform (SIFT) algorithm. Moreover, the extracted feature points were matched with the brute force (BF) algorithm. According to optimization results obtained with the random sample consensus (RANSAC) algorithm, the motion trajectories of these feature points were tracked, and an ice displacement rate field was finally established. The results indicated that the average ice velocities in the research area reached 2.00 and 0.74 m/s, and the maximum ice velocities on the right side of the river center were 2.65 and 1.04 m/s at 16:00 on 25 April 2021 and 8:00 on 26 April 2021, respectively. The ice velocity decreased from the river center toward the river banks. The proposed ice velocity monitoring technique and reported data in this study could provide an effective reference for the prediction of ice flood disasters