Number Of Nodes Research Articles

Distributed algorithm for parallel computation of the n queens solutions

The n-queen problem represents a classic challenge in artificial intelligence (AI) research. It involves the placement of n queens on an n x n chessboard, with the objective of ensuring that no queen threatens another. This problem has long been a source of fascination for mathematicians and computer scientists alike due to its inherent complexity. It is a well-known fact that as the value of ‘n’ increases, the problem becomes more challenging and falls into the NP problem class. Given the computational demands of the problem, parallel methods are of critical importance. It is noteworthy that scalable and parallel approaches for the n-queen problem remain to be developed. The majority of existing methods working on graphs attempt to parallelize a recursive sequential algorithm. However, the unpredictable nature of these algorithms makes it challenging to parallelize them on modern computer architectures. Consequently, we have selected an iterative algorithm from the literature in order to facilitate parallelization. This paper presents an innovative approach to parallelization that differs from traditional matrix-based strategies. The n-queen graph is distributed among a network of nodes, ensuring effective load balancing through dynamic partitioning and real-time computation. Our bespoke distributed algorithm, designed for the maximum clique problem on the n-queen graph, operates with true concurrency, thus obviating the necessity for resource or data sharing. The results of our assessment demonstrate that the parallel algorithm outperforms a cutting-edge sequential algorithm in terms of task completion time. The findings demonstrate that the speedups are almost perfect and that the workloads are distributed evenly across the network nodes. Furthermore, the results demonstrate high scalability, with task completion times decreasing as the number of nodes increases.

Three applications of semantic network analysis to individual student think-aloud data

Student during-learning data such as think-alouds or writing are often coded for use of strategies or moves, but less often for what knowledge the student is using. However, analyzing the content of such products could yield much valuable information. A promising technique for analyzing the content of student products is semantic network analysis, more widely used in political science, communication, information science, and some other social science disciplines. We reviewed the small literature on semantic network analysis (SemNA) of individuals with relevant outcomes to identify which network analysis metrics might be suitable. The Knowledge Integration (KI) framework from science education is discussed as focusing on amount and structure of student knowledge, and therefore especially relevant for testing with SemNA metrics. We then re-analyze three published think-aloud data sets from undergraduate students learning introductory biology with the metrics found in the literature review. Significant relations with posttest comprehension score are found for number of nodes and edges; degree and betweenness centrality; diameter, and mean distance. Inconsistent results possibly due to text-specific features were found for number of clusters, LCC, and density, and null results were found for PageRank centrality and centralization degree. Basic principles from the KI framework are supported—amount of information (nodes), connections (edges, average degree), key ideas (degree and betweenness centrality) and length of causal chains (mean distance and diameter) are related to posttest comprehension, but not density or LCC. Possible explanations for slight variations across data sets are discussed, and alternative theories and metrics are offered.

Effects on lymph node size, staging and primary tumor histology on diagnostic accuracy of axillary lymph node aspirate of breast cancers.

Fine-needle aspiration cytology is preferred for axillary lymph node metastasis with low costs and minimal risks. To improve diagnostic performance by incorporating clinical-radiological-pathological parameters, a large cohort pre-operative aspirates in were reviewed for parameters affecting adequacy rate and accuracy. Axillary nodal aspirates from three institutions with histologic correlation were retrieved. Case notes were reviewed for parameters pertaining to the primary tumor, nodal status, histologic and cytologic diagnoses. Totally 1361 specimens were included. The risk of malignancy for C1-C5 categories were 53.39%, 27.45%, 70.97%, 83.33% and 88.00%, increasing to 75.86%, 94.59% and 99.28% for C3/C4/C5 categories excluding cases with neoadjuvant therapy. Node size (p < 0.001) and histologic grade (p = 0.003) of primary tumor positively correlated with specimen adequacy. Presence of in situ component trended towards inadequacy (p = 0.069). Lymph node size remained a strong predictor of concordant cytologic diagnosis (p < 0.001). A higher percentage of involved node (p = 0.006) and HER2 overexpressed breast cancers (p = 0.027) increased concordance. Cases with ≥ 4 (up to ≥ 10) positive nodes were more likely to be concordant (p = 0.009- < 0.001), with improvements of 8.27%-12.37%. For size, cut-offs of ≥ 5 and ≥ 10mm were significant (p = 0.006- < 0.001). It is critical that clinical-radiological-pathological findings be interpreted together with cytology. Aspirates from smaller nodes are more likely to be non-informative, irrespective of the total number of suspicious nodes, or a high-grade primary. In axillae with less than 4 suspicious nodes and/or a target node of less than 5-10mm, the diagnostic accuracy of aspiration cytology decreases and should be interpreted cautiously.

Size, but not the number of positive lymph nodes is associated with worse overall survival in patients with small bowel neuroendocrine tumours

Size, but not the number of positive lymph nodes is associated with worse overall survival in patients with small bowel neuroendocrine tumours

Design of a Gomoku AI Based on the Alpha-Beta Pruning Search Algorithm

Abstract. This paper presents the implementation of a Gomoku AI based on the alpha-beta pruning search algorithm and the Negamax algorithm. Gomoku, a traditional board game known for its strategic depth, poses significant challenges in AI development due to the exponential increase in possible moves. The AI leverages the computational efficiency of Alpha-Beta Pruning, which enhances the Negamax algorithm by reducing the number of nodes that need to be evaluated in the game tree. This combination allows for faster decision-making without compromising accuracy. Additionally, a value evaluation function is used to assess board states and guide the AI in selecting optimal moves. In the results the performance of the AI was tested through simulations, demonstrating great performance in move selection and computational efficiency compared to traditional methods. The paper also explores potential improvements, including the integration of reinforcement learning (RL) techniques to further enhance the AI's adaptability and strategic decision-making capabilities.

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Advantage of Log Odds of Metastatic Lymph Nodes After Curative-Intent Resection of Gallbladder Cancer.

Lymph node metastasis (LNM) is among the most important predictors of poor prognosis after surgery for gallbladder cancer (GBC). Traditionally, staging has been based on the raw count of LNM, with a high risk of understaging patients who undergo inadequate lymph node dissection (LND). The log odds of metastatic lymph nodes (LODDS) may represent an alternative staging approach to stratify patients more accurately after resection of GBC. In this cross-sectional study, patients who underwent curative-intent surgery with LND for GBC were identified from an international database. Two predictive models were built and compared, each integrating a different lymph nodes status indicator [i.e., American Joint Committee on Cancer (AJCC) and LODDS]. Among 199 patients, the median number of lymph nodes examined was 5 [interquartile range (IQR): 3.0, 8.0]; most patients had T1 (n = 26, 13.1%) or T2 (n = 97, 48.7%) disease, and a subset had LNM (n = 87, 44.0%). Multivariable Cox analysis demonstrated LODDS was an independent predictor of overall survival [hazard ratio (HR) 1.84, 95% confidence interval (CI) 1.5-2.3; p < 0.001]. The LODDS model demonstrated better performance compared with a traditional model that utilized the AJCC N category [concordance (C) index: 0.814 versus 0.763; p < 0.001]. Patients classified as high- versus low-risk based on LODDS had much worse overall survival (OS) (4.9% versus 83.7%, respectively; p < 0.001). The LODDS model performance remained high even among patients with inadequateLND (< 6 LN) (C index: 0.87). An online calculator was developed ( https://catalano-giovanni.shinyapps.io/LoddsGBC/ ). A novel prognostic model based on LODDS may overcome the inherent limitations of the current AJCC staging system, reducing understaging among patients with fewer than six total lymph nodes evaluated.

Nomograms Predicting Survival, Recurrence and Beneficiary Identification of Adjuvant Chemotherapy in Treatment-naïve Patients with Rectal Cancer who Underwent Upfront Curative Resection: A multi-institutional study

Objective: To create and validate nomograms predicting overall survival and recurrence in treatment-naïve rectal cancer (RC) patients who underwent upfront surgery. Background: Although multidisciplinary treatment is standard for locally advanced RC, understanding surgical efficacy is important for determining indications for perioperative adjuvant therapy. Methods: RC patients who underwent upfront surgery at the Japanese Society for Cancer of the Colon and Rectum institutions were analyzed. A training cohort (n = 1925) of treatment-naïve patients who underwent surgery between 2007 and 2008 was analyzed to construct prognostic models predicting postoperative survival and recurrence. Discrimination and calibration were performed using an external validation cohort (n = 2957; Japanese colorectal cancer registry, procedures in 2005–2006). Effects of adjuvant chemotherapy on survival were evaluated based on nomogram prediction and Surveillance, Epidemiology, and End Results (SEER) data (n = 10,482; upfront surgery for RC in 2010–2015). Results: In the training cohort, age predicted survival, venous invasion predicted recurrence, and sex, tumor location, histological type, preoperative carcinoembryonic antigen, invasion depth, lymphatic invasion, positive radial margin, and numbers of metastatic nodes and examined nodes predicted both. Internal and external validated Harrell’s C-index values were respectively 0.77 and 0.75 for survival and 0.75 and 0.74 for recurrence. RC patients who underwent upfront surgery in the SEER database were stratified into 3 risk levels by nomogram score. Adjuvant chemotherapy did not improve 5-year survival in low-risk patients, but did so for middle-risk (62.4% vs 76.8%) and high-risk (39.4% vs 63.5%) patients. Conclusion: These nomograms could predict survival and recurrence after upfront curative resection of RC and identify cases expected to benefit more from adjuvant chemotherapy.

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system.

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by and , respectively. The performance of the average number of active sensor nodes decreased by and compared to existing algorithms, while the average coverage redundancy decreased by and relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

Guarantees on the structure of experimental quantum networks

AbstractQuantum networks connect and supply a large number of nodes with multi-party quantum resources for secure communication, networked quantum computing and distributed sensing. As these networks grow in size, certification tools will be required to answer questions regarding their properties. In this work we demonstrate a general method to guarantee that certain correlations cannot be generated in a given quantum network. We apply quantum inflation methods to data obtained in quantum group encryption experiments, guaranteeing the impossibility of producing the observed results in networks with fewer optical elements. Our results pave the way for scalable methods of obtaining device-independent guarantees on the network structure underlying multipartite quantum protocols.

Effects of reduced flow gradient on benthic biofilm communities' ecological network and community assembly.

The intensification of human activities has led to flow reduction and cut-off in most global rivers, seriously affecting riverine organisms and the biogeochemical processes. As key indicators of river ecosystems' structure and function, benthic biofilms play a critical role in driving primary production and material cycling in rivers. This research aimed to investigate the characteristics of microbial communities' complexity and stability during river flow reduction. Benthic biofilms were grown in artificial channels and subjected to eight gradients of flow reduction (represented by flow velocity from 0.4 to 110 cm/s). Biofilms' biodiversity, ecological networks and community assembly of bacteria, fungi and algae were investigated by high-throughput sequencing. Results showed significant differences in community composition and structure under different flow conditions. The eight flow gradients' microbial communities were divided into three groups: low, medium and high flows. The flow reduction led to significant decreases in bacterial and fungal communities' Chao1 index. Low flow conditions enriched the bacterial phyla Oxyphotobacteria, Alphaproteobacteria and Mollicutes, but significantly decreased the fungal phylum Chytridiomycota. Lowering flow reduced the fungal network's number of nodes and increased the algal network's number of edges. Cross-domain interactions network analysis showed a gradual increase in node and edge numbers with decreasing flow, while decreasing average path length. The neutral model predicted stochastic processes primarily drove biofilm community assembly, and that model's explanations decreased as the flow gradient decreased. The null model analysis revealed diffusion limitation as the most common stochastic ecological process for bacterial and algal communities, with reduced flow reducing heterogeneous selection and increasing diffusion-limited processes. This study provides an in-depth analysis of flow reduction's effects on biofilm communities' ecological networks and community assembly.

A Coverage Hole Recovery Method for 3D UWSNs Based on Virtual Force and Energy Balance

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs.

Trading Community Analysis of Countries’ Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America).

Graph neural link predictor based on cycle structure

AbstractCurrently, the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure, which predominantly rely on low‐order structural information and do not explore the multivariate interactions between nodes from the perspective of higher‐order structural information present in the network. The cycle structure is a higher‐order structure that lies between the star and clique structures, where all nodes within the same cycle can interact with each other, even in the absence of direct edges. If a node is encompassed by multiple cycles, it indicates that the node interacts and associates with a greater number of nodes in the network, and it means the node is more important in the network to some extent. Furthermore, if two nodes are included in multiple cycles, it signifies the two nodes are more likely to be connected. Therefore, firstly, a multi‐information fusion node importance algorithm based on the cycle structure information is proposed, which integrates both high‐order and low‐order structural information. Secondly, the obtained integrated structure information and node feature information is regarded as the input features, a two‐channel graph neural network model is designed to learn the cycle structure information. Then, the cycle structure information is utilised for the task of link prediction, and a graph neural link predictor with multi‐information interactions based on the cycle structure is developed. Finally, extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation, the proposed graph neural network model can effectively learn the cycle structure information, and using higher‐order structural information—cycle information proves to significantly enhance the overall link prediction performance.

Screening high-risk individuals for primary gastric adenocarcinoma: evaluating progression-free survival probability score in the presence and absence of Rictor expression after gastrectomy

ObjectiveDeveloping nomogram-based risk stratification model to determine 3-year and 5-year progression-free survival (PFS) and to identify high-risk patients with gastric adenocarcinoma based on different Rictor statuses.Methods1366 individuals who underwent radical gastric surgery to treat gastric adenocarcinoma at Shanxi Cancer Hospital from May 2002 to December 2020 were analyzed. Cox regression analysis was employed to create the nomograms. The nomograms’ performance was assessed using C-index, time receiver operating characteristic (t-ROC) curves, calibration curves, and decision curve analysis (DCA) curves in training and validation cohorts. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram’s risk scores.ResultsThe Rictor (-) nomogram for predicting PFS included variables such as age, number of positive lymph nodes, vascular invasion, maximum diameter of the tumor, omentum metastasis, and expression of MSH2. In the internal validation, the C-index of the Rictor (-) nomogram was 0.760 (95%CI: 0.720-0.799), which was superior to the C-index of the American Joint Committee on Cancer (AJCC) 8th edition TNM staging (0.683, 95%CI: 0.646-0.721). Similarly, the Rictor (+) nomogram for predicting PFS included variables such as gender, age, pT stage, number of positive lymph nodes, neural invasion, maximum diameter of the tumor, omentum metastasis, Clavien-Dindo classification for complications, and CGA expression. The C-index of the Rictor (+) nomogram was 0.795 (95%CI: 0.764-0.825), which outperformed the C-index of the AJCC 8th edition TNM staging (0.693, 95%CI: 0.662-0.723). The calibration curves, t-ROC curves, and decision curve analysis for both nomogram models demonstrated their excellent prediction ability.ConclusionThis study presents the first risk stratification for Rictor status in gastric adenocarcinoma. Our model identifies low-risk patients who may not require additional postoperative treatment, while high-risk patients should consider targeted therapies that specifically target Rictor-positive indicators.

Evolution from laparoscopic to robotic radical resection for gallbladder cancer: a propensity score-matched comparative study.

The use of robotic or laparoscopic surgery for gallbladder cancer (GBC) is increasing, with reported advantages over conventional open surgery. The purpose of this study was to compare the perioperative outcomes and postoperative overall survival (OS) associated with robotic radical resection (RRR) and laparoscopic radical resection (LRR) for GBC. A total of 109 patients with GBC who underwent radical resection with the same surgical team between January 2015 and December 2023 were enrolled, with 21 patients in the RRR group and 88 cases in the LRR group. A 1:1 propensity score matching (PSM) algorithm was used to compare the surgical outcomes and postoperative prognosis between the RRR and LRR groups. Logistic regression analysis was used to identify the risk factors of postoperative overall survival (OS) and complications of Clavien-Dindo (C-D) Grades III-IV. The median follow-up time was 46 (inter-quartile range, IQR 29-70) months for the LRR group and 16 (IQR 12-34) months for the RRR group. After PSM, the baseline characteristics of the RRR and LRR groups were generally well balanced, with 21 patients in each group. RRR was associated with significantly decreased intraoperative bleeding [100.00 (50.00, 200.00) mL vs 200.00 (100.00, 300.00) mL] and higher number of lymph nodes (LNs) yield [12.00 (9.00, 15.50) vs 8.00 (6.00, 12.00)]. The two groups showed comparable outcomes in terms of the incidence of biliary reconstruction, the range of liver resection, the length of operation, the incidence of postoperative morbidity, the incidence of C-D Grades III-IV complications, number of the days of drainage tubes indwelling and postoperative hospital stay, and mortality by postoperative days 30 and 90. After PSM, the 1-, 2-, and 3-year overall survival rates were 78, 70, and 37%, respectively, in the RRR group, and 71, 59, and 48%, respectively, in the LRR group (P = 0.593). Multivariate analysis showed that the preoperative TB level ≥ 72µmol/L and biliary reconstruction were found to be the independent risk factors of C-D Grades III-IV complications. T3 stage was identified to be the risk factor for postoperative OS. Compared with LRR, RRR showed comparable perioperative outcomes in terms of length of operation, and postoperative complications, recovery, and OS. In our case series, RRR of GBC can be accomplished safely and tends to show less intraoperative bleeding and higher LNs yield.

An Anchor-Free Location Algorithm Based on Transition Coordinates

In some location scenarios where the location information of nodes cannot be mastered in advance, the anchor-free location technology is particularly important. In order to reduce the complicated calculation and eliminate the accumulated error in the traditional anchor-free location algorithm, a new anchor-free location algorithm based on transition coordinates is proposed in this paper. This algorithm is different from the traditional methods such as minimum cost function or inverse matrix. Instead, N initial coordinates are randomly generated as the starting position of the transition coordinates, and the position increment between the transition coordinates and the real coordinates of the node is constantly modified. After K iterations, the convergent position coordinates are finally infinitely close to the real position coordinates of N nodes, and the computational complexity is less than most existing algorithms. As follows, the factors that affect the performance of the algorithm are investigated in the simulation experiment, including the topology structure, positioning accuracy and the total number of nodes, etc. The results show great advantages compared with the traditional anchor-free positioning algorithm. When the topology structure of the initial coordinates changes from a square to a random graph, the number of iterations increases by 15.79%. When the positioning accuracy increased from 1% to 1‰, the number of iterations increased by 36.84%. When the number of nodes N is reduced from 9 to 4, the number of iterations is reduced by 63.16%. In addition, the algorithm can also be extended to the field of moving coordinates or three-dimensional spatial positioning, which has broad application prospects.

Combined Use of External Iliac Lymph Node Count and Bone Scintigraphy for PJI Diagnosis: A Prospective Study

Background: The reactive enlargement of external iliac lymph nodes and increased blood flow in the infected region are commonly observed in lower limb infections. We aimed to differentiate between aseptic loosening and periprosthetic joint infection (PJI) after joint replacement surgery by quantifying the number of enlarged external iliac lymph nodes and using bone scintigraphy to monitor blood flow. Methods: We recruited 124 patients undergoing revision surgery for aseptic loosening or PJI. All the patients underwent preoperative dual-energy computed tomography (DECT) imaging for external iliac lymph nodes and bone scintigraphy. The diagnostic value was evaluated using ROC curve analysis. Results: The number of enlarged external iliac lymph nodes was significantly higher in the PJI group than in the aseptic failure group (4.0 versus. 1.0, p value &lt; 0.001). The median affected/unaffected side ratio in the blood pool phase of ECT in the PJI group was 1.49, significantly higher than the aseptic failure group’s median ratio of 1.04 (p value &lt; 0.001). The AUC for diagnosing PJI using the number of enlarged lymph nodes alone was 0.91, and when using the bone scintigraphy blood pool phase alone, the AUC was 0.89. When both metrics were combined, the AUC increased to 0.95, which was higher than the AUCs for the ESR (AUC = 0.83), CRP (AUC = 0.76), and synovial fluid PMN% (AUC = 0.62). Conclusions: Combining the enlargement of the lymph node count with the bone scintigraphy blood pool phase is a promising approach for diagnosing PJI.

Identification and Categorization of Technical Errors and Hazard-zones of Robotic versus Laparoscopic total Gastrectomy for Gastric Cancer: A Single Center Prospective Randomized Controlled Study.

The current research aimed to conduct a detailed analysis of intraoperative surgical performance, short-term outcomes, identify and categorize technical errors, and hazard-zones enacted during total gastrectomy performed robotically and laparoscopically by surgeons. Prospective research is needed to determine whether the technical advantages of robotic surgery translate to patient outcomes. Identify and process risk areas in robot-assisted total gastrectomy (RTG) and laparoscopic total gastrectomy (LTG) to get the best patient results. Patients undergoing RTG and LTG were recruited and randomized into the study. Six consultant/attending surgeons participated in this study and all surgical procedures were recorded. The unedited surgical video-recordings were handed over to third-party experts for granular analysis of the procedures using objective clinical human reliability analysis (OC-HRA) for the quality of intraoperative performance, technical errors, intraoperative complications. This study is a single center prospective randomized controlled trial. 82 patients were recruited and participated in this study with 40 cases undergoing RTG and 42 cases for LTG. RTG vs LTG. Determine whether RTG or LTG can provide the better intraoperative technical performance and identify the most hazardous zone (area) during total gastrectomy (TG). The technical errors enacted and identified in the RTG and the LTG were (46.11±5.63 VS 58.79±8.45, P＜0.001) respectively. The highest number of technical errors was identified during the dissection of the supra-pancreatic lymph nodes (Task Zones3, TZ3), including No.5, 7, 8a, 9, 11p, and 12a to complete the nodal clearance around the celiac artery and its trifurcation (7.29±1.88 VS 9.43±2.24, P <0.001) in both RTG and LTG. The number of lymph nodes harvested with RTG was higher than LTG (35.36±7.51 VS 30.54±6.95, P=0.016), especially in the upper margin of the pancreas (13.32±4.17 VS 9.36±3.81, P＜0.001). The total cost of hospitalization in the RTG group cost 3% more than LTG group ($15953.41±3533.91 VS $12198.26±2761.27, P＜0.001). This study offers compelling OC-HRA evidence demonstrating that RTG facilitates significantly superior technical performance compared to LTG. Whether examining short-term clinical outcomes or intraoperative operations, the robotic surgery system consistently outperforms laparoscopic surgery. Lymph node dissection in the supra-pancreatic region emerged as a major hazard zone in both procedures. chictr.org.cn: ChiCTR2000039193.

Waveflow: Boundary-conditioned normalizing flows applied to fermionic wave functions

An efficient and expressive wave function Ansatz is key to scalable solutions for complex many-body electronic structures. While Slater determinants are predominantly used for constructing antisymmetric electronic wave function Ansätze, this construction can result in limited expressiveness when the targeted wave function is highly complex. In this work, we introduce Waveflow, an innovative framework for learning many-body fermionic wave functions using boundary-conditioned normalizing flows. Instead of relying on Slater determinants, Waveflow imposes antisymmetry by defining the fundamental domain of the wave function and applying necessary boundary conditions. A key challenge in using normalizing flows for this purpose is addressing the topological mismatch between the prior and target distributions. We propose using O-spline priors and I-spline bijections to handle this mismatch, which allows for flexibility in the node number of the distribution while automatically maintaining its square-normalization property. We apply Waveflow to a one-dimensional many-electron system, where we variationally minimize the system’s energy using variational quantum Monte Carlo (VQMC). Our experiments demonstrate that Waveflow can effectively resolve topological mismatches and faithfully learn the ground-state wave function.