Number Of Nodes Research Articles

Nanocarbon tracer applied to explore the relationship between the number of exposed parathyroid glands and parathyroid function

After nanocarbon tracer was applied to thyroidectomy, we aimed to investigate the risk factors of hypoparathyroidism and the relationship between the number of exposed parathyroid glands and parathyroid function. This study retrospectively collected clinical data of patients who underwent thyroidectomy at Haining People’s Hospital from January 2019 to December 2022. Levels of parathyroid hormone (PTH) and blood calcium were observed before surgery, 1 day after surgery, and one month after surgery to assess the occurrence of postoperative parathyroid hypofunction. Patients were grouped based on their postoperative PTH levels. Furthermore, basic patient data and surgical data were collected to analyze the risk factors associated with hypoparathyroidism after thyroidectomy using single factor analysis. ANOVA analysis was used to compare the correlation between intraoperative parathyroid exposure and postoperative PTH and blood calcium levels. This study involved 80 patients. On the first day after surgery, the serum PTH and blood calcium levels of patients decreased, and 59 patients (73.75%) had normal serum PTH level after surgery, which was the normal group. Twenty-one patients (26.25%) developed hypoparathyroidism after operation and were divided into hypoparathyroidism group. Single factor analysis showed that the main risk factors of postoperative hypoparathyroidism were lesion location, lymph node dissection, nano carbon, number of lymph node metastasis, intraoperative parathyroid exposure, and inaccurate parathyroidectomy. ANOVA analysis showed that the more parathyroid exposure during operation was accompanied by the more decrease of serum PTH and blood calcium levels after operation. Therefore, the location of thyroid lesions, lymph node dissection, nano-carbon, number of lymph node metastases, intraoperative parathyroid exposure, and parathyroidectomy are risk factors for transient hypoparathyroidism after thyroid surgery. Moreover, intraoperative parathyroid exposure significantly affected serum PTH and blood calcium levels. Performing more accurate thyroidectomy and reducing parathyroid exposure help reduce the occurrence of postoperative hypoparathyroidism and hypocalcemia.

Enhancement of Harris Hawks optimization applied in path planning for an indoor navigation mobile application

The research focuses on the enhancement of Harris Hawks Optimization (HHO) algorithm in achieving an efficient path planning, specifically in indoor environments. It solves problems encountered in HHO; firstly, the exploration operator of HHO is modified to incorporate the survival of the fittest principle, this ensures a controlled diversity by using an Exponential Ranking selection method. The method aims to guide the algorithm to find a more optimal solution while allowing it to search for alternative paths. Secondly, Linear Path Strategy is used to reduce the number of nodes in the paths, therefore minimizing its length and simplifying trajectories. In addition, Linear Path Strategy aims to create smoother paths and avoid obstacles, improving the overall performance of the algorithm. Lastly, general multi-objective formula is defined to evaluate path length and travel time. Considering these factors established a balanced evaluation metric, which provided a detailed assessment of path quality for scenarios like indoor navigation. Comparative analysis was done, and results highlighted the effectiveness of EHHO in generating better paths, in comparison with Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and with the current HHO algorithm. It outperformed the algorithms compared in terms of path length, travel time, and efficiency of the execution, especially in a complex environment. In conclusion, EHHO algorithm showed promising results in providing shorter, faster, and more efficient routes, with potential applications across different domains that requires optimal path planning solutions.

Laparoscopic versus open abdominal lymph node dissection for esophageal squamous cell carcinoma: a propensity score matching analysis.

To compare the short- and long-term outcomes of laparoscopic and open abdominal lymph node dissection using propensity score matching (PSM) analysis. The subjects of this retrospective analysis were 459 patients who underwent curative resection for esophageal squamous cell carcinoma (ESCC) between May, 2005 and December, 2019, at our hospital. Patients were divided into two groups: the Laparoscopic (Lap group) and the Open (Open group). Post-PSM, 139 patients from each group were selected for the analysis to compare the short- and long-term outcomes between the groups. The Lap group experienced fewer Clavien-Dindo (CD) Grade ≥ 2 complications (28.1% vs. 40.3%, P = 0.04) and lower rates of abdominal surgical site infections (SSI) (2.9% vs. 7.9%, P = 0.02) than the Open group. The number of lymph nodes harvested was similar in the Lap and Open groups (14.8 ± 7.5 vs. 15.7 ± 8.6, P = 0.34). There was no significant difference in 3-year overall survival rates (81.2% vs. 69.5%, P = 0.12) or relapse-free survival rates (61.1% vs. 58.2%, P = 0.54) between the groups. Laparoscopic abdominal lymph node dissection for ESCC can be performed safely and appears to be beneficial.

The number of resected lymph nodes from the upper mediastinal area predicts long-term outcomes of esophageal squamous cell carcinoma after minimally invasive esophagectomy.

The total number of resected lymph nodes (LNs) is an important determinant of longer survival after esophagectomy for esophageal squamous cell carcinoma (ESCC). However, the resected LN counts from areas that affect long-term outcomes remain unclear. This study included 406 patients who underwent minimally invasive esophagectomies (MIEs) at Kobe University Hospital. Resected LN counts were evaluated in the following areas: upper mediastinal (UM), middle mediastinal (MM), lower mediastinal (LM), and abdominal (Abd). Cut-off values for LN counts from each area were determined using receiver operating characteristics analysis of the survival status. Cox proportional hazards regression analyses were performed to identify prognostic factors. The cut-off values for large or small numbers of resected LN counts in the UM, MM, LM, and Abd areas were 4, 8, 5, and 18, respectively, in patients with upper and middle thoracic (Ut/Mt) ESCC and 7, 6, 5, and 24, respectively, in patients with lower thoracic (Lt) ESCC. Multivariate analysis in patients with Ut/Mt ESCC revealed that tumor invasion depth, LN metastasis, and the resected LN count from the UM area were independent risk factors for overall survival [hazard ratio (HR), 7.04; 95% confidence interval (CI) 4.47-11.1; HR, 4.01; 95% CI 1.96-8.21; HR, 2.18; 95% CI 1.24-3.82, respectively]. In patients with Lt ESCC, tumor invasion depth, LN metastasis, and pulmonary complications were independent risk factors for overall survival (HR, 4.23; 95% CI 2.14-8.35; HR, 3.83; 95% CI 1.75-8.38; HR, 2.80; 95% CI 1.38-5.65, respectively). Resected LN counts from no areas were prognostic factors. The number of resected LNs from the UM area influenced the survival outcomes of patients with Ut/Mt ESCC after MIE.

Consideration of Age for Quantitative Measurements in Ecological Studies

The physiological age of a plant or its parts is important for quantitative studies in Ecology. It is demonstrated by measurements of leaf size that the number of nodes from the apex in individuals is a good indicator of the physiological age among natural populations. Hence, it is suggested that for measurements of leaf and internode of individuals in a heterogenous population a definite size range for the uppermost or the first visible leaf or internode be fixed for counting downwards. The plastochron index be, however, used for other quantitative estimations in ecological studies.

Majority opinion diffusion: when tie-breaking rule matters

Consider a graph G, which represents a social network, and assume that initially each node is either blue or white (corresponding to its opinion on a certain topic). In each round, all nodes simultaneously update their color to the most frequent color in their neighborhood. This is called the Majority Model (MM) if a node keeps its color in case of a tie and the Random Majority Model (RMM) if it chooses blue with probability 1/2 and white otherwise. We study the convergence properties of the above models, including stabilization time, periodicity, and the number of stable configurations. In particular, we prove that the stabilization time in RMM can be exponential in the size of the graph, which is in contrast with the previously known polynomial bound on the stabilization time of MM. We provide some bounds on the minimum size of a winning set, which is a set of nodes whose agreement on a color in the initial coloring enforces the process to end in a coloring where all nodes share that color. Furthermore, we calculate the expected final number of blue nodes for a random initial coloring, where each node is colored blue independently with some fixed probability, on cycle graphs. Finally, we conduct some experiments which complement our theoretical findings and also let us investigate other aspects of the models.

The use of indocyanine green for lateral lymph node dissection in rectal cancer—preliminary data from an emerging procedure: a systematic review of the literature

IntroductionLateral lymph node dissection (LLND) for rectal cancer is still not a widely established technique owing to the existing controversy between Eastern and Western countries and the lack of well-designed studies. The risk of complications and the paucity of long-term oncological results are significant drawbacks for further applying this technique. The use of indocyanine green (ICG) near-infrared (NIR) fluorescence for LLND appears as a promising technique for enhancing postoperative and oncological outcomes. This review aims to evaluate the emerging role of ICG during LLND and present the benefits of its application.Materials and methodsSystematic electronic research was conducted in PubMed and Google Scholar using a combination of medical subject headings (MeSH). Studies presenting the use of ICG during LLND, especially in terms of harvested lymph nodes, were included and reviewed. Studies comparing LLND with ICG (LLND + ICG) or without ICG (LLND-alone) were further analyzed for the number of lymph nodes and postoperative outcomes.ResultsIn total, 13 studies were found eligible and analyzed for different parameters. LLND + ICG is associated with significantly increased number of harvested lateral lymph nodes (p < 0.05), minor blood loss, decreased operative time, and probably decreased urinary retention postoperatively compared with LLND-alone.ConclusionsThe use of ICG fluorescence during LLND is a safe and feasible technique for balancing postoperative outcomes and the number of harvested lymph nodes. Well-designed studies with long-term results are required to elucidate the oncological benefits and establish this promising technique.

Multi-agent reinforcement learning based algorithm detection of malware-infected nodes in IoT networks

Abstract The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model.

An encompassed representation of timescale hierarchies in first-order reaction network

Complex networks are pervasive in various fields such as chemistry, biology, and sociology. In chemistry, first-order reaction networks are represented by a set of first-order differential equations, which can be constructed from the underlying energy landscape. However, as the number of nodes increases, it becomes more challenging to understand complex kinetics across different timescales. Hence, how to construct an interpretable, coarse-graining scheme that preserves the underlying timescales of overall reactions is of crucial importance. Here, we develop a scheme to capture the underlying hierarchical subsets of nodes, and a series of coarse-grained (reduced-dimensional) rate equations between the subsets as a function of time resolution from the original reaction network. Each of the coarse-grained representations guarantees to preserve the underlying slow characteristic timescales in the original network. The crux is the construction of a lumping scheme incorporating a similarity measure in deciphering the underlying timescale hierarchy, which does not rely on the assumption of equilibrium. As an illustrative example, we apply the scheme to four-state Markovian models and Claisen rearrangement of allyl vinyl ether (AVE), and demonstrate that the reduced-dimensional representation accurately reproduces not only the slowest but also the faster timescales of overall reactions although other reduction schemes based on equilibrium assumption well reproduce the slowest timescale but fail to reproduce the second-to-fourth slowest timescales with the same accuracy. Our scheme can be applied not only to the reaction networks but also to networks in other fields, which helps us encompass their hierarchical structures of the complex kinetics over timescales.

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities.

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

A Lightweight Secure Scheme for Underwater Wireless Acoustic Network

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level.

Conical-Shaped Shells of Non-Uniform Thickness Vibration Analysis Using Higher-Order Shear Deformation Theory

Conical-Shaped Shells of Non-Uniform Thickness Vibration Analysis Using Higher-Order Shear Deformation Theory

Sequential Optimal Trajectory Planning Scheme for Robotic Manipulators along Specified Path Based on Direct Collocation Method

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics.

Comparison of short-term outcomes of laparoscopic surgery, robot-assisted laparoscopic surgery, and open surgery for lateral lymph-node dissection for rectal cancer: a network meta-analysis.

This study attempted to compare short-term outcomes of laparoscopic surgery (LS), robot-assisted laparoscopic surgery (RS), and open surgery (OS) for lateral lymph-node dissection (LLND) in treatment of rectal cancer through network meta-analysis.Embase, Web of Science, PubMed, and The Cochrane Library databases were searched to collect cohort studies on outcomes of LS, RS, and OS for LLND for rectal cancer. Newcastle-Ottawa Scale (NOS) was utilized to evaluate the quality of cohort studies. Primary outcomes should at least include one of the following clinical outcome measures: operative time, blood loss, total lymph-node harvest, positive resection margin rate, postoperative complications, and postoperative hospital stay. A network meta-analysis was conducted using STATA software.Fourteen cohort studies including 8612 patients were eligible for inclusion. The network meta-analysis results showed that, in terms of intraoperative outcomes, the RS group had the longest operative time, while the OS group had the shortest; the LS and RS groups had significantly less blood loss than the OS group. In terms of histological outcomes, there were no significant differences in the total number of lymph nodes harvested and the positive margin rate among the LS, RS, and OS groups (P > 0.05). Regarding postoperative outcomes, the OS group had the highest probability of postoperative complications and the longest hospital stay, followed by the LS group, with the RS group being the lowest.RS was the best method in blood loss, postoperative complication rate, and postoperative hospital stay, followed by LS. OS had the shortest operative time and the highest blood loss.

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

A Hybrid Charging Scheme for Minimizing the Number of Energy-Exhausted Nodes in Wireless Rechargeable Sensor Networks

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study

BackgroundThe value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.MethodsPatients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.ResultsIn total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1–6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18–4.52), cohort 2 (HR = 1.64, 95% CI: 1.26–2.14), cohort 3 (HR = 2.53, 95% CI: 1.45–4.3), cohort 4 (HR = 1.24, 95% CI: 1.01–1.52), cohort 5 (HR = 2.56, 95% CI: 0.73–9.02), cohort 6 (HR = 2.30, 95% CI: 0.53–10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60–0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50–0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46–0.70), and RPI (C-index: 0.65, 95% CI: 0.54–0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40–0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.ConclusionsWe developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

Controversies in minimally invasive surgery for invasive cervical cancer

Background. Determination of the feasibility of performing minimally invasive surgery for invasive cervical cancer and identification of criteria for optimal surgical access that ensures safety, effectiveness, and satisfactory immediate and long-term results remain challenging in gynecological oncology. The aim of the study was to evaluate the immediate and long-term treatment outcomes in patients with invasive cervical cancer who underwent radical hysterectomy via laparotomy compared to those who underwent minimally invasive surgery. Material and methods. Treatment outcomes of 91 patients with invasive carvical cancer were analyzed. The assessment of the objective tumor response to neoadjuvant chemotherapy using the RECIST scale (Response Evaluation Criteria in Solid Tumors) was confirmed by clinical, ultrasound and CT/ MRI findings. The Kaplan-Maier curves and the log-rank criterion were used to compare the time of relapse-free and overall survivals. Statistical analysis and visualization of the analysis results were performed using prism 9.5.1 (GraphPad, USA). Results. The presence of parametrial lymphovascular space invasion and the frequency of lymphadenopathy were comparable for both groups of patients. there were no statistically significant differences in the number of the resected lymph nodes, but there was a statistically significant difference in volume of blood loss and the duration of surgery between the groups (p=0.0001). Every third patient was diagnosed with stage IIIC after surgery (pelvic lymph node lesion). Intraoperative complications were significantly lower in the laparoscopy group than in the laparotomy group (6.7 % vs 13 %). No significant differences in the proportion of relapses of the disease between the groups were found (9.6 % in the laparotomy group vs 11.7 % in the laparoscopy group). There were no significant differences in overall and relapse-free survival between the groups. Conclusion. Minimally invasive surgery is a promising and adequate surgical technique for the treatment of cervical cancer. However, additional studies are needed to determine the indications for these surgeries.

Surrogate-assisted sine Phasmatodea population evolution algorithm applied to 3D coverage of mobile nodes

AbstractDeploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine Phasmatodea population evolution algorithm (SASPPE) is used to evaluate the network coverage. A $$50 \times 50$$ 50 × 50 hill simulation environment was tested for the number of nodes of 30 and 40 and radii of 3, 5 and 7, respectively. The results show that the SASPPE algorithm has the highest coverage, which can be up to 23.624% higher than the PPE algorithm, and up to 5.196% higher than the PPE algorithm, ceteris paribus. The SASPPE algorithm mixes the GSAM with LSAMs, which balances the computational cost of the algorithm and the algorithm’s ability to find optimal results. The use of hierarchical clustering enhances the stable type of the LSAMs. In addition, LSAMs are easy to fall into local optimality when they are modeled with local data, and the use of sine Phasmatodea population evolution algorithm (Sine-PPE) for searching in LSAMs alleviates the time for the algorithm to fall into local optimality. On 30D, 50D, and 100D, the proposed algorithm was tested by 7 test functions. The results show that the algorithm has significant advantages on most functions.

Robotic versus laparoscopic anterior resection for the treatment of stage II and III sigmoid colon cancer: a propensity score-matched analysis.

Robot-assisted laparoscopic anterior resection is a novel technique. However, evidence in the literature regarding the advantages of robot-assisted laparoscopic surgery (RLS) is insufficient. The aim of this study was to compare the outcomes of RLS versus conventional laparoscopic surgery (CLS) for the treatment of sigmoid colon cancer. We performed a retrospective study at the Northern Jiangsu People's Hospital. Patients diagnosed with sigmoid colon cancer and underwent anterior resection between January 2019 to September 2023 were included in the study. We compared the basic characteristics of the patients and the short-term and long-term outcomes of patients in the two groups. A total of 452 patients were included. Based on propensity score matching, 212 patients (RLS, n = 106; CLS, n = 106) were included. The baseline data in RLS group was comparable to that in CLS group. Compared with CLS group, RLS group exhibited less estimated blood loss (P = 0.015), more harvested lymph nodes (P = 0.005), longer operation time (P < 0.001) and higher total hospitalization costs (P < 0.001). Meanwhile, there were no significant differences in other perioperative or pathologic outcomes between the two groups. For 3-year prognosis, overall survival rates were 92.5% in the RLS group and 90.6% in the CLS group (HR 0.700, 95% CI 0.276-1.774, P = 0.452); disease-free survival rates were 91.5% in the RLS group and 87.7% in the CLS group (HR 0.613, 95% CI 0.262-1.435, P = 0.259). Compared with CLS, RLS for sigmoid colon cancer was found to be associated with a higher number of lymph nodes harvested, similar perioperative outcomes and long-term survival outcomes. High total hospitalization costs of RLS did not translate into better long-term oncology outcomes.

GPU-based parallel programming for FEM analysis in the optimization of steel frames

ABSTRACT Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a reasonable level is possible by running GPU (Graphical Processing Unit) processors, which can be found on standard computers. This study presents an algorithm for the acceleration of size optimization of steel frames by using the BBO (Biogeography-Based Optimization) method that is suitable for GPU architecture. The GPU-based parallel algorithm, designed for FEM (Finite Element Method) analysis, is applied to three hypothetical steel-frame case structures with different numbers of members and nodes; and processed on four different computers which are available on the market. The presented case studies revealed that the proposed solution’s efficiency increases as the number of members increases and confirmed the ability of the acceleration algorithm for optimization of large-scale frame structures and provided time efficiency.