Maximum Node Research Articles

Prediction of bone ingrowth into a porous novel hip-stem: A finite element analysis integrated with mechanoregulatory algorithm.

Bone ingrowth into a porous implant is necessary for its long-term fixation. Although attempts have been made to quantify the peri-implant bone growth using finite element (FE) analysis integrated with mechanoregulatory algorithms, bone ingrowth into a porous cellular hip stem has scarcely been investigated. Using a three-dimensional (3D) FE model and mechanobiology-based numerical framework, the objective of this study was to predict the spatial distribution of evolutionary bone ingrowth into an uncemented novel porous hip stem proposed earlier by the authors. A CT-based FE macromodel of the implant-bone structure was developed. The bone material properties were assigned based on CT grey value. Peak musculoskeletal loading conditions, corresponding to level walking and stair climbing, were applied. The geometry of the implant-bone macromodel was divided into multiple submodels. A suitable mapping framework was used to transfer maximum nodal displacements from the FE macromodel to the cut boundaries of the FE submodels. CT grey value-based bone materials properties were assigned to the submodels. Thereafter, the submodels were solved and simulations of bone ingrowth were carried out using mechanoregulatory principle. A gradual increase in the average Young's modulus, from 1200 to 1500 MPa, of the bone tissue layer was observed considering all the submodels. The distal submodel exhibited 82% of bone ingrowth, whereas the proximal submodel experienced 65% bone ingrowth. Equilibrium in the bone ingrowth process was achieved in 7 weeks postoperatively, with a notable amount of bone ingrowth that should lead to biological fixation of the novel hip stem.

Grid Optimization of Free-Form Spatial Structures Considering the Mechanical Properties

In recent years, the application of free-form surface spatial grid structures in large public buildings has become increasingly common. The layouts of grids are important factors that affect both the mechanical performance and aesthetic appeal of such structures. To achieve a triangular grid with good mechanical performance and uniformity on free-form surfaces, this study proposes a new method called the “strain energy gradient optimization method”. The grid topology is optimized to maximize the overall stiffness, by analyzing the sensitivity of nodal coordinates to the overall strain energy. The results indicate that the overall strain energy of the optimized grid has decreased, indicating an improvement in the structural stiffness. Specifically, compared to the initial grid, the optimized grid has a 30% decrease in strain energy and a 43.3% decrease in maximum nodal displacement. To optimize the smoothness of the grid, the study further applies the Laplacian grid smoothing method. Compared to the mechanically adjusted grid, the structural mechanical performance does not significantly change after smoothing, while the geometric indicators are noticeably improved, with smoother lines and regular shapes. On the other hand, compared to the initial grid, the smoothed grid has a 21.4% decrease in strain energy and a 28.3% decrease in maximum nodal displacement.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

Boosting tree with bootstrap technique for pre-stress design in cable dome structures

Tensegrity structures, known for their rigidity derived from feasible pre-stresses, present unique challenges in structural engineering. Traditional force-finding methods, though comprehensive, rely heavily on intricate matrix computations, making them computationally intensive and often uncomfortable for considering external loads in practical engineering scenarios. This paper introduces a novel approach to compute pre-stresses in cable dome structures by integrating machine learning and probability theory, collectively termed the boosting tree with bootstrap technique (BTWBT). This method reduces the sample size to as few as 100 per iteration, while improving computational efficiency by randomly generating internal forces. By reframing the force determination as an inverse problem, it ensures that structural displacement converges to zero under feasible pre-stresses. The effectiveness of BTWBT is demonstrated across three distinct cable dome structures: the Geiger dome, Kiewitt dome, and rotating hyperboloid cable dome. Results show that BTWBT achieves the preset displacement requirement (maximum nodal displacement below 0.01 mm) with fewer iterations and reduced computational cost compared to traditional machine learning methods. BTWBT's capability to manage complex structural configurations with minimal data, while incorporating random internal force generation ranges, highlights its potential as a superior alternative for force determination in tensegrity structures.

Maximum Diameter of Ileocecal Lymph Nodes Measured Using Abdominal Ultrasonography Allows for the Discrimination of Yersinia pseudotuberculosis Infection from Kawasaki Disease.

This study aimed to determine whether the maximum diameter of ileocecal lymph nodes measured using abdominal ultrasonography is useful for differentiating Yersinia pseudotuberculosis infection from Kawasaki disease in the acute phase. The optimal maximum diameter cutoff of the ileocecal lymph nodes was also explored to optimize differentiation between these 2 diseases. We included pediatric patients <15 years old who met the diagnostic criteria for Kawasaki disease. Stool culture testing, loop-mediated isothermal amplification of stool specimens, and serological diagnosis were performed to confirm the presence or absence of Y. pseudotuberculosis infection. Of the 122 patients included in the analysis, 17 were confirmed to have Y. pseudotuberculosis infection and 105 were not. The age (in months), white blood cell count, C-reactive protein level, prediction score (risk score) for nonresponse to intravenous immunoglobulin, and number of intravenous immunoglobulin doses did not differ significantly between the Y. pseudotuberculosis-positive and -negative groups. The maximum diameter of ileocecal lymph nodes was 6.0 (5.5-9.5) mm in the Y. pseudotuberculosis-positive group and 3.0 (2.5-3.8) mm in the Y. pseudotuberculosis-negative group (numbers presented as median, interquartile range), with a significantly larger diameter in the Y. pseudotuberculosis-positive group (P < 0.01, Mann-Whitney U test), suggesting potency of ultrasonography. In patients meeting the diagnostic criteria for Kawasaki disease, the possibility of Y. pseudotuberculosis infection is significantly higher if the maximum ileocecal lymph node diameter ≥5.1 mm. Its sensitivity and specificity being 100%, and 89.5%, respectively.

Optimal truss design with MOHO: A multi-objective optimization perspective.

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO's solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

Optimization of truss structures using multi-objective cheetah optimizer

In this study, a multi-objective version of the recently proposed cheetah optimizer called multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration from the targeted hunting strategy employed by cheetahs, which involves a sequence of actions: searching for prey, patiently waiting for the right moment to attack, swiftly launching the attack, and then retreating from the prey and returning to their habitat. MOCO is the result of modification and enhancement from its single-objective counterpart, utilizing a Pareto dominance-based approach. This adaptation allows MOCO to efficiently handle multiple objectives, explores and exploits promising areas in the optimization landscape, and identifies non-dominated solutions, offering valuable tradeoff choices for decision-makers. To demonstrate its practical applications, the MOCO method has been employed to address five intricate structural design problems. These problems involve a pair of competing objectives: the minimization of structural weight and the reduction of maximum nodal displacement. To gauge the efficacy and efficiency of the proposed algorithm, a comparative analysis is conducted against three alternative state-of-the-art multi-objective algorithms. Furthermore, a rigorous evaluation is carried out utilizing hypervolume testing. The findings reveal that the MOCO algorithm surpasses the performance of the other algorithms, underscored by its capacity to uncover a diverse array of non-dominated solutions. To delve deeper into the experimental results, statistical analysis employing Friedman’s rank test is employed. The solutions generated and the convergence patterns exhibited by the MOCO approach underscore its exceptional proficiency in resolving intricate design problems.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Prognostic Value of Preoperative N Subcategories in Patients with Stage IIIA N2 Non-Small Cell Lung Cancer.

Purpose To evaluate the preoperative risk factors in patients with pathologic IIIA N2 non-small cell lung cancer (NSCLC) who underwent upfront surgery and to evaluate the prognostic value of new N subcategories. Materials and Methods Patients with pathologic stage IIIA N2 NSCLC who underwent upfront surgery in a single tertiary center from January 2015 to April 2021 were retrospectively reviewed. Each patient's clinical N (cN) was assigned to one of six subcategories (cN0, cN1a, cN1b, cN2a1, cN2a2, and cN2b) based on recently proposed N descriptors. Cox regression analysis was used to identify the significant prognostic factors for recurrence-free survival (RFS) and overall survival (OS). Results A total of 366 patients (mean age ± SD, 62.0 years ± 10.1; 202 male patients [55%]) were analyzed. The recurrence rate was 55% (203 of 366 patients) over a median follow-up of 37.3 months. Multivariable analysis demonstrated that cN (hazard ratios [HRs] for cN1 and cN2b compared with cN0, 1.66 [95% CI: 1.11, 2.48] and 2.11 [95% CI: 1.32, 3.38], respectively) and maximum lymph node (LN) size at N1 station (≥12 mm; HR, 1.62 [95% CI: 1.15, 2.29]), in addition to clinical T category (HR, 1.51 [95% CI: 1.14, 1.99]), were independent prognostic factors for RFS. For OS, clinical N subcategories (cN1, cN2a2, and cN2b vs cN0; HRs, 1.91 [95% CI: 1.11, 3.27], 1.89 [95% CI: 1.13, 2.18], and 2.02 [95% CI: 1.07, 3.80], respectively) and LN size at N1 station (HR, 1.75 [95% CI: 1.12, 2.71]) were independent prognostic factors. For clinical N1, OS was further stratified according to LN size (log-rank test, P < .001). Conclusion Assessing the proposed N subcategories by reporting single versus multistation involvement of N2 disease and maximum size of metastatic LN, reflecting metastatic burden, at preoperative CT may offer useful prognostic information for planning optimal treatment strategies. Keywords: CT, Lung, Staging, Non-Small Cell Lung Cancer Supplemental material is available for this article. ©RSNA, 2024.

Mesh antenna cable net form finding optimization by an improved NSGA-II

The tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic Algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution.

The risk factors for postoperative temporary vocal cord paralysis after thyroid cancer surgery: an observational retrospective cohort study.

To analyze the potential factors influencing new-onset postoperative transient vocal cord paralysis (VCP) in thyroid cancer patients. Case information of 8340 thyroid cancer patients hospitalized at China-Japan Union Hospital of Jilin University, Jilin Province, China, in the Thyroid Surgery Department from January 2018 to December 2020 was collected retrospectively and analyzed. The possible influencing factors were analyzed using a χ2 test, rank-sum test, and multiple logistic regression analysis. A nomogram was used to construct the clinical prediction model that was validated in the validation set by receiver operating characteristic, calibration curves, and Decision curve analysis. The strengthening the reporting of cohort, cross-sectional, and case-control studies in surgery (STROCSS) guideline was followed to conduct a retrospective cohort study. A total of 8340 patients, including 1817 (21.8%) men and 6523 (78.2%) women, were enrolled in this study. The rate of temporary VCP was 3.6% (308/8340). Based on the results of postoperative laryngoscopy, the patients were divided into VCP group and non-VCP group. Comparative analysis between the groups revealed that potential factors associated with postoperative transient VCP were tumor location on the dorsal side of the gland ( P =0.042), ultrasound showing a maximum nodal diameter >1cm ( P =0.002), multifocal carcinoma ( P <0.001), invasion of surrounding tissue ( P =0.005), lymph node metastases in the central compartment ( P =0.034), lateral cervical lymph node metastasis ( P <0.001), and prolonged operation ( P <0.001). A multiple logistic regression analysis showed that the independent risk factors in postoperative transient VCP were T stage (OR=1.411, P =0.013, 95% CI: 1.075-1.853), multifocal carcinoma (OR=1.532, P =0.013, 95% CI: 1.095-2.144), and duration of surgery (OR=1.009, P <0.001, 95% CI: 1.006-1.012). Finally, a clinical prediction model was established via a nomogram and was validated in the validation set, although its diagnostic efficacy needs to be improved further. High T stage, multifocal carcinoma, and prolonged operation time may be independent risk factors for the occurrence of postoperative transient VCP in patients undergoing initial surgery for thyroid cancer.

Optimizing the deployment of LID facilities on a campus-scale and assessing the benefits of comprehensive control in Sponge City

Rapid and intense urbanization has brought a wide range of serious issues like flooding and water pollution, which have become a great concern in a lot of cities. In response to these problems, Sponge City (SPC) has emerged as a potential solution. This study utilized the Stormwater Management Model (SWMM) to simulate urban runoff and determine an optimal combination of Low Impact Development (LID) strategies for reconstructing the study area. Upon analyzing the existing rainwater drainage system and revealed that it could only meet the design return period of 2-years, and identified the vulnerable flood-prone area and the maximum overflow node. After comprehensive consideration of various factors, the sponge reconstruction scenario selected four LID facilities combination: Porous pavement, Rain garden, Sunken green belt, and Green roof. Results indicated that when P = 1-yr, 5-yr, 10-yr and 20-yr, the total runoff in the LID scenario decreased by 43.91 %, 43.93 %, 45.95 % and 47.11 %, respectively. LID facilities substantially mitigate stormwater runoff and peak flows during moderate to light rainfall events. These LID facilities prove effective in reducing pollutant concentrations (reduction rate is all over 42 %), with notable reductions in Chemical Oxygen Demand (COD) and Suspended Solids (SS). This paper offers valuable insights that can serve as a reference for the construction or renovation of SPC, shedding light on effective strategies and considerations for managing stormwater and mitigating the impact of urbanization.

A Fast Detection Algorithm for Change Detection in National Forestland “One Map” Based on NLNE Quad-Tree

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data.

ASSOCIATION OF I/D POLYMORPHISM OF THE ACE GENE WITH THE PRESENCE/ABSENCE OF UTERINE BLEEDING AND THE CHARACTERISTICS OF LEIOMYOMATOUS NODES IN PATIENTS WITH A COMORBID COURSE OF UTERINE LEIOMYOMA AND GENITAL ENDOMETRIOSIS

Uterine leiomyoma (UL) is the most common benign tumor, which leads to reduced work capacity and loss of reproductive function in women of childbearing age.The aim of the study – to investigate the presence of probable associations of the polymorphic variant I/D of the angiotensin-converting enzyme (ACE) gene with the presence/absence of uterine bleeding and the characteristics of leiomyomatous nodes in patients with a comorbid course of intramural UL and genital endometriosis.Study methods. The molecular genetic study of the polymorphic variant I/D of the ACE gene was applied to 33 patients with a comorbid course of intramural UL and genital endometriosis and 30 patients with isolated intramural UL (at the same time, 5 patients had a medical history of cycles of controlled ovarian stimulation with the help of assisted reproductive technologies and a diagnosis of infertility) according to the standard operating procedure developed in the interdepartmental educational and research laboratory of the Ternopil State Medical University named after I.Ya. Horbachevskyi of the Ministry of Health of Ukraine. Study results. In patients with isolated intramural UL, uterine bleeding probably prevails in individuals with the DD genotype of the polymorphic variant of the ACE gene relative to individuals with the ID and II genotypes (χ2=7.07; p=0.029). At the same time, the presence of the D allele increases the risk of uterine bleeding by 5.8 times. Analysis of the dominant pattern of inheritance of the ACE gene showed that carriers of the D allele (genotype ID+DD) among patients with isolated intramural UL have a 6.3-fold increased risk of uterine bleeding. Analysis of the relationship between the maximum diameter of the leiomyomatous node in patients of the studied groups and the I/D polymorphism of the ACE gene in the group with isolated intramural UL revealed that the maximum diameter of the node in carriers of the DD genotype exceeds the corresponding indicator in carriers of the II genotype by 28.89% (p &lt;0.001), and the maximum node diameter in ID genotype carriers exceeds the corresponding indicator in II genotype carriers by 9.43% (р=0.032). In addition, the maximum node diameter is 14.29% greater in carriers of the D allele than the corresponding indicator in carriers of the I allele. As for the relationship between the number of leiomyomatous nodes in the patients of the studied groups and the I/D polymorphism of the ACE gene, probable differences were established only in the group with a comorbid course of intramural UL and genital endometriosis. At the same time, the individuals with a solid node predominate among carriers of the II and ID genotypes, while the individuals with multiple nodes predominate among carriers of the DD genotype; the distribution of persons with a solid node or multiple nodes is even among carriers of the D allele, and a solid node was found in 72.41% of individuals (р=0.045) among carriers of the I allele. In addition, the presence of the D allele increases the risk of multiple nodes by 3.09 times. Conclusions. It was found for the first time in the Ukrainian population, that the I/D polymorphism of the ACE gene (the presence of the D allele (genotype ID+DD)) is probably associated with an increased risk of multiple leiomyomatous nodes in patients with a comorbid course of intramural UL and genital endometriosis and with an increased risk of uterine bleeding and the size of the leiomyomatous node in patients with isolated intramural UL. The results of the logistic regression analysis show that the presence of the DD genotype of the ACE gene is associated with the development of uterine bleeding and the occurrence of multiple leiomyomatous nodes in patients with intramural UL, regardless of the presence of comorbid genital endometriosis.

MH‐ARO: Meta‐heuristic based adaptive routing for large scale opportunistic networks

SummaryIn wireless sensor networks, sensor nodes (SNs) are placed throughout a wide area and gather information from the surroundings. SN used to detect and send data consumes a lot of energy and dies instantly, which causes network overhead issues. Due to network overhead, network faults occur and do not cover a significant region for data transmission. A meta‐heuristic‐based adaptive routing for large‐scale opportunistic networks (MH‐ARO) is proposed in this work to overcome these problems. The network exploits the dragonfly approach (DA) with opportunistic routing in this protocol. The DA is based on local and global search (GS) optimization. In local search (LS), each node assigns/uses a rank for data transmission and selects the forwarder node after applying the decision level matrix in a single group. In GS, an opportunistic network has multiple groups. Each group sends optimal data to the next group. Each group assigns a relay ranking based on the forwarder node's highest rank. The BS receives critical data from the relay and increases the survival of the node. Based on LS and GS, MH‐ARO is categorized into two parts: (1) optimal forwarder opt (OFO) and (2) optimal route selection (ORS). In OFO, forwarder node selection is based on the following factors: relay ranking, node density, group ranking, residual energy, forwarder distance, and relay distance. ORS follows the adaptive routing and sends the data to BS using the best optimal route. The BS receives critical data without network failure and increases the survival of the node. The maximum active node participates in the network and consumes less energy so that a node covers a large region for communication. Comparing MH‐ARO's performance to competing routing protocols, key performance indicators such as PDR, MSR, alive node, survivability, AEC, AD, and throughput are examined. The results demonstrate that the MH‐ARO performs noticeably better than its rivals regarding energy efficiency.

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.

To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.

A Comparative Analysis of Energy Consumption in Various Wireless Sensor Network Techniques

The objective of this study is to analyze the energy consumption associated with modern methodologies utilized in wireless sensor networks and to conduct a comparative assessment with Reed Solomon (RS) codes. This paper presents three discrete techniques for wireless sensor networks. The strategies mentioned include the Self-Evolving Sensor System (SESS), the Secure and Adaptive Key Management utilizing Multipath Routing Protocol (SAKM-MRP), and the National Instruments Secure Reference-based Data Aggregation (NI-SRDA). A distinct algorithm was developed for each method to examine the energy use. Based on the experimental results, it has been shown that the RS-codes approach consumes a considerably greater quantity of energy compared to the SESS methods, which, in contrast, exhibit a significantly lower energy consumption. When comparing the efficiency of RS-codes and SESS methods, it is observed that the SAKN-MRP technique exhibits a more significant decrease in energy consumption. Compared to the RS-Codes system, the SESS scheme stands out with a significant 45.5% reduction in energy usage at the maximum delivery node. Similarly, the SAKM-MRP scheme showcases an average decrease of 35.7% in energy consumption. Notably, the NI-SRDA scheme achieves an impressive 60% reduction in energy consumption, underscoring its remarkable impact on energy efficiency. In a broader sense, it can be inferred that the NI-SDRA technique holds promise as an energy-efficient solution for wireless sensor networks in comparison to alternative strategies suggested in the current study.

Urban stormwater management under various runoff conditions for Arba Minch town of southern Ethiopia

Ineffective urban stormwater management causes increased surface runoff, and impacts on infrastructure and human life. However, urban stormwater condition is not quantified in Arba Minch area, which cause significant damage on the infrastructures as residence and roads. Therefore, the main objective of this study is to evaluate the condition of urban stormwater under different runoff conditions using Stormwater Management Model (SWMM) in Arba Minch town, specifically Sekela sub city. A combination of field, satellite, observed and laboratory test-based data on drainage systems, infiltration and % imperviousness was collected. The catchment is divided into 47 sub-catchments depend on the building block, flow direction, and drainage networks. The sub-catchments with the average surface runoff revealed 0.07 ​m3/s, 0.14 ​m3/s, 0.25 ​m3/s, 0.65 ​m3/s, and 0.75 ​m3/s over a 2-year, 5-year, 10-year, 25-year, and 30-year return period, respectively. The maximum nodes flooded occur at junctions’ number (J7, J8, J24, and J25), as well as conduits (C14, C18, and C27), according to simulation findings of 2-year return period design rainfall which align with the major cause area. For return periods of 2, 5, 10, 25, and 30 years, the peak runoff result was greatest in sub-catchments SC-1, SC-6, SC-8, SC-9, and SC-30. This depicts sub-catchments in the area are flooded, hence resizing of canals and other drainage structures are necessary to mitigate the stormwater. The SWMM simulated results for 2-year, 5-year, 10-year, 25-year, and 30-year return periods showed peak runoff was greatest in sub-catchments No.8, 1, 6, 30, and 9 because of the slope of the sub-catchment and their large area with high percent imperviousness that results in less infiltration into the soil. The finding of this study shows a rapid increase in surface runoff, which mainly related to impermeable surface and inadequate urban drainage systems leading to damage to infrastructures and properties. This study suggests, a mitigation measures should be adopted to minimize the effect of stormwater using stormwater various intervention approaches following multi program combined with various runoff condition which is very important for road protection authorities, decision makers, and the scientific community to support future water related researches.

Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest.

Facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficulty in capturing subtle features. This research introduced an ensemble neural random forest method that utilizes convolutional neural network (CNN) architecture for feature extraction and optimized random forest for classification. For feature extraction, four convolutional layers with different numbers of filters and kernel sizes are used. Further, the maxpooling, batch normalization, and dropout layers are used in the model to expedite the process of feature extraction and avoid the overfitting of the model. The extracted features are provided to the optimized random forest for classification, which is based on the number of trees, criterion, maximum tree depth, maximum terminal nodes, minimum sample split, and maximum features per tree, and applied to the classification process. To demonstrate the significance of the proposed model, we conducted a thorough assessment of the proposed neural random forest through an extensive experiment encompassing six publicly available datasets. The remarkable weighted average recognition rate of 97.3% achieved across these diverse datasets highlights the effectiveness of our approach in the context of FER systems.

Prestress analysis and geometry optimization for conical cable domes with zero Gaussian curvature

The conical cable dome with a rotating cone and zero Gaussian curvature is a new type of cable dome with the generatrix that follows a straight line. The paper studies the prestress design and mechanical properties of such cable domes. First, on the basis of the section and vertical diagrams of the proposed cone, a simplified method is developed to calculate feasible prestresses of cables and struts using the principle of nodal balance. Second, prestress distribution, load-bearing capacity, and prestress of the conical cable domes with 15 different combinations of rise-span ratio and inner triangular slope under both prestress and load states are studied. Additionally, the paper proposes five load conditions to assess the performance of the conical cable domes. Finally, the total strain energy, basic frequency, radial reaction, and gravity are optimized through single-criterion and multi-criterion based on the combination of a genetic algorithm and the Newton iteration method. To keep the outer appearance of the conical cable dome, the work selects the strut length as the optimization variable. An optimized conical cable dome is recommended with a low prestress uniformity index and high uniformity index. The results indicate that the conical cable dome is sensitive to the asymmetrical loads, especially the wind loads. For the single-criterion optimization, the structure with the basic frequency under loads as the objective function has the best load-bearing capacity. After optimization, the maximum nodal displacement is 32% and 43% of the original one under LC4 and LC5. Besides, to get a synthetic optimization result, a multi-criterion optimization is suggested.