Rank Analysis Research Articles

Soft computing approach on estimating the lateral confinement coefficient of CFRP veiled circular columns

For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions.

Interannual variation and machine learning simulation of organophosphate esters in Taihu Lake

Organophosphate esters (OPEs) are widespread in water bodies and have attracted public attention due to their hazards. This study investigated the presence of OPEs in surface water of Taihu Lake from 2012 and 2021–2022. The OPEs concentration was compared ten years ago and ten years later. Water and meteorological parameters were ranked using the random forest (RF) model, and OPEs concentration in lakes was simulated using selected parameters as inputs. The concentration of Σ7OPEs was higher ten years ago compared to ten years later. There was no significant seasonal difference in Σ7OPEs from 2021–2022, while the concentration of Σ7OPEs in 2012 was lower in summer than in other seasons. The spatial distribution of the two interannual Σ7OPEs exhibited a decreasing trend from the northwest region. The results of RF importance ranking and redundancy analysis showed that NH3-N, TN, TP, water temperature and relative humidity were the most influential factors affecting OPEs concentrations. RF models performed better for TnBP, as indicated by training R and test R values are excellent and relatively low errors. Our results demonstrated that machine learning models were useful in facilitating efficient monitoring and assessment of OPEs contamination in lakes.

Experimental Investigation on a Novel Hybrid Composite Developed by Laser Engineering Net Shaping: Optimization and Ranking Analysis

Experimental Investigation on a Novel Hybrid Composite Developed by Laser Engineering Net Shaping: Optimization and Ranking Analysis

Different classical estimation methods using ranked set sampling and data analysis for the inverse power Cauchy distribution

A recent two-parameter distribution that offers additional flexibility in modeling a real lifetime dataset is the inverse power Cauchy distribution (IPCD). The IPCD is user-friendly and analytically attractive, and it can be effectively utilized to analyze actual datasets. On the other hand, ranked set sampling (RSS) is a method of gathering data that first combines simple random sampling (SRS) with the expertise and judgment of the field investigator in determining where to take samples. The RSS is known to be more efficient than the SRS as well as less expensive since fewer sample units need to be procured and evaluated. In this article, various estimation techniques for IPCD are examined. The parameter estimates are determined utilizing the RSS and SRS of seven frequentist approaches, which are the maximum likelihood, Anderson-Darling, least squares, Cramér-von Mises, maximum product spacing, weighted least squares, and right-tail Anderson-Darling approaches. Using accuracy criteria, a simulation analysis is done to assess the efficacy of the provided RSS-based estimates. For further clarification, one real data analysis is provided.

Liquefaction susceptibility using machine learning based on SPT data

Assessing the potential for liquefaction using traditional experimental or empirical analysis procedures is both time-consuming and arduous. Employing a machine learning model that can accurately predict liquefaction potential for a specific site can reduce the time, effort, and associated costs. This study proposes several empirical machine learning models, including deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and bi-directional long short-term memory (BILSTM), to assess the liquefaction potential of soil deposits based on SPT-based post liquefaction datasets. To train the proposed models, a dataset comprising 834 liquefied and non-liquefied cases was collected to perform the liquefaction analysis. A Pearson correlation matrix was also conducted to examine the correlation between soil and seismic parameters and the probability of liquefaction. Furthermore, a sensitivity analysis was adopted to assess the impact of soil and seismic parameters on the probability of liquefaction. The proposed model's prediction capability was assessed using several performance indices, including rank analysis, accuracy matrix, and AIC criteria. The comparative analysis of the proposed models' predictive ability to determine liquefaction probability revealed that the RNN model outperformed the others, displaying the highest accuracy and lowest error index values. Subsequently, the RNN model achieved the first rank with a total score value of 70, followed by the CNN (55), DNN (52), BILSTM (47), and LSTM (16) models. The parametric analysis, rank analysis, accuracy matrix, and AIC criteria collectively demonstrate the proposed models' ability to predict liquefaction probability. Furthermore, the robustness of these models was assessed through external validation and comparative analysis.

Global practitioner assessment and management of mental fatigue and mental recovery in high-performance sport: Aneed for evidence-based best-practice guidelines.

Mental fatigue and mental recovery have gained scientific attention in relation to sporting performance, yet best practice assessment and management methods are lacking. A greater understanding of current knowledge and practices in high-performance sport are necessary. To understand the contemporary knowledge, beliefs, monitoring processes, management strategies, perceived responsibility, sources of evidence, and challenges, when assessing the mental fatigue and mental recovery of athletes in high-performance sport. A mixed-methods survey approach obtained information from 156 multi-disciplinary high-performance sport practitioners. Descriptive outputs were reported and potential differences between key concepts were detected using Wilcoxon-signed rank analysis. Thematic analysis interpreted open-text responses. Only 11.5% and 5.1% of respondents indicated they were "very" knowledgeable about mental fatigue and mental recovery, respectively. Knowledge (p < 0.001) and confidence in application (p = 0.001) were significantly greater for mental fatigue than mental recovery. Nearly all respondents perceived mental fatigue and mental recovery impacted training and competition performance, with a greater negative impact during competition (p < 0.001). A limited number of respondents reported deliberate assessment (31.1%) or management (51.2%) of mental fatigue and mental recovery. A combination of sources of evidence were used to inform practice, with common challenges to implementation including staff knowledge, athlete-buy in, time-availability, and a lack of evidence. Practitioners reported that assessing and managing mental fatigue and mental recovery was multi-disciplinary in nature. Practitioners reported that mental fatigue and mental recovery did impact performance, yet this was not reflected in the implementation of evidence-based assessment and management practices in high-performance sport.

Factors influencing oilseed growers for enrolling as members of Karnataka Co-operative Oilseed Growers Federation Limited, Raichur Regional Union, Karnataka

The study was conducted purposively in the selected organization Karnataka Co-operative Oilseed Growers Federation Limited (KOF) and factors influencing on enrolling as member of KOF was judged by selecting top three Oilseed Growers Co-operative Societies (OGCS) from Raichur Regional Union (RRU) based on highest volume of business. From selected OGCS 45 members and 45 non members were selected to collect primary data. The data on primary source were analysed using Simple Ranking and Garret analysis. Factors influencing majority of respondents for enrolling as members of society were hope of reducing market risk (mean score 57.82) and no commission charge (mean score 53.31). Further majority of non-members had not registered in the society due to non availability of credit facility from society (mean score 55.77), personal relation built with commission agentsover the year (mean score 54.31) and indebtedness to commission agent (51.71) as opined by non-member respondents.

Application of green technologies for the extraction of phytocompounds from C. pseudolimon residues; an approach for biological characterization

The citrus residue (peel and pomace), which is regarded as waste, is a promising source of bioactive compounds. Proper management through sustainable strategies is required to utilize citrus residue for value addition. In the present study, the efficiency of the shaking water bath extraction (SWE), microwave-assisted extraction (MAE), and ultrasonication-assisted (UAE) techniques on the extraction of phytocompounds from C. pseudolimon peel and pomace were evaluated. The ranking analysis was used to study the effect of the process parameters on the extraction of phytocompounds in terms of total phenols, flavonoids, antioxidant activity, and antidiabetic activity. Compared to the different techniques MAE (500 W; time 120 s; solid-liquid ratio 1:15 and pH 5.5) exhibited the highest rate of extraction for citrus peel phytocompounds, whereas UAE (300 W; time 30 min; solid-liquid ratio 1:15 and pH 5.5) was found more effective for citrus pomace phytocompounds extraction. However, minimum extraction was observed in the SWE technique for both samples. FTIR analysis also confirmed the quantitative data. Structural morphology revealed the better structural opening in MAE for citrus peel and in UAE for citrus pomace. In a nutshell, this study will provide the basis for a waste-to-profit solution using green technologies.

Ranking analysis of flyash – basalt fibre – polyamide 66 polymer composites based on the mechanical and sliding wear performance metrics using hybrid AHP-R method

This research work investigates the physical, mechanical, thermal, thermo-mechanical, and dry sliding wear characteristics of hybrid flyash particulates (F-class; 0 – 20 wt.% at the step of 5%)–basalt fibres (chopped; fixed 10 wt.%) reinforced polyamide 66 polymer composites fabricated using the twin screw extruder and injection moulding machine. Taguchi's design of experiment optimization approach is used for parameter optimization of the dry sliding wear process, followed by analysis of variance analysis. Further, the hybrid AHP-R method is used for ranking optimization based on the performance metrics. It is observed that the composition having 15 wt.% flyash particulates optimizes overall performance metrics, hence recommended for industrial parts fabrications. It has an experimental density of 1.23 g/cc, voids content of 5.93%, water absorption of 4.21%, tensile strength of 110.73 MPa, flexural strength of 146.72 MPa, Rockwell hardness of 62.44 HRM, fracture toughness of 4.17 MPa√m, impact strength of 2.06 J, the thermal conductivity of 1.24 W/mK, and specific wear rate of 7.55 × 10−4 mm3/Nm. The overall subjective ranking of the hybrid polymer composites attunes with the objective ranking by the hybrid AHP-R method.

STUDY ON HYDROGEOLOGICAL ASPECT OF GALANG SUB-DISTRICT, BATAM CITY FOR ACCEPTANCE OF PROSPECTIVE NPP SITES

This is a preliminary study in the selection of Nuclear Power Plant (NPP) site in Batam's Barelang area to support industrial growth in the area in the future. The initial site selection was conducted in 2015 and 2017 in the pre-survey phase, considering hydrogeological aspect. The results of previous research showed four potential areas, namely Pasir Panjang Beach, Tanjung Batu, Dapur 3 and Tanjung Rame. The hydrogeological aspect plays an important role in the consideration of site acceptance. This is related to the consideration of potential flow of radioactively contaminated groundwater seepage in the site area, in the event of a potential release. The acceptance of the NPP site from the hydrogeological aspect is based on the site’s permeable geological formation and porosity condition, where groundwater can be stored. The purpose of the study was to assess the potential site in Galang Sub-district, Batam City based on hydrogeological aspects which include surface geology, groundwater productivity and rock porosity. Research methods include primary and secondary data collection, literature review and ranking analysis. The results showed that Tanjung Batu, Dapur 3 and Tanjung Rame could be chosen as the potential sites for NPP based on the hydrogeological aspect. The three regions have surface geology in the form of sandstone, clay and claystone rock with medium rock porosity level and medium groundwater productivity. Pasir Panjang Beach is less preferable because it has a high porosity of rocks with the productivity of aquifers is being spread widely.&#x0D;

River bank erosion vulnerability demarcation based on the sensitivity analysis of geotechnical parameters: Majuli Island, India

The present study assesses the vulnerability of the river bank area most sensitive to erosion by combining the data obtained from existing literature and performing numerical simulation using finite element-based numerical code. Various geotechnical parameters, like average particle size (D50), Plasticity Index (PI), Elastic Modulus (E), Dry Unit Weight (γd) or Saturated Unit Weight (γsat), Coefficient of Permeability (k), Undrained Shear Strength (cu), Moisture Content (w%) and soil types based on the Unified Soil Classification System (USCS), were taken into account. Weighted Ranking Analysis (WRA) was performed based on the sensitivity of relevant geotechnical factors affecting bank erosion. The results indicate that the areas influenced by the Subansiri River (western bank) and the Brahmaputra River (southern bank) fall in the highly vulnerable zone category, while the Luhit and Kherkutia Suti Rivers (northern bank) fall in the moderately vulnerable category to river bank erosion.

P02.06.B DIELECTRIC PROPERTIES OF INTRACRANIAL TUMORS

Abstract BACKGROUND Recently, tumor treating fields (TTFields) were established for the treatment of newly diagnosed glioblastoma (GBM). One of the most crucial parameters defining the treatment efficacy of TTFields is the electric field intensity, which depends on the dielectric properties of the tumor tissue. In this study we determined the dieclectric properties of brain tumors by analyzing resected tissue following a fast acquisition protocol. In GBM patients, histological analysis for cellularity, vascularization and myelin content was performed. In addition, sensitivity analyses for specific parameters (tissue hydration, temperature, and saline irrigation) were conducted. MATERIAL AND METHODS A cohort of 130 patients with tumors of different histology and malignancy grade have been recruited (meningioma: n=36; brain metastases n=29; low grade glioma n=7; anaplastic glioma = 12; other= 7; glioblastoma n=39, all treated with TTFields). Tissue samples were placed into a cylindrical cell with a known diameter. The impedance was recorded at frequencies 20Hz-1MHz using a software specifically developed for this study. The measured impedance was translated into dielectric properties of the sample (conductivity and relative permittivity) based on the parallel plate model. Myelin, which is the most powerful electric isolator in the brain, was assessed in GBM samples by luxol fast blue staining, and quantified in a three-tier scale. To assess the impact of tissue conditions on the measurements, probes were warmed to 35 degree Celsius, dehydrated or irrigated with 0.9% saline solution. RESULTS We found significant differences between the conductivity of different types of tumors with meningiomas showing the lowest and GBM tissue exhibiting the highest conductivity values. GBM samples with very high median conductivity values displayed a consistently lower myelin content. In addition, GBM patients with particularly high conductivity had a significantly shorter overall survival (log rank analysis, p = 0.024). While tissue temperature had no detectable effects on the dielectric properties in GBM, saline irrigation tissue hydration significantly affected the results. CONCLUSION The dielectric properties of intracranial tumors depend on histological class and malignancy grade. GBM patients with high conductivity values showed a significantly poorer prognosis, indicating this parameter as potential marker for TTF efficacy.

Detection of diabetic patients in people with normal fasting glucose using machine learning

BackgroundDiabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG.MethodsThe physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors.ResultsThe prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application (http://www.cuilab.cn/dring).ConclusionsDRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System

Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation.

Oral chemotherapeutic agents in metastatic hormone-sensitive prostate cancer: A network meta-analysis of randomized controlled trials.

Multiple oral chemotherapeutic agents for metastatic hormone-sensitive prostate cancer (mHSPC) have been developed for conjugated use with conventional androgen deprivation therapy (ADT). Several randomized controlled trials (RCTs) report significant benefits in mHSPC patients. Therefore, we compared overall survival (OS) and progression-free survival (PFS) benefits among considerable mHSPC oral chemotherapeutic agents. We investigated mHSPC treatment efficacy through a systematic RCT-trial literature review (PubMed, Embase, Web of Science, the Cochrane Library, and Scopus). Two reviewers independently screened, extracted data, and assessed bias risk in duplicate. We identified 18 RCTs (n=13,509). Concerning OS, ADT+abiraterone, ADT+abiraterone+docetaxel, ADT+apalutamide, ADT+bicalutamide, ADT+darolutamide+docetaxel, ADT+enzalutamide, ADT+orteronel, and ADT+rezvilutamide were more effective than the standard of care (SOC). Comparing PFS, most treatments were more effective than SOC, excluding ADT+bicalutamide, nilutamide, flutamide, ADT+bicalutamide+palbociclib, and ADT+nilutamide. ADT+docetaxel with androgen receptor targeted agent (ARTA) triplet therapy was not among the top three treatments determined through ranking analysis. Novel oral chemotherapeutic agent combination therapies must replace current ADT monotherapy and ADT+docetaxel SOC. Even so, ADT+docetaxel with ARTA triplet therapy still is not the best mHSPC treatment and requires further study.

Association between Albuminuria and Serum Phosphate Levels in Non-Dialysis Stage 3-5 Chronic Kidney Disease Patients

Introduction: Chronic kidney disease (CKD) remains a global burden and catastrophic disease as about 697.5 million people suffering from it in 2017. About 42% of CKD mortality in Indonesia is related to cardiovascular complications. Hyperphosphatemia, a manifestation of chronic kidney disease-mineral bone disorder, could increase the risk of cardiovascular mortality. Albuminuria has been proven to inhibit the compensatory mechanisms for hyperphosphatemia, thereby aggravating this condition. This study was conducted to analyze the association between albuminuria and serum phosphate levels among CKD patients in Dr. Soetomo General Academic Hospital, Surabaya, Indonesia.Methods: This cross-sectional study used medical records of 129 non-dialysis stage 3-5 CKD patients at the outpatient clinic of Dr. Soetomo General Academic Hospital from March-November 2021. Descriptive analysis was performed on albuminuria, serum phosphate, age, sex, body mass index, comorbid, blood chemistry tests, and CKD stages data. Correlational analysis was conducted using the Spearman Rank test on albuminuria and serum phosphate levels.Results: The majority of the subjects in this study were male (55.81%); mean age was 55.21±11.99 years; mean BMI was 22.39±2.27 kg/m2; hypertension was found in 65.89% of the patients; mean eGFR was 25.01±16.1 ml/min/1.73 m2 and dominated by CKD stage 3-5. The distribution of albuminuria grade was dominated by heavy albuminuria (&gt;300 mg/g) and the mean serum phosphate level was 4.81±1.9 mg/dl. Spearman Rank analysis found a significant positive correlation with weak association strength (p&lt;0.001; rs=0.277) between albuminuria and serum phosphate levels.Conclusion: There was a significant positive correlation with weak association strength between albuminuria and serum phosphate levels.

1240 Life Expectancy of Octogenarians Following EVAR

Abstract Background The aim of elective Abdominal Aortic Aneurysm (AAA) repair is to prevent premature death from rupture. The Endovascular Aneurysm Repair (EVAR) 2 trial showed that patients with AAA who are not fit for open repair do not benefit in terms of life-expectancy from EVAR. In our region, the average life expectancy for men is above the national average but controversy remains when offering octogenarians expensive procedures with the aim of prolonging life. This study aimed to quantify the life-expectancy following an EVAR between octogenarians and younger patients. Method A retrospective review was performed of the electronic notes of all patients receiving EVAR at our unit between October 2009 to October 2019. Survival post EVAR was compared between the octogenarian group and the younger patient group. A survival analysis was undertaken using the SPSS software to calculate a Kaplan-Meier curve. Results 294 patients received EVAR between 2009-2019. Patients were between ages 45 and 89 (Median: 75); 87.4% were male. 169 patients died during follow up (n = 64 ≥80 years, n = 105 &amp;lt;80 years). Time of death post-EVAR in octogenarians (average age 82.91 years) ranged between 0 - 131 months, whilst those under 80 years (average age 71.77 years) ranged between 1 - 152 months. Log rank (Mantel-Cox) analysis demonstrated statistical significance (p = 0.017). Median years post-EVAR for ≥80 years was 3.02 - 4.98 and &amp;lt;80 years was 5.12 - 6.87. Conclusions Octogenarians undergoing EVAR have a shorter life-expectancy compared to those under 80 years of age. This should be considered when discussing elective options with patients and their relatives.

Government Initiatives for Enhancing Building Information Modeling Adoption in Saudi Arabia

Despite its numerous benefits, many countries are slow in adopting building information modeling (BIM). As a result, policymakers are implementing different government initiatives (GIs) for enhancing BIM adoption globally. However, it is critical to exercise caution when implementing GIs due to each country’s specific requirements and rules. Having country-specific GIs can ensure that BIM is appropriately adopted and fits a country’s needs and problems. Therefore, this study aims to investigate the effectiveness of the GIs in enhancing BIM adoption in Saudi Arabia. Data from 101 industry professionals were analyzed using a mean ranking analysis, normalization method, exploratory factor analysis (EFA), and fuzzy synthetic evaluation (FSE). Five critical GIs for enhancing BIM adoption were identified: developing programs for improving BIM competencies, developing programs to increase BIM awareness and understanding, developing programs to integrate BIM into education curricula and academia, developing BIM-related contractual frameworks, and providing financial aid to reduce the cost of BIM adoption. The EFA results indicate that the GIs can be grouped into two underlying constructs: national policies and organizational strategies. The FSE results confirmed that all GIs are effective. The study findings can serve as a significant reference for industry practitioners and policymakers in assuring successful BIM adoption.

106 Life expectancy of octogenarians following EVAR

Abstract Background The aim of elective Abdominal Aortic Aneurysm (AAA) repair is to prevent premature death from rupture. The Endovascular Aneurysm Repair (EVAR) 2 trial showed that patients with AAA who are not fit for open repair do not benefit in terms of life-expectancy from EVAR. In our region, the average life expectancy for men is above the national average but controversy remains when offering octogenarians expensive procedures with the aim of prolonging life. This study aimed to quantify the life-expectancy following an EVAR between octogenarians and younger patients. Methods A retrospective review was performed of the electronic notes of all patients receiving EVAR at our unit between October 2009 to October2019. Survival post EVAR was compared between the octogenarian group and the younger patient group. A survival analysis was undertaken using SPSS software to calculate a Kaplan-Meier curve. Results 294 patients received EVAR between 2009-2019. Patients were between ages 45 and 89 (Median 75); 87.4% were male. 169 patients died during follow up (n=64 ≥80 years, n=105 &amp;lt;80 years). Time of death post-EVAR in octogenarians (average age 82.91 years) ranged between 0- 131 months, whilst those under 80 years (average age 71.77 years) ranged between 1-152 months. Log rank (Mantel-Cox) analysis demonstrated statistical significance (p=0.017). Median years post-EVAR for ≥80 years was 3.02-4.98 and &amp;lt;80 years was 5.12 – 6.87. Conclusion Octogenarians undergoing EVAR have a shorter life-expectancy compared to those under 80 years of age. This should be taken into account when discussing elective options with patients and their relatives.

Critical success factors for implementing 3D printing technology in construction projects: academics and construction practitioners’ perspectives

Purpose The application of three-dimensional (3D) printing technology in construction projects is of increasing interest to researchers and construction practitioners. Although the application of 3D printing technology at various stages of the project lifecycle has been explored, few studies have identified the relative importance of critical success factors (CSFs) for implementing 3D printing technology in construction projects. To address this research gap, this study aims to explore the academics (i.e. researchers) and construction practitioners’ perspectives on CSFs for implementing 3D printing technology in construction projects. Design/methodology/approach To do this, a questionnaire was administered to participants (i.e. academics and construction practitioners) with knowledge and expertise in 3D printing technology in construction projects. The collected data were analysed using mean score ranking, normalization and rank agreement analysis to identify CSFs and determine the consistency of the ranking of CSFs between academics and construction practitioners. In addition, exploratory factor analysis was used to identify the relationships and underlying constructs of the measured CSFs. Findings Through a rank agreement analysis of the collected data, 11 CSFs for implementing 3D printing technology were retrieved (i.e. 17% agreement), indicating a diverse agreement in the ranking of the CSFs between academics and construction practitioners. In addition, the results show three key components of CSFs including “production demand enabling CSFs”, “optimize the construction process enabling CSFs” and “optimized design enabling CSFs”. Originality/value This study highlights the feasibility of implementing the identified CSFs for 3D printing technology in construction projects, which not only serves as a reference for other researchers but also increases construction practitioners’ awareness of the practical benefits of implementing 3D printing technology in construction projects. Specifically, it would optimize the construction lifecycle processes, enhance digital transformation and promote sustainable construction projects.