Software Performance Analysis Research Articles

Dyssynchronous Fetal Heart Failure in Maternal Diabetes: Evaluation with Speckle Tracking Echocardiography and Novel M-Mode Software

ObjectivesThis study aimed to investigate dyssynchronous heart failure in fetuses of mothers with diabetes mellitus (FDM) and fetal controls (FC) using two-dimensional speckle tracking echocardiography (2D-STE) and novel M-mode prototype software (PS). MethodsIn this cohort study 174 fetuses were analyzed, 87 in the FDM-cohort and 87 gestational age-matched fetuses in the FC-cohort. A subgroup of 38 fetuses formed the final case group, with a high median frame rate of approximately 160 frames/s. Using 2D Cardiac Performance Analysis software (TOMTEC, Unterschleissheim, Germany) we measured global longitudinal strain (GLS). TOMTEC PS detected annular displacement by assessing an artificial M-mode on the previously generated tracking. Dyssynchrony (DYS) was calculated as the inter- and intraventricular difference in time to peak GLS or annular displacement. ResultsGreater DYS was observed in all basal myocardial measurement sites and software between FDM-cohort compared to FC-cohort and no significant correlation was found between DYS measurements and gestational age. Intraventricular DYS between the basal segments was statistically significant (all p ≤ 0.036, Wald test of univariate regression models). The PS performed best in DYS measurements identifying right ventricular DYS as potentially predicting FDM (FDM: median, 18.5 (interquartile range [IQR], 13.9–25.0) ms vs. FC: median, 2.7 [IQR, 1.5–3.5] ms; p < 0.001). ConclusionIncreased intraventricular DYS demonstrated an impact of maternal diabetes mellitus on fetal hearts independent of gestational age. The prototype M-mode method identified cardiac dysfunction with higher accuracy than the conventional analysis. High-quality echocardiographic image acquisition is imperative for clinical application of 2D-STE and related advanced technologies.

Genotype-phenotype correlation of transthyretin cardiomyopathy in patients with stage C/D heart failure: atrial and myocardial mechanics and fibrosis

Abstract Objectives We sought to investigate: (1) the relationship between transthyretin (TTR) pathogenic variants and their corresponding echocardiographic atrial and ventricular strains in patients with TTR cardiomyopathy (TTR-CM) and clinical evidence of heart failure (HF), and (2) the relationship between TTR pathogenic variants and atrial fibrosis assessed by cardiac MRI. Methods and results Fifty Asian patients with biopsy or scintigraphy proven TTR-CM and stage C or D (C/D) HF were identified from an advanced HF clinic in a tertiary-care university hospital. Genetic testing for the pathogenic variants in the TTR genes associated with hereditary TTR-CM (hTTR-CM) was available in 22 patients (67 ±13 years; 77% were male). Of these, 3 different mutations were identified. The most prevalent pathogenic variant was Ala97Ser (67%) followed by Val30Met (27%), and Gly67Glu (6%). Wild-type TTR-CM was diagnosed in 32% of the cohort. Strain analysis was performed using the TomTec Arena 2D-cardiac performance analysis (CPA) software. Atrial and left ventricular (LV) late gadolinium enhancement (LGE) was quantified using a 9-segment and a 17-segment model, respectively. Patients with hTTR-CM had less impaired longitudinal strain (LS) (more negative strain values) than those with wild-type TTR-CM in the regional basal right ventricular free wall (RV-FW) (-21 ± -8.3 % vs.-12.6 ± -8.1%, p = 0.048) and LV basal inferoseptum (-7.3 ± -4.1 % vs.-3.1 ± -3.0 %, p = 0.045). In univariate analysis, more negative LS values in the basal LV inferoseptum and basal RV-FW were significant predictors of hTTR-CM (OR 5.1 and 2.3; p &amp;lt;0.05, respectively). However, MRI findings revealed similar regional LGE extent involving basal inferolateral segments between hTTR-CM and wild-type TTR-CM (38.3 ± 7.9 vs. 52.5 ± 34.5%, p =0.314). Left atrial (LA) and right atrial (RA) LGE was present in 90% and 81% of patients, respectively. Patients with hTTR-CM had reduced RA LGE when compared to those with wild-type TTR-CM (41% vs 67%, p =0.039). Conclusions In patients with TTR-CM and stage C/D HF, a reduced RA LGE extent and/or more preserved myocardial deformation in specific regions of basal LV and RV segments (basal LV inferoseptum and basal RV free wall) assessed by 2-D speckle tracking echocardiography were significantly more prevalent in patients with hTTR-CM than in wild-type TTR-CM. These echocardiographic and/or MRI findings may facilitate genotype testing in patients with TTR-CM, particularly in resource-limited settings. Further validation of these novel findings in patients with TTR-CM with HF is needed.

LA reservoir strain predicts elevated left atrial pressure in patients with Veno-Arterial (VA) extracorporeal membrane oxygenator (ECMO) support for cardiogenic shock

Abstract Background Veno-arterial (VA) extracorporeal membrane oxygenator (ECMO) is not a left ventricular (LV) support system as it increases aortic pressure and LV afterload due to retrograde blood flow in the aorta. This can lead to complications such as increased LV volume (LV distention), increased LV and left atrial (LA) pressures, and pulmonary edema. We sought to investigate the correlation between LA strain and invasive LA pressure (LAP) in patients with ECMO support for cardiogenic shock undergoing atrial septostomy with stenting. Methods and Results A total of 24 consecutive patients (52±20 years; 83% were male) undergoing atrial septostomy with stenting while being on ECMO support for cardiogenic shock for left heart decompression were analyzed. LAP was measured immediately after transeptal puncture and before deployment of interatrial septal stent. A comprehensive echocardiogram was performed in all patients prior to atrial septostomy. Median time difference between echocardiogram and LAP measurement was 3.5 (0.5-20) hours. LA strain analysis was performed using the TomTec Arena 2D-cardiac performance analysis (CPA) software. LA reservoir strain was measured at peak systole during the reservoir phase in a single apical 4-chamber view. Intraclass correlation coefficients between 2 different investigators were excellent (All&amp;gt; 0.95). Median LA reservoir strain and LAP were 3.2 (0.5-1) % and 23 (8-53) mmHg, respectively. LA reservoir strain significantly correlated with LAP (r=0.53, p=0.034). LA reservoir strain of &amp;lt; 1.5% can effectively predict markedly elevated LAP (LA&amp;gt; 30 mmHg) (OR =9.9, p =0.0016). Conclusions LA reservoir strain significantly correlated with invasive LAP. With the advent of strain imaging software, LA reservoir strain is a feasible, reproducible, noninvasive, and straightforward method to predict the need for left heart decompression and/or to guide optimal afterload management in patients with VA-ECMO for cardiogenic shock. Further a prospective simultaneous echocardiographic and invasive LAP study is needed.

Comparative Analysis of Kinetic Responses and Injury Risk During Landing from 45 cm Height: A Study of Barefoot vs. Sports Shoes Conditions

Jumping and landing biomechanics are closely related to the risk of acute injury due to prolonged exposure to high-ground reaction forces in basketball-like sports, which can lead to lower-limb musculoskeletal injuries in the hip, knee, and ankle. Footwear greatly impacts jumping mechanics, but going barefoot provides a unique perspective on how the human body interacts with the ground. This study aims to find out the kinetic responses during drop landing from 45 cm height with and without sports shoe conditions. Twenty-five healthy young adults were volunteers for this study. Kinetic parameters were recorded and processed by using Quattro Jump force plat and Mars Quarter performance analysis software. Data were presented as mean ± SD values and statistical analysis was performed using statistical software package SPSS-26. The drop landing data of with and without shoe shows scattered parameters with maximum forces of 4706.12N and 5393.04N at 45 cm height, indicating a 66% and 75% risk of metatarsal injury. The stabilization force was 632.64N and 623.64N at 45 cm, reached at 0.58s and 0.66s respectively. Time from Max Force to MFBS Regression analysis indicated a low R-squared value and a random fit plot. In barefoot (without shoe) conditions, the initial contact force, and maximum force were 85.71% and 13.60% higher (250N and 5393.04N respectively) compared to landing with shoes (100N and 4706.12N). However, the stabilization force was higher (632.64N) when landing with shoes compared to landing barefoot (623.64N). As a result, the risk of musculoskeletal injuries and joint stress was higher when landing barefoot due to the higher initial contact force and maximum force. On the other hand, landing with shoes enhances balance and stability due to the higher stabilization force.

Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis

PurposeThis study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.Design/methodology/approachThis study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.FindingsThe performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.Practical implicationsThe cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.Social implicationsThis study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.Originality/valueSome similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

TASC Software for HPC Performance Analysis: Current State and Latest Developments

TASC Software for HPC Performance Analysis: Current State and Latest Developments

Analysing the impact of the 50:22 kick law changes in South African rugby tournaments between 2021 and 2022

ABSTRACT Tactical kicking plays a crucial role in rugby union, influencing both attacking and defensive strategies and potentially impacting a team’s overall success. The introduction of the 50:22 law requires defensive teams to reposition players to anticipate and counter kicks, thereby reducing the number of players in their defensive front line. This study investigated the impact of the 50:22 kicking law on kicking profiles by comparing the 2021 and 2022 seasons, as well as examining the success rate of 50:22 kicks during the 2022 season in South African rugby union tournaments. A retrospective research design was employed, utilising video-based performance analysis software (Nacsport Scout Plus) for data collection. The analysis encompassed all general play kicks from 177 matches spanning two seasons and tournaments, amounting to a total of 6,479 kicks. Within this dataset, there were 53 successful 50:22 kicks out of 162 attempts. Key findings revealed an upswing in kicks by scrum halves (2021: 20%, 2022: 26%; p = 0.00) and a concurrent decline in kicks by fullbacks (2021: 21%, 2022: 18%; p = 0.01). Notably, there was a significant increase in the percentage of kicks occurring in the fourth quarter of the match (2021: 20%, 2022: 22%; p = 0.01). Furthermore, the study identified a noteworthy increase in the percentage of kicks following a turnover (2021: 2%, 2022: 3%; p < 0.01). In conclusion, this research contributes valuable insights into how the 50:22 law shapes kicking strategies in South African rugby union, shedding light on the shifts in player roles and temporal patterns of kicking within the context of this specific law.

Classification of architectural and MEP BIM objects for building performance evaluation

Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing (MEP), and other building objects have a wide range of shapes, features, and properties, which makes identifying them reliably and accurately with any single AI tool difficult. This study explored methods and techniques for automated classification of architectural and MEP BIM objects that can significantly reduce or eliminate the manual effort required to correct data exchange and interoperability issues in pre-processing building models for performance evaluation. A series of ensemble models using a stacking mechanism of four classification tools (semantic keyword search, rule-based inferencing, machine learning using object geometry features, and deep learning using visual shape features) were implemented and tested. A BIM object dataset with 3,410 instances belonging to 40 classes was compiled for training and testing the models. The dataset contains diverse architecture and MEP objects with high-quality selected instances and is available for public use. We found that an ensemble learning approach that exploits the various types of information available in instance models may exhibit superior object classification performance than any individual tool. The ensemble model applying all four tools achieved 91.0 % accuracy (F1 score 0.88), suggesting that automated classification using ensemble learning is an effective strategy.

Interior Space Design and Modelling Methods for Smart Homes - Integrating BIM and VR Technologies

Abstract In this study, the integration of Building Information Modeling (BIM) and Virtual Reality (VR) technologies with performance analysis software facilitates the selection of optimal indoor space layouts for settlement buildings. This is achieved through the use of information modeling and visualization tools to assess various aspects such as sunlight exposure, lighting conditions, architectural parameters, and energy consumption across different building layouts. A virtual environment was created using BIM and VR, within which the collision detection algorithm was refined using a chime-playing test scenario to enhance the quality control of 3D models. At 5000 triangular slices, the detection algorithm optimized in this study demonstrated a real-time performance that is 19 milliseconds faster than that of the comparative algorithm. Furthermore, in scenarios involving micro-speed air exchange, an open window area of 30 square meters facilitated an air volume of 250,168, indicating both economic and practical benefits. The overall satisfaction with interior design and spatial arrangement achieved through BIM and VR integration consistently exceeds 80%, underscoring the synergistic potential and future-oriented trajectory of combining these technologies.

Reading the Older People Researches in Architecture through Bibliometric Analysis (1975-2022)

The increase in the elderly population all over the world has made it necessary for many disciplines, from health to psychology, from economics to humanities, to conduct field-oriented studies. Architecture is one of the disciplines that play an important role in old age in terms of its relationship with the built environment. This study aims to reveal the intellectual structure, research topics, and trends of scientific studies on elderly users in the discipline of architecture. In this study, in which bibliometric analysis of articles for older adults was made in the Web of Science database between 1975 and 2022, the structure of the field and relations were analyzed by creating science maps through performance analysis and VOSviewer software. In the analyzes made, it has been seen that the number of articles on the elderly has increased in recent years. Although it has a wide distribution in terms of subject variety; housing, aging in place, age-friendly environment, and accessibility are the current topics in the field. This study contributes to the literature by presenting the current course of elderly user-oriented studies in architecture from a bibliographic perspective. On the other hand, it is thought to be a guide for new research by revealing the deficiencies in the field.

An analytical method for the meshing performance of deviation surface of spiral bevel gear based on a one-dimensional probe

The meshing performance is a significant indicator for evaluating the machining quality of spiral bevel gear. The flank contact accuracy (FCA) and flank geometric accuracy (FGA) are important aspects of meshing performance, and both are indispensable. In the actual production process, due to the lack of professional meshing performance analysis (MPA) software, the flank geometric structure cannot be reflected based on the FCA, and the actual meshing state cannot be reflected by the FGA, which cannot meet the manufacturing requirements of high-quality gear. Therefore, a MPA method for deviation surface of spiral bevel gear is proposed based on the gear measuring center using a one-dimensional probe. By constructing the deviation surface, the flank meshing model is established, and the meshing parameters are determined. Aiming at the complexity of solving the meshing model, a method for decreasing the dimension of the meshing equations is proposed by establishing the mathematical relationship between the rotation angle and the normal vector, which improves the solution accuracy of the meshing point. The intersection points of the adjacent transmission error curves are taken as the meshing in and out points, and then the actual position of the contact pattern is accurately determined. The instantaneous contact ellipse is obtained by using the method of a cylindrical surface intercepting the deviation surfaces, which not only simplifies the solution algorithm, but also obtains the contact pattern more in line with the actual situation. Finally, the transmission error and contact pattern are digitally characterized. The experimental results show that the digital and actual patterns have good consistency. Flank geometry and gear meshing are organically linked based on the proposed method, and the actual meshing state can be predicted according to the flank topology deviation, which provides important theoretical support for the improvement of the machining quality and meshing performance of the spiral bevel gear.

Trends and Future Directions of Accreditations in Higher Education : Bibliometric Analysis

Purpose : The study aimed to highlight the importance of accreditation in higher education and its relevance in enhancing the quality and standardization of the education system. The purpose of this research was to analyze the trends and future directions of accreditation in higher education using bibliometric analysis. Specifically, the study aimed to identify the relationship between accreditation-originating countries and top publishing countries in this domain, assess the relevance of accreditation over time, and propose future research areas through citation analysis and thematic evaluation. Design/Methodology/Approach : The bibliometric method was used in our research to study and explore accreditation in higher education research. Bibliometric analysis is famous for quantitative data analysis. Using the relevant keywords in the Scopus database, we performed a bibliometric analysis on 1,544 articles. We used the biblioshiny tool through “R” open-source software for performance analysis and thematic mapping analysis, and VOSviewer was used for co-occurrence keyword analysis and network analysis. Findings : The findings of the analysis substantiate with literature to infer the relationship between accreditation-originating countries and top countries publishing in this domain. The findings revealed that the relevance of accreditation has been increasing over the years, and the accreditation-originating countries were leading the research through publications in this area. The study proposed future research areas using citation analysis and thematic evaluation. Practical Implications : The implications are relevant to higher education institutes, policymakers, and other stakeholders. The study is expected to be a catalyst in driving the research in the area of accreditation in higher education. Novelty : This study's novelty is its use of bibliometric analysis to explore the trends and future directions of accreditations in higher education. The study also highlighted the increasing relevance of accreditation in enhancing the quality and standardization of the education system. The study is expected to be a catalyst for driving research in the area of accreditations in higher education.

Performance analysis of four PPP service software under different intensity geomagnetic storms

Currently, many precise point positioning (PPP) services provide through software or online websites for GNSS positioning studies and applications. Various PPP service software has different positioning performances due to their respective algorithms and versions. In addition, the positioning accuracy and reliability of PPP services lack systematic evaluation, especially in the case of geomagnetic storms. This study presents the PPP performance analysis of the common GNSS PPP software as CSRS-PPP (https://www.geod.nrcan.gc.ca/), APPS (https://apps.gdgps.net/), Net_Diff (http://center.shao.ac.cn/shao_gnss_ac/), and GAMP (https://www.ngs.noaa.gov/gps-toolbox/GAMP) under different geomagnetic storm intensities. The PPP experiments involve data from 916, 885, and 676 GPS stations data on 17 March 2015 (severe), 26 August 2018 (strong), and 15 January 2022 (moderate), respectively. The results showed that the positioning performance of the four PPP software is different in the magnetic storm environment. We found that during geomagnetic storms, the CSRS-PPP software has the highest positioning accuracy and reliability, while the APPS software shows the worst positioning performance. The performance of Net_Diff and GAMP during geomagnetic storms are comparable but are better than that of APPS. Statistical results indicate that the average values of 3D RMS of CSRS-PPP, APPS, Net_Diff, and GAMP under severe storm are 0.14 m, 0.60 m, 0.40 m, and 0.33 m, under strong storm are 0.18 m, 0.65 m, 0.27 m, and 0.33 m, and under moderate storm are 0.25 m, 1.66 m, 0.26 m, and 0.34 m, respectively. The main reason why the CSRS-PPP has the best positioning accuracy under different intensity storms is that it uses the ambiguity-fixed PPP solution, while the other three software use float solution.

A PERFORMANCE OF THE MALAYSIA RUGBY SEVENS TEAM BASED ON SELECTED PERFORMANCE INDICATORS ON A VARIOUS ASIA-LEVEL RUGBY SEVENS TOURNAMENT FROM 2018 - 2020.

Identification of performance indicators related to the status of the game (winning and losing) is needed for a tactical approach especially in improving the Malaysia Men’s Rugby Seven team. This study aims to characterize team performance indicators based on the game status of Malaysia men’s rugby sevens teams. A total of 16 matches (winning, n=8, losing, n=8) for the national team from various Asia-level tournaments from years 2018 to 2020 were collected using Sportscode performance analysis software and a notational analysis form. The performance indicators variables are extracted from the excel spreadsheet using the visual basic application of Microsoft excel before being exported to SPSS version 26 with the significant value is set at p&lt;0.05. Based on the analysis, there is no significant difference in the winning performance and losing performance of the Malaysia Men’s Rugby Sevens Team. The finding from this research can be utilized by the coaches and practitioners in improving the rugby sevens team performance.

Proposal of retrofit of historic buildings as cafes in Korea: Recycling biomaterials to improve building energy and acoustic performance

While the preservation of historic buildings is important, they must remain usable. The comfort of occupants using the historic building must be considered due to the change of use. In this study, a 100-year-old building in Korea, protected as a cultural heritage site, is retrofitted. Buildings currently in continuous use as cafes require improvement of low building performance due to aging materials. The energy performance and sound environment of the target building were improved by recycling café generated coffee waste. Recycled coffee waste uses a phase change material, increasing its latent heat. DesignBuilder simulation, a building energy performance analysis software, analyzed energy consumption and room temperature before and after retrofit. C10 containing 10% of the phase change material of coffee waste board showed high efficiency, saving heating energy after retrofit. After retrofit according to the material sound absorption coefficient, ODEON simulation confirmed improvement of the indoor sound environment. The measured sound pressure level was 73.95 and 75.15 dB in the 500 and 1,000 Hz frequency bands, respectively; however, after implementing improvements, the sound pressure level decreased by up to 7.39%. Reverberation time was observed as high as 1.47 s and 1.83 s for the 500 and 1000 Hz frequency bands, respectively, and decreased by up to 66.67%. Therefore, application of coffee waste board to the retrofit of historic buildings is effective in improving indoor environment.

The generalized method for estimating reserves of shale gas and coalbed methane reservoirs based on material balance equation

As the main unconventional natural gas reservoirs, shale gas reservoirs and coalbed methane (CBM) reservoirs belong to adsorptive gas reservoirs, i.e., gas reservoirs containing adsorbed gas. Shale gas and CBM reservoirs usually have the characteristics of rich adsorbed gas and obvious dynamic changes of porosity and permeability. A generalized material balance equation and the corresponding reserve evaluation method considering all the mechanisms for both shale gas reservoirs and CBM reservoirs are necessary. In this work, a generalized material balance equation (GMBE) considering the effects of critical desorption pressure, stress sensitivity, matrix shrinkage, water production, water influx, and solubility of natural gas in water is established. Then, by converting the GMBE to a linear relationship between two parameter groups related with known formation/fluid properties and dynamic performance data, the straight-line reserve evaluation method is proposed. By using the slope and the y -intercept of this straight line, the original adsorbed gas in place (OAGIP), original free gas in place (OFGIP), original dissolved gas in place (ODGIP), and the original gas in place (OGIP) can be quickly calculated. Third, two validation cases for shale gas reservoir and CBM reservoir are conducted using commercial reservoir simulator and the coalbed methane dynamic performance analysis software, respectively. Finally, two field studies in the Fuling shale gas field and the Baode CBM field are presented. Results show that the GMBE and the corresponding straight-line reserve evaluation method are rational, accurate, and effective for both shale gas reservoirs and CBM reservoirs. More detailed information about reserves of shale gas and CBM reservoirs can be clarified, and only the straight-line fitting approach is used to determine all kinds of reserves without iteration, proving that the proposed method has great advantages compared with other current methods.

A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis

A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis

Faktor-faktor yang Memengaruhi Keberhasilan Studi Penerima Bidikmisi di Universitas Sultan Ageng Tirtayasa

This study aims to analyze the influence of internal factors, external factors, service quality, and financial aid on the success of the study of Bidikmisi scholarship recipients at Sultan Ageng Tirtayasa University. The study used descriptive quantitative method with a retrospective survey approach. The sample of this study was 247 graduates of Bidikmisi recipients at Sultan Ageng Tirtayasa University. The data analysis technique used is Structural Equation Modeling (SEM) with LISREL software and Importance Performance Analysis (IPA). The results showed that internal, external, and service quality factors had a positive and significant relationship to the study's success, but financial aid did not affect the study's success. The indicator variable that best describes the internal factor is the academic self-concept, while for the external factor is the community environment, and for the service quality is responsiveness. Internal factors have the most significant influence on the success of the study of Bidikmisi recipients, while external factors have the least influence. It is advised to UNTIRTA to set their priority to increase the success of the study of Bidikmisi recipients by paying attention to internal factors, service quality, and external factors. Improving internal conditions is done by increasing academic interest and doing orientation at the beginning of the lecture. Improving the quality of service is done by improving the quality of information and the attitude of the manager's empathy, while improving external condition is done by completing studies with peer groups. Keywords: higher education management, SEM, success of study, bidikmisi, IPA

Application of machine learning algorithm in the prediction of fuel rod temperature performance

In order to predict the fuel rod temperature performance effectively, we establish a series of machine learning models based on <italic>k</italic>-nearest neighbor, decision tree and AdaBoost algorithms. The input parameters and calculation results of fuel rod performance analysis software JASMINE and data feature engineering are used as the training and test data for the machine learning models. The models are trained by the training data set which includes the characteristic parameters such as pellet and cladding type, axial height, local power, cladding corrosion thickness and core inlet temperature. After training, the models use the test data to predict the cladding outside surface temperature and pellet center temperature. The prediction results show that the model based on AdaBoost algorithm has the best prediction performances, and the mean square errors of cladding outside surface temperature and pellet center temperature are 0.605 ℃ and 8.347 ℃, respectively, and the average absolute errors are 0.273 ℃ and 3.814 ℃, respectively. Comparing the predicted values with the target values, the maximum deviation of Adaboost algorithm for the cladding outside surface temperature is 3 ℃, and the most of the prediction deviation of the pellet center temperature is less than 10 ℃, indicating that the model based on AdaBoost algorithm has the high prediction accuracy for the temperature performance of fuel rods.

An Enhanced Performance Analysis of Software Using Architectural Feedback

The importance of software products and their quality attributes attainment has been a thing of concern in recent time to both academia and industry experts. This research work evaluated an enhanced performance analysis of software using architectural feedback. Data collected were, classified and analysed using SPSS reveal that the Relative Importance Index (RII) in relations to an enhance performance analysis of software using the architectural feedback was 0.83 which led to the proposal of a framework for an enhanced performance analysis of software using architectural feedback.