Software Performance Research Articles

Automated Assessment of the DWI-FLAIR Mismatch in Patients with Acute Ischemic Stroke: Added Value to Routine Clinical Practice.

The DWI-FLAIR mismatch is used to determine thrombolytic eligibility in patients with acute ischemic stroke when the time since stroke onset is unknown. Commercial software packages have been developed for automated DWI-FLAIR classification. We aimed to use e-Stroke software for automated classification of the DWI-FLAIR mismatch in a cohort of patients with acute ischemic stroke and in a comparative analysis with 2 expert neuroradiologists. In this retrospective study, patients with acute ischemic stroke who had MR imaging and known time since stroke onset were included. The DWI-FLAIR mismatch was evaluated by 2 neuroradiologists blinded to the time since stroke onset and automatically by the e-Stroke software. After 4 weeks, the neuroradiologists re-evaluated the MR images, this time equipped with automated predicted e-Stroke results as a computer-assisted tool. Diagnostic performances of e-Stroke software and the neuroradiologists were evaluated for prediction of DWI-FLAIR mismatch status. A total of 157 patients met the inclusion criteria. A total of 82 patients (52%) had a time since stroke onset of ≤4.5 hours. By means of consensus reads, 81 patients (51.5%) had a DWI-FLAIR mismatch. The diagnostic accuracy (area under the curve/sensitivity/specificity) of e-Stroke software for the determination of the DWI-FLAIR mismatch was 0.72/90.0/53.9. The diagnostic accuracy (area under the curve/sensitivity/specificity) for neuroradiologists 1 and 2 was 0.76/69.1/84.2 and 0.82/91.4/73.7, respectively; both significantly (P < .05) improved to 0.83/79.0/86.8 and 0.89/92.6/85.5, respectively, following the use of e-Stroke predictions as a computer-assisted tool. The interrater agreement (κ) for determination of DWI-FLAIR status was improved from 0.49 to 0.57 following the use of the computer-assisted tool. This automated quantitative approach for DWI-FLAIR mismatch provides results comparable with those of human experts and can improve the diagnostic accuracies of expert neuroradiologists in the determination of DWI-FLAIR status.

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.

Modelling and Analysis of Electromechanical Stress in Transformers Caused by Short-Circuits

A common reason for internal faults in transformers windings is the weakness insulation caused by vibration / deformation related to electromechanical forces produced by high short circuit currents. This phenomenon significantly reduces the transformer life expectancy and may even lead to its instantaneous or timing destruction. Focusing this subject this paper is aimed at presenting the performance of a time domain model inserted in a finite element program i.e. the 3D software package and the investigation of the relationship between high current levels occurring at transformer windings and the internal mechanical stresses. To highlight the overall model and the software performance, a laboratory 15 kVA transformer is utilized to show the programme facilities and potentially. The transformer has been built with concentric double-layer windings and ferromagnetic core with three columns and this equipment has been submitted to a balanced three-phase short- circuit. In addition, the computational simulations were carried out using distinct geometries to the windings i.e. with and without deformation.

Design and Development of a CCSDS 131.2-B Software-Defined Radio Receiver Based on Graphics Processing Unit Accelerators

In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers.

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline.

Determining the appropriate computational requirements and software performance is essential for efficient genomic surveillance. The lack of standardized benchmarking complicates software selection, especially with limited resources. We developed a containerized benchmarking pipeline to evaluate seven long-read assemblers-Canu, GoldRush, MetaFlye, Strainline, HaploDMF, iGDA, and RVHaplo-for viral haplotype reconstruction, using both simulated and experimental Oxford Nanopore sequencing data of HIV-1 and other viruses. Benchmarking was conducted on three computational systems to assess each assembler's performance, utilizing QUAST and BLASTN for quality assessment. Our findings show that assembler choice significantly impacts assembly time, with CPU and memory usage having minimal effect. Assembler selection also influences the size of the contigs, with a minimum read length of 2,000 nucleotides required for quality assembly. A 4,000-nucleotide read length improves quality further. Canu was efficient among de novo assemblers but not suitable for multi-strain mixtures, while GoldRush produced only consensus assemblies. Strainline and MetaFlye were suitable for metagenomic sequencing data, with Strainline requiring high memory and MetaFlye operable on low-specification machines. Among reference-based assemblers, iGDA had high error rates, RVHaplo showed the best runtime and accuracy but became ineffective with similar sequences, and HaploDMF, utilizing machine learning, had fewer errors with a slightly longer runtime. The HIV-64148 pipeline, containerized using Docker, facilitates easy deployment and offers flexibility to select from a range of assemblers to match computational systems or study requirements. This tool aids in genome assembly and provides valuable information on HIV-1 sequences, enhancing viral evolution monitoring and understanding.

Tackling the radiotherapy (RT) shortage in Sub-Saharan Africa by gathering and using data from LMICs and HICs facilities for designing a future robust RT facility

Purpose Historically, highly sophisticated medical linear accelerators (linacs) produced for high- (HIC) and upper-middle-income country (MIC) markets frequently experience significant additional operational failures in low- and lower-middle-income countries (LMICs). This study focuses on LMICs in Africa where there is a substantial equipment shortfall, projected to be a gap by 2040 of about 5000 linacs. The purpose of this study was to gain an insight into the poor performance of linac components, the unreliable infrastructure often encountered in LMICs and the consequent linac-related treatment downtime. Methods and Materials A questionnaire was sent to at least one cancer center in each of the 28 African countries that had experience treating cancer patients with linacs at the time of the survey (4 more countries have acquired linacs) since we completed this survey. For comparison questionnaires were sent to selected facilities in four high-income countries (Canada, Switzerland, UK, US) and to Jordan, a middle-income country. To investigate factors influencing linac downtime, we first utilised flow diagrams to illustrate the dependence of linac subsystem performance on infrastructural/environmental factors, the availability of spare parts and local repair capability. Secondly, a univariate analysis correlated linac downtime with factors such as method of linac fault diagnosis and staffing. Finally, a multivariate analysis investigated the relationship between GDP per capita and cancer mortality to incidence ratio statistics and compared these with the surveyed linac downtime across low-, middle- and high- income countries. Results Responses to the survey confirmed significant multi-factorial issues that influence the extent of linac downtime especially the performance of multi-leaf collimators, electron guns, vacuum systems, RF power and software. Other challenges include electrical power instability, inadequate national funding (GDP/capita), and workforce capability as well as a significant shortfall in formal education and training programmes for the radiation therapy (RT) workforce. Conclusion This survey identified numerous modes of radiotherapy (RT) equipment failure causing treatment downtime in LMICs that can be overcome by improvements in the design of RT technology but they need to be accompanied by increased RT staff training, improved broadband access and increased annual national funding for RT. The collaborative network of linac-based RT facilities in 28 1 * African countries that was developed to conduct this study is available for further investigations as RT capacity and capability improve in Africa.

Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree

The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. Tree ensemble methods, such as Random Forests or XgBoost, are powerful learning tools for classification tasks. However, while combining multiple trees may provide higher prediction quality than a single one, it sacrifices the interpretability property resulting in “black-box” models. In light of this, we aim to develop an interpretable representation of a tree-ensemble model that can provide valuable insights into its behavior. First, given a target tree-ensemble model, we develop a hierarchical visualization tool based on a heatmap representation of the forest's feature use, considering the frequency of a feature and the level at which it is selected as an indicator of importance. Next, we propose a mixed-integer linear programming (MILP) formulation for constructing a single optimal multivariate tree that accurately mimics the target model predictions. The goal is to provide an interpretable surrogate model based on oblique hyperplane splits, which uses only the most relevant features according to the defined forest's importance indicators. The MILP model includes a penalty on feature selection based on their frequency in the forest to further induce sparsity of the splits. The natural formulation has been strengthened to improve the computational performance of mixed-integer software. Computational experience is carried out on benchmark datasets from the UCI repository using a state-of-the-art off-the-shelf solver. Results show that the proposed model is effective in yielding a shallow interpretable tree approximating the tree-ensemble decision function.

Identification and Quantification of Radiotherapy-related Protein Expression in Cancer Tissues Using the Qupath Software and Prediction of Treatment Response.

Automated measurement of immunostained samples can enable more convenient and objective prediction of treatment outcome from radiotherapy. We aimed to validate the performance of the QuPath image analysis software in immune cell markers detection by comparing QuPath cell counting results with those of physician manual cell counting. CD8- and FoxP3-stained cervical, CD8-stained oropharyngeal, and Ku70-stained prostate cancer tumor sections were analyzed in 104 cervical, 92 oropharyngeal, and 58 prostate cancer patients undergoing radiotherapy at our Institution. QuPath and manual counts were highly correlated. When divided into two groups using ROC curves, the agreement between QuPath and manual counts was 89.4% for CD8 and 88.5% for FoxP3 in cervical cancer, 87.0% for CD8 in oropharyngeal cancer and 80.7% for Ku70 in prostate cancer. In cervical cancer, the high CD8 group based on QuPath counts had a better prognosis and the low CD8 group had a significantly worse prognosis [p=0.0003; 5-year overall survival (OS), 65.9% vs. 34.7%]. QuPath counts were more predictive than manual counts. Similar results were observed for FoxP3 in cervical cancer (p=0.002; 5-year OS, 62.1% vs. 33.6%) and CD8 in oropharyngeal cancer (p=0.013; 5-year OS, 80.2% vs. 47.2%). In prostate cancer, high Ku70 group had worse and low group significantly better outcome [p=0.007; 10-year progression-free survival (PFS), 56.0% vs. 93.8%]. QuPath showed a strong correlation with manual counting, confirming its utility and accuracy and potential applicability in clinical practice.

Radiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artificial Intelligence Software to the Analysis of Senior and Junior Radiologists.

The aims of this study were: (a) to evaluate the performance of an artificial intelligence (AI) software package (Boneview Trauma, Gleamer) for the detection of post-traumatic bone fractures in radiography as a standalone; (b) used by two radiologists (osteoarticular senior and junior); and (c) to determine to whom AI would be most helpful. Within 14 days of a trauma, 101 consecutive patients underwent radiographic examination of the upper or lower limbs. The definite diagnosis for identifying fractures was: (a) radio-clinical consensus between the radiologist on-call who analyzed the images and the orthopedist (Group 1); (b) Cone Beam computed tomography (CBCT) exploration of the area of interest, in case of doubts or absence of consensus (Group 2). Independently of this diagnosis for both groups, the radiographic images were separately analyzed by two radiologists (osteoarticular senior: SR; junior: JR) prior without, and thereafter with the results of AI. AI performed better than the radiologists in detecting common fractures (Group 1), but not subtle fractures (Group 2). In association with AI, both radiologists increased their overall performances in both groups, whereas this increase was significantly higher for the JR (p < 0.05). AI is reliable for common radiographic fracture identification and is a useful learning tool for radiologists in training. However, the software's overall performance does not exceed that of an osteoarticular senior radiologist, particularly in case of subtle lesions.

Software evaluation on infant development to support teaching and professional training.

evaluate the functional performance and technical quality of the Wise Infant Development® educational software with experts. methodological research that followed the software evaluation process according to the ISO/IEC 25010 and NBR ISO-IEC 14598-6 standards. The software's functional performance was assessed by a group of nurse experts and its technical quality by information technology experts. The Content Validity Index and the Binomial test were used for statistical analysis. in both expert groups, agreement was greater than 70%, indicating that the software is suitable and pertinent to what was proposed in all its evaluated characteristics: functional suitability, reliability, usability, performance efficiency, compatibility, security, maintainability, and portability. The technology received suggestions for improvement, which were accepted. the Wise Infant Development® software was well evaluated by the experts and could contribute to teaching about infant development, both in undergraduate nursing courses and in professional training. (1) The software presents satisfactory technical quality and functional performance. (2) Technology is useful in the students' teaching-learning process. (3) The software can be used to train child health professionals.

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction.

Background Multiple commercial artificial intelligence (AI) products exist for assessing radiographs; however, comparable performance data for these algorithms are limited. Purpose To perform an independent, stand-alone validation of commercially available AI products for bone age prediction based on hand radiographs and lung nodule detection on chest radiographs. Materials and Methods This retrospective study was carried out as part of Project AIR. Nine of 17 eligible AI products were validated on data from seven Dutch hospitals. For bone age prediction, the root mean square error (RMSE) and Pearson correlation coefficient were computed. The reference standard was set by three to five expert readers. For lung nodule detection, the area under the receiver operating characteristic curve (AUC) was computed. The reference standard was set by a chest radiologist based on CT. Randomized subsets of hand (n = 95) and chest (n = 140) radiographs were read by 14 and 17 human readers, respectively, with varying experience. Results Two bone age prediction algorithms were tested on hand radiographs (from January 2017 to January 2022) in 326 patients (mean age, 10 years ± 4 [SD]; 173 female patients) and correlated strongly with the reference standard (r = 0.99; P < .001 for both). No difference in RMSE was observed between algorithms (0.63 years [95% CI: 0.58, 0.69] and 0.57 years [95% CI: 0.52, 0.61]) and readers (0.68 years [95% CI: 0.64, 0.73]). Seven lung nodule detection algorithms were validated on chest radiographs (from January 2012 to May 2022) in 386 patients (mean age, 64 years ± 11; 223 male patients). Compared with readers (mean AUC, 0.81 [95% CI: 0.77, 0.85]), four algorithms performed better (AUC range, 0.86-0.93; P value range, <.001 to .04). Conclusions Compared with human readers, four AI algorithms for detecting lung nodules on chest radiographs showed improved performance, whereas the remaining algorithms tested showed no evidence of a difference in performance. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Omoumi and Richiardi in this issue.

Music teaching software development based on neural network algorithm and user analysis

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience.

PLANNING STUDY OF HYBRID POWER PLANT SOLAR PV-DIESEL GENERATOR ON KODINGARE ISLAND, SINJAI REGENCY

Kodingare Island is located in Pulau Sembilan District, Sinjai Regency, one of nine islands. Currently, most people still rely on conventional energy from diesel power plants. The reason is that this island does not yet receive an electricity supply from the electricity grid due to the geographical limitations of the archipelago. It is known that the most potential renewable energy source on Kodingare Island is solar energy, with a potential for solar radiation reaching 5.86 kWh/m2/day. This research aims to analyze an innovation that combines PV and solar, where PV acts as the main electricity generator, while solar functions as a backup and additional energy source. The method used in this research uses simulation methods, layout modeling, and financial analysis using HOMER Pro simulation software to determine the potential and performance of hybrid power plants and SketchUp Pro software to produce three-dimensional layouts and economic and feasibility values obtained through financial analysis. Technical aspects include producing an electrical energy system of 37,029 Wh/year, consisting of PV of 32,981 Wh/year and solar of 4,048 Wh/year with energy consumption of 33,850 Wh/year. The required fuel consumption is 2,086 L/year, with excess electricity of 931 kWh/year and renewable energy penetration of 89.1%. From an economic perspective, planning this hybrid power system requires an investment of 258.290.000 IDR, O&amp;M costs of 19.350.600 IDR, and the cost of energy value of 1,352/kWh IDR. In contrast, from the feasibility aspect of planning a hybrid electric power system, it is said to be feasible because it produces a Net Present Value of 9,870,151 IDR, is more significant than zero, the Profitability Index is 1.03 greater than one, the Internal Rate of Return is 8.90% greater than the credit interest rate of 8.43% and the Payback Period required for return of capital is nine years nine months.

A large-scale empirical study on mobile performance: energy, run-time and memory

Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE’s GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues’ priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

Research into the world of Cloud-Virtual Reality

Virtual Reality technology has not been able to penetrate the mainstream consumer market due to restrictions on hardware and content. With the advancement of cloud computing technologies, Virtual Reality hardware and technology applications may now be supported strongly. The metaverse, cloud VR gaming, and cloud VR stimulator are just a few of the real-world applications that are now employing cloud computing in Virtual Reality. We can lower the cost of Virtual Reality technology which would begin a new era of many users to apply cloud-based technologies. With the aid of technology, it will be possible to operate from anywhere without having to transport a lot of equipment. The performance, usability, and compatibility of Virtual Reality software and hardware can be improved by cloud computing on the level of hardware and content. The VR dataset is made available for public collaboration through the Internet using a VR-Cloud server. The digital asset that represents the layout of our pedestrian bridge comprises images of all the streets, buildings, trees, and other urban amenities. In the virtual environment of the metropolitan area, where they run and stroll according to predetermined behaviour scenarios, the cars and people are created and entered. By employing cloud communication software to analyse simulations of vehicles and pedestrian crowds and to debate design ideas, users share the VR reality by connecting to the VR Cloud servers from remote devices. This research demonstrates how VR-cloud may be combined with Neuralink and NFT in the virtual world for future enhancement. This paper is a research note on cloud computing and its application using the Virtual Reality (VR) platform for the modern world with futuristic innovations. Keywords: Cloud Computing, Metaverse, Neuralink, Virtual Reality, VR-Cloud.)

Modeling more software performance antipatterns in cyber-physical systems

The design of cyber-physical systems (CPS) is challenging due to the heterogeneity of software and hardware components that operate in uncertain environments (e.g., fluctuating workloads), hence they are prone to performance issues. Software performance antipatterns could be a key means to tackle this challenge since they recognize design problems that may lead to unacceptable system performance. This manuscript focuses on modeling and analyzing a variegate set of software performance antipatterns with the goal of quantifying their performance impact on CPS. Starting from the specification of eight software performance antipatterns, we build a baseline queuing network performance model that is properly extended to account for the corresponding bad practices. The approach is applied to a CPS consisting of a network of sensors and experimental results show that performance degradation can be traced back to software performance antipatterns. Sensitivity analysis investigates the peculiar characteristics of antipatterns, such as the frequency of checking the status of resources, that provides quantitative information to software designers to help them identify potential performance problems and their root causes. Quantifying the performance impact of antipatterns on CPS paves the way for future work enabling the automated refactoring of systems to remove these bad practices.

Comparative Analysis of Energy Production and Simulation Software for Photovoltaic Systems in Multiple Moroccan Sites

This paper presents a comprehensive study conducted as part of the Propre.ma's project, comparing data from several identical photovoltaic systems installed at multiple sites in Morocco. The project capitalizes on twenty distinct locations equipped with the same solar photovoltaic system and three varieties of solar modules (Mono-si, Poly-si, and Amorphous-si) with nearly identical rated power (2kWp) and a standardized 2 kW inverter. The main objectives are to compare the energy production of different solar photovoltaic systems over two years (2015 to 2016) and to assess the performance of PVsyst and SAM simulation software, renowned for their precision. The study reveals that Mono-si and Poly-si systems produce higher energy outputs than Amorphous-si. Moreover, PVsyst demonstrates closer alignment with real-world energy production rates across all technologies (Mono-si, Poly-si, and Amorphous-si) compared to SAM. This research represents a pioneering effort in evaluating simulation software for photovoltaic systems deployed across multiple sites, providing valuable insights for future solar energy projects and technological advancements.

Enterprise architecture contribution in distributed agile software development

AbstractGeographically distributed agile development may experience a high failure rate due to communication issues, which has a negative influence on project performance. One suggested solution in the literature is to enhance both communication and project performance by implementing agile enterprise architecture. However, the empirical evidence that supports this claim is still scarce. To address this gap, this study empirically explores the role of agile enterprise architecture as an artifact in distributed agile development. The findings of an in‐depth qualitative case study from a dispersed agile development organization that involves teams distributed over three locations are used in this work. Over 2 months, data was gathered by interviewing 12 key members of the team and watching three Sprint sessions of agile software development. Text analysis qualitative approach was used to analyze the data. The findings imply that agile enterprise architecture has a positive effect on distributed agile software development communication, quality, and functionality. Agile enterprise architecture may also support on‐time completion, but a trade‐off with on‐budget may be necessary. These findings provide valuable insights, frameworks, and best practices that support organizations in achieving greater agility, collaboration, and success in their distributed software development initiatives. As this is one of the first studies to look at the influence of agile enterprise architecture on distributed agile software development communication and performance, further research is needed to confirm and expand on the conclusions of this study.

Efficiency Analysis of Compression Software (WINRAR and 7-Zip) Across Diverse Data Types on Windows 11 and Ubuntu 23.10

This paper presents a comprehensive analysis of the performance and efficiency of two widely used compression software, WINRAR and 7-Zip, across various data types. The study focuses on evaluating their effectiveness on different operating systems, specifically Windows 11 and Ubuntu 23.10. The analysis encompasses considerations such as compression ratios, resource utilization, and processing times. WINRAR and 7-Zip are examined in diverse scenarios, including the compression of text files (.txt), image files (.png), audio files (.flac), and video files (.mp4). The study reveals notable variations in compression outcomes influenced by intrinsic complexities of each file format. Moreover, the investigation extends beyond the initially studied operating systems, suggesting potential applications on other platforms like Kali Linux. The findings contribute insights into the nuanced performance of compression software across varied data types and operating environments, facilitating informed decision-making for users seeking optimal compression solutions.

Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort.

The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist. The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes. A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (± 13.7) months for boys and 1.7 (± 13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (± 13.7) months, while in the oblique plane it was 1.8 (± 13.9) months (p = 0.01). The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections. · Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.. · As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections.. · Pape J, Hirsch F, Deffaa O et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. Fortschr Röntgenstr 2024; 196: 600 - 606.