Performance evaluation in containerised cargo ports: a systematic analysis

Dynamic behavior of crack sealant bonding interface in the asphalt pavement crack repair structure under moving vehicle load

This study proposes a comprehensive seismic retrofit strategy for a 24-story steel building originally designed according to the outdated seismic code (GB 50011-2001). The building suffers from structural deficiencies, including excessive torsional displacement, insufficient axial load-bearing capacity of columns, and the presence of weak-story mechanisms. In this retrofit, BRB were adopted to make the structure comply with the modern seismic code (GB 50011-2010). Through systematic analysis using the YJK structural software, BRB were arranged at the mid-spans in the Y-direction of the structure. The results verified that the BRB system enhances the lateral stiffness of the structure while maintaining its architectural functionality. Compared with the base isolation alternative, it reduces the construction cost. Moreover, the structure can meet the requirements of GB 50011-2010 without disrupting its normal operation. This work establishes a reference framework for cost-effective seismic retrofitting of high-rise buildings.

The purpose of this study is to examine the effectiveness of Management Information Systems (MIS) followed in manufacturing industries by synthesizing existing scholarly literature through a systematic bibliometric analysis. The study aims to identify dominant research themes, theoretical foundations, and emerging directions that explain how MIS contributes to operational efficiency, decision-making effectiveness, and strategic competitiveness in manufacturing organizations. The study adopts a bibliometric research design based on peer-reviewed journal articles indexed in the Scopus database. A PRISMA-guided literature selection process was employed to ensure transparency and rigor. Keyword co-occurrence and network visualization techniques were applied using VOSviewer to identify thematic clusters and intellectual linkages within MIS and manufacturing research. The bibliometric analysis yielding five testable propositions for empirical validation via PLS-SEM. The findings indicate that MIS effectiveness in manufacturing industries is multi- dimensional, encompassing decision-support intelligence, operational integration, engineering–management coordination, performance evaluation, and sustainability orientation. The literature shows a clear transition from traditional reporting-based MIS to analytics-driven and strategically aligned information systems, particularly in the context of Industry 4.0. The study relies on secondary bibliometric data and does not empirically test causal relationships. While the findings provide a comprehensive conceptual synthesis, future research may validate the identified dimensions using empirical and longitudinal research designs. The study offers actionable insights for manufacturing managers to align MIS investments with decision intelligence, operational excellence, and long-term sustainability goals. It also highlights the importance of integrating shop-floor data with managerial decision systems. This study contributes to the MIS and manufacturing literature by structuring fragmented research into coherent thematic dimensions and by positioning MIS as a strategic organizational capability rather than a purely operational support system.

Performance of modern day computer systems greatly depends on the wide range of workloads, which run on the systems. Thus, a representative set of workloads, representing the different classes of real-world applications, need to be used by computer designers and researchers for processor design-space evaluation studies. While a number of different benchmark suites are available, a few common benchmark suites like the SPEC CPU2006 benchmarks are widely used by researchers either due to ease of setup, or simulation time constraints etc. However, as the popular benchmarks such as SPEC CPU2006 benchmarks do not capture the characteristics of the wide variety of emerging real-world applications, using them as the basis for performance evaluation may lead to either suboptimal designs or misleading results. In this paper, we characterize the behavior of the data analytics workloads, an important class of emerging applications, and perform a systematic similarity analysis with the popular SPEC CPU2006 & SPECjbb2013 benchmarks suites. To characterize the workloads, we use hardware performance counter based measurements and a variety of extracted micro-architecture independent workload characteristics. Then, we use statistical data analysis techniques, namely principal component analysis and clustering techniques, to analyze the similarity/dissimilarity among these different classes of applications. In this paper, we demonstrate the inherent differences between the characteristics of the different classes of applications and how to arrive at meaningful subsets of benchmarks, which will help in faster and more accurate targeted early hardware system performance evaluation.

BackgroundArtificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and reforms of curricula. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community.ObjectiveTo explore research trends in the field and identify future directions for AI-enabled medical education, we conducted a systematic bibliometric analysis focusing on temporal trajectories in the field.MethodsDocuments were collected from the Web of Science and Scopus databases covering the period from 2000 to 2024. A multistep search strategy combining information retrieval, a definitive journal list, and cocitation analysis was used to identify relevant publications. Journal and author impact were assessed using both publication and citation metrics. Research trends and hot spots were examined through citation burst detection, frequency analysis, and co-occurrence networks, with a color gradient used to indicate the average occurrence year of keywords. The citation lineage structure of the field was evaluated using a k-means clustering-based analysis of cocitation networks to trace influential references.ResultsOur analysis revealed a significant increase in publications since 2021, with foundational works emerging as early as 2019. Influential journals in this domain included JMIR Medical Education, Anatomical Sciences Education, and Medical Education. The evolving research trajectory exhibited a shift from conventional computer-assisted learning tools toward generative AI platforms. Earlier applications of AI in medical education were predominantly concentrated at the undergraduate level, indicating substantial potential for expansion into graduate and continuing medical education. Furthermore, limited cocitation connections were observed between recent generative AI research and conventional medical AI studies, and investigations into medical students’ attitudes toward generative AI remain scarce.ConclusionsThere are critical needs for (1) interdisciplinary studies that intentionally integrate generative AI with foundational medical AI work and (2) involving medical educators and students in AI development. Future research should focus on building theoretical frameworks and collaborative projects that connect these currently separate domains to foster a more cohesive knowledge base.

Machine Learning (ML) has been a critical computational paradigm that has shaped contemporary applications in such domains as finance, healthcare, and cybersecurity, such that its performance evaluation cannot be less critical. However, its selection and interpretation of metrics has remained inconsistent, often leading to misleading conclusions. This study presents a systematic analysis of the most commonly used performance evaluation metrics in ML, integrating conceptual taxonomy, mathematical definitions, and empirical assessment under controlled perturbations. There are three dimensions to ML performance evaluation metrics categorization: robustness, discrimination, and calibration. Experiment conducted on classification and regression, and using synthetic datasets and benchmarks, evaluate threshold variation, class imbalance and label noise. Results obtained showed that no single metric captures model performance comprehensively and widely used metrics may yield conflicting or misleading assessments under certain conditions. Also, context-aware selection and multi-dimensional reporting were necessary for reliable evaluation. By empirically linking metric behaviour to data characteristics, this study provides guidance for context-aware metric selection and reporting that is not only standardized but also evidence-based.

BackgroundPrediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learning approaches. For the end user it is often problematic to evaluate the true performance and applicability of computational tools as some knowledge about computer science and statistics would be needed.ResultsInstructions are given on how to interpret and compare method evaluation results. For systematic method performance analysis is needed established benchmark datasets which contain cases with known outcome, and suitable evaluation measures. The criteria for benchmark datasets are discussed along with their implementation in VariBench, benchmark database for variations. There is no single measure that alone could describe all the aspects of method performance. Predictions of genetic variation effects on DNA, RNA and protein level are important as information about variants can be produced much faster than their disease relevance can be experimentally verified. Therefore numerous prediction tools have been developed, however, systematic analyses of their performance and comparison have just started to emerge.ConclusionsThe end users of prediction tools should be able to understand how evaluation is done and how to interpret the results. Six main performance evaluation measures are introduced. These include sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. Together with receiver operating characteristics (ROC) analysis they provide a good picture about the performance of methods and allow their objective and quantitative comparison. A checklist of items to look at is provided. Comparisons of methods for missense variant tolerance, protein stability changes due to amino acid substitutions, and effects of variations on mRNA splicing are presented.

The banking sector plays an indispensable role in the global economy by facilitating economic growth, providing financial services, and ensuring the stability of the financial system. The banking industry is one of the most dynamic sectors, where financial institutions are constantly striving to achieve a strategic advantage over their peers. One such effective strategy is mergers and acquisitions (M&A), where banks consolidate their operations through either a merger or an acquisition to achieve strategic objectives. The purpose of this paper is to undertake a review of mergers and acquisitions in the banking industry using bibliometric analysis to identify their technical reasons, significance, and effects and to offer thematic and methodological directions for further studies to move forward on the topic. Bibliometric analysis, including citation, bibliographic coupling, coauthorship, and co-occurrence analysis, was utilized to discover trends in and conduct descriptive analysis of articles. The study used keyword and reference search methods to discover 616 research articles from the Scopus database between 1994 and 2022. Publication trends, highly influential authors, leading nations, and top journals are addressed. Hence, the systematic review of the most cited articles has been performed significantly. The study summarizes the significance, effects, and performance evaluation of mergers and acquisitions in the banking sector using both bibliometric and systematic analysis, which will enable academicians and researchers to make significant contributions to future research directions on mergers and acquisitions in the banking sector.

A special type of Stewart mechanism, the orthogonal mechanism, is declared to have special advantage in performance. However, the current study of orthogonal mechanism lacks a systematic analysis that accounts for the advantage. Towards this goal, this paper studies multi-dimensional performance evolution and evaluation from traditional Stewart to 3-3 orthogonal parallel mechanisms. Six evaluation indicators named Good Displacement and Orientation Velocity Workspace (GDVW, GOVW), Good Force and Torque Payload Workspace (GFPW, GTPW), and Good Translation and Rotation Stiffness Workspace (GTSW, GRSW) are established to comprehensively evaluate the performance. This method can intuitively reveal the evolution trends of different performances, including motion/force transmission capacity, displacement and orientation velocity isotropy, force and torque payload isotropy, and translation and rotation deformation stiffness isotropy, within the different parameter spaces, which can provide guidance on the design of high-performance mechanism. Results indicate that the performance of orthogonal mechanism is better than traditional Stewart mechanisms in motion/force transmission capacity, velocity and torque payload. Finally, the orthogonal mechanism is designed and adopted for application to validate the effectiveness.

Introduction. Modern organizations operate with large volumes of data from various sources, in particular: intelligent sensors; tools for monitoring, controlling and diagnostics; syslogs and human-generated content, etc. others Artificial intelligence technologies increase the capabilities of data analysis and use them for effective business, can create original images and texts for marketing purposes and give intelligent recommendations for analytics. The above indicates that artificial intelligence is gaining more and more importance as a tool for increasing efficiency, optimizing resources and providing competitive advantages to the enterprise's business strategies. Purpose. Identification of the key advantages of using artificial intelligence technology for enterprise management and analysis of its impact on productivity, quality and efficiency of business processes, as well as prospects for the development of this technology in the modern business environment. Methodology. Systematic analysis of scientific publications on this topic. The summarization of materials on the research topic was carried out in accordance with the established requirements for a comprehensive search of scientific publications in open access in order to identify general trends and assess the prospects for the development of this technology in the modern business environment. The results. In the materials of the article, the justification of the key advantages of using artificial intelligence for the management of enterprises is provided and it is established that artificial intelligence has a significant potential for automating routine tasks, in particular, such as document processing, database management or creating reports, etc. It is noted that artificial intelligence contributes to the rational justification of management decisions based on the analysis of large volumes of data in the company's activities, taking into account the behavior of consumers of products (services), relevant financial and economic indicators and market trends. It is advisable to use artificial intelligence in the optimization of supply chains taking into account the risk and justification of alternative logistics solutions. In the field of quality control, artificial intelligence provides automatic detection of defects with the help of computer vision, which reduces the amount of defects and increases compliance with standards. In addition, it is an effective tool for personnel management, assisting in recruitment, performance evaluation and forecasting of training needs. Based on the results of the research, it was concluded that artificial intelligence is becoming an integral part of modern enterprise management, providing opportunities to optimize business processes by reducing costs and introducing innovations. It has been established that in order for enterprises to make the most of the advantages of artificial intelligence technology, it is necessary to invest in the adaptation of internal processes and personnel training, which contributes to the creation of prospects for long-term progressive development, ensuring flexibility and competitiveness in conditions of rapid changes in the market environment.

As cloud adoption accelerates across enterprise environments, organizations increasingly face the complexity of managing large-scale, distributed systems spanning multiple regions and accounts. The challenge of maintaining effective monitoring and alerting mechanisms across such vast infrastructures has become paramount for ensuring system reliability, security, and performance. This paper presents a comprehensive investigation into the implementation of centralized alerting frameworks within cloud environments, with particular emphasis on Amazon Web Services (AWS) cloud-native tools including CloudWatch, Simple Notification Service (SNS), and Lambda, alongside third-party monitoring solutions such as New Relic and Splunk. The centralized approach to alerting addresses the inherent complexities of distributed cloud architectures by consolidating alert management, reducing operational overhead, and improving response times to critical system events. Through systematic analysis of architectural patterns, implementation strategies, and best practices, this research demonstrates how organizations can achieve scalable, resilient, and cost-effective alerting solutions. The paper examines multi-region deployment strategies, distributed processing mechanisms, and high-availability patterns that ensure continuous monitoring capabilities even during regional service disruptions. Performance evaluation and case study analysis reveal significant improvements in mean time to detection (MTTD) and mean time to resolution (MTTR) when compared to traditional decentralized alerting approaches.

<div class="htmlview paragraph">The simulation and evaluation of land vehicle performance requires accurate representations of driving conditions. Currently, many driving cycles have been developed and used in the determination of vehicle fuel economy and emission performance. These driving cycles are typically defined as vehicle speed and power trajectories over time. However, most of the standard driving cycles are short in duration and distance and inadequate in representing a wide range of actual driving conditions. There are also many specialized driving cycles designed for specific types of vehicles, but these are often not applicable to more than a specific class of vehicles. Moreover, more detailed driving schedules are often kept as proprietary information within the vehicle manufacturers.</div> <div class="htmlview paragraph">This study developed a general methodology to address two interrelated objectives: i) the systematic analysis of real-world driving data and cataloging of actual driving patterns and ii) the synthesis of a broad class of driving cycles with specific statistical properties, based on available databases. Both of these objectives are addressed through the use of multivariate statistical methods.</div>

In the present study, steady numerical simulations performed on the counter rotating turbo fan (CRTF) COBRA are compared with experimental data carried at the CIAM C-3A test-bench in Moscow. For this purpose, a systematic analysis of the measurement uncertainties was performed for the global aerodynamic performances of the CRTF, namely, the massflow, the total pressure ratio, the isentropic efficiency, as well as the torque ratio applied on both fan rows. Several numerical models are investigated to highlight their effects on the aforementioned predicted quantities. Differences in modeling consist in grid resolutions and the use of two turbulence models popular in the turbomachinery community. To match as much as possible the experiment running conditions, the performance map of the CRTF is simulated using the exact measured speed ratio and massflow. The comparisons show good estimations of the numerical simulation over the entire performance map. The main differences between the turbulence models occur at part-speed close to stall conditions. More surprisingly at aerodynamic design point, the importance of the turbulence modeling on the predicted torque ratio has been pointed out.

Rice diseases significantly reduce crop yield and threaten global food security, particularly in developing agricultural systems. Deep learning approaches, mostly CNN, have shown promising performance in automated rice disease detection. However, despite high reported accuracies, concerns remain regarding model overfitting, limited dataset diversity, and insufficient evaluation of generalization performance under real-world conditions. This research provides a structured review of deep learning-based rice leaf disease detection methods published between 2018 and 2026. Using a structured search and predefined selection criteria, relevant studies were analyzed based on method/model architecture, and performance. The review identifies dominant adoption of transfer learning models such as VGG, ResNet, DenseNet, EfficientNet, and YOLO-based detection frameworks. While most studies report classification accuracies above 90%, demonstrating the capability of deep learning techniques in accurately identifying rice diseases from images. The analysis further reveals that explicit application and systematic evaluation of regularization techniques are rarely emphasized. Similarly, data augmentation strategies are commonly applied but often lack detailed investigation regarding their impact on generalization performance. These gaps indicate the need for focused research on improving robustness and preventing overfitting in rice disease detection systems. The findings provide a foundation for developing more generalized and reliable deep learning models for practical agricultural deployment.

Comprehensive cancer care in the Nordic countries; Strengths, opportunities and improvement profiles.