Performance Issues Research Articles

Experimental determination of lateral sway of a pneumatic spring when passing through a railroad switch in conditions of provision of high-speed trains` safety

Experimental determination of lateral sway of a pneumatic spring when passing through a railroad switch in conditions of provision of high-speed trains` safety

Interscalable material microstructure organization in performance-based computational design

Various parameters can be integrated in material-based computational design in architecture. Materials are the main driver of these processes and evaluated with the constraints related to the form, performance, and fabrication techniques. However, current methodologies mostly involve investigating already existing materials. Studies on computational material design, in which new materials are developed by designing their microstructures in response to the performative issues, are generally undertaken at the material scale, and not adopted to the architectural design process yet. To resolve this issue, the methodology titled Interscalable Material Microstructure Organization in Performance-based Computational Design (I2MO_PCD) is developed and presented in three stages, including (1) identification of different types of material microstructures, (2) computational material design, and (3) prototyping. Data-based material modelling and visualization, and algorithmic modelling techniques are utilized, followed by various performance simulations as a part of an iterative process. New microstructure organizations are designed computationally, organized under three main groups as linear-curvilinear, crystal and metaball-voronoi. The outcomes of different performance analyses, including structure, radiation, direct sun hours, acoustics and thermal bridge were compared. Thus, the role of geometrical organization of microstructures, scales and material types in different performance computations were identified, by designing and fabricating synthetic materials.

AI-driven anomaly detection in cloud computing environments

The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.

Optimizing and Analyzing Traffic Data Using Data Mining Techniques

This is the project titled Optimizing and Analyzing of Traffic Data using Data Mining techniques and under this project goals will be defined which may include enhancing network perfomance ,reducing latency and strenghtening security measures. Next, critical network componemts are identified ,such as routers, switches and firewalls which require analysis and optimization. Network traffic data is collected using monitoring tools or packet sniffers, encompassing both inbound and outbound traffic. This data is then analyzed to identify patterns trends and potential security threats. Network bottlenecks and performance issues are also identified. Based on the analysis, optimizaion techniques are implemented. This may involve reconfigurring network settings, adjusting routing protocols or upgrading hard watre components, Simultaneously, security measures are implemented to address identified vulnerabilities .Perfomance improvements are continously monitored and measured using metrics such as network latency, throughput and packet loss. The optimizations are finetuned periodically based on ongoing and new traffic patterns

Residential building forms and energy efficiency in the Saharan climate: the case of Adrar, Algeria

Algeria is a country that has witnessed rapid development as a result of its abundant oil supplies. The country’s GDP to energy consumption ratio clearly shows the need to adopt energy efficiency policies. Taking into account the regulation of excess solar radiation in the desert environment, this study examines the relationship between the shape of different buildings and their energy use. This research was divided into two stages. In the first stage, the ideal building shape for the city of Adrar, in terms of energy consumption, was determined by studying three basic geometric shapes, including square, rectangular, and triangle shapes. According to simulation research conducted using Design Builder (version 6.1.0), a square building was the best shape to maximize energy performance. To analyze the thermal behavior of different building shapes, all structures with expanded shapes were simulated based on the ideal shape discovered in the first stage. Of the three extended alternative cases, the case of the Mini Arrival offers the best energy efficiency and sufficient natural lighting thanks to its 90 cm depth and no vertical offset from the top of the window. By adding design principles to the design process, this study helps improve the energy efficiency of new buildings and also gives another angle on research methods for solving energy performance issues regarding desert buildings.

Gwydion: Efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. As dependencies increase, communication between microservices becomes more complex and frequent, leading to slower response times and higher resource consumption, especially during high demand. In addition, performance issues in one microservice can also trigger a ripple effect across dependent services, exacerbating the performance degradation across the entire application. This paper studies the impact of microservice inter-dependencies in auto-scaling by proposing Gwydion, a novel framework that enables different auto-scaling goals through Reinforcement Learning (RL) algorithms. Gwydion has been developed based on the OpenAI Gym library and customized for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL algorithms on real cloud environments for two opposing reward strategies: cost-aware and latency-aware. Gwydion focuses on improving resource usage and reducing the application’s response time by considering microservice inter-dependencies when scaling horizontally. Experiments with microservice benchmark applications, such as Redis Cluster (RC) and Online Boutique (OB), show that RL agents can reduce deployment costs and the application’s response time compared to default scaling mechanisms, achieving up to 50% lower latency while avoiding performance degradation. For RC, cost-aware algorithms can reduce the number of deployed pods (2 to 4), resulting in slightly higher latency (300μs to 6 ms) but lower resource consumption. For OB, all RL algorithms exhibit a notable response time improvement by considering all microservices in the observation space, enabling the sequential triggering of actions across different deployments. This leads to nearly 30% cost savings while maintaining consistently lower latency throughout the experiment. Gwydion aims to advance auto-scaling research in a rapidly evolving dynamic cloud environment.

Tuning Earth System Models Without Integrating to Statistical Equilibrium.

This paper proposes algorithms for estimating parameters in Earth System Models (ESMs), specifically focusing on simulations that have not yet achieved statistical equilibrium and display climate drift. The basic idea is to treat ESM time series as outputs of an autoregressive process, with parameters that depend on those of the ESM. The maximum likelihood estimate of the parameters and the associated uncertainties are derived. This method requires solving a nonlinear system of equations and often results in unsatisfactory parameter estimates, especially in short simulations. This paper explores a strategy for overcoming this limitation by dividing the estimation process into two linear phases. This algorithm is applied to estimate parameters in the convection scheme of the Community Earth System Model version 2 (CESM2). The modified algorithm can produce accurate estimates from perturbation runs as short as 2years, including those exhibiting climate drift. Despite accounting for climate drift, the accuracy of these estimates is comparable to that of algorithms that do not. While these initial results are not optimal, the autoregressive approach presented here remains a promising strategy for model tuning since it explicitly accounts for climate drift in a rigorous statistical framework. The current performance issues are believed to be technical in nature and potentially solvable through further investigation.

Bond-Slip Behavior Between C-Shaped Steel and Foamed Concrete in CTS Composite Structural Members

The bond-slip behavior between cold-formed thin-walled steel (CTS) and foamed concrete (FC) is a critical issue in the mechanical performance of FC-filled CTS composite wall structures. Thus, this study provides experimental and theoretical research on the bond-slip behavior between CTS and FC. A total of eleven specimens were tested using push-out configurations, considering the number of web holes, foamed concrete (FC) strength, anchorage length, and CTS section splice form. A constitutive model for bond-slip was proposed, and the regression formulas for accurately predicting the characteristic bond strength between CTS and foamed concrete were established. A finite element model was developed to investigate the bond-slip mechanism at the interface between CTS and FC. The bond-slip constitutive model accurately fits the experimental and finite element results. The results indicate that the ultimate bond strength of the specimens increases with the number of web holes; when the number of web holes reaches two, the ultimate bond strength is 155.4% of that of the non-perforated specimens. As the concrete strength increases from 3.43 MPa to 11.26 MPa, the ultimate bond strength of specimens with two web holes improves by 23.1%, while non-perforated specimens have a 54.7% enhancement. When the anchorage length is extended from 200 mm to 400 mm, the ultimate bond strength decreases by 29.3%. Additionally, when steel sections are joined in a double-span I form, the bond strength increases by 91.6% and 95.8% compared to the single-span form and the double-span box form, respectively.

SIMTAM: Generation Diversity Test Programs for FPGA Simulation Tools Testing Via Timing Area Mutation

Field-Programmable Gate Array (FPGA) timing simulation is essential in electronic circuit design, allowing for the verification of timing characteristics like delays and clock frequencies. However, bugs in timing simulation tools can lead to inaccurate results, potentially causing designers to miss critical issues in chip performance. Traditional testing methods often fall short in thoroughly assessing these tools, as current FPGA testing primarily focuses on synthesis and behavioral simulation, neglecting timing aspects. To address this issue, we propose SIMTAM for testing timing simulation tools. Specifically, SIMTAM consists of three components: equivalent delay region construction, diversity program segment generation, and differential testing. Given a seed circuit design file written by hardware description language such as Verilog, the delay region construction component randomly identifies delay structures for inertial delay in the design file to construct equivalent delay sleep regions. In the sleep region, the simulator skips the signal pulse whose width is less than the specified delay, thus ensuring the equivalence of the variations. The diversity program segment generation component combines Verilog expressions using generation operators and inject them into the sleep region to generate diverse design files. The differential testing component compares the seed and variant design files to find compilation inconsistency issues. In five months, SIMTAM reported 16 bugs to developers in two popular timing simulation tools Iverilog and Vivado; ten of which are confirmed.

THE RELATIONSHIP BETWEEN ISOLATION, ACTIVITIES AND URBAN MOBILITY FOR THE ELDERLY DURING THE COVID-19 PANDEMIC

Objective: to identify the impact that the elderly population had during the quarantine on issues of social isolation, urban mobility and performance of instrumental activities of daily living. And as specific objectives: to carry out a comparative analysis between the mobility patterns of the elderly before and during the COVID-19 pandemic, to verify the impact of the reduction in urban mobility during the pandemic on the performance of their IADLs and to investigate symptoms of depression. Method: cross-sectional and quantitative study, carried out with 43 subjects from the interior of the state of São Paulo, with inclusion criteria of active individuals, with chronological age equal to or greater than 60 years, of both genders and, as exclusion criterion, diagnosis related to cognitive or physical impairments that compromised their functional independence. The instruments used for data collection were a questionnaire and the Yesavage Geriatric Depression Scale Abbreviated (GDS-15). Data analysis performed using statistics using the Statistical Package for the Social Sciences (SPSS version 18), McNemar's test and Pearson's chi-square test, with Yates' continuity correction, with a significance level of 5% (p < 0.05). Results: there was a predominance of female participants, 28 (65.12%), most aged between 70 and 79 years, totaling 23 (53.49%) and 25 (58.14%) married. Regarding education, 16 (37.21%) had higher education and 15 (34.88%) had elementary education. For 37 (86.05%) of the elderly, the frequency of visits to the places decreased during the pandemic, while for 4 (9.30%) it remained the same and for 2 (4.65%) it increased. With the pandemic, fewer individuals used the bus as a means of transportation, with the percentage going from 34.88% to 18.6%. Regarding car use, the number went from 72.09% to 79.07%. Regarding essential activities and social interaction, there was a decrease in individuals attending these places (p-0.04123) and (p-0.37110), respectively. The number of women with some indication of depression was higher during the pandemic (p-0.01333). Of the total number of subjects with depressive symptoms, 41.7% lived alone, while 58.3% lived with someone. Conclusions: The COVID-19 pandemic changed the daily lives of older adults, and this study contributed to identifying the impact of social isolation on urban mobility patterns and activity performance for the subjects. Highlighting the importance of seeking strategies and actions to overcome the possible consequences of this context for the health of older adults.

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities through collecting movement data. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets available for deriving effective HAR systems and less than ideal recognition capabilities. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and / or only few samples from a target application domain are available – each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework–Cross-Domain HAR–which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Cross-Domain HAR enables substantial performance improvements over the state-of-the-art in sensor-based HAR scenarios. Through our extensive experimental evaluation on a range of benchmark datasets we specifically demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance and provide practical suggestions for using the framework in real-world applications.

Unraveling the Dynamics of e-Government Digitization, Penetration and User Experience: A Case Study of Greek Municipalities

This study examines the user experience (UX) of Greek municipal websites, the first in-depth look at local e-Government services in Greece. Using Lighthouse for technical performance and an Adapted UX Audit, it explores factors beyond digital skills affecting citizen adoption of these platforms. Findings reveal widespread deficiencies, with no municipality scoring above 55%, highlighting significant challenges in usability, functionality, and user satisfaction. Critical issues in Technical Performance and Missing Features, both scoring below 50%, suggest that inherent design flaws hinder user acceptance. The study emphasizes the need for improvements in feedback mechanisms, multilingual support, and accessible help sections to better serve diverse populations. Addressing these gaps could enhance e-democracy by fostering more inclusive, user-friendly services. The results advocate for a strategic overhaul of municipal websites to increase engagement, improve e-Government effectiveness, and strengthen democratic participation. Limitations and recommendations for future research and policy-making are also provided.

Deep Encodings vs. Linguistic Features in Lexical Complexity Prediction

AbstractIn this work, we present a novel approach to lexical complexity prediction (LCP) that combines diverse linguistic features with encodings from deep neural networks. We explore the integration of 23 handcrafted linguistic features with embeddings from two well-known language models: BERT and XLM-RoBERTa. Our method concatenates these features before inputting them into various machine learning algorithms, including SVM, Random Forest, and fine-tuned transformer models. We evaluate our approach using two datasets: CompLex for English (a high-resource language) and CLexIS2 for Spanish (a relatively low-resource language in ), allowing us to study performance issues from a cross-lingual perspective. Our experiments involve different combinations of linguistic features with encodings from pretrained deep learning models, testing both token-based and sequence-related encodings. The results demonstrate the effectiveness of our hybrid approach. For the English CompLex corpus, our best model achieved a mean absolute error (MAE) of 0.0683, representing a 29.2% improvement over using linguistic features alone (MAE 0.0965). On the Spanish CLexIS2 corpus, we achieved an MAE of 0.1323, a 19.4. These findings show that handcrafted linguistic features play a fundamental role in achieving higher performance, particularly when combined with deep learning approaches. Our work suggests that hybrid approaches should be considered over full end-to-end solutions for LCP tasks, especially in multilingual contexts.

Studying the growth and morphology of metal microstructures in sodium metal batteries with ionic liquid electrolytes by operando 23Na NMR spectroscopy

Sodium metal batteries are a potentially promising energy storage solution due to the abundance and low cost of sodium and high energy density of metal anodes. However, the growth of microstructural metal during cycling remains a crucial safety and performance issue to be addressed. Herein we investigate this process in sodium metal symmetric cells using operando 23Na nuclear magnetic resonance spectroscopy. We analyse the NMR signal arising from the microstructural sodium metal to obtain quantitative and qualitative information on its growth and morphology, observing significant differences in the evolution of the microstructures for different electrolytes, including ionic liquids with different sodium salt concentrations, water as an additive, and comparing with a more traditional organic electrolyte. The results correlate well with the previously reported cycling performance of these electrolytes, as well as ex situ SEM images of the electrode surface after cycling.

Performance issue monitoring, identification and diagnosis of SaaS software: a survey

Performance issue monitoring, identification and diagnosis of SaaS software: a survey

Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.

Mitochondria and plastids import thousands of proteins. Their experimental localisation remains a frequent task, but can be resource-intensive and sometimes impossible. Hence, hundreds of studies make use of algorithms that predict a localisation based on a protein's sequence. Their reliability across evolutionary diverse species is unknown. Here, we evaluate the performance of common algorithms (TargetP, Localizer and WoLFPSORT) for four photosynthetic eukaryotes (Arabidopsis thaliana, Zea mays, Physcomitrium patens, and Chlamydomonas reinhardtii) for which experimental plastid and mitochondrial proteome data is available, and 171 eukaryotes using orthology inferences. The match between predictions and experimental data ranges from 75% to as low as 2%. Results worsen as the evolutionary distance between training and query species increases, especially for plant mitochondria for which performance borders on random sampling. Specificity, sensitivity and precision analyses highlight cross-organelle errors and uncover the evolutionary divergence of organelles as the main driver of current performance issues. The results encourage to train the next generation of neural networks on an evolutionary more diverse set of organelle proteins for optimizing performance and reliability.

What automatic speech recognition can and cannot do for conversational speech transcription

Transcripts are vital in any research involving conversation. Most transcription is conducted manually, by experts; a process which can take many times longer than the conversation itself. Recently, there has been interest in using automatic speech recognition (ASR) to automate transcription, driven by the wide availability of ASR platforms such as OpenAI’s Whisper. However as studies typically focus on metrics such as the word error rate, there is a lack of detail about ASR transcript quality and the practicalities of ASR use in research. In this paper we review six state-of-the-art ASR technologies, three commercial and three open-source. We assess their capabilities as automatic transcription tools. We find that the commercial ASR systems mostly capture an accurate representation of what was said, and overlapping speech is handled well. Unlike prior work, we show that commercial ASR also preserves the location, but not necessarily the spelling of a large majority of non-lexical tokens: short words such as uh-hum which play vital roles in conversation. We show that the open-source ASR systems produce substantially more errors than their commercial counterparts. However, we highlight how the cost and privacy advantages of open-source ASR may outweigh performance issues in certain applications. We discuss practical considerations for ASR deployment in research, concluding that present ASR technology cannot yet replace the trained transcriber. However, a high-quality initial transcript generated by ASR can provide a good starting point and may be further refined by manual correction. We make all ASR-generated transcripts available for future research in the supplementary material.

Eco-friendly, stable, and high-performance biochar prepared by a twice-modification scheme: Saccharification of raw materials & thermal air oxidation of biochar

Organic pollutants, such as phenolic compounds, pose significant risks to both the environment and human health. While biochar is an effective adsorbent for removing these pollutants, its dissolved solid (DS) components can lead to the loss of functional groups, structural disintegration, unstable performance, and environmental issues. This study introduces a twice-modification scheme designed to produce a biochar (BC-M) that combines high stability with superior performance. The process begins with the preparation of a stable biochar from cellulase-treated lignocellulose. This precursor biochar is then subjected to thermal air oxidation to enhance its oxygen-containing functional groups, thereby improving its adsorption capabilities. A mathematical model was developed to explore the relationship between different thermal air oxidation conditions and the properties of BC-M, aiming to optimize both adsorption capacity and DS. The model's multi-objective optimization indicated the optimal modification conditions. Compared to unmodified biochar (BC-O), BC-M showed significant improvements: its specific surface area increased by 63.6%, pore volume by 139%, and functional groups by 50%–1271%. Notably, the DS of BC-M was reduced to just 1.08 mg/L, representing a 97.5% reduction from BC-O, with a minimal mass loss of only 0.78 ± 0.45% during modification. BC-M also demonstrated a remarkable enhancement in the adsorption of phenolic compounds, with a capacity 21%–2408% higher than BC-O. Furthermore, calculations indicated that BC-M could reduce carbon emissions by 0.70 t CO2/yr/t, outperforming activated carbon in this regard. This study offers valuable insights into biochar modification, providing a low-cost, high-stability, and high-efficiency alternative for environmental cleanup.

Speech Enhancement Algorithm Based on Microphone Array and Lightweight CRN for Hearing Aid

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources.

DIGITAL TRANSFORMATION: IS THERE ANY LINK WITH MARKET PERFORMANCE OF PHARMACEUTICAL MICRO, SMALL AND MEDIUM-SIZED ENTERPRISES IN KENYA

The business-operating environment in Kenya and especially in the City County of Nairobi has caused a large number of pharmaceutical Micro, Small, and Medium-Sized Enterprises report profit warnings and thus experience performance issues. Data from the World Bank for the previous two years indicate pharmaceutical MSMEs operating in Kenya have seen a decline in profits and stagnation. The study therefore sought to establish the effect of digital transformation elements, including e-procurement, e-marketing, e-payment, and telemedicine, on the market performance of pharmaceutical Micro, Small, and Medium Enterprises (MSMEs) in Nairobi City County, Kenya. The research examines the limited understanding of how these digital technologies influence market performance, particularly in the pharmaceutical MSME sector. The study employs a cross-sectional descriptive research design, surveying 122 MSMEs from an initial target population of 175, selected through proportionate and simple random sampling. The questionnaire was tested for validity and reliability using Cronbach's alpha with a threshold of 0.7. Quantitative data were analyzed through multiple regression and correlation analysis to determine the strength of the relationships between variables. The results reveal that e-procurement, e-marketing, e-payment, and telemedicine significantly and positively affect market performance. The findings suggest that these digital strategies enhance procurement efficiency, marketing outreach, financial transactions, and healthcare service delivery, ultimately improving the market performance of pharmaceutical MSMEs.