Performance Issues Research Articles

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.

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.

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.

Flight-Based Control Allocation: Towards Human–Autonomy Teaming in Air Traffic Control

It is widely recognized that airspace capacity must increase over the coming years. It is also commonly accepted that meeting this challenge while balancing concerns around safety, efficiency, and workforce issues will drive greater reliance on automation. However, if automation is not properly developed and deployed, it represents something of a double-edged sword, and has been linked to several human–machine system performance issues. In this article, we argue that human–automation function and task allocation may not be the way forward, as it invokes serialized interactions that ultimately push the human into a problematic supervisory role. In contrast, we propose a flight-based allocation strategy in which a human controller and digital colleague each have full control authority over different flights in the airspace, thereby creating a parallel system. In an exploratory human-in-the-loop simulation exercise involving six operational en route controllers, it was found that the proposed system was considered acceptable after the users gained experience with it during simulation trials. However, almost all controllers did not follow the initial flight allocations, suggesting that allocation schemes need to remain flexible and/or be based on criteria capturing interactions between flights. In addition, the limited capability of and feedback from the automation contributed to this result. To advance this concept, future work should focus on substantiating flight-centric complexity in driving flight allocation schemes, increasing automation capabilities, and facilitating common ground between humans and automation.

The Role of Grit in Developing Intrapreneurship Through the Mediation of Career Adaptability Among Generation-Z University Students

Since the latter half of the last decade, there has been a rapid transformation in workforce demographics as Generation Z enters the workplace dynamic and takes over the responsibilities of retiring Baby Boomers. However, as Generation Z employees gradually enter the workforce, organizational human resource practices have yet to evolve to address the performance issues this generation poses, resulting in higher employee turnover ratios among Generation Z employees. Despite this problem, Generation Z has a potential characteristic that will benefit organizations: intrapreneurship. This research examines the influence of grit on intrapreneurship as a valuable construct for Generation Z through the mediation of career adaptability. This research uses quantitative methods with mediation analysis. The results of this research show no direct influence of grit on intrapreneurship. When career adaptability is included as a moderator variable, the analysis results show a significance value below 0.05, which shows the role of career adaptability as a full mediator between grit and intrapreneurship. Keywords: Z Generations, intrapreneurship, grit, career adaptability

Reinforcement learning in sentiment analysis: a review and future directions

Sentiment analysis in natural language processing (NLP) is used to understand the polarity of human emotions (e.g., positive and negative) and preferences (e.g., price and quality). Reinforcement learning (RL) enables a decision maker (or agent) to observe the operating environment (or the current state) and select the optimal action to receive feedback signals (or reward) from the operating environment. Deep reinforcement learning (DRL) extends RL with deep neural networks (i.e., main and target networks) to capture the state information of inputs and address the curse of dimensionality issue of RL. In sentiment analysis, RL and DRL reduce the need for a large labeled dataset and linguistic resources, increasing scalability and preserving the context and order of logical partitions. Through enhancement, the RL and DRL algorithms identify negations, enhance the quality of the generated responses, predict the logical partitions, remove the irrelevant aspects, and ultimately capture the correct sentiment polarity. This paper presents a review of RL and DRL models and algorithms with their objectives, applications, datasets, performance, and open issues in sentiment analysis.

DREAM: Debugging and Repairing AutoML Pipelines

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems have shortcomings in their design. We identify two common and severe shortcomings in AutoML, performance issue (i.e., searching for the desired model takes an unreasonably long time) and ineffective search issue (i.e., AutoML systems are not able to find an accurate enough model). After analyzing the workflow of AutoML, we observe that existing AutoML systems overlook potential opportunities in search space, search method, and search feedback, which results in performance and ineffective search issues. Based on our analysis, we design and implement DREAM, an automatic and general-purpose tool to alleviate and repair the shortcomings of AutoML pipelines and conduct effective model searches for diverse tasks. It monitors the process of AutoML to collect detailed feedback and automatically repairs shortcomings by expanding search space and leveraging a feedback-driven search strategy. Our evaluation results show that DREAM can be applied on two state-of-the-art AutoML pipelines and effectively and efficiently repair their shortcomings.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

DC-SoC: Optimizing a Blockchain Data Dissemination Model Based on Density Clustering and Social Mechanisms

Due to its partially decentralized and highly scalable features, the consortium blockchain has currently overtaken other blockchain technologies as the one most frequently used and studied across various industries. However, performance issues such as low transaction efficiency and redundant communication processes continue to hinder the development of consortium blockchains. In the Hyperledger Fabric consortium blockchain system, transaction efficiency is largely influenced by the consensus protocol and broadcast protocol. This paper introduces a novel consortium blockchain network model, DC-SoC, focused on optimizing broadcast protocols. By incorporating the concept of density clustering, a stable propagation structure is established for the blockchain network, thus optimizing data dissemination in the Gossip protocol. Additionally, the concept of social networks is integrated, using trustworthiness scores and economic incentives to evaluate node security. The experimental results demonstrate that when DC-SoC is applied in a large-scale consortium blockchain environment, it significantly improves communication performance between nodes and ensures transmission reliability.

Interface properties study of black silicon solar cells with front surface a-Si:H/c-Si heterojunction

Abstract The exceptionally low reflectance of black silicon (Si) across a broad wavelength range makes it an intriguing surface texture for solar cell applications. Si heterojunction (SHJ) solar cells fabricated on black Si formed by dry reactive ion etching using inductive coupled plasma on n-type Si are explored. The study is focused on the properties of the a-Si:H/ crystalline silicon interface, being a key issue for the photovoltaic performance of SHJ. Deep-level transient spectroscopy detected no radiation defect in Si after the etching. The surface of black Si was passivated with an intrinsic a-Si:H layer, followed by the deposition of p-type and n-type a-Si:H on the front and back sides, respectively. The SHJ solar cell photovoltaic performance based on black Si is strongly influenced by defect density at the a-Si:H/Si interface. Admittance spectroscopy and effective charge carrier lifetime measurements demonstrate that interface defect density decreases with the increase of (i) a-Si:H thickness. The value of 0.75 ms effective charge carrier lifetime was reached for single (i) a-Si:H layer passivation and 0.25 ms when (p)a-Si:H was deposited over the intrinsic a-Si:H layer. The measured open circuit voltage values for the SHJ solar cells increase with the (i) a-Si:H layer thickness reaching 658 mV. However, the fill factor decreases with increasing (i) a-Si:H layer thickness, limiting the efficiency at the maximum value below 14% due to the thickness uniformity of the a-Si:H layer. The development of conformal growth of a-Si:H is a key issue for further improvement of black Si heterojunction solar cell photovoltaic performance.

The Growing Trend of Cloud-Based Data Integration and Warehousing

This article examines the growing trend of cloud-based data integration and warehousing solutions in response to the exponential growth of data generation and the need for scalable, flexible analytics capabilities. It explores the key drivers behind cloud adoption, including data volume increases, demand for real-time insights, cost efficiencies, and advanced analytics requirements. The benefits of cloud solutions are discussed, such as scalability, cost-effectiveness, agility, and enhanced analytical capabilities. The article also addresses challenges like data security, vendor lock-in, and performance issues, providing best practices for successful implementation. Emerging trends in automated ETL processes, machine learning integration, and multi-cloud approaches are highlighted, showcasing the evolving landscape of cloud-based data management. Through analysis of market projections, case studies, and industry statistics, this comprehensive overview equips organizations with the knowledge to leverage cloud technologies to transform their data management capabilities and gain competitive advantages in the digital age.

Structured multi-view k-means clustering

K-means is a very efficient clustering method and many multi-view k-means clustering methods have been proposed for multi-view clustering during the past decade. However, since k-means have trouble uncovering clusters of varying sizes and densities, these methods suffer from the same performance issues as k-means. Improving the clustering performance of multi-view k-means has become a challenging problem. In this paper, we propose a new multi-view k-means clustering method that is able to uncover clusters in arbitrary sizes and densities. The new method simultaneously performs three tasks, i.e., sparse connection probability matrices learning, prototypes aligning, and cluster structure learning. We evaluate the proposed new method by 5 benchmark datasets and compare it with 11 multi-view clustering methods. The experimental results on both synthetic and real-world experiments show the superiority of our proposed method.

The Role of Excess Charge Mitigation in Electromagnetic Hygiene: An Integrative review.

The electromagnetic characteristics of many environments have changed significantly in recent decades. This is in large part due to the increased presence of equipment that emits electromagnetic radiation and materials that may often readily gain excess charge. The presence of excess charge can often increase risk of infection from pathogens, and likelihood of individuals experiencing compromised performance, respiratory problems and other adverse health issues from increased uptake of particulate matter. It is proposed that adopting improved electromagnetic hygiene measures, including optimized humidity levels, to reduce the presence of inappropriate levels of electric charge can help reduce the likelihood of ill health, infection and poor performance arising from contaminant inhalation and deposition, plus reduce the likelihood of medical devices and other electronic devices getting damaged and/or having their data compromised. It is suggested that such measures should be more widely adopted within clinical practice guidelines and water, sanitation and hygiene programs.

Transformer- and joint learning-based dual-domain networks for undersampled MRI segmentation.

Recently, magnetic resonance imaging (MRI) has become a crucial medical imaging technology widely used in clinical practice. However, MRI faces challenges such as the lengthy acquisition time for k-space data and the need for time-consuming manual annotation by radiologists. Traditionally, these challenges have been addressed individually through undersampled MRI reconstruction and automatic segmentation algorithms. Whether undersampled MRI segmentation can be enhanced by treating undersampled MRI reconstruction and segmentation as an end-to-end task, trained simultaneously, rather than as serial tasks should beexplored. We introduce a novel Transformer- and Joint Learning-based Dual-domain Network (TJLD-Net) for undersampled MRIsegmentation. This method significantly enhances feature recognition in the segmentation process by fully utilizing the rich detail obtained during the image reconstruction phase. Consequently, the method can achieve precise and reliable image segmentation even with undersampled k-space data. Additionally, it incorporates an attention mechanism for feature enhancement, which improves the representation of shared features by learning the contextual information in MRimages. Simulation experiments demonstrate that the segmentation performance of TJLD-Net on three datasets is significantly higher than that of the joint model (RecSeg) and six baseline models (where reconstruction and segmentation are regarded as serial tasks). On the CHAOS dataset, the Dice scores of TJLD-Net are, on average, 9.87%, 2.17%, 1.90%, 1.80%, 9.60%, 0.80%, and 6.50% higher than those of the seven compared models. On the ATLAS challenge dataset, the average Dice scores of TJLD-Net improve by 4.23%, 5.63%, 2.30%, 1.53%, 3.57%, 0.93%, and 6.60%. Similarly, on the SKM-TEA dataset, the average Dice scores of TJLD-Net improve by 4.73%, 12.80%, 14.83%, 8.67%, 4.53%, 11.60%, and 12.10%. The novel TJLD-Net model provides a promising solution for undersampled MRI segmentation, overcoming the poor performance issues encountered by automated segmentation algorithms in low-quality acceleratedimaging.