Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure

Avaliação do desempenho do processo de manufatura do café/ Performance evaluation of the coffee manufacturing process

Globalization and advanced manufacturing technologies have forced manufacturing firms to increase productivity while reducing costs. At the same time, customers are increasingly demanding better products considering tangi- ble (e.g., smell, color, taste) and intangible (e.g., mark, fair treading, and envi- ronmental responsability) attributes. Currently, Brazil consolidates a position as the largest producer and exporter of coffee, accounting for 30% of the inter- national coffee market. This paper presents a stochastic model for performance evaluation and planning of coffee manufacturing process aiming at reducing the cost and time of the production cycle. An industrial case study shows the practical usability of the proposed models and techniques

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities

Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure

Factors leading to business process noncompliance and its positive and negative effects: Empirical insights from a case study

Many organizations face noncompliance in their business processes. Such noncompliant behavior can range from well-intended actions to the deliberate omission of essential tasks. The current view on noncompliance is mostly negative and many researchers discuss how to avoid it altogether. A gap in the research is a lack of empirical insights on when noncompliance has positive and when it has negative effects. Against this background, we conduct a qualitative study in the customer service department of a company hosting one of Europe's leading online project platforms. Differing from previous studies on business process noncompliance, the starting point of our study is direct observations of how employees conduct their work. We found that noncompliant behavior with a positive intention had a mostly positive effect on business process outcomes. Unintended factors of noncompliance, such as a lack of knowledge or carelessness, caused the most severe negative impact on business process outcomes

A Mapping Study of Machine Learning Methods for Remaining Useful Life Estimation of Lead-Acid Batteries

Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the battery's State of Health (SoH) and Remaining Useful Life (RUL) contributes to enhancing predictive maintenance, reliability, and longevity of battery systems. Besides improving the cost savings, correct estimation of the SoH can lead to reduced pollution though reuse of retired batteries. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries. These two indicators are critical in the battery management systems of electric vehicles, renewable energy systems, and other applications that rely heavily on this battery technology. In this study, we analyzed the types of machine learning algorithms employed for estimating SoH and RUL, and evaluated their performance in terms of accuracy and inference time. Additionally, this mapping identifies and analyzes the most commonly used combinations of sensors in specific applications, such as vehicular batteries. The mapping concludes by highlighting potential gaps and opportunities for future research, which lays the foundation for further advancements in the field