Search Results (42)

The environmental burden of conventional plastics has sparked interest in sustainable alternatives such as polyhydroxyalkanoates (PHAs). However, despite ample research in bioprocess development and the use of inexpensive waste streams, production costs remain a barrier to widespread commercialization. Complementary to this, genetic engineering offers another avenue for improved productivity. Cupriavidus necator stands out as a model host for PHA production due to its substrate flexibility, high intracellular polymer accumulation, and tractability to genetic modification. This review delves into metabolic engineering strategies that have been developed to enhance the production of poly(3-hydroxybutyrate) (PHB) and related copolymers in C. necator. Strategies include the optimization of central carbon flux, redox and cofactor balancing, adaptation to oxygen-limiting conditions, and fine-tuning of granule-associated protein expression and the regulatory network. This is followed by outlining engineered pathways improving the synthesis of PHB copolymers, PHBV, PHBHHx, and other emerging variants, emphasizing genetic modifications enabling biosynthesis based on unrelated single-carbon sources. Among these, enzyme engineering strategies and the establishment of novel artificial pathways are widely discussed. In particular, this review offers a comprehensive overview of promising engineering strategies, serving as a resource for future strain development and positioning C. necator as a valuable microbial chassis for biopolymer production at an industrial scale. Full article

The importance of real-time big data has become crucial in the digital revolution of modern society, in the context of increasing data flows from multiple sources, including social media, internet connected devices (IOT) and financial systems, real-time analysis and processing is becoming a strategic tool for fast and accurate decision making, we find applications in different domains such as healthcare, finance, and digital marketing, which is revolutionizing traditional business models. In this article, we explore the recent advances and future prospects of real-time big data. Our research is based on recent work published between 2020 and 2025, examining the technological advances, the difficulties encountered and suggesting ways of optimizing the efficiency of these technologies. Full article

The rapid proliferation of Internet of Things (IoT) devices across industries has created a need for robust, scalable, and real-time data processing architectures capable of supporting intelligent analytics and predictive maintenance. This paper presents a novel comprehensive architecture that enables end-to-end processing of IoT data streams, from acquisition to actionable insights. The system integrates Kafka-based message brokering for the high-throughput ingestion of real-time sensor data, with Apache Spark facilitating batch and stream extraction, transformation, and loading (ETL) processes. A modular machine-learning pipeline handles automated data preprocessing, training, and evaluation across various models. The architecture incorporates continuous monitoring and optimization components to track system performance and model accuracy, feeding insights to users via a dedicated Application Programming Interface (API). The design ensures scalability, flexibility, and real-time responsiveness, making it well suited for industrial IoT applications requiring continuous monitoring and intelligent decision-making. Full article

Stream processing frameworks have become key enablers of real-time data processing in modern distributed systems. However, robust and scalable mechanisms for verifying temporal properties are often lacking in existing systems. To address this gap, a new runtime verification framework is proposed that integrates linear temporal logic (LTL) monitoring into stream processing applications, such as Apache Spark. The approach introduces reusable LTL monitoring patterns designed for seamless integration into existing streaming workflows. Our case study, applied to real-time financial data monitoring, demonstrates that LTL-based monitoring can effectively detect violations of safety and liveness properties while maintaining stable latency. A performance evaluation reveals that although the approach introduces computational overhead, it scales effectively with increasing data volume. The proposed framework extends beyond financial data processing and is applicable to domains such as real-time equipment failure detection, financial fraud monitoring, and industrial IoT analytics. These findings demonstrate the feasibility of real-time LTL monitoring in large-scale stream processing environments while highlighting trade-offs between verification accuracy, scalability, and system overhead. Full article

Electrostatic techniques have introduced innovative approaches to devise efficient tools for pest control across various categories, encompassing pathogens, insects, and weeds. The focus on electric discharge technology has proven pivotal in establishing effective methods with simple device structures, enabling cost-effective fabrication using readily available materials. The electric discharge-generating devices can be assembled using commonplace conductor materials, such as ordinary metal nets linked to a voltage booster and a grounded electric wire. The strategic pairing of charged and grounded conductors at specific intervals generates an electric field, leading the charged conductor to initiate a corona discharge in the surrounding space. As the applied voltage increases, the corona discharge intensifies and may eventually result in an arc discharge due to the breakdown of air when the voltage surpasses the insulation resistance limit. The utilization of corona and arc discharges plays a crucial role in these techniques, with the corona-discharging stage creating (1) negative ions to stick to pests, which can then be captured with a positively charged pole, (2) ozone gas to sterilize plant hydroponic solutions, and (3) plasma streams to exterminate fungal colonies on leaves, and the arc-discharging stage projecting electric sparks to zap and kill pests. These electric discharge phenomena have been harnessed to develop reliable devices capable of managing pests across diverse classes. In this review, we elucidate past achievements and challenges in device development, providing insights into the current status of research. Additionally, we discuss the future directions of research in this field, outlining potential avenues for further exploration and improvement. Full article

Cloud Manufacturing enables the integration of geographically distributed manufacturing resources through advanced Cloud Computing and IoT technologies. This paradigm promotes the development of scalable and adaptable production systems. However, existing frameworks face challenges related to scalability, resource orchestration, and data security, particularly in rapidly evolving decentralized manufacturing settings. This study presents a novel nine-layer architecture designed specifically to address these issues. Central to this framework is the use of Apache Kafka for robust, high-throughput data ingestion, and Apache Spark Streaming to enhance real-time data processing. This framework is underpinned by a microservice-based architecture that ensures a high scalability and reduced latency. Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. Key components, such as RabbitMQ, contribute to low-latency performance, whereas Kafka ensures data durability and supports real-time application. Additionally, the in-memory data processing of Spark Streaming enables rapid and dynamic data analysis, yielding actionable insights. The experimental results highlight the potential of the framework to enhance operational efficiency, resource utilization, and data security, offering a resilient solution suited to the demands of modern industrial applications. This study underscores the contribution of the framework to advancing Cloud Manufacturing by providing detailed insights into its performance, scalability, and applicability to contemporary manufacturing ecosystems. Full article

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains. Full article

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era. Full article

Increasing demand for electrical and electronic equipment results in the generation of a rapidly growing waste stream, known by the acronym WEEE (waste electrical and electronic equipment). The purpose of this study was to evaluate the effectiveness of green sink–float treatment in sorting plastic polymers typically found in WEEE (PP, ABS, PA6, PS, and PVC). Molasses, a by-product of sugar bio-refining, was added in various concentrations to water to form solutions at different densities. The methodology was initially tested on virgin polymers; later, it was applied to plastics from a WEEE treatment plant. The polymers were characterised through near infrared spectroscopy (NIRS) and Fourier-transform infrared spectroscopy (FTIRS) analyses; the detection of any additives and flame retardants was conducted using the sliding spark technology (SSS2) and scanning electron microscope (SEM—EDX). The results showed that, for plastics from WEEE, the recovery efficiency was 55.85% for PP in a solution of tap water while the remaining part of PP (44.15%) was recovered in a solution of water to which 90% molasses was added. Furthermore, 100% recovery efficiency was obtained for PS and 93.73% for ABS in a solution of tap water with the addition of 10% w/v molasses. A recovery efficiency of 100% was obtained for PVC and 100% for PA6 in a solution consisting solely of molasses. Full article

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations. Full article

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy. Full article

The problem of flow control under conditions of a turbulent boundary layer at transonic and supersonic free-stream velocities is considered. Such flows are integral components of the flight process and exert significant effects on the flow around both the aerodynamic object as a whole and its individual elements. The present paper describes investigations of a combined control device (“plasma wedge”), which is a wedge mounted along the flow with the energy supply at one side of the wedge owing to a spark discharge. The strategy of flow control by this device is based on increasing the momentum in the boundary layer, which enhances its resistance to the adverse pressure gradient and, as a consequence, its resistance to flow separation further downstream. The study includes experimental and computational aspects. The examined flow evolves on a rectangular flat plate with a sharp leading edge at the free-stream Mach number M = 1.45 and unit Reynolds numbers Re₁ = 11.5·10⁶ 1/m. The experiments are performed to study the velocity fields and the pressure distribution in the wake behind the actuator. The results show that a streamwise vortex is formed in the wake behind the actuator when the discharge is initiated. Reasonable agreement of the experimental data with numerical simulations allows one to conclude that the Reynolds-averaged Navier–Stokes equations are suitable tools for solving the problem considered. Full article

Big Data and primarily distributed stream processing systems (DSPSs) are growing in complexity and scale. As a result, effective performance management to ensure that these systems meet the required service level objectives (SLOs) is becoming increasingly difficult. A key factor to consider when evaluating the performance of a DSPS is CPU efficiency, which is the ratio of the workload processed by the system to the CPU resources invested. In this paper, we argue that developing new performance tools for creating DSPSs that can fulfill SLOs while using minimal resources is crucial. This is especially significant in edge computing situations where resources are limited and in large cloud deployments where conserving power and reducing computing expenses are essential. To address this challenge, we present a novel task-level approach for measuring CPU efficiency in DSPSs. Our approach supports various streaming frameworks, is adaptable, and comes with minimal overheads. This enables developers to understand the efficiency of different DSPSs at a granular level and provides insights that were not previously possible. Full article

Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments. Full article

The prediction and early warning efficiency of mine gas concentrations are important for intelligent monitoring of daily gas concentrations in coal mines. It is used as an important means for ensuring the safe and stable operation of coal mines. This study proposes an early warning model for gas concentration prediction involving the Spark Streaming framework (SSF). The model incorporates a particle swarm optimisation algorithm (PSO) and a gated recurrent unit (GRU) model in the SSF, and further experimental analysis is carried out on the basis of optimising the model parameters. The operational efficiency of the model is validated using a control variable approach, and the prediction and warning errors is verified using MAE, RMSE and R². The results show that the model is able to predict and warn of the gas concentration with high efficiency and high accuracy. It also features fast data processing and fault tolerance, which provides a new idea to continue improving the gas concentration prediction and warning efficiency and some theoretical and technical support for intelligent gas monitoring in coal mines. Full article