The world is becoming increasingly dependant in computing intensive applications. The appearance of new paradigms, such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) are creating a demand for high-performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. These devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all these applications, there is a recent tendency to offload these computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address these problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of these devices in the Cloud.

Traditional manufacturing industries are currently immersed in an automation process, integrating new techniques and tools, driven by the demands from producers to improve the manufacturing process as well as the working conditions of employees. For the footwear industry, bonding is a key operation in the manufacturing process where the outsole is assembled onto the lasted shoe. However, in this operation, workers are often subjected to hazardous substances (i.e., organic solvents) and perform repetitive tasks with limited added value. Against this background, this paper describes the results of a research project, whose aim was to obtain the maximum benefit from different technologies analyzed, such as collaborative robotics, artificial vision and multirobot control, for the manipulation of flexible/deformable objects. The main result of this project is a robotic workcell for shoe bonding that has been introduced in the production line to fully automate the operation. This workcell integrates three collaborative robots, one for (hot melt) adhesive application and another two, with two-finger electric grippers, to carry out the bonding synchronously. Different vision systems have also been embedded to conduct the various processes involved. The entire operation is controlled and coordinated through ROS (Robot Operating System). The key findings of this research showcase the automation of a process traditionally undertaken by humans. In this novel approach, two robots collaborate to manipulate flexible objects, liberating the operator from engaging in repetitive, non-value-added tasks and the handling of hazardous substances.

Efficient representation of data is a fundamental prerequisite for addressing computational problems effectively using computers. The continual improvement in methods for representing numbers in computers serves as a critical step in expanding the scope and capabilities of computing systems. In this research, we conduct a comprehensive review of both fundamental and advanced techniques for representing numbers in computers. Additionally, we propose a novel model capable of representing rational numbers with absolute precision, catering to specific high precision applications. Specifically, we adopt fractional positional notation coupled with explicit codification of the periodic parts, thereby accommodating the entire rational number set without any loss of accuracy. We elucidate the properties and hardware representation of this proposed format and provide the results of extensive experiments to demonstrate its expressiveness and minimal codification error when compared to other real number representation formats. This research contributes to the advancement of numerical representation in computer systems, empowering them to handle complex computations with heightened accuracy, making them more reliable and versatile in a wide range of applications.

Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases.

Purpose – This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories. Design/methodology/approach – This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments. Findings – The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations. Originality/value – This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

Recent advances in the area of the Internet of Things shows that devices are usually resource-constrained. To enable advanced applications on these devices, it is necessary to enhance their performance by leveraging external computing resources available in the network. This work presents a study of computational platforms to increase the performance of these devices based on the Mobile Cloud Computing (MCC) paradigm. The main contribution of this paper is to research the advantages and possibilities of architectures with multiple offloading options. To this end, a review of architectures that use a combination of the computing layers in the available infrastructure to perform this paradigm and outsource processing load is presented. In addition, a proof-of-concept application is introduced to demonstrate its realization along all the network layers. The results of the simulations confirm the high flexibility to offload numerous tasks using different layers and the ability to overcome unfavorable scenarios.

Number processing is an essential task in resolving many computational problems for which a global solution is difficult to find: amount and type of operations, functional domain, nature of the data, numerical range of operands and results, etc. In particular, certain applications require high-performance computing related to elaborate mathematical calculations and, at the same time, they impose considerable restrictions on accuracy; for example, the calculation of trajectories for moving bodies in space or over long distances, guidance and positioning systems, quantum theory calculations, intensive financial transactions, climate modelling, etc. [1]. In these computations, a slight imprecision in the operations may cause considerable deviations in the results obtained. With this type of problem, the representation of the data involved in the problem itself is a basic issue for computational resolution: Which numerical set is appropriate for codification of the data? How many significant digits are necessary to perform the calculations? Which representational format should be used? These and other questions will lead us to an appropriate numerical codification and will help us find a balance between the accuracy and complexity of the calculations. The relevance of this topic is evident from the number of proposals and works that attempt to resolve specific aspects of the problem. Given the need for greater accuracy in some applications, the most common practice is to make use of variable precision software tools for reasons of portability. This solution forms a layer of abstraction on top of the computer system that provides structures and operations with a length that can be adjusted to the needs of each application. Among these tools are function libraries and types of data, arithmetic packages and other extensions of common programming languages (C, Pascal, Fortran, etc.) [2, 3]. Their main contribution is to extend the range of representation formats to store a larger number of significant digits and achieve greater accuracy. Nevertheless, the complexity of calculating the operations restricts their use for the general case. In addition, their final codification is based on formats and numerical representations supported by the machine hardware, which adds its own restrictions.

The problem of nesting frequently arises in the industrial environment, and it has a strong ecological and economic impact in the manufacturing processes. It basically consists of placing a set of pieces (polygons) on a material sheet, making sure that the pieces do not overlap and that they do not exceed the boundaries of the sheet. With regard to irregular 2D polygons, the problem is NP-complete. Therefore, different heuristics have been developed so as to cope with the problem. In this paper, the application of the Jaya metaheuristic algorithm to the nesting problem is proposed. This algorithm has been already applied to several engineering problems and has generally demonstrated better results than most metaheuristic algorithms. In this paper, the Jaya algorithm has been adapted to the specific features of the nesting problem so as to optimize the placement of pieces on a sheet, with the objective of minimizing material waste and computational time. The results of our experimentation demonstrate the algorithm's effectiveness in reducing the convex hull area across various datasets, showing potential in solving complex, irregular shape nesting problems. This research provides a new application of the Jaya algorithm and opens ways for future work in optimization techniques and parameter-free heuristic algorithms for nesting.

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.

Denial of Wallet (DoW) attacks refers to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment. These attacks are particularly relevant in the context of serverless architectures due to characteristics like pay-as-you-go model, auto-scaling, limited control and cost amplification. Serverless computing, often referred to as Function-as-a-Service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. However, the lack of realistic and representative datasets that simulate function invocations in serverless environments has been a challenge for research and development of solutions in this field. The aim is to create a dataset for simulating function invocations in serverless architectures, that is a valuable practice for ensuring the reliability, efficiency, and security of serverless applications. Furthermore, we propose a methodology for the generation of the dataset, which involves the generation of synthetic data from traffic generated on cloud platforms and the identification of the main characteristics of function invocations. These characteristics include SubmitTime, Invocation Delay, Response Delay, Function Duration, Active Functions at Request, Active Functions at Response. By generating this dataset, we expect to facilitate the detection of Denial of Wallet (DoW) attacks using machine learning techniques and neural networks. In this way, this dataset available in Mendeley data repository could provide other researchers and developers with a dataset to test and evaluate machine learning algorithms or use other techniques based on the detection of attacks and anomalies in serverless environments.

The development of smart vehicles has been a major focus of the automotive industry in recent years. Smart vehicles, equipped with advanced sensors and communication technologies, represent a transformative paradigm in modern transportation systems. Some of the challenges associated with the introduction of smart vehicles include developing reliable sensors, creating robust communication networks, and ensuring the security of vehicle systems. This paper proposes a Blockchain-based framework for accident prevention on the Internet of Vehicles, where vehicles monitor the state of the route and transmit information about hazardous situations to the Blockchain network. Once the event is confirmed, warnings are sent to all vehicles, and their speed is automatically reduced to avoid accidents. The tests in a 3D graphical simulator, combined with Hyperledger Besu technology for the creation of the Blockchain network, demonstrated both horizontal and vertical scalability, validating the potential of this framework for real-world integration. Moreover, it presents a fast reaction to anomalous situations on a route compared to human reactions under similar circumstances.

Serverless architectures enable application execution in environments where computational resources are allocated dynamically. Unlike traditional models, functions are invoked based on discrete events, offering scalability and reduced operational overhead. However, the ephemeral and distributed nature of these environments introduces significant monitoring challenges, rendering them vulnerable to exploitation. A Denial of Wallet (DoW) attack represents a specific threat in cloud-native ecosystems, where an attacker deliberately exhausts a target’s financial resources by forcing consumption beyond budget thresholds. Such attacks exploit the pay-per-use billing model, leading to substantial financial liabilities. This study introduces a hybrid detection model for DoW attacks that combines traditional statistical methods with advanced AI techniques to reduce computational costs while maintaining detection efficacy. The proposed framework operates in two sequential phases. First, time-series entropy is derived from the distribution of function invocation types and execution-duration buckets, generating a temporal sequence reflecting system behavior. Second, a supervised anomaly detection algorithm is applied to this sequence to identify deviations indicative of a DoW attack. This entropy-driven approach aims to enhance accuracy while minimizing false positives. By quantifying the randomness and predictability of serverless workloads, the model provides an effective mechanism for early threat identification, enabling proactive identification of financial risks associated with Denial of Wallet attacks in offline and batch evaluation settings. This dual-phase strategy ensures the effectiveness of previously developed methods while significantly improving computational efficiency in serverless deployments.