The emergence of the Internet of Things and the interconnection of systems and machines enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction and communication between physical and digital entities, leading to the creation of cyber-physical systems. The digital twin and the standard for the Asset Administration Shell are concepts derived from Industry 4.0 that exploit the advantages of connecting the physical and virtual domains, improving the management and display of the collected data. Furthermore, the increasing availability of data has enabled the implementation of data-driven approaches, such as machine and deep learning models, for predictive maintenance in industrial and automotive applications. This paper provides a two-dimensional review of the Asset Administration Shell and data-driven methods for predictive maintenance, including fault diagnosis and prognostics. Additionally, a digital twin architecture combining the Asset Administration Shell, predictive maintenance and data-driven methods is proposed within the context of the WaVe project.

Depression detection from social media texts such as Tweets or Facebook comments could be very beneficial as early detection of depression may even avoid extreme consequences of long-term depression i.e. suicide. In this study, depression intensity classification is performed using a labeled Twitter dataset. Further, this study makes a detailed performance evaluation of four transformer-based pre-trained small language models, particularly those having less than 15 million tunable parameters i.e. Electra Small Generator (ESG), Electra Small Discriminator (ESD), XtremeDistil-L6 (XDL) and Albert Base V2 (ABV) for classification of depression intensity using Tweets. The models are fine-tuned to get the best performance by applying different hyperparameters. The models are tested by classification of depression intensity of labeled tweets for three label classes i.e. ’severe’, ’moderate’, and ’mild’ by downstream fine-tuning the parameters. Evaluation metrics such as accuracy, F1, precision, recall, and specificity are calculated to evaluate the performance of the models. Comparative analysis of these models is also done with a moderately larger model i.e. DistilBert which has 67 million tunable parameters for the same task with the same experimental settings. Results indicate that ESG outperforms all other models including DistilBert due to its better deep contextualized text representation as it gets the best F1 score of 89% with comparatively less training time. This study helps to achieve better classification performance of depression detection as well as to choose the best language model in terms of performance and less training time for Twitter-related downstream NLP tasks.

Sequential Consistency (SC) is the most intuitive memory model, and SC Violations (SCVs) produce unintuitive, typically incorrect executions. Most prior SCV detection schemes have used data races as proxies for SCVs, which is highly imprecise. Other schemes that have targeted data-race cycles are either too conservative or are designed only for two-processor cycles and snoopy-based systems. This paper presents Volition, the first hardware scheme that detects SCVs in a relaxed-consistency machine precisely, in a scalable manner, and for an arbitrary number of processors in the cycle. Volition leverages cache coherence protocol transactions to dynamically detect cycles in memory-access orders across threads. When a cycle is about to occur, an exception is triggered. Volition can be used in both directory- and snoopy-based coherence protocols. Our simulations of Volition in a 64-processor multicore with directory-based coherence running SPLASH-2 and Parsec programs shows that Volition induces negligible traffic and execution overhead. In addition, it can detect SCVs with several processors. Volition is suitable for on-the-fly use.

In the attempt to understand the behavior of HfO${}_2$-based resistive switching devices at low temperatures, TiN/Ti/HfO${}_2$/W metal–insulator–metal devices were fabricated; the atomic layer deposition technique was used to grow the high-k layer. After performing an electroforming process at room temperature, the device was cooled in a cryostat to carry out 100 current–voltage cycles at several temperatures ranging from the “liquid nitrogen temperature” to 350 K. The measurements showed a semiconducting behavior in high and low resistance states. In the low resistance state, a hopping conduction mechanism was obtained. The set and reset voltages increased when temperature decreased because the thermal energies for oxygen vacancies and ions were reduced. However, the temperature did not influence the power absorbed in the reset transition, indicating the local temperature in the filament controls the transition. The set transition turned from gradual to abrupt when decreasing the temperature, due to a positive feedback between the current increase and the Joule heating at low temperatures.

Automated industrial welding processes depend on a large number of factors interacting with high complexity resulting in some sporadic and random variability of the manufactured product that may affect its quality. It is therefore very important to have an accurate and stable quality control. In this work, a deep learning (DL) model is developed for semantic segmentation of weld seams using 3D stereo images of the seam. The objective is to correctly identify the shape and volume of the weld seam as this is the basic problem of quality control. To achieve this, a model called UNet $${\mathcal {L}}$$L ++ has been developed, based on the UNet and UNet++ architectures, with a more complex topology and a simple encoder to achieve a good adaptation to the specific characteristics of the 3D data. The proposed model receives as input a voxelized 3D point cloud of the freshly welded part where noise is abundantly visible, and generates as output another 3D voxel grid where each voxel is semantically labeled. The experiments performed with parts built by a real weld line show a correct identification of the weld seams, obtaining values between 0.935 and 0.941 for the Dice Similarity Coefficient (DSC). As far as the authors are aware, this is the first 3D analysis proposal capable of generating shape and volume information of weld seams with almost perfect noise filtering.

Multiprocessor architectures that continuously execute atomic blocks (or chunks) of instructions can improve performance and software productivity. However, all of the prior proposals for such architectures assume single-context cores as building blocks - rather than the widely-used Simultaneous Multithreading (SMT) cores. As a result, they are underutilizing hardware resources. This paper presents the first SMT design that supports continuous chunked (or transactional) execution of its contexts. Our design, called BulkSMT, can be used either in a single-core processor or in a multicore of SMTs. We present a set of BulkSMT configurations with different cost and performance. We also describe the architectural primitives that enable chunked execution in an SMT core and in a multicore of SMTs. Our results, based on simulations of SPLASH-2 and PARSEC codes, show that BulkSMT supports chunked execution cost-effectively. In a 4-core multicore with eager chunked execution, BulkSMT reduces the execution time of the applications by an average of 26% compared to running on single-context cores. In a single core, the average reduction is 32%.