Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet-of-Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model-based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we call the Massive Open Online Laboratory.

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals.

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives.

Existing unsupervised anomaly detection methods suffer from insufficient parameter precision, poor robustness to noise, and limited generalization capability. To address these issues, this paper proposes an Adaptive Diffusion Adversarial Evolutionary Network (ADAEN) for unsupervised anomaly detection in tabular data. The proposed network employs an adaptive hierarchical feature evolution generator that captures multi-scale feature representations at different abstraction levels through learnable attribute encoding and a three-layer Transformer encoder, effectively mitigating the gradient vanishing problem and the difficulty of modeling complex feature relationships that are commonly observed in conventional generators. ADAEN incorporates a multi-scale adaptive diffusion-augmented discriminator, which preserves scale-specific features across different diffusion stages via cosine-scheduled adaptive noise injection, thereby endowing the discriminator with diffusion-stage awareness. Furthermore, ADAEN introduces a multi-scale robust adversarial gradient loss function that ensures training stability through a diffusion-step-conditional Wasserstein loss combined with gradient penalty. The method has been evaluated on 14 UCI benchmark datasets and achieves state-of-the-art performance in anomaly detection compared to existing advanced algorithms, with an average improvement of 8.3% in AUC, an 11.2% increase in F1-Score, and a 15.7% reduction in false positive rate.

The environment of today’s companies is marked by increasing dynamism. Rapid technological developments, strong innovation impulses, and continual market entry of new competitors create volatile conditions that make the delivery of valuable products challenging. Long-term corporate success therefore depends on offering a product portfolio consistently aligned with evolving market needs. Customers expect products that show continuous improvements in performance and functionality over time, making systematic product upgrading a key success factor. Release planning addresses this need by enabling continuous product evolution through planned product upgrades. It focuses on selecting and combining functional units for structured publication within releases. This proactive management of product value offers substantial potential but also demands comprehensive know-how, particularly given rising product complexity and the interplay of multiple technologies. The objective of this work is to develop a methodology that supports effective planning of product upgrades. The method assists in the product-specific selection of release types and the derivation of suitable release strategies. It yields release units defined by product structure and provides recommendations for appropriate release strategies. The methodology is demonstrated through its application to an electric vehicle, illustrating its practical relevance for software-intensive products.

Since the compressor system in underground gas storage (UGS) facilities operates under highly dynamic and complex injection conditions, traditional rule-based operation and mechanism-based modeling approaches prove inadequate for meeting the stringent requirements of high-accuracy prediction under such variable conditions. To address this, a data-driven two-phase prediction framework for compressor energy consumption is proposed. In the first phase, a convolutional neural network with efficient channel attention (CNN-ECA) is developed to accurately forecast key operating condition parameters. Based on these outputs, the second phase employs a compressor performance prediction model to estimate unit energy consumption with improved precision. In addition, a hybrid prediction strategy integrating a Transformer architecture is introduced to capture long-range temporal dependencies, thereby enhancing both single-step and multi-step forecasting performance. The proposed method is evaluated using operational data from eight compressors at the Xiangguosi underground gas storage. Experimental results show that the framework achieves high prediction accuracy, with a MAPE of 4.0779% (single-step) and 4.2449% (multi-step), outperforming advanced benchmark models.

Coordinating operating room schedules with downstream inpatient bed availability remains a critical challenge for hospitals, particularly under emergency-driven uncertainty. Emergency arrivals introduce variability that propagates congestion across surgical and inpatient systems, reducing elective surgery throughput and resource utilization. Existing approaches often treat operating rooms and inpatient beds as isolated planning problems, limiting the ability to anticipate system-wide congestion effects. This study proposes a system-level decision-support framework that integrates elective operating room scheduling, emergency arrivals, and inpatient bed capacity within a unified stochastic optimization model. Uncertainty in surgical duration and patient length of stay is represented through scenario-based stochastic modeling. Computational experiments examine system performance under varying levels of emergency demand and bed availability. The results identify critical congestion thresholds beyond which elective throughput deteriorates rapidly, highlighting the role of downstream bed constraints in governing system capacity under uncertainty. The proposed framework provides hospital managers with practical insights for coordinated surgical and inpatient capacity planning, bridging operations research optimization with operations management principles at the system level.

Robot navigation refers to a robot’s ability to determine its position within a reference frame and plan a path to a target location. Visual navigation, which relies on visual sensors such as cameras, is one approach to this problem. Among visual navigation methods, Visual Teach and Repeat (VT&R) techniques are commonly used. To develop an effective robot navigation framework based on the VT&R method, accurate and fast depth estimation of the scene is essential. In recent years, event cameras have garnered significant interest from machine vision researchers due to their numerous advantages and applicability in various environments, including robotics and drones. However, the main gap is how these cameras are used in a navigation system. The current research uses the attention-based UNET neural network to estimate the depth of a scene using an event camera. The attention-based UNET structure leads to accurate depth detection of the scene. This depth information is then used, together with a hybrid deep neural network consisting of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), for robot navigation. Simulation results on the DENSE dataset yield an RMSE of 8.15, which is an acceptable result compared to other similar methods. This method not only provides good accuracy but also operates at high speed, making it suitable for real-time applications and visual navigation methods based on VT&R.

Interferometers are essential tools for quality control of optical surfaces. While interferometric techniques like phase-shifting interferometry offer high accuracy, they involve complex setups, require stringent calibration, and are sensitive to phase shift errors, noise, and surface inhomogeneities. In this research, we introduce an alternative algorithm that integrates Moving Average and Fast Fourier Transform (MAFFT) techniques with Polynomial Fitting. The proposed method achieves results comparable to a Zygo interferometer under standard conditions, with an error margin under 2%. It also maintains measurement stability in noisy environments and in the presence of significant local inhomogeneities, operating in real-time to enable wavefront measurements at 30 Hz. We have validated the algorithm through simulations assessing noise-induced errors and through experimental comparisons with a Zygo interferometer.

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making.