ABSTRACT Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low‐frequency data, privacy concerns, and battery‐constrained gateways. We propose a novel hybrid semi‐supervised federated domain adaptation architecture for fault detection in DH. We use a one‐class variational autoencoder (VAE) to leverage heterogeneous sensor streams from 434 distributed substations. First, we perform cross‐network unsupervised pre‐training on multi‐sourced data from two independent real‐world DH networks, fusing their return temperature dynamics into a robust shared manifold. Second, we leverage maintenance metadata to selectively allow verified‐normal clients for per‐round fine‐tuning of the model. Third, we drastically reduce uplink costs by compressing each client's weight delta using 10% top‐ k sparsification and demonstrate that our pipeline enables robust few‐shot finetuning with 20% of the normal operational data while retaining high detection performance. By strategically training, our method achieves F 1 and G‐mean scores of up to 97% and an AUC ≥ 99% on real‐world DH data. To our knowledge, this is the first work to study cross‐domain data fusion in the DH field for fault detection, aiming to enhance and enable effective, scalable, and energy‐efficient monitoring of substations.

Automatic fault detection and diagnosis (FDD) are critical for maintaining reliable and efficient industrial systems. However, conventional methods rely heavily on manual inspections or threshold-based techniques, which often fail to capture the dynamic patterns in time series (TS) sensor data. As a result, faults persist for extended periods, leading to suboptimal system operations, increased energy waste, and significant economic losses. This work proposes a cross-modal framework that facilitates the efficient deployment of state-of-the-art pretrained vision models for enhanced FDD, with two novel TS-to-image transformations: first, an adapter deep encoder that learns optimal, task-specific representations from raw sensor data while generating outputs that are input-compliant with pretrained models. Second, an enhanced line plot that creates geometric shapes of two related signals. Comparative experiments against fixed methods, including spectrograms, Gramian angular fields, Markov transition fields, recurrence plots, and five deep learning baseline models, showed substantial performance gains across diverse domains. InceptionTime achieved the highest average baseline performance with an F$_{1}$of 88.6%, while the adapter and shapes achieved 94.4% and 92.4%, respectively. The findings highlight the potential of the cross-modal framework for FDD to facilitate early intervention and efficient system maintenance in industrial settings.

Abstract This paper proposes SHIODEG, a hybrid metaheuristic that integrates the success-history intelligent optimizer (SHIO) with differential evolution (DE) and a Gaussian transformation (GT) to tackle two persistent challenges in optimization for engineering design: (i) the absence of a universally best optimizer across problem classes (as implied by the No-Free-Lunch perspective) and (ii) the limited ability of purely gradient-based methods to produce substantial improvements in complex, constrained, and often non-smooth real-world problems, motivating hybrid strategies that balance exploration and exploitation. SHIODEG follows a staged search process in which DE generates diverse trial solutions, GT injects normally distributed perturbations to reduce premature convergence and diversity collapse, and SHIO refines promising regions using success-history guidance from the best three leaders. SHIODEG is evaluated on the IEEE CEC2022 benchmark suite (12 functions) using 30 independent runs, a population size of 100, and a budget of 1000D function evaluations. The results show that SHIODEG consistently delivers top-tier performance across the benchmark suite, showing strong competitiveness, low variability, and statistically significant improvements over a wide range of alternative optimizers. It also demonstrates robust effectiveness on multiple constrained engineering design problems, achieving high-quality solutions across diverse real-world constraints.

This study introduces FLAME (Fault Localization using Augmented Model Enhancement), a novel fault diagnosis framework for District Heating (DH) substations. Automated Fault Detection and Diagnosis (FDD) has become imperative as many DH substations perform sub-optimal due to faults. The main challenges complicating accurate fault diagnosis are increasing operational complexities and a scarcity of labelled data. FLAME integrates a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with an attention mechanism and introduces the Fault Augmentation Signature Technique (FAST). FAST overcomes the limitations of traditional stochastic data augmentation methods by leveraging pattern mixing of the time series. The FLAME framework uses transfer learning, initially trained on augmented data using FAST and fine-tuned using original substation data. Experimental results reveal that FLAME outperforms conventional methods, obtaining F1 scores of approximately 0.95 and 0.92 on lab-simulated and real-world datasets, respectively. Additionally, the research found the importance of the temperature difference measurement ( Δ T ) and median-based sampling strategies for optimal fault pattern identification. These findings establish FLAME as a new benchmark in DH system diagnostics, offering a robust framework to enhance fault diagnosis accuracy and operational efficiency of DH substations.

Federated Learning (FL) enables decentralized learning while preserving data privacy. However, ensuring security and optimizing resource utilization in FL remains challenging, particularly in untrusted environments. To address this, we propose SecureFedPROM, a novel zero-trust FL framework that integrates Attribute-Based Access Control (ABAC) for secure client authorization and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) for dynamic, multi-criteria client selection. Unlike traditional FL client selection methods that prioritize security or efficiency, SecureFedPROM optimizes trustworthiness, computational efficiency, and performance, ensuring robust participation in each training round. We evaluate SecureFedPROM across multiple real-world datasets, demonstrating its superiority over state-of-the-art client selection protocols. Our results show that SecureFedPROM achieves a 7.19% improvement in model accuracy, accelerates convergence, and reduces the number of training rounds. Additionally, it minimizes wall-clock time and computational overhead, making it highly scalable for edge AI environments. These findings highlight the importance of integrating zero-trust security principles with multi-criteria decision-making to enhance security and efficiency in FL.

Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain. In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection. For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator’s profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process. A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost.

Federated machine learning promises to overcome the input privacy challenge in machine learning. By iteratively updating a model on private clients and aggregating these local model updates into a global federated model, private data is incorporated in the federated model without needing to share and expose that data. Several open software projects for federated learning have appeared. Most of them focuses on supporting flexible experimentation with different model aggregation schemes and with different privacy-enhancing technologies. However, there is a lack of open frameworks that focuses on critical distributed computing aspects of the problem such as scalability and resilience. It is a big step to take for a data scientist to go from an experimental sandbox to testing their federated schemes at scale in real-world geographically distributed settings. To bridge this gap we have designed and developed a production-grade hierarchical federated learning framework, FEDn. The framework is specifically designed to make it easy to go from local development in pseudo-distributed mode to horizontally scalable distributed deployments. FEDn both aims to be production grade for industrial applications and a flexible research tool to explore real-world performance of novel federated algorithms and the framework has been used in number of industrial and academic R&D projects. In this paper we present the architecture and implementation of FEDn. We demonstrate the framework's scalability and efficiency in evaluations based on two case-studies representative for a cross-silo and a cross-device use-case respectively.

Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, such as Kappa, ROC, and MCC, have been used in the assessment of the classifier behavior.

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCs.