In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem.
Musharafa Saleem, A. Al-Zubaidi, Neyara Radwan
et al.
The present research analyzes the properties of a Casson ternary nanofluid over a stretching sheet with Thomson and Troian slip conditions, taking into consideration the influences of electromagnetohydrodynamic (EMHD). The ternary nanofluid comprises three different nanoparticles, which include titanium dioxide (TiO2), copper (Cu), and silver (Ag), all being suspended in oil, which is the base fluid. They are involved because of their good thermal conductivity and chemical stability, and AgCuTiO2 /Oil nanofluid is a composite of copper, titanium oxide, and oil. Hence, carrying out the said procedure, the ternary nanofluid becomes AgCuTiO2/Oil. The sheet is, however, thought to be stretching vertically while the flow is determined by the effect of the gravity force through the free convention. Moreover, the phenomena of EMHD, porous medium, thermal slip, thermal radiation, Joule heating, and heat source/sink are included to make the energy equation more real-life. This leads to a set of partial differential equations (PDEs) based mathematical models transformed into ordinary differential equations (ODEs)-appropriate similarity transformation. The Runge–Kutta–Fehlberg (RKF-45) method solves the given ordinary differential system. According to the research’s findings, the temperature of the ternary Casson nanofluid rises when the suspension of silver, copper, and titanium dioxide nanoparticles increases, and the velocity of flow for merely silver and copper decreases when the density decreases. This causes the flow rate to be constricted through the velocity slip condition, at the same time as the nanofluid’s temperature increases.
Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao
et al.
[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.
Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.
Technology and innovations have fueled the evolution of Industry 4.0, the fourth industrial revolution. Industry 4.0 encourages growth and development through its efficiency capacity, as documented in the literature. The growth of the construction industry is a subset of the universal set of the gross domestic product value; thus, Industry 4.0 has a spillover effect on the engineering and construction industry. In this study, we aimed to map the state of Industry 4.0 in the construction industry, to identify its key areas, and evaluate and interpret the available evidence. We focused our literature search on Web of Science and Scopus between January 2015 and May 2019. The search was dependent on the following keywords: “Industry 4.0” OR “Industrial revolution 4.0” AND TOPIC: “construction” OR “building”. From the 82 papers found, 20 full-length papers were included in this review. Results from the targeted papers were split into three clusters: technology, security, and management. With building information modelling (BIM) as the core in the cyber-physical system, the cyber-planning-physical system is able to accommodate BIM functionalities to improve construction lifecycle. This collaboration and autonomous synchronization system are able to automate the design and construction processes, and improve the ability of handling substantial amounts of heterogeneity-laden data. Industry 4.0 is expected to augment both the quality and productivity of construction and attract domestic and foreign investors.
There has been a significant increase in studies related to Industry 4.0 alongside the development of new technologies, devices and software, becoming one of the most relevant topics for years within the so-called Fourth Industrial Revolution (4IR). The Architecture, Engineering and Construction sector (AEC) sector is one step behind other engineering fields in productivity, and digitalisation can help reduce this gap. Building Information Modelling (BIM) implementation in various project phases with other technologies such as the Internet of Things, Big Data, Blockchain or Geographic Information System (GIS) are the main drivers of Smart Construction. This paper provides an updated state-of-art of the BIM applications through different civil engineering projects and towards the use of new Information and Communication Technologies (ICTs). Hence, the integration of BIM in the Facility Management through ICTs allows decision making based on data analysis and the optimization of available resources.
Raenald Syaputra, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto
Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.
Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco
et al.
Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.
In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs) associated with it are novel and dynamic. This makes it challenging for faculty who are looking to incorporate AI within their courses to create a mental map of how to tackle this challenge. In this paper, we advance a role-based conception of competencies to assist disciplinary faculty with identifying and implementing AI competencies within engineering curricula. We draw on prior work related to AI literacy and competencies and on emerging research on the use of AI in engineering. To illustrate the use of the framework, we provide two exemplary cases. We discuss the challenges in implementing the framework and emphasize the need for an embedded approach where AI concerns are integrated across multiple courses throughout the degree program, especially for teaching responsible and ethical AI development and use.
Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber
et al.
Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.
Purpose Smart cities provide fully integrated and networked connectivity between virtual/digital assets and physical building/infrastructure assets to form digital economies. However, industrial espionage, cyber-crime and deplorable politically driven cyber-interventions threaten to disrupt and/or physically damage the critical infrastructure that supports national wealth generation and preserves the health, safety and welfare of the populous. The purpose of this paper is to present a comprehensive review of cyber-threats confronting critical infrastructure asset management reliant upon a common data environment to augment building information modelling (BIM) implementation. Design/methodology/approach An interpretivist, methodological approach to reviewing pertinent literature (that contained elements of positivism) was adopted. The ensuing mixed methods analysis: reports upon case studies of cyber-physical attacks; reveals distinct categories of hackers; identifies and reports upon the various motivations for the perpetrators/actors; and explains the varied reconnaissance techniques adopted. Findings The paper concludes with direction for future research work and a recommendation to utilize innovative block chain technology as a potential risk mitigation measure for digital built environment vulnerabilities. Originality/value While cyber security and digitization of the built environment have been widely covered within the extant literature in isolation, scant research has hitherto conducted an holistic review of the perceived threats, deterrence applications and future developments in a digitized Architecture, Engineering, Construction and Operations (AECO) sector. This review presents concise and lucid reference guidance that will intellectually challenge, and better inform, both practitioners and researchers in the AECO field of enquiry.
ABSTRACT Can you ask students from three different bachelor programmes to help solve planning and routeing problems for hospitals? In the presented case an interdisciplinary approach was shown to be successful after some redesign. Students from Applied Mathematics, Civil Engineering and Industrial & Engineering Management jointly designed solutions for ‘traffic’ to and through the hospital using stochastic modelling. Importantly this project was scaffolded through coursework, supervision and problem-design. The particular scaffolding strategy employed by the teaching team offers other teacher teams ideas for making interdisciplinary project-based learning a more effective learning opportunity. At the same time we need to ensure that students feel at home in their own programme and will be empowered to work with other specialists.
Dor Freidin, MD, Roei Singolda, MD, Shai Tejman-Yarden, MD, MSc, MBA
et al.
Introduction:. This study was designed to compare VR stereoscopical three-dimensional (3D) imaging with two-dimensional computed tomography angiography (CTA) images for evaluating the abdominal vascular anatomy before autologous breast reconstruction.
Methods:. This prospective case series feasibility study was conducted in two tertiary medical centers. Participants were women slated to undergo free transverse rectus abdominis muscle, unilateral or bilateral deep inferior epigastric perforator flap immediate breast reconstruction. Based on a routine CTA, a 3D VR model was generated. Before each procedure, the surgeons examined the CTA and then the VR model. Any new information provided by the VR imaging was submitted to a radiologist for confirmation before surgery. Following each procedure, the surgeons completed a questionnaire comparing the two methods.
Results:. Thirty women between 34 and 68 years of age were included in the study; except for one, all breast reconstructions were successful. The surgeons ranked VR higher than CTA in terms of better anatomical understanding and operative anatomical findings. In 72.4% of cases, VR models were rated having maximum similarity to reality, with no significant difference between the type of perforator anatomical course or complexity. In more than 70% of the cases, VR was considered to have contributed to determining the surgical approach. In four cases, VR imaging modified the surgical strategy, without any complications.
Conclusions:. VR imaging was well-accepted by the surgeons who commented on its importance and ease compared with the standard CTA presentation. Further studies are needed to determine whether VR should become an integral part of preoperative deep inferior epigastric perforator surgery planning.
Software engineering capabilities are increasingly important to the success of economic and political blocs. This paper analyzes quantity and quality of software engineering research output originating from the US, Europe, and China over time. The results indicate that the quantity of research is increasing across the board with Europe leading the field. Depending of the scope of the analysis, either the US or China come in second. Regarding research quality, Europe appears to be lagging the other blocs, with China having caught up to and even having overtaken the US over time.
Fluorescent molecules are versatile nanoscale emitters that enable detailed observations of biophysical processes with nanoscale resolution. Because they are well-approximated as electric dipoles, imaging systems can be designed to visualize their 3D positions and 3D orientations, so-called dipole-spread function (DSF) engineering, for 6D super-resolution single-molecule orientation-localization microscopy (SMOLM). We review fundamental image-formation theory for fluorescent di-poles, as well as how phase and polarization modulation can be used to change the image of a dipole emitter produced by a microscope, called its DSF. We describe several methods for designing these modulations for optimum performance, as well as compare recently developed techniques, including the double-helix, tetrapod, crescent, and DeepSTORM3D learned point-spread functions (PSFs), in addition to the tri-spot, vortex, pixOL, raPol, CHIDO, and MVR DSFs. We also cover common imaging system designs and techniques for implementing engineered DSFs. Finally, we discuss recent biological applications of 6D SMOLM and future challenges for pushing the capabilities and utility of the technology.
In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.
In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.