During the last decade a new trend of approaches has emerged, which considers models not just documentation artefacts, but also central artefacts in the software engineering field, allowing the creation or automatic execution of software systems starting from those models. These proposals have been classified generically as Model-Driven Engineering (MDE) and share common concepts and terms that need to be abstracted, discussed and understood. This paper presents a survey on MDE based on a unified conceptual model that clearly identifies and relates these essential concepts, namely the concepts of system, model, metamodel, modeling language, transformations, software platform, and software product. In addition, this paper discusses the terminologies relating MDE, MDD, MDA and others. This survey is based on earlier work, however, contrary to those, it intends to give a simple, broader and integrated view of the essential concepts and respective terminology commonly involved in the MDE, answering to key questions such as: What is a model? What is the relation between a model and a metamodel? What are the key facets of a modeling language? How can I use models in the context of a software development process? What are the relations between models and source code artefacts and software platforms? and What are the relations between MDE, MDD, MDA and other MD approaches?
This study evaluates the performance and energy trade-offs of three popular data processing libraries—Pandas, PySpark, and Polars—applied to GreenNav, a CO<sub>2</sub> emission prediction pipeline for urban traffic. GreenNav is an eco-friendly navigation app designed to predict CO<sub>2</sub> emissions and determine low-carbon routes using a hybrid CNN-LSTM model integrated into a complete pipeline for the ingestion and processing of large, heterogeneous geospatial and road data. Our study quantifies the end-to-end execution time, cumulative CPU load, and maximum RAM consumption for each library when applied to the GreenNav pipeline; it then converts these metrics into energy consumption and CO<sub>2</sub> equivalents. Experiments conducted on datasets ranging from 100 MB to 8 GB demonstrate that Polars in lazy mode offers substantial gains, reducing the processing time by a factor of more than twenty, memory consumption by about two-thirds, and energy consumption by about 60%, while maintaining the predictive accuracy of the model (R<sup>2</sup> ≈ 0.91). These results clearly show that the careful selection of data processing libraries can reconcile high computing performance and environmental sustainability in large-scale machine learning applications.
Jotiar H. Mohammed Kochar, Nizar M. Ali. ALSulaifani
The current research aims to explore the impact of participative leadership represented by (decision-making participation, delegation of authority, human relations, and justice and equality) in human resource sustainability, defined by (human resource well-being, human resource development, human resource retention, and work-life Equilibrium). At the same time, testing the mediating role of the organizational Equilibrium variable represented by (contributions and inducements) in the influential relationship between the two variables. The study community comprised all employees working in private hospitals in the governorate, totaling approximately 650 individuals across 15 hospitals. Data were collected from a random sample of 306 medical staff members working in private hospitals in Duhok Governorate, Kurdistan Region, Iraq, using a questionnaire. The research employs a descriptive-analytical approach, and the results, analyzed using SPSS V.25, indicate a significant direct effect of both participative leadership and organizational Equilibrium on human resource sustainability. Furthermore, the findings confirm the potential of organizational Equilibrium as a partial mediator in the relationship between participative leadership and human resource sustainability. The study recommends that private hospital management enhance human resource sustainability by promoting participative leadership practices and leveraging the dimensions of organizational Equilibrium to achieve this goal..
Management information systems, Economic history and conditions
Mateusz Zajac, Tomislav Rožić, Justyna Swieboda-Kutera
et al.
<i>Background</i>: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. <i>Methods</i>: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. <i>Results</i>: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. <i>Conclusions</i>: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape.
Transportation and communication, Management. Industrial management
Henrique Rodrigues Lelis, Daniel Jardim Pardini, Eloy Pereira Lemos Junior
Compliance programs have legal, administrative and technological attributes that help organizations find solutions related to strategy, management and organizational governance. In turn, blockchain has been described as a digital system with potential for use in numerous activities, as any activity whose function is to protect and transfer digital assets can be impacted by the system. However, there are criticisms and reservations regarding its adoption by organizations, especially regarding issues related to the regulatory framework, corporate governance and technological management. From this perspective, it becomes relevant to relate the attributes of compliance programs to the development of blockchain in the organizational dimension, which is the proposal of this research. The gap explored with this research is to describe the implications that the attributes of compliance programs can bring to the development of blockchain technology, in the context of organizations. To explore the topic, a panel of experts and a Delphi round were created to structure a survey that sought evidence that demonstrates the existence or not of contributions from compliance programs to the development of the blockchain. This article presents the results relating to the organizational dimension of the doctoral thesis “Attributes of Compliance Programs for the blockchain, in the context of the Dimensions of the State, Organization and Individual”, defended by the first author, in the Doctoral program in Information and Management Systems of Knowledge at Universidade Fumec, with UNIVERSIDADE FUMEC and FAPEMIG as funding institutions.
Social sciences (General), Bibliography. Library science. Information resources
Tareq Salameh, Mena Maurice Farag, Abdul-Kadir Hamid
et al.
This study addresses the fundamental challenge of accurately forecasting power generation from photovoltaic (PV) systems, which is crucial for effective grid integration and energy management. The intermittency and variability of solar power due to environmental factors pose significant difficulties in achieving reliable predictions. An adaptive neuro-fuzzy inference system (ANFIS) model is proposed for forecasting the performance of a 2.88 kW on-grid PV system in Sharjah, UAE. The model leverages extensive real-time data collected during the peak energy generation season to predict critical variables such as the maximum power point (MPP), voltage, and current. The ANFIS model achieves high prediction accuracy, with a Coefficient of Determination (R2) of 0.9967 for power generation, 0.9076 for voltage generation, and 0.9913 for current generation. These results highlight the model’s robustness in capturing the nonlinear dependencies between environmental factors and PV output. The study compares the ANFIS model with other established machine learning models, including Linear Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The ANFIS model outperforms these models in terms of prediction accuracy, demonstrating its superior generalization capabilities. The findings underscore the potential of the ANFIS model for robust forecasting and effective PV performance management, providing a reliable tool for early fault detection and system assessment. Future work will focus on integrating fault detection capabilities and extending model validation across different seasons to ensure a comprehensive investigation of the system dynamics under fluctuating weather conditions.
Summary: Pseudouridine is the most abundant epitranscriptomic modification, but its cellular functions remain poorly understood. Here, we identify pseudouridine synthase 1 (PUS1) as a key driver of tumor immune evasion. Specifically, we find that PUS1 is aberrantly overexpressed in tumors and correlates with tumor malignant progression. Notably, genetic ablation of PUS1 effectively suppresses tumor progression, increases T cell infiltration, and boosts T cell function in both MYC/Trp53−/− mouse liver cancer and chemically induced liver cancer models. Mechanistically, PUS1 loss induces the expression of retrotransposon sequences, resulting in elevated levels of double-stranded RNA (dsRNA) and subsequent activation of innate antiviral immune signaling. Importantly, PUS1 depletion sensitizes tumors to anti-PD-1 therapy in a MYC/Trp53−/− mouse liver cancer model. Similarly, 5-fluorouracil inhibits pseudouridine synthesis and significantly enhances the efficacy of PD-1 inhibition. Overall, our findings demonstrate PUS1 as a critical regulator of tumor immune evasion, and targeting pseudouridine synthesis can enhance immunotherapy efficacy by activating dsRNA-sensing pathways.
Undoubtedly, wind turbines are currently one of the most significant contributors to clean energy. Therefore, it is crucial to enhance the capability of wind turbines, which in turn leads to an increase in their dimensions. Nevertheless, only advanced control systems can guarantee the optimal and secure operation of these huge machines. On the other hand, the precise control of these modern wind turbines is only achievable through the use of highly specialised actuators. Despite their importance, actuators have historically been overlooked and seen as secondary components in control systems. However, in modern machines, actuators are required to manipulate multiple tonnes or manage thousands of volts and amperes within short times. Consequently, greater emphasis must be placed on their handling and operation. This study aims to review actuators for modern large wind energy converters from a control engineering perspective, using a tutorial approach.
Materials of engineering and construction. Mechanics of materials, Production of electric energy or power. Powerplants. Central stations
Dimitrios G. Katehakis, Dimitrios Filippidis, Konstantinos Karamanis
et al.
Digital innovation can significantly enhance public health services, environmental sustainability, and social welfare. To this end, the European Digital Innovation Hub (EDIH) initiative was funded by the European Commission and national governments aiming to facilitate the digital transformation on various domains (including health) via the setup of relevant ecosystems consisting of academic institutions, research centres, start-ups, small and medium-sized enterprises, larger companies, public organizations, technology transfer offices, innovation clusters, and financial institutions. The ongoing goal of the EDIHs initiative is to bridge the gap between high-tech research taking place in universities and research centres and its deployment in real-world conditions by fostering innovation ecosystems. In this context, the smartHEALTH EDIH started its operation in Greece in 2023, offering technical consultation services to companies and public sector organizations to accelerate digitalization in precision medicine and innovative e-health services by utilizing key technologies such as artificial intelligence, high-performance computing, cybersecurity, and others. During its first 20 months of operation, over 50 prospective recipients have applied for consulting services, mainly seeking “test-before-invest” services. This paper aims to provide insights regarding the smartHEALTH initiative, preliminary outcomes and lessons learned during this first period of operation. To this end, this paper outlines smartHEALTH’s approach to attracting recipients and providing expert guidance on utilizing state-of-the-art technologies for innovative services, product development, and process creation to accelerate digital transformation.
The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.
Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization.
Information technology, Management information systems
Hamid Reza Vanaei, Sofiane Khelladi, Ivan Dobrev
et al.
Cross-Flow Fans (CFFs) have been widely applied in the automotive and domestic air conditioning industries in recent decades. They are high-pressure coefficient turbomachines compacted diametrically, and thus, the complex interactions of these fans require thorough evaluation. Their innovation has opened up new directions in turbomachinery, and both academic research and industry have seen numerous efforts to develop these types of fans. Despite extensive work, optimizing and improving their performance remains a challenge. Enhancing their efficiency necessitates improvements in structural characteristics, aerodynamic features, and acoustic properties. In this review, we aim to demonstrate the essential aspects of CFFs by introducing their fundamentals and primary characteristics. Furthermore, we delve into a discussion on the acoustic performance of these fans. We also summarize the flow characteristics and different flow-field patterns in CFFs and their impact on aeroacoustic behavior. The main objective of this review paper is to provide an overview of the research in this field, summarizing the critical factors that play a significant role in studying CFFs’ performance.