ABSTRACT This paper brings a new understanding to the relative importance of different uncertainty sources across forecast horizons up to 7 days ahead. It presents a method for probabilistic wind power forecasting that quantifies uncertainty from weather forecasts and weather‐to‐power conversion separately. The study reveals that weather‐to‐power uncertainty is more significant for short‐term forecasts, while weather forecast uncertainty dominates mid‐term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore. By addressing both uncertainty sources, the proposed forecast method achieves state‐of‐the‐art results for lead times of 6 to 162 h, eliminating the need for separate models for short‐ and mid‐term forecasting. Importantly, it also significantly improves short‐term forecasts during high weather uncertainty periods, where methods based on deterministic weather forecasts dramatically underestimate total uncertainty. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over 5 years.
The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.
Imen Tounsi, Fadi Karkafi, Mohammed El Badaoui
et al.
Mechanical vibration monitoring often requires high sampling rates and generates large data volumes, posing challenges for storage, transmission, and power efficiency. Compressive Sensing (CS) offers a promising approach to overcome these constraints by exploiting signal sparsity to enable sub-Nyquist acquisition and efficient reconstruction. This study presents a comprehensive comparative analysis of the key components of the CS framework: sparse basis, measurement matrix, and reconstruction algorithm for machinery vibration signals. In addition, a hardware-efficient measurement matrix, the Wang matrix, originally developed for image compression, is introduced and evaluated for the first time in this context. Experimental assessment using the HUMS2023 and the CETIM gearbox datasets demonstrates that this matrix achieves superior reconstruction quality, with higher SNR, compared to conventional Gaussian and Bernoulli matrices, especially at high compression ratios.
Bangladesh is strongly seeking renewable energy sources to meet its increasing electricity demand as part of the worldwide shift to sustainable energy. Even in areas where the use of solar energy would be quite practical, many educational institutions nationwide still rely on the traditional power grid despite having significant solar potential. This paper fills a crucial research gap by providing a thorough technoeconomic analysis of a grid-tied solar photovoltaic (PV) system designed for an educational institution in Narail, Bangladesh, an application that is little documented in national literature. Essential design criteria, including the best system orientation, comprehensive loss analysis, precise component specifications, and energy yield projections, were all included in the system model, which was created using local meteorological data. According to the operational study, the suggested system can generate about 133 MWh of energy per year, of which 100 MWh is exported to the national grid and 33 MWh is set aside for self-consumption. A remarkably competitive levelized cost of energy (LCOE) of $0.0577/kWh is attained by the design. Additionally, the system is expected to lower carbon dioxide emissions by around 77.5 tonnes annually or more than 1900 tonnes throughout its 25-year operating lifespan, assuming a conservative 1% annual degradation rate. These findings highlight the economic feasibility and environmental sustainability of grid-connected solar PV systems for educational institutions in Bangladesh, which have the potential to improve energy security and significantly contribute to national carbon reduction efforts.
Md. Shahazan Parves, Md. Abu Bakkar Siddique, Md. Tarekuzzaman
et al.
Considering the environmental concerns of lead Hazardousness and durability concerns in lead-based perovskite solar cells (PSCs), lead-free alternatives like X2NaIrCl6 (X = Rb, Cs) have gained significant attention. This investigation carries out an analysis of the structural and optoelectronic behaviour of X2NaIrCl6 (X = Rb, Cs) using DFT to assess its potential for absorber material for solar cells (SCs). Structural stability of X2NaIrCl6 (X = Rb, Cs) double perovskites was analysed using tolerance factors (τ1, μ, τ2), with dynamical stability ensured through phonon dispersion. Negative and binding energy (Eb) and formation energy (Ef) further validated their stability. Direct band gaps, determined utilizing the TB-mBJ (GGA-PBE) approach, the values were determined to be 2.02 eV (0.97 eV) for Rb2NaIrCl6 and 1.93 eV (0.92 eV) for Cs2NaIrCl6, placing them in the recommended range (0.8- 2.2 eV) for photoelectric conversion. X2NaIrCl6 (X = Rb, Cs) double perovskites exhibit remarkable potential for photovoltaic applications, driven by their high absorption coefficients (∼104) and favourable optical properties, including low energy loss and minimal reflectivity (<15 %). These attributes highlight their promise for high efficiency and low-cost materials for advanced optoelectronic and solar energy devices. SCAPS-1D software employed to identify the most efficient solar cell designs by incorporating various HTLs and ETLs. Among 40 tested configurations, the ITO/ZnO/Cs2NaIrCl6/Cu2O structure attains the maximum PCE of ∼20.39 %, while ITO/ZnO/Rb2NaIrCl6/Cu2O achieves ∼19.16 %. Additionally, the study examines the effects of varying ETL/absorber thicknesses and series and shunt resistances, and temperature on photovoltaic performance. A detailed investigation was conducted on the principal photovoltaic indicators, such as current-voltage characteristics, capacitance, quantum efficiency, Mott Schottky parameters, and the processes governing photocarrier generation and recombination. These findings highlight X2NaIrCl6 (X = Rb, Cs) as a suitable material for high-performance optoelectronic and photovoltaic real-world applications.
Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae
et al.
Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.
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.
The integration of robotics into agricultural machinery has the potential to revolutionize the agricultural sector by improving efficiency, reducing labor costs, and enhancing precision. Robotics technology offers the promise of automating repetitive and labor-intensive tasks such as planting, harvesting, and crop monitoring. This paper explores the current advancements in agricultural robotics, with a particular focus on their role in enhancing machinery automation. Key benefits, challenges, and future trends in the field are discussed, alongside case studies and real-world applications. The paper also explores the impact of robotics on sustainable farming practices and resource optimization.
AbstractMechanical penetrations, as important pressure pipelines penetrating the reactor compartment, withstand high temperatures and pressures. The current complete design and verification process for mechanical penetrations. This article focuses on the problem of stress concentration and easy damage of the penetration components in the reactor compartment under high temperature and high pressure environment. Combining with the existing regulations of nuclear power plants and ships, finite element analysis method is used to analyze the stress of the penetration components under specific high temperature and high pressure and ship ultimate load coupling. At the same time, based on the simulation analysis results, the structural dimensions of the penetration components are optimized, and a mechanical penetration verification process is designed. The coupled thermal stress results of the penetration indicate that the stress of the penetration is too large at the tail of the sleeve, with the values of primary film stress Pm and primary bending stress Pb being 228.2 and 275.91 MPa, respectively. From this, it can be seemed that there is obvious stress concentration at the junction of the support ring and sleeve, as well as at the transition point of the insulation layer, which is the weakest area of the penetration.
This paper presents a minimally invasive surgical robot system for endoluminal gastrointestinal endoscopy through natural orifices. In minimally invasive gastrointestinal endoscopic surgery (MIGES), surgical instruments need to pass through narrow endoscopic channels to perform highly flexible tasks, imposing strict constraints on the size of the surgical robot while requiring it to possess a certain gripping force and flexibility. Therefore, we propose a novel minimally invasive robot system with advantages such as compact size and high precision. The system consists of an endoscope, two compact flexible continuum mechanical arms with diameters of 3.4 mm and 2.4 mm, respectively, and their driving systems, totaling nine degrees of freedom. The robot’s driving system employs bidirectional ball-screw-driven motion of two ropes simultaneously, converting the choice of opening and closing of the instrument’s end into linear motion, facilitating easier and more precise control of displacement when in position closed-loop control. By means of coordinated operation of the terminal surgical tools, tasks such as grasping and peeling can be accomplished. This paper provides a detailed analysis and introduction of the system. Experimental results validate the robot’s ability to grasp objects of 3 N and test the system’s accuracy and payload by completing basic operations, such as grasping and peeling, thereby preliminarily verifying the flexibility and coordination of the robot’s operation in a master–slave configuration.
Daylí Covas Varela, Gilberto Dionisio Hernández Pérez, Juan José Cabello Eras
et al.
Este artículo presenta un modelo de ecuaciones estructurales que mide la calidad de vida urbana (CVU), tomando como referencia la ciudad de Cienfuegos (Cuba). El objetivo es determinar las variables que influyen en la CVU mediante un procedimiento que permite el diseño de dos modelos: uno desde la dimensión objetiva y otro desde la dimensión subjetiva. Los resultados muestran un modelo de relaciones entre indicadores de gestión, basado en la información de los decisores locales, y otro modelo que relaciona indicadores de percepción obtenidos de los ciudadanos. La comparación de ambos modelos generaliza que variables como salud, vivienda, ingresos personales y carga contaminante son factores determinantes en la CVU.
Mechanical engineering and machinery, Industrial engineering. Management engineering
The large-scale integration of doubly-fed induction generator (DFIG) based wind power plants poses stability challenges for power system operation. This study investigates the transient stability and dynamic performance of a modified 3-machine, 9-bus Western System Coordinating Council (WSCC) system. The investigation was conducted by connecting the DFIG wind farm to the sixth bus via a low-impedance transmission line and installing power system stabilizers (PSSs) on all automatic voltage regulators (AVRs). A three-phase fault simulation was carried out to test the system, with and without power system stabilizers and a static synchronous compensator (STATCOM) device. Time-domain simulations demonstrate improved transient response with PSS-STATCOM control. A 50% reduction in settling time and 70% decrease in power angle undershoots at the slack bus are achieved following disturbances, even at minimum wind penetration levels. Load flow analysis shows the coordinated controllers maintain voltages within 0.5% of nominal at 60% wind penetration, while voltages at load buses can deviate up to 15% without control. Eigenvalue analysis indicates the PSS-STATCOM boosts damping ratios of critical oscillatory modes from nearly 0% to over 30% under high wind injection. Together, the present findings provide significant evidence that PSS and STATCOM cooperation enhances dynamic voltage regulation, angle stability, and damping across operating ranges, thereby maintaining secure operation in systems with high renewable integration.
By considering a discrete tape where each cell corresponds to an integer, thus to a possible sum, a pseudo-polynomial solution can be given to subset sum problem, which is an NP-complete problem and a cornerstone application for this study, using shifts and element-wise summations. This machinery can be extended symbolically to continuous case by thinking each possible sum as a single frequency impulse on the frequency band. Multiplication with a cosine in this case corresponds to the shifting operation as modulation in communication systems. Preliminary experimentation suggests that signal generation thus solution space calculation can be done in polynomial time. However, reading the value at a specific frequency (sum value) is problematic, namely cannot be simulated in polynomial time currently. Dedicated hardware implementation might be a solution, where both circuit-based and wireless versions might be tried out. A polynomial representation is also given that is claimed to be analogous to a tape of a Turing machine. Both rational and real number versions of the subset sum problem are also discussed, where the rational version of the problem is mapped to 0-1 range with specific patterns of True values. Although this machinery may not be totally equivalent to a non-deterministic Turing machine, it may be helpful for non-deterministic universal Turing machine actualization. It may pave way to both theoretical and practical considerations that can help computing machinery, information processing, and pattern recognition domains in various ways.
Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyze the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.
Ahmed Fadlelmoula, Diana Pinho, Vitor Hugo Carvalho
et al.
Since microorganisms are evolving rapidly, there is a growing need for a new, fast, and precise technique to analyse blood samples and distinguish healthy from pathological samples. Fourier Transform Infrared (FTIR) spectroscopy can provide information related to the biochemical composition and how it changes when a pathological state arises. FTIR spectroscopy has undergone rapid development over the last decades with a promise of easier, faster, and more impartial diagnoses within the biomedical field. However, thus far only a limited number of studies have addressed the use of FTIR spectroscopy in this field. This paper describes the main concepts related to FTIR and presents the latest research focusing on FTIR spectroscopy technology and its integration in lab-on-a-chip devices and their applications in the biological field. This review presents the potential use of FTIR to distinguish between healthy and pathological samples, with examples of early cancer detection, human immunodeficiency virus (HIV) detection, and routine blood analysis, among others. Finally, the study also reflects on the features of FTIR technology that can be applied in a lab-on-a-chip format and further developed for small healthcare devices that can be used for point-of-care monitoring purposes. To the best of the authors’ knowledge, no other published study has reviewed these topics. Therefore, this analysis and its results will fill this research gap.
The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.
Chenyin Feng, Christopher L. Frewin, Md Rubayat-E Tanjil
et al.
Carbon containing materials, such as graphene, carbon-nanotubes (CNT), and graphene oxide, have gained prominence as possible electrodes in implantable neural interfaces due to their excellent conductive properties. While carbon is a promising electrochemical interface, many fabrication processes are difficult to perform, leading to issues with large scale device production and overall repeatability. Here we demonstrate that carbon electrodes and traces constructed from pyrolyzed-photoresist-film (PPF) when combined with amorphous silicon carbide (<i>a-</i>SiC) insulation could be fabricated with repeatable processes which use tools easily available in most semiconductor facilities. Directly forming PPF on <i>a</i>-SiC simplified the fabrication process which eliminates noble metal evaporation/sputtering and lift-off processes on small features. PPF electrodes in oxygenated phosphate buffered solution at pH 7.4 demonstrated excellent electrochemical charge storage capacity (CSC) of 14.16 C/cm<sup>2</sup>, an impedance of 24.8 ± 0.4 kΩ, and phase angle of −35.9 ± 0.6° at 1 kHz with a 1.9 kµm<sup>2</sup> recording site area.
In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.
We rigorously prove that a non-elliptical inhomogeneity continues to permit an internal uniform stress field despite the presence of a nearby non-circular Eshelby inclusion undergoing uniform anti-plane eigenstrains when the surrounding matrix is subjected to uniform remote anti-plane stresses. Here, we adopt a specific representation of the non-circular Eshelby inclusion as a Booth’s lemniscate inclusion. Our analysis indicates that the internal uniform stress field inside the non-elliptical inhomogeneity is independent of the existence of the Booth’s lemniscate inclusion whereas the non-elliptical shape of the inhomogeneity is attributed entirely to its presence. Representative numerical examples are presented to demonstrate the feasibility of the proposed method of general solution.
Engineering (General). Civil engineering (General), Mechanical engineering and machinery
Vahid Monfared, Hamid Reza Bakhsheshi-Rad, Seeram Ramakrishna
et al.
In this research article, a mini-review study is performed on the additive manufacturing (AM) of the polymeric matrix composites (PMCs) and nanocomposites. In this regard, some methods for manufacturing and important and applied results are briefly introduced and presented. AM of polymeric matrix composites and nanocomposites has attracted great attention and is emerging as it can make extensively customized parts with appreciably modified and improved mechanical properties compared to the unreinforced polymer materials. However, some matters must be addressed containing reduced bonding of reinforcement and matrix, the slip between reinforcement and matrix, lower creep strength, void configurations, high-speed crack propagation, obstruction because of filler inclusion, enhanced curing time, simulation and modeling, and the cost of manufacturing. In this review, some selected and significant results regarding AM or three-dimensional (3D) printing of polymeric matrix composites and nanocomposites are summarized and discuss. In addition, this article discusses the difficulties in preparing composite feedstock filaments and printing issues with nanocomposites and short and continuous fiber composites. It is discussed how to print various thermoplastic composites ranging from amorphous to crystalline polymers. In addition, the analytical and numerical models used for simulating AM, including the Fused deposition modeling (FDM) printing process and estimating the mechanical properties of printed parts, are explained in detail. Particle, fiber, and nanomaterial-reinforced polymer composites are highlighted for their performance. Finally, key limitations are identified in order to stimulate further 3D printing research in the future.