Abstract: In this study, effect of inlet turbulence intensity on the friction coefficient for transitional boundary layer has been investigated computationally. For this purpose, two equation turbulence models of Std. k-ε, RNG k-ε, Std. k-ω ve SST k-ω have been compared with Gamma-Theta (GT) transitional model, and it has been found that Gamma Theta model is the most consistent model with the experimental values of the ERCOFTAC T3A test case. Then, the effect of inlet turbulence intensity on the friction coefficient has been computed by using this Gamma-Theta model. Transition from laminar to turbulence is shortened with increasing turbulence intensity by changing it from 0.01 to 0.1. The most suitable inlet turbulence intensity value with the experimental results of ERCOFTAC T3A test case is found as Tu=0.033.

Abstract: Lamb waves offer a series of desirable features for SHM-applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline-online ray tracing approach is combined with FRF and transmissibility functions. Each ray is thereby represented either by a parametrized FRF, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves.

Abstract: This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they’re needed most. It also shows how precise forecasting isn’t just about saving costs it’s about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike.

Abstract: This review explores the transformative role of digital technologies in advancing sustainable manufacturing practices and promoting environmental stewardship. As industries face increasing pressure to minimize their ecological footprint, the integration of digital tools such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics emerges as a pivotal strategy. This paper synthesizes current literature on various digital technologies and their applications in manufacturing processes, highlighting their potential to optimize resource utilization, reduce waste, and enhance energy efficiency. Furthermore, the review examines case studies that illustrate successful implementations of these technologies, revealing best practices and challenges faced by organizations. By identifying key trends and future directions, this study aims to provide valuable insights for researchers and practitioners seeking to leverage digital innovations for sustainable development in the manufacturing sector.

Abstract: The increasing global interest in clean energy sources and the decreasing costs of solar panels position solar power as an advantageous option for wider adoption. However, the rapid uptake of intermittent renewable energy presents challenges, potentially causing power instability due to f luctuations between power generation and demand. Therefore, the accuracy of solar Photovoltaic (PV) power prediction becomes crucial to ensure stable system operations and optimize the integration of renewable sources. The current methods for forecasting solar PV power play a vital role in upholding system reliability and maximizing renewable energy integration. This scholarly paper offers a comprehensive and comparative evaluation of different Machine Learning (ML) techniques employed for PV power prediction, specifically focusing on short-term forecasts. The study provides insights into the factors influencing solar PV power prediction and presents an overview of existing prediction methods in the literature, with an emphasis on models based on Machine Learning approaches like Mutliple linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression and ensemble laerning methods like Random forest Regression ,Gradient boosting Regressor,ADA boost Regressor. To facilitate a more insightful comparison and a deeper understanding of advancements in this domain, the research conducts simulations to assess the performance of various ML methods used in predicting solar PV power. The article concludes a best machine learning model with a thorough discussion of the study's findings and their implications

Abstract: This study introduces a novel state estimation framework that incorporates Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE), shifting from traditional physics-based models to rapidly developed data-driven techniques. A DNN model with Long Short-Term Memory (LSTM) nodes is trained on synthetic data generated by a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), which undergoes thermal derating as part of the torque control strategy in a battery electric vehicle. The MHE is constructed by integrating the trained DNN with a simplified driving dynamics model in a discrete-time formulation, incorporating the LSTM hidden and cell states in the state vector to retain system dynamics. The resulting optimal control problem (OCP) is formulated as a nonlinear program (NLP) and implemented using the \texttt{acados} framework. Model-in-the-loop (MiL) simulations demonstrate accurate temperature estimation, even under noisy sensor conditions or failures. Achieving threefold real-time capability on embedded hardware confirms the feasibility of the approach for practical deployment. The primary focus of this study is to assess the feasibility of the MHE framework using a DNN-based plant model instead of focusing on quantitative comparisons of vehicle performance. Overall, this research highlights the potential of DNN-based MHE for real-time, safety-critical applications by combining the strengths of model-based and data-driven methods.

Abstract: Plastic injection molding is a fundamental manufacturing process utilized in diverse areas, producing approximately 30% of global plastic products. This review examines the optimization methodologies in injection molding, emphasizing the integration of advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review highlights the significance of cooling, which constitutes 50-80% of the cycle time, and explores innovative strategies like conformal cooling channels (CCC) to improve uniformity and reduce defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. Integration of surrogate models like Kriging, response surface methodology, and artificial neural networks has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization.

Abstract: Pears are highly valued by consumers worldwide due to their unique taste and flavor profile, leading to their extensive cultivation and global consumption. Pesticides are vital in the prevention and management of pests and diseases in pear production; however, the intensive application of these agrochemicals has resulted in significant contamination issues, which adversely affect the quality and safety of pear products. As a result, the monitoring of pesticide residues in pears is essential to ensure the safety of the fruit and to safeguard the public health. This review paper attempts to provide readers with an overview of the occurrence and dissipation of pesticide residues in pears, as well as the analytical techniques employed for their detection. Furthermore, potential directions for future research are suggested, with the goal of contributing valuable insights to ongoing studies on pesticide residues in pears.

Abstract: The escalating global imperative for sustainable waste management has prompted innovative research into valorizing organic waste streams in construction materials. This study investigates coffee bagasse (BC) as a potential fine aggregate replacement in concrete, addressing both environmental challenges and material performance optimization. Through systematic analysis, the effect of coffee bagasse incorporation as fine aggregate in the compressive resistance of concrete was studied. Coffee bagasse was processed in raw and pyrolyzed states and incorporated into concrete mixtures at 5%, 10%, and 20% volume replacement levels. Differential scanning calorimetry (DSC) and comprehensive compressive strength assessments were carried out to analyze the performance and behavior of the material. The findings revealed critical insights: raw coffee bagasse lixiviation significantly impaired cement hydration, negatively affecting concrete mechanical properties, whereas pyrolysis at 349.5°C transformed BC into coffee biocarbon, yielding a remarkable 22.39% increase in concrete compressive strength. The thermal treatment emerged as a pivotal intervention for effective waste material integration, demonstrating a promising pathway for converting agricultural by-products into high-value construction materials. By bridging waste management strategies with material science innovation, this research contributes to circular economy principles, offering a sustainable approach to reducing waste and enhancing infrastructure material performance through advanced thermal processing techniques.

Abstract: This paper proposes a methodology for the co-expansion planning of transmission lines and energy storage devices, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program (MINLP) and solved using a decomposition-based approach that combines a genetic algorithm with mixed-integer linear programming (MILP). Uncertainties are modeled through representative day scenarios obtained via clustering. The proposed methodology is validated on a modified IEEE 24-bus system. Results show that co-planning reduces wind curtailment, fuel costs, and total investment costs compared to transmission-only expansion.

Abstract: This work proposes a hybrid deep learning-based framework to visual feedback control an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResNet-18 backbone is augmented to fuse interest-point maps with RGB channels, enabling the network to capture scene geometry better. A manipulator robot with an eye-in-hand configuration provides a reference image, while subsequent poses and images are generated synthetically, removing the need for extensive real data collection. The experimental results reveal that this enriched input representation significantly improves convergence accuracy and velocity smoothness compared to a baseline that processes real images alone. Specifically, including feature point maps allows the network to discriminate crucial elements in the scene, resulting in more precise velocity commands and stable end-effector trajectories. Thus, integrating additional, synthetically generated map data into convolutional architectures can enhance the robustness and performance of the visual servoing system, particularly when real-world data gathering is challenging.

Abstract: The heat transfer coefficient or the HTC is an industry-standard indicator of building energy performance. It has been predicated on an assumption that it is of a constant value, and several different methods have been developed to measure and calculate the HTC as a constant. Whilst there are limited variations of results obtained from these different methods, none of these methods consider a possibility that the HTC could be dynamically variable. Our experimental work shows that the HTC is not a constant. Experimental evidence base from our environmental chambers, which contain detached houses, and in which ambient air temperature can be controlled between -20 °C and +40 °C, with additional relative humidity control and with weather rigs that can introduce solar radiation, rain and snow, shows that the HTC is dynamically variable. Analysis of data from fully instrumented and monitored houses in a combination with calibrated simulation models and data processing scripts based on genetic algorithm optimization provide experimental evidence of dynamic variability of the HTC. This research increases the understanding of building physics properties and has a potential to change the way the heat transfer coefficient is used in building performance analysis.

Abstract: Fluid-structure interaction (FSI) is a critical phenomenon in various engineering applications, including aerospace, civil, and mechanical engineering, where the interaction between fluid flow and structural dynamics significantly influences performance and safety. This study presents a comprehensive experimental validation of computational models used to simulate FSI scenarios, focusing on the accuracy and reliability of numerical predictions against experimental data. A series of controlled experiments were conducted using a state-of-the-art wind tunnel facility, where flexible structures were subjected to varying fluid flow conditions. The experimental setup included high-speed cameras and advanced measurement techniques such as particle image velocimetry (PIV) to capture the fluid flow characteristics and structural responses in real-time. The results demonstrated a strong correlation between the computational predictions and experimental observations, validating the computational models' ability to accurately capture the complex interactions between fluid and structure. Key parameters such as displacement, stress distribution, and flow patterns were analyzed, revealing insights into the underlying mechanics of FSI. The findings underscore the importance of experimental validation in enhancing the credibility of computational models, ultimately contributing to more reliable design practices in engineering applications. This research not only provides a robust framework for future studies in FSI but also emphasizes the need for continuous refinement of computational techniques to address the challenges posed by complex fluid-structure interactions.

Abstract: This work proposes a High-Brightness LED (HB-LED) driver for Visible Light Communication (VLC) based on two converters, a high frequency Buck DC/DC converter and a low frequency Boost DC/DC converter, connected in series with respect to the LED load and connected in parallel at the input. This topology is called a Series/Parallel Boost/Buck DC/DC Converter. A VLC system needs to do two different tasks: biasing the HB-LED and generating the communication signal. These typically have different power requirements, the bias power is 3/4, while the communication power is 1/4 of the total power. The requirements of each are also different: the communication signal requires a high frequency, fast output response, while the biasing control requires a converter with a slow output voltage response. The proposed architecture takes advantage of the differences between the two tasks and achieves high efficiency and high communication performance by means of splitting the power between the two DC/DC converters. A high frequency Buck DC/DC converter generates the communication signal, while the low frequency Boost DC/DC converter is responsible for biasing the LEDs. This technique allows most of the DC biasing power to be processed by the low frequency converter (achieving high efficiency), keeping the high frequency converter delivering the communication power (achieving high communication performance). To provide experimental results, the proposed VLC HB-LED driver was built and validated by reproducing a 64-QAM with a bit rate up to 1.5 Mbps, reaching 91.5% overall efficiency.

Abstract: This review presents the band selective frequency technology of Electromagnetic Interference (EMI) noise spectrum spread in the DC-DC switching converter for communication devices. This technology generates notch characteristic spectrum bands with a low noise level in the received frequency band spectrum. It detects the received frequency and generates a notch band there using a switching pulse control technology. First, we introduce the conventional spread spectrum technology. By modulating the clock frequency, EMI noise is dispersed to avoid concentrating at specific frequency bands. There are both analog modulation techniques and digital modulation methods. Next, we explain the main technology of this review, the notch band generation technology. This technique involves modulating the phase or pulse width of clock to produce notch band characteristics in the EMI noise spectrum. Then we present its simulation results, theoretical analysis, and implementation results. Finally, we demonstrate a technique that tunes the notch band frequency to the received signal one automatically.

Abstract: Peptide-drug conjugates (PDCs) have emerged as a next-generation therapeutic platform, combining the target specificity of peptides with the pharmacological potency of small-molecule drugs. As an evolution beyond antibody-drug conjugates (ADCs), PDCs offer distinct advantages, including enhanced cellular permeability, improved drug selectivity, and versatile design flexibility. This review provides a comprehensive analysis of the fundamental components of PDCs, including homing peptide selection, linker engineering, and payload optimization, alongside strategies to address their inherent challenges, such as stability, bioactivity, and clinical translation barriers. Therapeutic applications of PDCs span oncology, infectious diseases, metabolic disorders, and emerging areas like COVID-19, with several conjugates advancing in clinical trials and achieving regulatory milestones. Innovations, including bicyclic peptides, supramolecular architectures, and novel linker technologies, are explored as promising avenues to enhance PDC design. Additionally, this review examines the clinical trajectory of PDCs, emphasizing their therapeutic potential and highlighting ongoing trials that exemplify their efficacy. By addressing limitations and leveraging emerging advancements, PDCs hold immense promise as targeted therapeutics capable of addressing complex disease states and driving progress in precision medicine

Abstract: Including ultra-high performance fiber reinforced concrete (UHPFRC) layer in tension with normal strength concrete (NSC) significantly enhances the structural properties of concrete infrastructures. However, the durability of the interfacial bond between the two materials under aggressive chemical exposure remains uncertain. This study investigates the impact of severe magnesium sulfate exposure in conjunction with drying-wetting cycles, a common environmental challenge for infrastructures, on the mechanical properties of composite concrete systems (CCS) consisting of a UHPFRC tension layer and an NSC compression layer. In addition, the effect of varying steel fiber concentrations (0%, 1%, 1.5%, and 2%) in the UHPFRC layer was examined. The results show that the reduction in compressive strength was approximately 40% regardless of the fiber content. However, the use of fibers highly enhanced the mechanical interaction between the NSC and UHPFRC layers, resulting in superior mechanical resistance against the effect of the magnesium sulfate exposure. Adding 1% steel fibers slightly increased toughness, further increasing the fiber content to 2% resulted in a negligible effect on the energy absorption capacity under the severe magnesium sulfate environment.

Abstract: The rapid advancement in computing power, coupled with the ability to collect vast amounts of data, has created new opportunities for industrial applications. While time-domain industrial signals typically do not allow for direct stability assessment or the detection of abnormal situations, alternative representations can reveal hidden features. This paper introduces a simple algorithm that is not directly linked to contemporary machine learning methods, designed to analyze industrial data from a standard automation system. The algorithm generates clear graphical representations to aid in controlling the production process. Specifically, we propose using time-shifted maps derived from data series collected by an acceleration sensor mounted on a robot base. Furthermore, we numerically simulated three distinct anomalous scenarios and presented their corresponding graphical representations.

Abstract: The archive explores the intersection of Artificial Intelligence (AI) and quantum computing in architectural design, specifically in the pixelation of renderings. AI, through convolutional neural networks, improves the accuracy and speed of architectural visualization, enabling realistic details and advanced environmental simulations. Quantum computing, meanwhile, offers unprecedented processing power, facilitating complex calculations and optimizing sustainable designs. Although promising, this integration faces challenges such as public skepticism, accessibility, a shortage of skilled labor, and high costs. The future points to closer collaboration between AI and quantum computing, which could democratize advanced tools and foster innovations in diverse sectors, including architecture.

Abstract: The optimization of rocket nozzle design remains critical for advancing the efficiency and performance of space propulsion systems. This investigation presents an in-depth analysis of supersonic nozzle configurations and their aerodynamic behaviors, with a specific focus on methodologies to design a contour capable of enhancing thrust by reducing losses under. Further concepts of compressible aerodynamics and CFD are also reported, compassing the state of the art in chapters 1 and 2 necessary to base the studies reported. Chapter 3 investigates traditional nozzle designs, specifically conical and bell nozzles, employing the Method of Characteristics (MOC) to compute their flow characteristics and optimize geometrical parameters for minimum length and maximum thrust efficiency. Various design contours, including parabolic and truncated ideal configurations, are evaluated for their applicability in diverse applications such as supersonic wind tunnels and propulsion systems.In Chapter 4, the research extends to advanced nozzle geometries, including aerospike, dual-bell, expansion-deflection, and multi-grid nozzles, each offering unique advantages in adapting to varying pressure environments. These designs are analyzed for their ability to mitigate under- and over-expansion losses, improved thrust coefficient, and enhance specific impulse. The studies emphasize the critical balance between design complexity, manufacturing constraints, and aerodynamic performance, establishing guidelines for integrating innovative design features into modern propulsion systems.Computational simulations and theoretical formulations underscore the effectiveness of improved MOC techniques and boundary-layer corrections in accurately predicting nozzle performance. The findings have broad implications for the development of propulsion systems in high-demand applications, including single-stage-to-orbit (SSTO) vehicles and hypersonic flight systems.