Abstract: A new Common-Mode (CM) choke with asymmetrical winding capable of attenuating Differential-Mode (DM) and Common-Mode (CM) noise for AC to DC Power Converters is proposed in this paper. For a conventional CM choke with symmetrical winding, the CM and DM noise are also attenuated by the magnetizing inductance (LM) and very small leakage inductance (Lk) due to the higher coupling factor, respectively, of the CM choke. However, because the number of turns is limited according to the shape and size of the CM choke, it is very difficult to find the proper leakage inductance (Lk) to eliminate the DM noise thoroughly. For this reason, a bulky CM choke is required for a larger leakage inductance (Lk). Typically, another DM choke is used, for the sole purpose of DM noise attenuation. Therefore, the cost and size of the EMI filters for use with a conventional CM choke can increase. To solve these problems, the proposed CM choke with asymmetrical winding is introduced in this paper. Different from the conventional CM choke with symmetrical winding, the magnetizing inductance (LM) of the proposed CM choke with asymmetrical winding can suppress not only CM but also DM noise. Therefore, the proposed CM choke with asymmetrical winding can effectively attenuate CM and DM types of noise even with fewer and smaller turns. This method can be applied to all types of power converters which use two- or three-stage systems. To confirm the validity and superiority of the proposed CM choke, comparisons of the total noise as well as the DM and CM noise characteristics between conventional and proposed CM chokes are presented in this paper. Lastly, a practical approach to the design of an EMI filter and the design procedures are also presented and addressed.

Abstract: Digital twin (DT) will revolute network autonomy. Current studies have promoted DT-native 6G network by deeply integrate DT into mobile network architectures to improve the timeless of physical-digital synchronization and network optimizations. However, DT has mainly acted as just a tool for network autonomy, leading a gap towards the ultimate goal of network self-evolution. This paper analyzes the future direction of the DT-native network. Specifically, the proposed architecture introduces a key concept called "future shots", which gives accurate network predictions under different time scales of self-evolution strategies for various network elements. To realize the future shots, we propose a long-term hierarchical convolutional graph attention model for cost-effectively network predictions, a conditional hierarchical graph neural network for strategy generations, and methods for efficient small-large scale interactions. The architecture is expected to facilitate high-level network autonomy for 6G networks.

Abstract: This paper presents an analysis of the current state of processing lead-zinc ores from the Koskudyk deposit (Kazakhstan). At present, polymetallic ores are being extracted from the Ridder-Sokolnoye, Zyryanovskoye, Maleevskoye, and Achisai deposits. However, the reserves of rich and easily beneficiable ores are being depleted, and the supply of raw materials from the developed deposits does not exceed 25 years. As a result, more complex and difficult-to-enrich oxidized and mixed ores are being involved in production, and the extraction of non-ferrous metals from these ores presents a significant technological challenge. The most effective method for enriching oxidized polymetallic ores is flotation with preliminary sulfidization. Laboratory studies were conducted on a sample of oxidized lead-zinc ore from the Koskudyk deposit, which contains 79.69% oxidized lead compounds and 84.72% oxidized zinc compounds. This study examines the effect of sulfidization using sodium sulfide and determines the oxidative-reductive potential (ORP) levels for various reagent dosages. The results showed that increasing the sodium sulfide dosage to 700 g/t and achieving an ORP of −200 mV led to a 50.07% lead extraction, while the quality and quantity of zinc extraction remained stable. Determining the optimal ORP level significantly improves the efficiency of the enrichment process, enhancing both qualitative and quantitative indicators and simplifying the regulation of the technological process.

Abstract: Aviation operations are increasingly impacted by Clear Air Turbulence (CAT) encounters, a growing concern in both media and academic circles. Research into CAT focuses on generation, prediction, detection and monitoring of the occurring events (thanks to weather related methodologies and instruments) along with technologies and operational aspects to mitigate their effects, from the perspective of both the flight segment (aircraft and pilot and related onboard systems) and the ground segment (ATM and ATC and related tools). Climate changes have led to more frequent and severe CAT events, highlighting the need for sustainable aviation solutions, aiming to achieve improved theoretical knowledge and technological and operational management advancements. This paper addresses the CAT topic under two main perspectives: the one of scientific understanding of the phenomena and the one of the technological management of such occurrence in aviation operations. With reference to the first addressed domain, the paper provides a comprehensive review of the currently used and perspective proposed methodologies and tools for understanding, detecting and predicting CAT phenomena. With reference to the second addressed domain, then, the paper aims analyzing the state-of-the-art and trends in the technological and operational management and mitigation of the CAT occurrences at tactical level (i.e., while in flight) by the aviation, covering the technologies and procedures implemented onboard and in the ground segment. Overall, therefore, the paper assesses the state-of-the-art and identifies the most promising innovations that promote safer, more sustainable future aviation operations, by bridging weather and climate science with aviation engineering, in presence of CAT events.

Abstract: The peak particle velocity (PPV) of blasting vibration is a primary indicator to evaluate the explosion effect in an open pit mine. In the blasting scenario of an open pit mine, existing methods for predicting the peak velocity of blasting vibration are difficult to achieve ideal outcomes, leading to inappropriate designs of blasting parameters and detonation network. As a result, the peak velocity of blasting vibration cannot be accurately forecasted. Aiming at improving the prediction accuracy of blasting vibration peak velocity, based on the monitoring data of blasting vibrations at Yuanbaoshan open-pit mine with different coal storage conditions, distance from the explosive center, maximum charge per delay, elevation difference and longitudinal wave speed are chosen as the input parameters. The key parameters C and g of the support vector machine (SVM) algorithm are optimized through the global optimization of the particle swarm optimization (PSO) algorithm, so that the prediction performance of SVM is at the optimal state, and then the PSO-SVM model for predicting the peak velocity of blasting vibration is constructed. By analyzing the relationship among the input parameters and the blasting vibration peak velocity, it is concluded that the longitudinal wave speed, which represents the site conditions, is also a significant factor influencing the propagation of blasting vibration velocity. Comparing to the test results of the RS-dept and the improved Sadovsky formula, it is found that the maximum error of blasting vibration prediction by the PSO-SVM model is reduced by 48.19% and 53.6% compared with that by the RS-dept model and the improved Sadovsky formula, respectively. Also, the average error of blasting vibration prediction by the combined PSO-SVM algorithm is 3.37%, which is 29.94% and 17.86% lower than that by the improved Sadovsky formula and the RS-dept model, respectively. The predicted values of the improved PSO-SVM model match best with the measured values and the predicted results are most reliable. The proposed research method can provide theoretical guidance and practical reference for the blast design of an open pit mine.

Abstract: The energy consumption of rotary wing unmanned aerial vehicles has become an important factor restricting their long-term application. This article focuses on decoupling the motion channel and reducing control energy consumption, and proposes a decoupling controller based on dynamic inversion for the complete dynamics of quadcopter unmanned aerial vehicles. Firstly, design a direct closed-loop feedback control for the z-channel to exhibit second-order linear dynamic characteristics with adjustable parameters. Then, the specific functions of pitch angle and yaw angle are combined as virtual control variables for the comprehensive decoupling design of x-direction and y-direction, so that the x-channel and y-channel also exhibit independent parameter adjustable second-order linear dynamic characteristics. Next, by solving the actual control variables, a fast convergence system is dynamically formed by the deviation between the virtual control variables and their actual values, ensuring that the specific function combination of pitch angle and yaw angle quickly converges to the expected value. Finally, the effectiveness and low energy consumption control characteristics of the decoupling control scheme were demonstrated through simulation comparison with other control methods (such as classical PID) in terms of energy consumption.

Abstract: This paper presents a comprehensive investigation into dead reckoning algorithms. The study begins with an in-depth analysis of the fundamental mathematical formulation for navigation position calculations, then introduces the concept of local invariance to refine traditional methods by examining state parameters with inherent stability. Building on this foundation, we propose an efficient iterative optimization algorithm designed specifically for real-time trajectory prediction under sparse sensor data conditions(Only three samples are required). This approach effectively mitigates the impact of data scarcity, enabling robust and accurate trajectory predictions in challenging environments. The proposed method is anticipated to play a pivotal role in following control systems, thereby significantly improving their operational reliability and performance.

Abstract: The article reviews the current state of Marine Spatial Planning through key references such as Intergovernmental Ocean Commission (IOC-UNESCO), the High-Level Panel for a Sustainable Ocean Economy, and the Global Partnership for Oceans, clarifying the concepts widely accepted by the scientific community through these organizations. It also examines the current state of Terrestrial Spatial Planning (TSP), its basic and general concepts, regardless of specific national legislations, as territorial planning is managed and analyzed from a state, regional, and municipal perspective in all countries. From this starting point, the article analyzes a proposal for the integration of both planning systems, their benefits, and challenges. It proposes a methodology for integration from both technical and administrative perspectives to ensure governance, sustainability, and resilience against the effects of climate change, which will significantly impact the coastal strip. Proposing a joint zoning of both land and ocean, which will form the basis for the planning and governance methodology, involves integrating terrestrial and marine spatial planning into a unified framework. This approach ensures a holistic view of the environment, considering the interconnectedness of ecosystems, socio-economic activities, and the governance structures that manage them. Proper management of the land-sea interaction will guarantee both sustainability and governance, as well as a prosperous economy that respects ecosystems.

Abstract: Ports serve as logistical hubs through which approximately 80% of the world's goods are transported annually. New regulations from the International Maritime Organization (IMO) and the European Union require both ships and ports to implement measures designed to reduce the environmental, global, and local impact of port activities and mitigate climate change. These measures involve investing in renewable energy generation systems to transition from fossil fuel-based energy to renewable electricity. Consequently, constructing new power generation plants is necessary to meet ports' energy demands. However, ports are primarily logistics-focused platforms with limited space for other activities. Therefore, the use of port service areas, inner docks, and exterior/adjacent water zones for installing marine renewable energy generation plants is under consideration. This study employs high-resolution meteorological and oceanographic modelling, including wave agitation models validated with real-world data, to assess the feasibility of integrating marine renewable energy within port service areas.

Abstract: There are many parts in industrial gluing systems, and the temperature characteristics of these parts vary greatly. In response to this situation, a segmented adaptive PID temperature control method is proposed in this paper. This method combines a segmented temperature control algorithm with a variable control coefficient temperature control algorithm based on output power, which not only ensures that the system has a small overshoot but also ensures that the system has faster convergence speed and better robustness. At the same time, it greatly improves the scope of application and control accuracy of the temperature controller. The experimental results show that, under the same experimental conditions, compared with the traditional PID method, the overshoot of the proposed method is reduced by 2 to 4 degrees Celsius, the convergence speed is increased by about 30%, and the temperature fluctuation amplitude after being disturbed is reduced by about 0.2 degrees Celsius.

Abstract: This paper focuses on the ship control system and studies the problem of unknown control direction. Considering that the traditional Nussbaum gain method has to consider the complex situation where the gain converges to both positive and negative infinity when proving the system stability, a unilateral Nussbaum function is defined in this paper. By constructing this function, the design and proof process of the adaptive Nussbaum gain method are simplified. Taking the ship course - keeping control system as the research object, a course angle tracking controller is designed by combining neural network, robust adaptive and sliding mode control techniques. A dual - input RBF single - output neural network is used to approximate the uncertain part of the system, and the robust adaptive control is adopted to deal with the unknown disturbance. The simulation results show that the proposed method can effectively cope with the problems of unknown control direction and its jump, keeping the system stable, which has great theoretical and engineering application value.

Abstract: Twin robotic X-ray computed tomography (CT) systems enable flexible CT scans by using robots to move the X-ray source and the X-ray detector around an object’s region of interest. This allows scans of large objects, image quality optimization and scan time reduction. Despite these advantages, robotic CT systems still face challenges that limit their widespread adoption. This paper discusses the state of twin robotic CT and its current main challenges. These challenges include optimization of scanning trajectories, precise geometric calibration and advanced 3D reconstruction techniques.

Abstract: This work presents an alternative method for defining feasible joint-space boundaries and their corresponding geometric workspace in a planar robotic system. Instead of relying on traditional numerical approaches that require extensive sampling and collision detection, the proposed method constructs a continuous boundary by identifying key intersection points of boundary functions. The feasibility region is further refined through centroid-based scaling, addressing singularity issues and ensuring a structured trajectory. Comparative analyses demonstrate that the final robot pose and reachability depend on the selected traversal path, highlighting the nonlinear nature of the workspace. Additionally, an evaluation of traditional numerical methods reveals their limitations in generating continuous boundary trajectories. The proposed approach provides a structured method for defining feasible workspaces, improving trajectory planning in robotic systems.

Abstract: In this study, a Brönsted-Lewis bifunctional acidic catalyst PW/UiO/CNTs-OH was synthesized via hydrothermal method. The esterification reaction parameters between oleic acid and methanol catalyzed by PW/UiO/CNTs-OH were optimized using central composite design-response surface methodology (CCD-RSM). The process achieved 92.9% biodiesel yield under optimized reaction conditions, retaining 82.3% biodiesel yield after four catalytic cycles. The enhanced catalytic performance of PW/UiO/CNTs-OH can be attributed as follows: the [Zr6O4(OH)4]12+ anchored on the surface of MWCNTs provides nucleation sites of UiO-66, enabling dual functions of HPW stabilization and Lewis acid site generation via quadrupole inversion. In addition, HPW introduction during synthesis of UiO-66 reduces solution pH, inducing the protonation of the p-Phthalic acid (PTA) to disrupt the coordination with the [Zr6O4(OH)4] cluster, thereby creating an unsaturated Zr4+ site with electron pair-accepting capability as additional Lewis acid sites. EIS analysis revealed that PW/UiO/CNTs-OH exhibited superior electron migration efficiency compared to UiO-66 and PW/UiO. Furthermore, NH3-TPD and Py-IR analysis showed that PW/UiO/CNTs-OH possessed high densities of Lewis acidic sites of 83.69 μmol/g and Brönsted acidic sites of 9.98 μmol/g.

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.