Abstract: Zonal day-ahead (DA) electricity markets followed by redispatch (RD) markets for congestion management are vulnerable to the strategic bidding behavior known as Inc-Dec gaming. Although previous literature has demonstrated the effects of Inc-Dec gaming, it has neglected the participation of flexible demand in RD markets. This paper addresses this gap by developing a novel multi-period bi-level optimization model of a strategic producer participating in DA and RD markets, accounting for the inherent time-coupling operating characteristics of demand flexibility (DF) in the RD market. This model includes an upper level problem determining the optimal bidding decisions of the strategic producer in the DA and RD markets, and two lower level problems representing the clearing process of the two markets. The impacts of DF in mitigating Inc-Dec gaming are demonstrated through a small-scale case study involving a 2-node system and a 2-period market horizon, as well as a large-scale case study involving a modified IEEE RTS 24-node system and a daily (24-hour) market horizon.

Abstract: The present paper investigates generation of the alternating almost zero and strong homogeneous magnetic field for rotary magnetic refrigeration. In order to achieve alternating magnetic field with eight regions, a soft magnetic rod is inserted in the bore. Four high flux density regions (FDR) for magnetization and four low FDR demagnetization of magnetocaloric materials are obtained by the proposed design. The designing procedure step of the four poles and its implementation using 3D finite element simulation is presented. To achieve a predefined requirement, some parts of magnet material are replaced by a high permeability soft magnetic material. The proposed design for the rotary refrigeration magnetic allowing to achieve a good field distribution in the air gap, maximization ratio of high field and minimization ratio of low field volumes to the permanent magnet volume, reduction of the amount of magnet material used, and augmentation of flux density between a low and high field region.

Abstract: The next generation of wireless communication, 6G, promises a leap beyond the advances of 5G, aiming not only to increase speed but also to redefine how people, machines, and environments interact. This paper examines the evolution from 5G Advanced to 6G through a detailed review of 3GPP Releases 15-20, outlining the progression from enhanced mobile broadband to intelligent services supporting holographic communication, remote tactile interaction, and immersive XR applications. Three foundational service pillars are identified in this evolution: immersive communication, everything connected, and high-precision positioning. These advances enable transformative use cases such as virtual surgery, cooperative drone swarms, and AI-driven agriculture, demanding innovations in spectrum utilization (including sub-THz bands), AI-native network architectures, and energy-efficient device ecosystems. Future networks are expected to deliver peak data rates up to 1~Tbps, localization accuracy below 10~cm, and device densities reaching 10M/km2, while sustaining end-to-end latency under 1~ms. Across Releases 15-20, 3GPP has progressively standardized capabilities for XR, positioning, scheduling, and sustainability, while initiatives such as RedCap, Ambient IoT, and NTN extend connectivity toward global, low-power, and cost-effective coverage. Supported by programs like Hexa-X and the Next G Alliance, 6G is positioned as a fundamental redesign of wireless communication centered on intelligence, adaptability, inclusivity, and sustainability.

Abstract: In this study, a traffic flow analysis was conducted using a multi-agent simulation to evaluate the effect of reducing CO2 emissions through the penetration of connected autonomous vehicles (CAVs) equipped with vehicle-to-vehicle (V2V) communication functionality. By exchanging local information among CAVs, alleviating traffic congestion without the need for cooperative vehicle control, and reducing CO2 emissions by up to 20% is possible. In addition, we analysed the impact of CAV penetration rate and communication range on CO2 emissions, demonstrating that the reduction effect in CO2 emissions tends to appear more prominently once the penetration of CAVs reaches a certain threshold. In particular, when the communication range is narrow, a significantly high penetration rate is required before the benefits of CO2 reduction become evident. Furthermore, a wider communication range is not necessarily more desirable. These findings suggest that limiting the communication range may enable more efficient use of road traffic information. Although each CAV acts solely based on its own self-interest, route selection based on local information leads to the emergence of swarm intelligence, resulting in improved efficiency at the collective level.

Abstract: The work introduces a leading-edge system of scrutiny of the identities of users based on their voices, combining FSK modulation with the versions of MORSA encryption with improvements to the security system. Enhancing the provision of secure message delivery across chaotic networks of communications by modulating FSK. The ability to distinguish the separate vocal characteristics from the bandwidths has the benefit of increasing resilience to any disturbances. At the same time, the MORSA technique improves the security of encryption with a composite number RSA configuration, which increases the performance and strengthens the decryption attacks. Combining the two systems will result in an integrated security approach, in which the privacy of data accuracy protection is offered simultaneously in the identity verification processes. As empirical evaluations reveal, the proposed solution possesses excellent validation accuracy and negligible processing time, which is the reason why it can be considered an appealing choice when it comes to immediate data processing in common critical communications systems.

Abstract: Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Trajectory Error (ATE) RMSE by 11.2%–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments.

Abstract: KY converters are interesting DC/DC converter concepts. In this paper the basic posi-tive step-up converter with limited voltage transformation ratio, two types of the neg-ative output voltage step-up converter, the negative output step-up down converter, and two types of the positive output step-up-down converter based on the KY concept are treated. The disadvantage of all these converters is a recharging current peak at all switching periods leading to stress of the components and an additional elec-tro-magnetic compatibility problem. Two methods to improve the converters are pre-sented. First an included limiting resistor reduces the spike of the recharging pulse, and second a small inductor is placed in the recharging loop making a resonant charg-ing possible, that improves the efficiency. The function of the basic converters is ex-plained and the two improvements are included. The considerations are proved by simulations and measurements of a small converter laboratory design are shown.

Abstract: Neuroscience adopts a multidimensional approach to decode thoughts and actions originating inside the brain, aka the Brain Computer Interface (BCI). However, achieving high accuracy in these decodings remains a challenge and an open research topic in BCI research. This study aims to enhance the accuracy of signal classification for identifying human emotional states. We utilized the publicly available EEG-Audio-Video (EAV) dataset that comprises EEG recordings from 42 subjects across five emotional categories. Our key contribution is to exploit the 2-dimensional contrast enhancement applied to the spectrogram for feature extraction, followed by classification using the EEGNet model. As a result, 12.5% improvement in classification accuracy over the baseline was achieved. This contribution demonstrates a potential advancement in BCI-based EEG signal processing in neuroscientific research.

Abstract:

The accurate forecasting of electricity sales volumes constitutes a critical task for power system planning and operational management. Nevertheless, subject to meteorological perturbations, holiday effects, exogenous economic conditions, and endogenous grid operational metrics, sales data frequently exhibit pronounced volatility, marked nonlinearities, and intricate interdependencies. This inherent complexity compounds modeling challenges and constrains forecasting efficacy when conventional methodologies are applied to such datasets. To address these challenges, this paper proposes a novel decomposition-integration forecasting framework. The methodology first applies Variational Mode Decomposition (VMD) combined with the Zebra Optimization Algorithm (ZOA) to adaptively decompose the original data into multiple Intrinsic Mode Functions (IMFs). These IMF components, each capturing specific frequency characteristics, demonstrate enhanced stationarity and clearer structural patterns compared to the raw sequence, thus providing more representative inputs for subsequent modeling. Subsequently, an improved RevInformer model is employed to separately model and forecast each IMF component, with the final prediction obtained by aggregating all component forecasts. Empirical validation on an annual electricity sales dataset from a commercial building demonstrates the proposed method’s effectiveness and superiority, achieving Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Percentage Error(MSPE）values of 0.044783, 0.211621, and 0.074951 respectively – significantly outperforming benchmark approaches.

Abstract: Unmanned underwater vehicles (UUVS) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, temporal phase overlap governs thrust augmentation, while power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles.

Abstract: The railway industry is increasingly pressured to adopt sustainable practices, seeking alternatives to virgin natural aggregates that reduce environmental impact and lifecy-cle costs. The extraction of slate for ornamental purposes generates significant waste, approximately 30% by mass, which is typically disposed of in landfills, causing envi-ronmental and economic concerns. This study comprehensively investigates the poten-tial of slate waste as a primary component in sub-ballast layers for heavy-haul rail-ways. Laboratory tests were conducted on mixtures of slate waste and a clayey soil, with granular contents ranging from 60% to 90%. The key geotechnical parameters evaluated included the California Bearing Ratio (CBR), Resilient Modulus (RM), com-paction characteristics, and Atterberg limits. The results indicate that mixtures with slate waste (SLT) exhibit a performance comparable to conventional mixtures with gneiss aggregate (REF). The RM and CBR values for the SLT mixtures increased by 48.5% and 38.4%, respectively, when the slate waste content was raised from 60% to 90%. A non-linear relationship was found between RM and CBR for both materials types. Furthermore, the study integrates findings from related research on recycled ballast and tropical soils, highlighting the synergistic benefits of using industrial by-products. It concludes that slate waste, potentially stabilized with low cement per-centages, presents a viable, high-performance, and sustainable solution for railway sub-ballast, contributing to circular economy principles in railway infrastructure.

Abstract: This paper introduces new methods of a mathematical framework for analyzing and reducing harmonic distortion in small-signal DC–DC converters. Traditional methods often depend on large passive components or trial-and-error tuning, which can be costly and lack precise predictive power. In contrast, this work defines a harmonic reduction coefficient (Δ), derived analytically from small-signal transfer functions, serving as a design tool to quantify and minimize harmonic content. Closed-form formulas for the resonant frequency and Δ are developed for Buck, Boost, and Buck–Boost converters operating in continuous conduction mode (CCM). The proposed approach enables the optimal selection of passive components to effectively suppress second-harmonic distortion, eliminating the need for additional filtering hardware. Simulations confirm the theoretical findings, showing significant improvements in total harmonic distortion (THD). Overall, the Δ-based design method offers a practical and versatile tool for enhancing converter performance in sensitive applications, including radar systems, audio equipment, and renewable energy interfaces.

Abstract: On-field calibration for SINS often uses right hexahedron, but the influence of the structure errors, such as mutual position tolerances towards parallelism or the per-pendicularity of two arbitrary planes of the hexahedron, on the calibration accuracy is often neglected. In this paper, a hexahedron structure error model and a comprehen-sive corresponding SINS calibration error model are developed based on hemispherical resonator gyroscope (HRGs). The proposed method introduces the comprehensive hexahedron errors through defining the normal vectors of the exterior surfaces of the hexahedron. A 24-position calibration scheme is designed to identify accelerome-ter-related errors, while a 48-rotation scheme is developed to identify gyro-related er-rors. The complete calibration procedure enables simultaneous identification of hexa-hedron structure errors, installation misalignments, scale factor errors, and biases. Ex-perimental validation is conducted using a high-precision three-axis turntable, which simulates the hexahedron structure errors. The results show that the proposed method significantly improves the calibration accuracy of both accelerometers and HRGs compared with traditional methods. Furthermore, it reduces the accuracy require-ments for the hexahedron structure, thus lowering the cost of SINS on-field calibration.

Abstract: In a globalized world where data plays an important role in system operation, process automation, decision-making, etc., the development of real-time control systems is essential because it allows operators or supervisors to know the current status of a process based on the physical variables that are part of it. Therefore, there is currently a wide range of software/hardware tools available through which control systems can be implemented to operate in real time, some of which are Arduino, ESP32, and PIC microcontrollers. As an alternative to the current limitations of the aforementioned systems, this paper presents a novel proposal for the implementation of digital controllers using Texas Instruments embedded systems, which is based on an experimental framework using different test plants for which different control strategies were designed. The results obtained highlight the ability of Texas Instruments microcontrollers to execute real-time control loops applied to different physical systems and operated under different parameters. In conclusion, it was evident that Texas Instruments embedded systems equipped with different microcontrollers are an interesting alternative in the development of control systems, not only on a small scale but also in industrial applications.

Abstract: A novel precision operational amplifier scheme has been developed and investigated, implemented using complementary bipolar transistors and input field-effect transistors controlled by a p-n junction. Computer simulation of the developed circuit was performed in LTSpice environment, which demonstrates that the proposed schematic solution provides a high voltage gain (over 80 dB) with low static current consumption and relatively low load resistances (RL = 2 kΩ). The systematic component of the zero voltage offset does not exceed 100 µV.

Abstract: An increasing number of different types of dielectric liquids are appearing on the market. This is undoubtedly related to sustainable development goals. This paper presents comparative studies of the lightning impulse breakdown voltage (LIBV) of six dielectric liquids with different chemical compositions: naphthenic uninhibited mineral oil (UMO), naphthenic inhibited mineral oil (IMO), natural ester (NE), synthetic ester (SE), bio-based hydrocarbon (BIO), and an inhibited liquid produced using gas-to-liquids technology (GTL). Tests were conducted in a point to sphere electrode configuration with a 5 mm thick pressboard barrier placed between them. This configuration was designed to more closely replicate the actual configuration found in transformers, where the oil channels are separated by pressboard barriers. Tests were performed for two inter-electrode gap distances of 25 mm and 40 mm, and for both lightning impulse voltage polarities. Pressboard barrier was placed so that the distance between point electrode and the barrier was always the same (10 mm). Measurements were performed using the step method. Before measurements began, pressboard barrier was impregnated in dielectric liquid being tested. The obtained measurement results were compared with previous studies conducted by authors, which used a similar electrode system but without pressboard barrier. The results confirmed that inserting pressboard barrier between electrodes effectively inhibits development of discharges and significantly increases the electrical strength of the entire insulation system.

Abstract: This paper introduces a deep learning-based methodology for the automated identification of spalling defects in tunnel linings, emphasizing the fusion of intensity and depth information. A novel network architecture is presented, leveraging Mobile Laser Scanning (MLS) data to generate a dataset of paired intensity and depth images. The network effectively integrates these multi-modal inputs to enhance the precision of spalling segmentation. Results demonstrate the superior performance of the proposed approach in comparison to methods relying solely on intensity data, highlighting the critical role of depth information for accurate defect characterization in complex underground environments.

Abstract: Flow quality at the engine face—especially total-pressure recovery (PR) and swirl—is central to the performance and stability of external-compression supersonic inlets. This work combines a two-stage assessment with steady RANS to quantify bleed–swirl trade-offs in a single-ramp intake. Stage-I is a no-bleed (WOB) screening over M∞ = 1.4-1.9 to locate the practical onset of a bleed requirement; deterioration in PR and swirl beyond M∞ ≈ 1.6, consistent with a pre-shock strength near the turbulent-separation threshold, motivates Stage-II. Stage-II comprises with/without bleed comparisons at M∞ = {1.6, 1.8, 1.9} across operating-map traverses parameterized by the flow ratio λ. Simulations in ANSYS Fluent (realizable k-ε model with enhanced wall treatment, pressure-based coupled solver) provide engine-face distortion metrics using standardized ring/sector swirl measures (SIRA/SISA) alongside PR. Results show that bleed removes low-momentum near-wall fluid and stabilizes the terminal-shock interaction, raising PR and lowering peak swirl and swirl intensity across the map, while extending the stable operating range to lower λ at fixed M∞. The analysis delivers a design-oriented linkage between shock–boundary-layer interaction control and AIP swirl: when bleed is applied at and above M∞ = 1.6, separation footprints shrink and organized swirl sectors weaken, yielding improved operability with modest bleed fractions.

Abstract: This review examines the progress made in the field of polymer nanocomposites for additive manufacturing. This study focuses on developing sustainable filaments from nanokaolin and recycled high-density polyethylene (HDPE) waste. Adding nanokaolin as a filler to recycled HDPE matrices created filaments with significantly enhanced mechanical and thermal properties. They achieve up to 35% higher tensile strength, 25% greater thermal stability, and 40% reduction in material costs compared to traditional biobased and virgin-polymer filaments Using the Taguchi method, a well-known optimization technique, we systematically adjusted the extrusion parameters of the filaments. This method is part of a broader strategy known as the Design of Experiments (DOE) framework. This helps to identify the best production settings. This review investigates the links between processing conditions, microstructure, and material properties, supported by advanced characterization and modeling methods. In addition to economic factors, we also detail the environmental benefits of using recycled HDPE and nanokaolin, such as reduced carbon footprint and plastic waste, compared to standard filaments. This highlights the sustainability of this method. This study establishes a scientific basis for circular material flow in additive manufacturing. This promotes the adoption of high-performance, cost-effective, and environmentally friendly 3D printing solutions.

Abstract:

Superhydrophobic micropillar surfaces, inspired by the lotus leaf, have been extensively studied over the past two decades for their self-cleaning, anti-friction, anti-icing, and anti-corrosion properties. In this study, we introduce a simple and effective method for introducing porosity into polydimethylsiloxane (PDMS) micropillar arrays using salt templating. We then evaluate the wetting behaviour of these surfaces before and after infusion with perfluoropolyether (PFPE) oil. Apparent contact angle and sliding angle were measured relative to a non-porous control surface. Across five porous variants, the contact angle decreased by approximately 5° (from 157° to 152° on average), while the sliding angle increased by about 3.5° (from 16.5° to 20° on average). Following PFPE infusion, the porous arrays exhibited reduced sliding angles while maintaining superhydrophobicity. These results indicate that introducing porosity slightly reduces water repellency and droplet mobility, whereas PFPE infusion restores mobility while preserving high water repellency. The change in wettability following PFPE infusion highlights the potential of these surfaces to function as robust, self-cleaning materials.