Search Results (2,766)

Stable adhesion on non-magnetic, steep, and irregular underwater surfaces (e.g., concrete dams with cracks or biofilms) remains a challenge for inspection robots. This study develops a novel adsorption mechanism based on the synergistic operation of a Venturi-ejector and a composite suction cup. The mechanism utilizes the Venturi effect to generate stable negative pressure via hydrodynamic entrainment and innovatively adopts a composite suction cup—comprising a rigid base and a dual-layer EPDM sponge (closed-cell + open-cell)—to achieve adaptive sealing, thereby reliably applying the efficient negative-pressure generation capability to rough underwater surfaces. Theoretical modeling established the quantitative relationship between adsorption force (F) and key parameters (nozzle/throat diameters, suction cup radius). CFD simulations revealed optimal adsorption at a nozzle diameter of 4.4 mm and throat diameter of 5.8 mm, achieving a peak simulated F of 520 N. Experiments demonstrated a maximum F of 417.9 N at 88.9 W power. The composite seal significantly reduced leakage on high-roughness surfaces (Ra ≥ 6 mm) compared to single-layer designs. Integrated into an inspection robot, the system provided stable adhesion (>600 N per single adsorption device) on vertical walls and reliable operation under real-world conditions at Balnetan Dam, enabling mechanical-arm-assisted maintenance. Full article

This study presents the design and validation of an automated skip loading system for vertical shaft hoisting operations, aimed at addressing inefficiencies in current manual systems that contribute to consistent underperformance in meeting daily production targets. Initial assessments revealed a task completion rate of 91.6%, largely due to delays and inaccuracies in manual ore loading and accounting. To resolve these challenges, an automated system was developed using a bin and conveyor mechanism integrated with a suite of industrial automation components, including a programmable logic controller (PLC), stepper motors, hydraulic cylinders, ultrasonic sensors, and limit switches. The system is designed to transport ore from the draw point, halt when one ton is detected, and activate the hoisting process automatically. Digital simulations demonstrated that the automated system reduced loading time by 12% and increased utilization by 16.6%, particularly by taking advantage of the 2 h post-blast idle period. Financial evaluation of the system revealed a positive Net Present Value (NPV) of $1,019,701, a return on investment (ROI) of 69.7% over four years, and a payback period of 2 years and 11 months. The study concludes that the proposed solution significantly improves operational efficiency and recommends further enhancements to the hoisting infrastructure to fully optimize performance. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

Biobanks rely on ultra-low-temperature (ULT) storage for irreplaceable specimens, where precise 3D temperature field reconstruction is critical to preserve integrity. This is the first study to apply geostatistical methods to ULT field reconstruction in cryogenic biobanking systems. We address critical gaps in sparse-sensor environments where conventional interpolation fails due to vertical thermal stratification and non-stationary trends. Our physics-informed universal kriging framework introduces (1) the first domain-specific adaptation of universal kriging for 3D cryogenic temperature field reconstruction; (2) eight novel lag-binning methods explicitly designed for sparse, anisotropic sensor networks; and (3) a leave-one-out cross-validation-driven framework that automatically selects the optimal combination of trend model, binning strategy, logistic weighting, and variogram model fitting. Validated on real data collected from a 3000 L operating cryogenic chest freezer, the method achieves sub-degree accuracy by isolating physics-guided vertical trends (quadratic detrending dominant) and stabilising variogram estimation under sparsity. Unlike static approaches, our framework dynamically adapts to thermal regimes without manual tuning, enabling centimetre-scale virtual sensing. This work establishes geostatistics as a foundational tool for cryogenic thermal monitoring, with direct engineering applications in biobank quality control and predictive analytics. Full article

In order to solve the problem encountered by traditional potato–stem separation devices, that is, they cannot meet the requirements when installed in small-scale harvesters, a new type of vertical differential roller potato–stem separation device was developed. The device features a compact structure and simultaneously possesses both separating and conveying functions. Through the analysis of the separation force between potato and stem, the structure and parameters of the separation device were determined. The simulation and the field test of the potato–stem separation process were carried out with the vertical differential roller speed, the vertical differential roller gap width and the conveyor chain speed as the influencing factors. The simulation test analysed the influence law of different working parameters on the performance of potato–stem separation. The field test revealed the order of the effects of various factors on the impurity rate and skin-breaking rate, concluding that the optimal combination of operational parameters was a vertical differential roller rotational speed of 6 s⁻¹, a vertical differential roller gap width of 7 mm, and a conveyor chain speed of 1.4 m·s⁻¹. This experiment fills the research gap in the study of potato–stem separation devices suitable for small-scale potato harvesters and promotes the development of compact potato harvesters. Full article

Differentiating genuine hemiplegic gait (HG) in stroke survivors from hemiplegic-like gait voluntarily imitated by healthy adults (MHG) is essential for reliable assessment and intervention planning. Treadmill-based gait data were obtained from 79 participants—39 stroke patients (HG) and 40 healthy adults—instructed to mimic HG (MHG). Forty-eight spatiotemporal and force-related variables were extracted. Random Forest, support vector machine (SVM), and logistic regression classifiers were trained with (i) the full feature set and (ii) the 10 most important features selected via Random Forest Gini importance. Performance was assessed with 5-fold stratified cross-validation and an 80/20 hold-out test, using accuracy, F1-score, and the area under the receiver operating characteristic curve (AUC). All models achieved high discrimination (AUC > 0.93). The SVM attained perfect discrimination (AUC = 1.000, test set) with the full feature set and maintained excellent accuracy (AUC = 0.983) with only the top 10 features. Temporal asymmetries, delayed vertical ground reaction force peaks, and mediolateral spatial instability ranked highest in importance. Reduced-feature models showed negligible performance loss, highlighting their parsimony and interpretability. Supervised machine learning algorithms can accurately distinguish true hemiplegic gait from mimicked patterns using a compact subset of gait features. The findings support data-driven, time-efficient gait assessments for clinical neurorehabilitation and for validating experimental protocols that rely on gait imitation. Full article

The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

Sustainability concerns and rising consumer environmental awareness (CEA) have fundamentally reshaped competitive dynamics in modern supply chains. This study examines the influence of CEA on pricing and environmental effort competition between store brand (SB) and national brand (NB) products in a two-stage supply chain with one manufacturer and one retailer. We develop a mathematical model to evaluate strategic interactions under three power structures: Manufacturer Stackelberg (MS), Retailer Stackelberg (RS), and Vertical Nash (VN), considering two environmental investment scenarios: NB-only investment and bilateral SB-NB investment. Our findings indicate that (i) when only NB products invest environmentally, CEA increases environmental effort levels, wholesale prices, and retail prices for both brands, expanding total channel value rather than merely redistributing profits; (ii) CEA and channel competition on jointly determine optimal channel power structure, with MS dominating in differentiated markets with low CEA while RS yields superior outcomes under high competition and high CEA; (iii) retailers consistently achieve maximum profits under VN structure through balanced negotiation positions; and (iv) bilateral environmental investment causes price convergence across structures, shifting competitive focus from governance to operational excellence. By integrating environmental investment, channel power structure, and channel competition into a unified framework, this study offers managers practical decision tools for selecting optimal channel structures based on observable market conditions. Furthermore, it demonstrates how grocery retail chains and consumer goods manufacturers can transform environmental initiatives from compliance costs into value creation mechanisms that enhance both profitability and sustainability. Full article

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control. Full article

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful. Full article

Molecular quantum-dot cellular automata (QCA) are attracting much attention as an alternative that can improve the problems of digital circuit design technology represented by existing CMOS technology. In particular, they are well suited to the upcoming nanoquantum environment era with their small size, fast switching speed, and low power consumption. In this study, we propose a 5 × 5 × 1 ultra-slim vertical panel type multi-layer 2-to-1 multiplexer (Mux) using molecular QCA, departing from conventional multi-layer formats, and show its expansion to 4-to-1 Mux and application to vertical panel type D-latch and RAM cells. In addition, the polarization phenomenon of cells is physically proven using the potential energy, distance among electrons, and the relative positions of cells, and the secure RAM design takes noise elimination and polarization of the output signal into consideration. The circuits are simulated in terms of operation and performance using QCADesigner 2.0.3 and QCADesignerE, and the proposed multi-layer 2-to-1 Mux shows a significant improvement of at least 1473% and 277% in two representative standard design costs compared to the state-of-the-art multi-layer Muxes. Full article

Inspired by the free-flight capabilities of the gannet in both aerial and underwater environments, a foldable-wing air–water cross-medium vehicle was designed. To enhance its propulsive performance and transition stability across these two media, aero-hydrodynamic performance analyses were conducted under three representative operating states: aerial flight, underwater navigation, and water entry. Numerical simulations were performed in ANSYS Fluent (Version 2022R2) to quantify lift, drag, lift-to-drag ratio (L/D), and tri-axial moment responses in both air and water. The transient multiphase flow characteristics during water entry were captured using the Volume of Fluid (VOF) method. The results indicate that: (1) in the aerial state, the lift coefficient increases almost linearly with the angle of attack, and the L/D ratio peaks within the range of 4–6°; (2) in the folded (underwater) configuration, the fuselage still generates effective lift, with a maximum L/D ratio of approximately 2.67 at a 10° angle of attack; (3) transient water entry exhibits a characteristic two-stage force history (“initial impact” followed by “steady release”), with the peak vertical load increasing significantly with water entry angle and velocity. The maximum vertical force reaches 353.42 N under the 60°, 5 m/s condition, while the recommended compromise scheme of 60°, 3 m/s effectively reduces peak load and improves attitude stability. This study establishes a closed-loop analysis framework from biomimetic design to aero-hydrodynamic modeling and water entry analysis, providing the physical basis and parameter support for subsequent cross-medium attitude control, path planning, and intelligent control system development. Full article

The low-altitude economy represents an important facet of emerging productive forces, and flying cars serve as key vehicles driving its development. This paper proposes an aerodynamic design for the aerial vehicle module of split-type flying cars, which meets the functional requirements for vertical takeoff, climb, and cruising, and provides a reference solution for urban air mobility. A multidisciplinary constraint-based approach was employed to define the design requirements of the aerial vehicle module, ensuring its capability to operate in various complex environments. Through theoretical analysis and Computer-Aided Design (CAD) methods, key geometric, aerodynamic, and stability parameters were developed and evaluated. After finalizing the design concept of the aerial vehicle module, aerodynamic analysis was conducted, and aerodynamic coefficients were assessed using Computational Fluid Dynamics (CFD) simulations across angles of attack ranging from −5° to 20°. The results indicated that the aerial vehicle module achieved a maximum lift-to-drag ratio of 13.40 at an angle of attack of 2°, and entered a stall condition at 13°. The aerodynamic design enhances the module’s stability under various operating conditions, thereby improving handling performance. Overall, the aerial vehicle module demonstrates favorable aerodynamic characteristics during low-altitude flight and low-speed cruising, satisfying the design requirements and constraints. Full article

This study presents the development and experimental validation of a dynamic simulation model for a transcritical CO₂ heat pump system coupled with a stratified water tank, with particular focus on strong transient behavior and detailed heat exchanger characteristics. Due to the unique thermophysical properties of CO₂ under transcritical conditions, conventional modeling approaches are insufficient. The model was validated against experimental results under a range of operating conditions. It accurately predicted outlet water temperatures within ±3.2 °C and system COP within ±6.8% deviation from measurements. In contrast to previous models, this approach offers improved accuracy in capturing dynamic system responses, including startup transients, and demonstrates high adaptability across varying ambient temperatures and load profiles. Importantly, the model also considers the vertical installation layout of components, enabling analysis of gravitational effects on system dynamics and offering insights into optimal configuration strategies. The validated model serves as a powerful tool for system optimization and advanced control design in residential CO₂ heat pump applications. Full article