Search Results (155)

To address large fluctuations in fertilizer flow and the difficulty of real-time quantitative blockage monitoring during fertilization by an integrated tillage, land preparation, and seeding machine, a fertilization monitoring system combining real-time detection and intelligent alarm functions was designed and developed. The system uses an STC32G12K128 microcontroller as the core control unit and integrates fiber-optic sensors, fiber-optic amplifiers, and associated peripheral hardware. Supporting host computer software was also developed on the Python3.13 platform. Based on the light-blocking principle, the optical signal generated by fertilizer particles passing through the sensing area is converted into a digital signal by the fiber-optic amplifier. A quantitative correlation model between the amount of blocked light and the fertilizer discharge rate was then established, enabling indirect and non-contact measurement of the fertilizer discharge rate. Indoor bench tests demonstrated a highly significant positive linear correlation between the amount of blocked light and the fertilizer discharge rate. The overall mean absolute percentage error of the fitted model was below 10%. Based on this detection system, a fertilization monitoring and alarm module was further developed for indoor bench conditions, together with discrimination logic for fertilizer blockage and fertilizer shortage. At rotational speeds of 30–50 r/min, the system achieved an average blockage detection rate of 98%, an average false alarm rate of 4.4%, and an alarm response time of no more than 3 s. Full article

Recursive evidence fusion gives an intrusion detection system temporal memory, but it also gives unreliable windows and erroneous review outcomes a path to influence later diagnoses. Existing drift-handling, open-set, conformal, continual-learning, and human-in-the-loop methods provide useful signals or update classifiers and memories; they do not, by themselves, specify when a post-classification evidential state may be written or replaced. We present RTEF-IDS, a protocol that separates current action, model-evidence admission, reviewed-feedback admission, and history replacement. The protocol retains the history-relative reliability principle from our previous work, instantiates it for singleton-plus-ignorance IDS evidence, and assigns operation-specific credentials. Reviewed windows make no base-state change, mapped-known feedback may be appended, and replacement requires persistent confirmation. On 33,384 frozen windows, 30% retrospective admission excludes 26.3% of held-out-or-misclassified mass while retaining 94.8% of known-correct evidence. Under paired imperfect feedback, retrospective replacement increases one-window future history-state agreement by 0.107 in the primary block and 0.129 in IoT-23 leave-scenario-out replay. Under a past-only rolling-budget gate within externally supplied frozen partitions, the corresponding increments are 0.001 and 0.000, indicating that the tested gate exposes few qualifying replacement opportunities; bounded external short streams show the same opportunity constraint. Independent second review reduces false authorization from 5.66 to 0.124 per 1000 first-stage reviewed windows under independent errors and from 34.27 to 0.181 under five-window correlated errors, with a corresponding increase in review demand and a reduction in admitted corrective feedback. A shared systematic label alias remains unresolved by the tested review arms. These results support explicit, auditable state-mutation control while identifying the causal-opportunity and feedback-provenance conditions under which it operates. Full article

The position of the center of mass directly affects the attitude control and flight safety of moving bodies such as unmanned aerial vehicles (UAVs). Therefore, high-precision measurement of the center of mass is required. Existing methods require changing the posture of the measured object multiple times. This introduces repeated positioning errors and suffers from poor equipment versatility. To address these issues, this paper proposes a static and dynamic combined measurement method for the center of mass based on multi-view vision. First, the relationship between the swing period and the pendulum length under the simple pendulum principle is analyzed. The basic principle of determining the direction of the center of mass using the line of gravity is also examined. Second, an under-constrained compound pendulum fixture is designed. A binocular vision system is used to track circular markers, perform FFT-based period verification, and fit the gravity line using singular value decomposition (SVD). Third, using a standard cubic iron block as the test object, the influence of pendulum length and swing angle on measurement accuracy is studied. Finally, experiments verify that the proposed method can obtain three-dimensional coordinates of the center of mass under a single suspension condition. The results show that with a pendulum length of 330 mm and an initial swing angle of 4°, the root mean square error of the center of mass measurement is 0.70 mm, and the maximum deviation over five repeated measurements is 1.45 mm. This method does not require repeated lifting or changes in posture. It can meet the need for in-situ, high-precision center of mass measurement of UAVs and other aircraft. Full article

MXenes have attracted considerable attention as anode materials for sodium-ion batteries (SIBs) because of their metallic conductivity, hydrophilic surfaces, tunable surface terminations, and layered structures. However, pristine MXenes are limited by nanosheet restacking, oxidation instability, heterogeneous surface chemistry, low initial Coulombic efficiency, and insufficient electrode-level ion accessibility. These issues indicate that MXenes should be regarded not simply as standalone active materials but as multifunctional building blocks for composite electrode design. This review discusses recent progress in MXene-based composite anodes for SIBs, focusing on MXene/carbon composites, MXene/metal compound composites, polymer-assisted composites, and three-dimensional structured MXene composites for improving structural stability, interfacial chemistry, and sodium-storage kinetics. We emphasize that composite engineering can reshape sodium storage from diffusion-limited intercalation toward hybrid mechanisms involving interfacial adsorption, pseudocapacitive storage, heterointerface-driven redox reactions, ion desolvation regulation, and solid-electrolyte interphase stabilization. Key practical challenges, including oxidation control, initial Coulombic efficiency, high-mass-loading electrode design, gravimetric–volumetric performance trade-offs, scalable synthesis, and full-cell validation, are also discussed. Finally, we propose future design principles based on integrated materials chemistry, interfacial science, multiscale architecture engineering, and realistic cell-level evaluation for advancing MXene composites toward practical SIB anodes. Full article

Grape molasses, known as pekmez in Turkish, is a traditional concentrated fruit product that may be adulterated with cheaper sweeteners or water. This study evaluated broadband microwave S-parameter measurements as a rapid, non-destructive screening approach for detecting glucose syrup substitution and water dilution in grape molasses. Nine physical mixture groups were prepared, including pure grape molasses, glucose syrup–substituted mixtures at 5–30%, pure glucose syrup as an endpoint reference, and water-diluted mixtures at 10–30%. For each group, five consecutive technical measurements were recorded using a Libre vector network analyzer connected to a WR-229 waveguide-based two-port setup over 3.30–4.90 GHz. All mixtures were prepared from a single commercial grape molasses source and a single glucose syrup source; the results should therefore be interpreted as pilot-scale, single-source evidence rather than a generalizable screening method. Glucose syrup substitution produced a systematic upward shift in S₁₁ resonance frequency. In the practical 0–30% range, S₁₁ resonance frequency showed a strong linear relationship with glucose syrup content on the calibration data (R² = 0.9955; inverse prediction error = 0.72 percentage points), though this reflects calibration fit rather than independently validated prediction accuracy. Water dilution produced stronger S₂₁ attenuation. The detection principle is based on the complementary use of S₁₁ resonance behavior for glucose syrup substitution and S₂₁ transmission loss for water dilution. In group-blocked point-wise classification, the Ensemble Bagged Trees classifier achieved 88.79% accuracy and a macro-F1 score of 0.874. These results indicate that S₁₁ and S₂₁ provide complementary proof-of-concept indicators for controlled-mixture screening of grape molasses adulteration. Full article

The convergence of optical burst switching (OBS) and elastic optical networks (EON) offers a promising pathway for 6G IoT backhaul. However, existing solutions treat OBS and EON separately and rely on heuristic resource allocation that fails to meet stringent QoS demands. This paper proposes a fully AI-driven converged OBS/EON architecture integrating a hybrid switching fabric, a multi-agent deep reinforcement learning (DRL) orchestrator, and a federated learning (FL) plane for autonomous, QoS-aware resource provisioning. The control plane implements multi-agent Proximal Policy Optimization (PPO) for joint burst scheduling, routing, modulation selection, and spectrum allocation. The orchestration plane employs q-fair FL for privacy-preserving cross-domain traffic prediction. Mathematical formulations of the optimization problem with spectrum, GSNR, and delay constraints are provided, along with pseudo-algorithms. Simulations over a 14-node NSFNET topology demonstrate a 78% reduction in blocking probability, a 42% improvement in spectral efficiency, sub-millisecond URLLC delays, and a Jain’s fairness index of 0.92, while preserving data privacy. The framework builds upon SDN principles for seamless integration with optical transport infrastructures. Full article

Structured mesh generation is critical for high-fidelity Computational Fluid Dynamics (CFD) simulations; however, the automatic generation of multi-block structured meshes for complex three-dimensional (3D) wings remains a long-standing challenge. Traditional methods heavily rely on manual multi-block topology decomposition, while recent intelligent methods suffer from limited generalizability, reliance on labeled training data, and inadequate boundary-layer controllability. This paper proposes a fully automatic structured mesh generation method for arbitrary 3D wing configurations based on adaptive parametric multi-block topology. First, a geometric parameterization framework for 3D wings is established using the Gordon surface method. Then, the multi-block topology is adaptively generated analytically via parametric construction of control vertices and edges. Finally, a four-level assembly process is employed to generate the final mesh. Systematic tests on wings with various aspect ratios, sweep angles, and deformations demonstrate that the method exhibits excellent robustness. Generating a one-million-cell mesh requires only 0.391 s (single-threaded), which is approximately six times faster than existing intelligent methods. CFD results of benchmark wings show good agreement with available reference data, while the static aeroelastic characteristics of the custom flying-wing model conform to physical principles. This study provides a generalizable, interpretable, and fully automatic solution for structured mesh generation of 3D wings. Full article

This study examined how educational robotics and machine learning activities contributed to the understanding of artificial intelligence (AI) among elementary school students, pre-service teachers, and practicing teachers. The intervention involved 1207 participants, including 1009 fifth-grade students, 93 practicing elementary school teachers, and 105 pre-service teachers. Participants engaged in a series of structured activities focused on educational robotics, visual block-based programming, and machine learning concepts. A pre-experimental design was employed to assess changes in understanding and to explore participants’ perceptions of the learning experience. Statistical analyses included Student’s t-tests, the Kruskal–Wallis H test, and Bonferroni-corrected post hoc comparisons. Results indicated pre–post gains in elementary school students’ understanding of machine learning, AI modeling processes, and fundamental computational concepts following participation in the intervention. Across the three groups, participants reported positive perceptions of the activities and recognized their value for learning about AI. Elementary school students reported higher motivation and stronger engagement in coding-related tasks, likely reflecting their previous experience with Scratch in school settings. Practicing teachers expressed a more interdisciplinary perspective on the educational applications of AI, whereas pre-service teachers demonstrated stronger conceptual understanding of the underlying principles. Overall, the findings suggest that educational robotics and machine learning activities are associated with positive learning experiences and observed gains in AI-related understanding. However, given the pre-experimental design without a control group or random assignment, the results should be interpreted as evidence of observed changes and participant perceptions rather than demonstrated effectiveness. Full article

InfraRed Thermography (IRT), a non-invasive, non-contact, and non-destructive testing (NDT) technique, has become an established tool in the assessment of a building’s behavior and energy performance. However, the inherent low spatial resolution of thermal infrared (TIR) cameras has led recent work to fuse thermographic and geometric data to generate accurate 3D representations of buildings encapsulating temperature information. Whilst existing data fusion methods have relied on sensors in fixed relative orientation (RO), the co-registration of independent TIR and RGB blocks using ground control points (GCPs), or the reprojection of TIR images onto additional geometric or parametric models, approaches that directly match multi-modal images remain limited. In principle, if multi-modal tie points were available, it would be possible to directly align the RGB block with the TIR block; however, such matching is extremely challenging due to the substantial differences in radiometric properties. The main contribution of this paper is to demonstrate the applicability of off-the-shelf deep learning-based image matching algorithms, originally trained on mono-modal datasets, to multi-modal matching tasks for InfraRed Thermography 3D-Data Fusion (IRT-3DDF). We conduct a comparative evaluation of the principal algorithms developed in recent years, with particular emphasis on 3D accuracy and computational efficiency, under the hypothesis that, owing to the inherently local nature of the problem they address, these algorithms can generalize from a mono-modal training domain to a multi-modal application domain. The results are benchmarked against existing hand-crafted open-source multi-modal reference methods. Importantly, the proposed method is fully-automatic, obviating the need for sensor pre-calibration, manual co-registration, or associated positioning information. Results demonstrate that DL-based image matching, using pre-trained neural networks outside of their expected training domain, provides a viable approach for IRT-3DDF capable of co-registering blocks of multi-modal images across varying scales, settings, sensors, and subjects. Our results indicate accuracy in 3D is up to seven times better than multi-modal hand-crafted algorithms, while hand-crafted mono-modal methods fail to co-register images in their entirety. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article

Electrocatalytic nitric oxide reduction reaction (NORR) for ammonia synthesis has emerged as a research focus in artificial nitrogen fixation. Unlike previous reviews that primarily focus on experimental catalyst development, this work offers a comprehensive and systematic summary of recent theoretical progress in NORR, with special emphasis on low-dimensional materials. We connect four important areas: atomic-level design principles for active sites, emerging mechanistic ideas that go beyond conventional scaling relations, realistic simulations of the electrochemical microenvironment, and data-driven machine learning approaches for catalyst discovery. We begin by discussing the reaction mechanism, analyzing the orbital interactions that control NO activation and the thermodynamic and kinetic features of different reaction pathways. For active-site construction, we examine electronic synergy in single-atom and dual-atom catalysts, coordination microenvironment tuning, electronic structure modulation through doping and strain, and heterojunction interfaces that allow multi-degree-of-freedom regulation. To explore new mechanistic concepts, we introduce p-block element synergy, reverse activation, magnetic and spin control, and surface electronic singularities as strategies to overcome traditional scaling relations. Regarding the reaction microenvironment, we analyze how coverage, solvation, local pH, and applied potential jointly affect selectivity and activity. Finally, we summarize the role of machine learning in building descriptors and accelerating catalyst screening. This review aims to provide theoretical guidance for the rational design of efficient NORR electrocatalysts with high activity, selectivity, and long-term stability. Full article

Adductor canal block (ACB) is widely used for perioperative analgesia in knee surgery because it provides effective pain control while preserving quadriceps muscle strength. With the increasing use of ultrasound-guided regional anesthesia, interest has expanded beyond conventional morphologic imaging toward quantitative assessment of peripheral nerve function and biomechanics. Shear wave elastography (SWE) is an emerging ultrasound-based technique that enables real-time quantification of tissue stiffness and has recently gained attention in peripheral nerve evaluation. Previous SWE studies have primarily focused on chronic neuropathic conditions, including entrapment neuropathy and diabetic neuropathy, in which increased nerve stiffness is commonly observed. However, emerging observations suggest that peripheral nerve stiffness may dynamically decrease following regional anesthesia procedures such as ACB. This finding raises the possibility that nerve stiffness reflects not only chronic structural pathology but also transient physiologic and biomechanical modulation. Potential mechanisms underlying reduced stiffness after ACB include perineural hydrodissection, decreased fascial compression, sympathetic blockade-induced vasodilation, altered intraneural pressure, and changes in surrounding muscle tension. Because the saphenous nerve within the adductor canal is superficial and consistently visualized under ultrasound guidance, ACB represents an attractive model for investigating dynamic changes in peripheral nerve biomechanics. This narrative review summarizes current evidence regarding SWE assessment of peripheral nerves and discusses the potential implications of dynamic stiffness changes after regional anesthesia. We review the biomechanical principles of SWE, factors affecting nerve stiffness, current evidence in neuropathic and perioperative settings, technical limitations, and future clinical applications. Understanding the dynamic behavior of peripheral nerve stiffness may expand the role of SWE from a diagnostic tool for neuropathy to a quantitative biomarker for regional anesthesia and perioperative nerve physiology. Full article

The series-connected chain MMC with DC-blocking capacitor (C-SCMMC) is an emerging topology for HVDC tapping applications with high voltage and relatively low power capacity. However, the DC-blocking capacitor can be bulky and costly, which deteriorates its economy and flexibility. This paper investigates the feasibility of DC-blocking capacitor reduction, with special emphasis on the characteristics, control, and parameter design. The principle of C-SCMMC considering the DC-blocking capacitor dynamics is firstly modeled and analyzed, and the ripples and harmonics, as well as their influences on the external performance of the converter, are analyzed; then, improved control strategies, namely, the grid-tied harmonics suppression and current control, are proposed considering the enlarged DC-blocking capacitor voltage ripple; after that, the influence of reduced DC-blocking capacitor on the operation range and parameter design are analyzed, and the economic advantage is demonstrated via parameter design and comparison based on a typical bench mark. Analysis shows that the DC-blocking capacitor voltage ripple is coupled with other parameters, such as the arm output voltage and SM capacitance, and the advised range is from 0.1 to 0.3 p.u. at unity power factor to reduce the overall cost. In a typical design, the C-SCMMC can reduce the number of SMs by 2/3 and the capacitor energy storage capacity by 15% compared to the conventional MMC. Finally, simulation results obtained in MATLAB/Simulink 2024b are provided to verify the feasibility of the proposed converter and the correctness of the parameter design. Full article

Infrastructure expansion in Indonesia has increased demand for paving blocks, raising concerns over cement production costs and environmental impact. This study investigates the comparative effectiveness of pineapple leaf fiber (PALF, Ananas comosus) and sisal fiber (Agave sisalana) as reinforcements in paving blocks, evaluating water absorption and 28-day compressive strength at fiber contents of 0%, 1%, 3%, 5%, and 7% by cement volume. A full-factorial two-way ANOVA with post-hoc Tukey HSD was employed. A dosage of 3% for both fiber types resulted in compressive strengths of 14.5 MPa (PALF, +59% vs. control) and 15.2 MPa (sisal, +67% vs. control), both of which met the requirements of SNI 03-0691-1996 Class B. Sisal fiber demonstrated superior compressive performance, consistent with its higher stiffness and tensile strength as reported in the literature. Water absorption increased monotonically with fiber content for both types, with SNI Class D compliance (≤10%) maintained only at 0% for PALF and 0–1% for sisal, a known consequence of the inherently hydrophilic nature of plant-based natural fibers. A statistically significant interaction term (F = 3.697, p = 0.012) confirmed that the two fibers respond differently to dosage increases, providing nuanced practical guidance beyond what single-factor studies can offer. These findings demonstrate the promising compressive strength of agricultural waste fiber-reinforced paving blocks, warranting further investigation of abrasion resistance, flexural strength, and long-term durability before practical deployment. Such utilization supports circular economy principles in the construction industry. Full article

Despite the widespread adoption of model predictive control (MPC) in railway research, the integration of intelligent learning mechanisms into train control systems operating under moving block signaling remains limited, particularly in approaches that preserve constraint satisfaction and industrial feasibility. To address this gap, this paper presents a novel learning-based model predictive control (LMPC) framework for high-speed train control under the moving block signaling principle. Moving block signaling dynamically enforces safe inter-train separation based on the absolute braking distance, imposing stringent safety, comfort, and performance constraints on train operation. The proposed LMPC exploits the repetitive nature of railway operations by progressively improving its control policy through the incorporation of historical operational data into the terminal set of the optimization problem. This learning capability enables the controller to optimize train behavior on a given line while pursuing different control objectives, namely maximum-speed operation for leading trains and minimum safe inter-train separation for following trains, in full compliance with signaling requirements, speed limits, actuator constraints, and comfort-related jerk bounds. Simulation results on a representative high-speed line show that, compared with a conventional non-learning MPC, the proposed LMPC achieves a measurable reduction in traction-related energy consumption while maintaining comparable speed profiles, travel times, and strict constraint satisfaction. These improvements are achieved through a single software-level modification of the train control algorithm, without requiring additional onboard hardware or infrastructure upgrades, positioning the proposed LMPC as a promising and practically viable solution for energy-efficient deployment in high-speed railway operations. Full article