Search Results (4,832)

Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection. Full article

Breastfeeding is a complex biological system and a bidirectional physiological dialogue in which the infant may contribute to maternal breast health. This review synthesizes current evidence, clearly separating established findings from emerging hypotheses, to examine the possible infant-driven mechanisms that influence hormonal and immune homeostasis in the mammary gland. We evaluate how neonatal suckling coordinates interconnected hormonal reflexes and immune activity, and we explore the hypothesis that the retrograde flow of infant saliva to the breast tissue could activate maternal enzymatic defenses, particularly the xanthine oxidase and lactoperoxidase systems. We also consider the activation of antimicrobial peptides through direct contact at the nipple and areola, including cathelicidin and defensins, as well as the potential roles of fetal microchimerism and microbial transfer from the infant’s mouth in strengthening breast resilience. Although much of the evidence remains indirect and based on in vitro and animal models, the convergence of data supports a reformulated conceptual model that presents the infant as an active physiological partner rather than a passive recipient of milk. Recognizing this shift has important clinical implications for the prevention of inflammatory conditions such as mastitis, the improvement of breastfeeding support strategies, and the optimization of maternal and infant health outcomes. The review also identifies significant gaps in current knowledge and cautiously proposes hypotheses to explore these mechanisms. While preliminary, this framework offers an original perspective that may guide future research and open new paths in the study of human lactation biology. Full article

Brucellosis remains an underreported zoonotic disease in Ecuador. Its control program in cattle integrates diagnostic testing, vaccination, and eradication incentives, although participation is largely voluntary. Since 2025, vaccination has become compulsory nationwide. Human surveillance remains largely passive, and strain-level data are very limited. This study applied an integrated approach, combining serology (Rose Bengal and SAT-EDTA), microbiological culture, and molecular diagnostics, to assess the presence and diversity of Brucella spp. in cattle and pigs from six slaughterhouses in the northern Andean highlands. A total of 2054 cattle and 1050 pigs from Carchi, Imbabura, and Pichincha were sampled. Among cattle, 133 (6.5%; 95% CI: 5.5–7.6) were seropositive, and viable B. abortus strains were isolated from 17 (12.8%). Genus identification was confirmed by IS711-PCR, while species- and biovar-level differentiation was achieved with AMOS-PCR; additional assays targeting the ery gene and RB51 marker were used to distinguish field from vaccine strains. Biotyping and molecular analysis revealed a predominance of B. abortus biovar 4 (13/17 isolates) over biovar 1, all confirmed as field strains. In pigs, 10 animals (0.95%) tested seropositive, but no isolates were recovered, highlighting limitations of serology in swine. Most livestock, including the positives, originated locally, reinforcing the representativeness of our findings. The successful isolation and molecular characterization of B. abortus demonstrates the value of combining diagnostic strategies beyond serology. These results underscore the utility of active surveillance when supported by traceability systems; this approach may also contribute to guide interventions to reduce infection risk in livestock and humans. Full article

Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable. Full article

RNA-targeting techniques have emerged as powerful tools in cancer research and therapeutics, offering precise and programmable control over gene expression at the post-transcriptional level. Once viewed as passive intermediates in the central dogma, RNA molecules are now recognized as dynamic regulators of cellular function, capable of influencing transcription, translation, and epigenetic regulation. Advances in high-throughput sequencing technologies, transcriptomics, and structural RNA biology have uncovered a diverse landscape of coding and non-coding RNAs involved in oncogenesis, drug resistance, and tumor progression. In response, several RNA-targeting strategies have been developed to modulate these transcripts, including antisense oligonucleotides (ASOs), RNA interference (RNAi), CRISPR-Cas13 systems, small molecules, and aptamers. This review provides a comparative analysis of these technologies, highlighting their molecular mechanisms, therapeutic potential, and current limitations. Emphasis is placed on the translational progress of RNA-targeting agents, including recent FDA approvals and ongoing clinical trials for cancer indications. Through a critical comparison of these strategies, this review underscores the growing significance of RNA-targeting technologies as a foundation for next-generation cancer therapeutics and precision oncology. Full article

The paper presents an innovative theoretical concept of a bio-inspired soft gripper system with two parallel jaws, a fixed and a mobile one. It is conceived for gripping fragile or soft objects with complex, irregular shapes that are easily deformable. This novel gripper is designed for handling small objects of masses up to 0.5 kg. The maximum gripping stroke of the mobile jaw is 13.5 mm. The driving motor is a pneumatic muscle, an actuator with inherently compliant, spring-like behavior. Compliance is the feature responsible for the soft character of the gripper system, ensuring its passive adaptability to the nature of the object to be gripped. The paper presents the structural, kinematic, static, and dynamic models of the novel gripper system and describes the compliant behavior of the entire assembly. The results of the dynamic simulation of the gripper have confirmed the attaining of the imposed motion-related performance. Full article

Battery cell balancing is crucial in series-connected lithium-ion packs to maximize usable capacity, ensure safe operation, and prolong cycle life. This paper presents a comprehensive study and a novel adaptive duty-cycled hybrid balancing system that combines passive bleed resistors and an active switched-capacitor (SC) balancer, specifically designed for a 4-cell series-connected battery pack. This work also explored open circuit voltage (OCV)-driven adaptive pulse-frequency modulation (PFM) active balancing to achieve higher efficiency and better balancing speed based on different system requirements. Finally, this paper compares passive, active (SC-based), and adaptive duty-cycled hybrid balancing strategies in detail, including theoretical modeling of energy transfer and efficiency for each method. Simulation showed that the adaptive hybrid balancer speeds state-of-charge (SoC) equalization by 16.24% compared to active-only balancing while maintaining an efficiency of 97.71% with minimal thermal stress. The simulation result also showed that adaptive active balancing was able to achieve a high efficiency of 99.86% and provided an additional design degree of freedom for different applications. The results indicate that the adaptive hybrid balancer offered an excellent trade-off between balancing speed, efficiency, and implementation simplicity for 4-cell Li-ion packs, making it highly suitable for applications such as high-voltage portable chargers. Full article

This study presents an innovative bimodal approach for laboratory partial discharge (PD) analysis using a YOLOv8-based convolutional neural network (CNN). The main contribution consists, first, in the transformation of a conventional DDX-type electrical detector into a smart and autonomous data source. By training the CNN, a system capable of automatically reading and interpreting the data from the detector display—discharge magnitude and applied voltage—is developed, achieving an average training accuracy of 0.91 and converting a passive instrument into a digitalized and structured data source. Second, and simultaneously, an optical visualization system captures direct images of the PDs with a high-resolution camera, allowing for their morphological characterization and spatial distribution. For electrical voltages of 10, 13, and 16 kV, PDs were detected with a confidence level of up to 0.92. The fusion of quantitative information intelligently extracted from the electrical detector with qualitative characterization from optical analysis offers a more complete and robust automated diagnosis of the origin and severity of PDs. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

The dynamics of port-Hamiltonian systems is based on energy balance principles (the first law of thermodynamics) embedded in the structure of the model. However, when dealing with thermodynamic subsystems, the second law (entropy production) should also be explicitly taken into account. Several frameworks were developed as extensions to the thermodynamic domain of port-Hamiltonian systems. In our work, we study three of them, namely irreversible port-Hamiltonian systems, entropy-based generalized Hamiltonian systems, and entropy-production-metric-based port-Hamiltonian systems, which represent alternative approaches of selecting the state variables, the storage function, simplicity of physical interpretation, etc. On the example of a simplified lumped-parameter model of a heat exchanger, we study the frameworks in terms of their implementability for an IDA-PBC-like control and the simplicity of using these frameworks for practitioners already familiar with the port-Hamiltonian systems. The comparative study demonstrated the possibility of using each of these approaches to derive IDA-PBC-like thermodynamically consistent control and provided insight into the applicability of each framework for the modeling and control of multiphysics systems with thermodynamic subsystems. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

High parasitic capacitance from poly-insulator-poly capacitors in complementary metal oxide semiconductor (CMOS) processes presents a major bottleneck to achieving high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) in imaging systems. This study proposes a Phase-Adjustable SAR ADC that addresses this limitation through a reconfigurable architecture. The design utilizes a phase-adjustable logic unit to switch between a conventional SAR mode for high-speed operation and a noise-shaping (NS) SAR mode for high-resolution conversion, actively suppressing in-band quantization noise. An improved SAR logic unit facilitates the insertion of an adjustable phase while concurrently achieving an 86% area reduction in the core logic block. A prototype was fabricated and measured in a 0.35-µm CMOS process. In conventional mode, the ADC achieved a 7.69-bit effective number of bits at 2 MS/s. By activating the noise-shaping circuitry, performance was significantly enhanced to an 11.06-bit resolution, corresponding to a signal-to-noise-and-distortion ratio (SNDR) of 68.3 dB, at a 125 kS/s sampling rate. The results demonstrate that the proposed architecture effectively leverages the trade-off between speed and accuracy, providing a practical method for realizing high-performance ADCs despite the inherent limitations of non-ideal passive components. Full article

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

Pipeline systems are essential across various industries for transporting fluids over various ranges of distances. A notable application is natural circulation through thermo-syphoning, driven by temperature-induced density variations that generate fluid flow in closed loops. This passive mechanism is widely employed in sectors such as process engineering, oil and gas, geothermal energy, solar water heaters, fertilizers, etc. Natural Circulation Loops eliminate the need for mechanical pumps. While this passive mechanism reduces energy consumption and maintenance costs, maintaining stability and efficiency under varying operating conditions remains a challenge. This study investigates thermo-syphoning in a rectangular closed-loop system and develops optimal control strategies like using a Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) to ensure stable and efficient heat removal while explicitly addressing physical constraints. The results demonstrate that MPC improves system stability and reduces energy usage through optimized control actions by nearly one-third in the initial energy requirement. Compared to the LQR and unconstrained MPC, MPC with active constraints effectively manages input limitations, ensuring safer and more practical operation. With its predictive capability and adaptability, the proposed MPC framework offers a robust, scalable solution for real-time industrial applications, supporting the development of sustainable and adaptive natural circulation pipeline systems. Full article