Abstract: Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of an autonomous robotic aspirator capable of detecting and removing intraoperative bleeding without continuous human intervention. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level actions execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in-vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate fast reaction times (below 0.04 s) and high blood removal rates (above 80% in all cases). The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased autonomy and improved workflow efficiency in minimally invasive procedures.

Abstract: Reliable assessment of small-strain soil stiffness is essential for geotechnical site characterization and for analysing the behaviour of embankments and other earth structures. Surface-wave methods provide an efficient non-destructive means of estimating shear-wave velocity profiles; however, their application is limited by the non-uniqueness of the inversion process. This paper presents a multimodal inversion procedure for Rayleigh-wave dispersion curves based on the particle swarm optimization algorithm. The procedure involves the calculation of theoretical dispersion curves for a horizontally layered medium and their matching with experimental data through a global search scheme. The proposed procedure was first verified using two synthetic soil profiles, and its robustness was further assessed by considering perturbations of the theoretical dispersion curve of up to 10%. Particular attention was given to the influence of higher modes on the inversion results. The results show that including higher modes leads to a more accurate and reliable determination of shear-wave velocity profiles than an inversion based solely on the fundamental mode. The procedure was subsequently validated on a transverse embankment profile using an experimental MASW dispersion curve and comparison with SCPT results. Good agreement was obtained, and the eight-layer model proved to be a good compromise between accuracy and model complexity. The proposed multimodal approach therefore represents a reliable tool for the geotechnical characterization of layered soil profiles.

Abstract: This paper presents a comprehensive experimental and simulation study on the stick–slip characteristics of pneumatic cylinders operating at low velocities. A pneumatic servo experimental system is constructed to systematically investigate stick–slip motion by measuring piston position, piston velocity, pressures in the two-cylinder chambers, and friction force. Extensive experiments are conducted on three pneumatic cylinders of different types and sizes to examine the influences of airflow rate, air source pressure, external load, and initial piston position on stick–slip behavior. Based on experimental observations, a complete mathematical model of the pneumatic servo system is developed. Unlike conventional approaches that simulate stick–slip motion using friction models driven solely by piston velocity, the proposed system-level model explicitly describes the entire dynamic process from valve control inputs to airflow, pressure evolution in the cylinder chambers, piston motion, and friction force. In addition, a new dynamic friction model is proposed by improving the revised LuGre friction model through the incorporation of a dwell-time-dependent static friction force, which is experimentally observed to play a critical role in governing stick–slip motion. Simulation studies are performed using both the proposed friction model and the revised LuGre friction model. The simulated results are systematically compared with experimental data for all tested cylinders. The results demonstrate that the proposed system model with the new friction formulation significantly improves the prediction of stick–slip characteristics, including the number of stick–slip cycles and the evolution of pressure and friction force, compared with conventional friction-model-based simulations.

Abstract: Introduction: Portable non-enzymatic electrochemical glucose sensors offer potential for decentralized healthcare and medical education; however, their integration into clinically meaningful teleconsultation workflows remains limited. This study presents the functional integration of a portable copper-modified electrochemical glucose sensor into a rural web- and Android-based telemedicine platform within a simulation-based medical education framework. Materials and Methods: Screen-printed carbon electrodes were electrochemically activated and modified via copper electrodeposition. Electrochemical characterization was performed using cyclic voltammetry to identify the glucose oxidation region and chronoamperometry for quantitative detection. Glucose solutions in PBS (pH 10) were measured using 70 µL samples, and the resulting signals were converted into glucose values (mg/dL) through a calibration model and incorporated into simulated gynecological teleconsultation workflows. Results: The sensor exhibited a stable amperometric response at +0.60 V, with a linear range of 3.125–50 mM (R2 = 0.9822), an area-normalized sensitivity of 0.061 µA·mM−1·cm−2, and a limit of detection of 1.39 mM. Implementation within the simulation scenario (n = 26) demonstrated 69% high/very high perceived usability and 88% high/very high educational value. Conclusion: These results support the feasibility of integrating portable electrochemical sensing into teleconsultation-based training environments and establishing a practical framework for future validation and deployment in rural telemedicine applications.

Abstract: To address the demanding requirements for high gain, wide bandwidth, and stable circularly polarized (CP) radiation in Wireless Local Area Network (WLAN) applications, this paper proposes and implements a broadband circularly polarized array antenna operating in the 2.4 GHz ISM band. The design employs a coplanar waveguide (CPW)-fed broadband CP monopole antenna as the radiating element. A sequential rotation (SR) technique is utilized to form a four-element array. Furthermore,​ a windmill-shaped defected ground structure (DGS) is innovatively introduced to further extend the bandwidth. The antenna is fabricated on a low-cost FR4 substrate with overall dimensions of 126 mm × 126 mm × 1 mm. Simulation and measurement results show that the array antenna achieves a -10 dB impedance bandwidth of 1.22–2.78 GHz (87.1% relative bandwidth) and a 3-dB axial ratio (AR) bandwidth of 1.85–2.66 GHz (35.0% relative bandwidth), completely covering the target band. At the center frequency of 2.2 GHz, the antenna exhibits left-hand circular polarization (LHCP) radiation, with a measured peak gain of 8.2 dBi and a cross-polarization isolation better than 15 dB. To verify its performance advantages in practical systems, the designed antenna was integrated into a ZigBee wireless communication system for data transmission testing. The results indicate that, in a complex multipath environment, the system employing the proposed antenna achieves a significantly lower packet loss rate (approximately 3.0%) compared to using a traditional linear-polarized whip antenna (19.0%), effectively optimizing the wireless link quality. The designed antenna features wide bandwidth, high gain, and strong anti-interference capability, making it suitable for WLAN, Internet of Things (IoT), and other wireless communication systems.

Abstract: This paper studies the effect of the movement of a single-sided vibro-impact nonlinear energy sink (SSVI NES) in the direction opposite to the obstacle on its dynamics and efficiency in mitigating vibrations of the primary structure (PS) subjected to the harmonic excitation. The damper efficiency is assessed by the reduction of PS maximum mechanical energy. All damper parameters are optimized simultaneously. The paper focuses on the SSVI NES with free movement in the direction opposite to the obstacle, without any constraints, which ensures its high efficiency. Its dynamics and efficiency are compared with those of other dampers, namely SSVI NES with limited motion away from the obstacle and the tuned mass damper (TMD). The preservation of damper tuning when changing the structural parameters such as the natural frequency of the PS, its damping and the intensity of the harmonic exciting force is also being studied. The dynamics of SSVI NES with free motion away from the obstacle is quite calm with periodic motion over almost the entire frequency range. Rapid alternation of modes with different periodicity and different numbers of impacts per cycle, as well as irregular modes, is observed only at high frequencies of the exciting force.

Abstract: Using a parameter that characterizes the damping capacity of the bar material, a mathematical model was developed to control the transverse vibration motion of a slender bar under boundary conditions defined by various support configurations. The model was validated for composite bars reinforced with natural fabrics made of flax, cotton, silk, or hemp fibers, and a hybrid resin matrix containing a 60% volumetric fraction of natural Dammar resin.

Abstract: The problem of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks is addressed. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices often prevents these methods from satisfying stringent accuracy requirements. As an alternative, this paper proposes a positioning framework based on Angle-of-Arrival (AoA) measurements. The framework incorporates a low-complexity AoA estimation algorithm derived from the analysis of the spatial covariance matrix. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The proposed localization algorithm belongs to the class of Weighted Least Squares (WLS) estimators and provides a unified approach to UE positioning in both 2D and 3D physical space. Simulation results demonstrate the effectiveness of the proposed framework under the challenging high-multipath conditions inherent to 5G indoor deployments.

Abstract: Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided-wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided-wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems.

Abstract: The aerodynamic behaviour of buildings equipped with porous outer envelopes is governed by the interaction between millimetre-scale geometric features and building-scale flow structures. Explicitly resolving these scales in numerical simulations is computationally prohibitive, making homogenised porous-medium formulations a practical alternative. Among them, the Darcy–Forchheimer (D–F) model is widely adopted; however, the reliability of building-scale predictions critically depends on how its resistance coefficients are identified and validated. This study proposes and assesses a consistent procedure for the determination and application of D–F coefficients for porous screens used in double-skin façade systems. Porous elements are first characterised at element scale through an analytical derivation based on aerodynamic force coefficients, from fully resolved CFD simulations of representative periodic modules. The resulting D–F coefficients are cross-compared and validated against available wind tunnel data. Secondly, the calibrated homogenised model is applied to a building-scale double-skin façade configuration. The porous layer is represented as a finite-thickness porous region governed by the identified D–F parameters and analysed through unsteady Reynolds-averaged Navier–Stokes simulations. The model’s capability to reproduce global aerodynamic loads, local pressure distributions, and wake characteristics is evaluated against experimental data. The results demonstrate that a properly calibrated D–F formulation provides an accurate and computationally efficient representation of porous façade systems, bridging element-scale characterisation and structural-scale aerodynamic performance.

Abstract: The basic aim of this research was to compare the experimentally evaluated flow field at the stern region of a hull form with large block coefficient with the respective numerical results. To this end, a Five-Hole Pitot tube was used to capture the wake flow at the stern region of a scaled model of a bulk carrier in the towing tank of the Laboratory for Ship and Marine Hydrodynamic (LSMH) of the National Technical University of Athens (NTUA). The measurements were carried out at three aft sections of the model, where large scale vortices are usually generated: the section at the propeller, a section ahead of it and another one under the transom stern. The model was towed at a speed of 1.214 m/s, corresponding to Fn =0.17. The tube was calibrated on air at an equivalent Re, while a second in-house calibration technique was developed to consider installation misalignments and to increase overall measurement accuracy. The numerical calculation of the flow was performed using CFD tools developed at LSMH of NTUA. The method solves the RANS equations by applying the finite volume approach underneath a prescribed free surface which is derived by a potential flow code. The numerical results are in good agreement with the experimental ones, confirming the robustness of both methods.

Abstract: Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-time Responsive (CTR) scheme by selecting the phase with the highest cumulative travel time (CTT) first and then identifying the compatible phase combination with the greatest group CTT, allowing more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion aware mechanism, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. By comparing with other baselines, our simulation results show that under moderate-to-heavy traffic conditions, CATS reduces mean end-to-end travel time by up to 23.72% and improves throughput by up to 93.19% over the baselines, confirming that the co-design of navigation and signal control produces complementary benefits.

Abstract: Accurate characterization of rock mechanical parameters in heterogeneous geological formations remains fundamentally challenging because lithological heterogeneity induces mapping ambiguity: similar logging responses may correspond to different mechanical properties. Existing approaches, including empirical formulas, pure machine learning models, and feature-augmented learning methods, generally assume a single global mapping between logging data and geomechanical response, which limits their ability to resolve heterogeneity-induced bias. To address this issue, this study proposes a heterogeneity-aware residual learning framework for rock mechanical parameter characterization from well logs. Rather than treating lithotype information as a simple auxiliary feature, the method explicitly models lithotype-dependent deviations as structured conditional corrections to the global geomechanical response. In this way, heterogeneity is represented as a learnable source of systematic bias rather than being implicitly absorbed into a single global predictor. The proposed framework is potentially extendable to other heterogeneous subsurface systems and applicable to heterogeneous geological systems where conditional bias exists. By explicitly accounting for lithology-controlled response deviations, it alleviates the non-uniqueness caused by heterogeneity and improves the physical consistency of prediction. Cross-well validation demonstrates that the proposed method effectively reduces lithotype-induced bias and achieves stable generalization under varying geological conditions. Further analysis shows that the performance gain does not arise from additional information alone, but from structural modeling of conditional bias under heterogeneous lithological regimes. This study provides a generalizable modeling paradigm for geomechanical characterization in heterogeneous subsurface systems and offers a physically consistent basis for reliable prediction in complex geological environments.

Abstract: Detection of leaks in hydrogen (H2) infrastructure is required on a large scale to enable a safe widespread use of this clean energy source. Sensing solutions must be low-cost, use scalable fabrication methods and allow multiplexed detection, while providing reliable safety alarms as fast as possible. Optical methods can make this possible while avoiding the risk of ignition due to electronics at the point of detection. Metal hydride-based micro-mirror configurations benefit from a simple interrogation scheme, as long as the sensitive element can produce a large optical response. Magnesium thin films undergo a drastic variation of properties when hydrogenated, making them suitable for this application. In this work, a micro-mirror device using single-mode fibers capable of detecting the presence of H2 with a loading t10 and t90 of 1.2 and 3.0 seconds, respectively, is demonstrated. A complete interrogation unit was developed, presenting a solution suited for widespread deployment using industry-standard optical components and equipment.

Abstract: This study describes a procedure for preparing microfluidic chips made from polydimethylsiloxane (PDMS). It uses a UV ozone cleaner for surface activation instead of the more common oxygen plasma treatment. Although this process has some limitations compared to oxygen plasma bonding, it can be used when a low-cost procedure for a small number of microfluidic chips is required or when an oxygen plasma etcher is unavailable. The challenges of this process arise from the slight hardening of the PDMS surface when it is activated for 70 minutes, which is necessary for reliable bonding. It is demonstrated that damage resulting from this hardening in conjunction with careless handling of the microfluidic chip is mitigated by incorporating predetermined break structures and using tubes with an outer diameter that is smaller than the inlets. Additionally, pre-polymerized PDMS glue and PDMS seals are suggested to ensure that the tubes are properly sealed. To demonstrate the concept, two microfluidic structures were prepared and tested, achieving flow rates of at least 170 µL/min at ±180 mbar. Bonding remains stable up to a pressure of approximately 0.5 bar.

Abstract:

Methicillin-resistant Staphylococcus aureus (MRSA) is a major global health threat responsible for significant morbidity and mortality, accounting for approximately 19,000 deaths annually in the United States. MRSA resistance is primarily mediated by the mecA and mecC genes, which are carried on the staphylococcal cassette chromosome mec (SCCmec) integrated at the OrfX locus of Staphylococcus aureus, resulting in reduced susceptibility to β-lactam antibiotics. Rapid and accurate diagnostic methods are therefore essential to improve clinical outcomes and limit disease transmission. This mini-review evaluates current MRSA diagnostic approaches, including polymerase chain reaction (PCR) and its variants, isothermal amplification techniques (LAMP and RPA), CRISPR-based diagnostics, and electrochemical biosensors. These methods are compared in terms of diagnostic accuracy, clinical utility, cost-effectiveness, and practical limitations. Overall, isothermal amplification demonstrated a more favorable balance in cost-effectiveness and practical limitations compared to other methods. However, when considering clinical utility and diagnostic accuracy, the results were context dependent. No single method was universally optimal, and the choice of diagnostic approach depends on the clinical context and resource availability.

Abstract: Leaks in pipe systems result in significant economic losses, environmental hazards, and public health risks. Transient-based leak detection methods, which exploit the dynamics of pressure waves in response to system anomalies, have emerged as efficient techniques for identifying and characterizing leaks in pressurized pipelines. These methods offer dis-tinct advantages, including minimal data requirements, high sensitivity to low-pressure anomalies, and resilience to the ill-posed conditions often affecting steady-state models. This paper reviews transient-based leak detection, synthesizing findings from over 138 peer-reviewed publications spanning the past three decades. The review categorizes tran-sient-based methods into transient damping, transient reflection, system response, and inverse transient methods, analyzing the prevalence, evolution, and research rate of each category over time. By structuring the review around key aspects such as simulation do-main type, analysis approach, system response, solver strategies, adaptability to noise, viscoelasticity, and network complexity, this paper identifies significant trends and shifts in research focus. A comprehensive tabular dataset of 138 studies captures how research activity in various areas has accelerated, slowed, or reached stability, offering insights into the evolving priorities within the field. This review highlights areas for further develop-ment, particularly in addressing AI-enhanced applications, transient excitation and measurement sites design, noise resilience, comprehensive leak characterization, valida-tion approaches, and scalability for complex network applications, providing a resource to guide future research in transient-based leak detection.

Abstract:

This study presents an analytical–experimental investigation of the mechanical and tribological behaviour of two coating systems applied to deep, internally profiled cylindrical components manufactured via Electrochemical Rifling (ECR): a hard anodised aluminium oxide (AAO) coating on an aluminium alloy and a hard chromium coating on alloy steel. The experimental characterisation includes microhardness measurements, coefficient of friction determination, and controlled sliding wear tests. The results indicate that the chromium coating exhibits approximately 3.2 times higher microhardness and a 16% lower average coefficient of friction compared to the anodised aluminium layer, leading to significantly improved wear resistance.A good agreement is observed between analytical predictions and experimental results. For the steel specimen, values of approximately 26,800 cycles (analytical) and 36,000 cycles (experimental) were obtained, while for the aluminium specimen the corresponding values are approximately 2,050 and 2,012 cycles.Considering the degradation mechanisms typical of hard chromium coatings, a conservative reliability-oriented criterion yields a functional service life of approximately 12,000 cycles for the chromium coating and around 1,000 cycles for the anodised aluminium coating. A Weibull-based reliability analysis (R = 0.95) indicates service lives of approximately 5,200 cycles and 433 cycles, respectively.

Abstract: Real-time ultrasound imaging through sonolucent cranial implants is an emerging modality for post-neurosurgical monitoring of the adult brain, but quantitative interpretation remains challenging due to speckle, attenuation, shadowing, and the difficulty of consistently delineating thin anatomical landmarks. We present a deep learning system developed at Longeviti Neuro Solutions for segmenting key intracranial structures–the ipsilateral and contralateral lateral ventricles and the cranial midline–in coronal-plane adult cranial ultrasound images from patients with Longeviti ClearFit^® Acoustic Brain Interface (ABI)TM implants. Our dataset comprises 456 proprietary, de-identified ultrasound frames (JPEG with known pixel spacing) annotated in CVAT with ventricle and midline labels. We benchmark multiple encoder–decoder segmentation architectures and address severe class imbalance via class-weighted optimization, test-time augmentation (horizontal flip with left–right label swapping), and class-specific post-processing to reduce spurious components and improve mask coherence. The best-performing configuration achieves a foreground macro Dice of 0.869 on a held-out test set, with ventricle Dice values above 0.92 and midline Dice of approximately 0.75. Finally, we transform predicted masks into geometry-based metrology by estimating maximal perpendicular ventricle spans and ventricle-to-midline distances, producing standardized measurement overlays suitable for downstream review.

Abstract: Quadriplegic (QP) patient’s emotion detection using Electroencephalogram (EEG) signal is challenging due to daily medication. During medications of existing QP patients, EEG signal bands such as: alpha, theta, and beta bands overlaps. Accurate detection of emotion from overlapped EEG band signal is the first major problem here. EEG signal acquisition during medication is termed as Pharmaco-EEG (pEEG) Emotion recognition methods used in drug-free EEG signals are not suitable for pEEG based emotion recognition. Quadriplegic patient’s pEEG signal has second major problem is the artefacts such as involuntary spasms, respiratory artefacts, or caregiver interactions. To solve the above two major problems RBMT frame work is proposed, which consists of SiO2, Nano Coated electrode and optimized algorithms to detect emotion from pEEG signals. Sio2 Nano coating-based graphene EEG electrode is developed in this paper which prevents overlapping of bands. pEEG signals are pre-processed with Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) and removes the artefacts in pEEG signal. In this paper, proposed Rehabilitation BCI system with music therapy (RBMT) framework consists of Secretary Bird Optimization Algorithm (SBOA-LSTM) for spatial and temporal feature extraction. The RBMT framework consists of a Mutual-Cross-Attention mechanism tuned by SBOA and integrated with a SoftMax layer to classify the emotional states as anxiety or depression more effectively based on measurement of valence and arousal levels. Based on predicted emotion levels, a music therapy module in RBMT proposed framework is triggered through BCI audio interface to play songs. Hearing the play, QP patients reduces their anxiety / depression levels. The framework continuously evaluates emotion level through measuring valence and arousal levels, and dynamically changes the songs from the play list using the Fisher-Yates Shuffle algorithm to reduce emotions levels. The proposed RBMT performs as personalized therapeutic devices for QP patients. The emotion prediction accuracy of proposed RBMT framework is 98%, when compared to other traditional methods and EEG sensors.