Search Results (443)

Upper limb disabilities resulting from stroke affect millions worldwide, yet current rehabilitation systems face limitations in portability, cost-effectiveness, and multi-joint integration. This study presents a cable-driven parallel exoskeleton integrating elbow, wrist, and finger assistance into a single portable device. The design strategically separates actuation components, housing all motors in a backpack unit, while limb-mounted modules serve as cable routing guides, achieving seven degrees of freedom within practical constraints of portability (1.2–1.5 kg) and cost-effectiveness (3D-printed components). The device incorporates seven servo motors controlled via Arduino with IMU feedback and PID algorithms. Kinematic and dynamic analyses informed mechanical design, while ARMAX system identification enabled controller optimization achieving 87.96% model fit. Experimental validation with eight healthy participants performing four upper limb exercises demonstrated consistent trends toward reduced activation in four monitored agonist muscles with exoskeleton assistance (21.3% average reduction, p = 0.087), with moderate effect sizes for proximal muscles (Cohen’s d = 0.70–0.79) and significant reductions in brachioradialis during radial/ulnar deviation (23.4%, p = 0.045). These findings provide preliminary evidence of the device’s potential to reduce muscular effort during assisted movements, warranting further clinical validation with patient populations. Full article

The advancement of miniaturized liquid chromatography (M-LC) systems has drawn considerable attention for their ability to enhance sensitivity, expedite analysis, and minimize the environmental impact of chemical usage in various analytical processes. This review explores the fundamental principles and recent innovations in M-LC technology, including diverse pump designs, advanced column techniques, and the reduction in connection devices. Emphasizing the need for components that operate efficiently at the capillary or nanoscale with minimal dead volumes, we also discuss the development of benchtop instruments and mass spectrometry integrations. The review further highlights the growing applications of M-LC in food, environmental, and biological analyses, highlighting its potential as a powerful and emerging tool in separation science. Looking forward, addressing problems such as limited robustness, fabrication complexity, and integration with sensitive detectors will be instrumental to advancing M-LC technology. Modern innovation in microfabrication, materials science, and hyphenated methods holds great promise for allowing real-time, high-throughput, and portable analytical solutions in the near future. Full article

The Monastery of San Nicolò l’Arena in Catania, a UNESCO World Heritage site, embodies a complex architectural and historical stratigraphy, reflecting successive construction phases, functional changes, and the impact of catastrophic events, including the 1669 lava flow and the 1693 earthquake. As part of the CHANGES project, this study combines historical–archaeological research with non-invasive in situ scientific analyses to investigate the materials and the conservation state of the monumental complex. Stratigraphic analysis identified multiple masonry and plaster units, allowing the reconstruction of five main construction phases and related functional changes. Portable X-ray Fluorescence (pXRF), Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFT), and handheld optical microscopy provided rapid insights into the chemical and mineralogical composition of plasters and mortars, highlighting lime-based binders with variable aggregate, including volcanic clasts, sand, and cocciopesto. In situ diagnostic analyses allowed us to distinguish pre- and post-earthquake materials, while historical data contextualized construction phases and functional transformations. The integration of archaeological and scientific approaches proved to be complementary: historical evidence guides the selection of analytical targets, while diagnostic results enrich and validate the interpretation of the building’s evolution. This interdisciplinary methodology establishes a robust framework for the understanding and valorization of complex cultural heritage sites. Full article

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit. Full article

High-renewables grids are planned in min but judged in milliseconds; credible studies must therefore resolve both horizons within a single model. Current adequacy tools bypass fast frequency dynamics, while detailed simulators lack multi-hour optimization, leaving investors without a unified basis for sizing storage, shifting demand, or upgrading transfers. We present a two-layer Hierarchical Model Predictive Control framework that links 15-min scheduling with 1-s corrective action and apply it to Germany’s four TSO zones under a stringent zero-exchange stress test derived from the NEP 2045 baseline. Batteries, vehicle-to-grid, pumped hydro and power-to-gas technologies are captured through aggregators; a decentralized optimizer pre-positions them, while a fast layer refines setpoints as forecasts drift; all are subject to inter-zonal transfer limits. Year-long simulations hold frequency within ±2 mHz for 99.9% of hours and below ±10 mHz during the worst multi-day renewable lull. Batteries absorb sub-second transients, electrolyzers smooth surpluses, and hydrogen turbines bridge week-long deficits—none of which violate transfer constraints. Because the algebraic core is modular, analysts can insert new asset classes or policy rules with minimal code change, enabling policy-relevant scenario studies from storage mandates to capacity-upgrade plans. The work elevates predictive control from plant-scale demonstrations to system-level planning practice. It unifies adequacy sizing and dynamic-performance evaluation in a single optimization loop, delivering an open, scalable blueprint for high-renewables assessments. The framework is readily portable to other interconnected grids, supporting analyses of storage obligations, hydrogen roll-outs and islanding strategies. Full article

Background: Impaired balance is one of the most common and functionally limiting problems in children with cerebral palsy (CP), significantly affecting their motor abilities and quality of life. Although force platforms are considered the gold standard for evaluating postural stability, they are often costly, non-portable, and require specialized laboratory environments, limiting their accessibility in routine clinical settings. Objective: This study aimed to develop a novel software program based on image processing techniques to assess static balance in children with CP and to evaluate its validity against traditional force platform measurements. Methods: A total of 83 children aged 5–15 years (63 with CP, GMFCS levels I–II; 20 healthy controls) participated. Static balance was assessed under four different standing conditions using both a force platform and a newly developed video-based software tool. The software utilized the frame difference method to detect center of mass movements, and parameters such as velocity and total displacement were calculated. Correlation analyses were conducted between the image processing and force platform data. Results: The software demonstrated moderate to strong positive correlations with force platform parameters in the majority of test conditions, particularly when participants stood with eyes open. In more challenging balance scenarios (e.g., eyes closed, feet together), correlations were weaker but still significant. Conclusions: The findings suggest that this image-based software is a valid, low-cost, and portable alternative for static balance assessment in children with CP. It has the potential for use in diverse clinical or home settings, supporting individualized rehabilitation strategies. Full article

Electrogenerated chemiluminescence (ECL) is a powerful analytical technique that combines the best features of both electrochemical and photoluminescence methods. In this work, we present a direct ECL-based method for the detection of fentanyl using unmodified screen-printed electrodes. The analysed system consists of tris(2,2′-bipyridyl)ruthenium(II) (Ru(bpy)₃²⁺) as the luminophore and fentanyl as the co-reactant. A comprehensive optimization of the experimental parameters, such as buffer pH, luminophore concentration and working electrode material, was performed in order to maximize the ECL response. The optimal conditions are identified as PBS buffer pH 6, 2.5 × 10⁻³ M Ru(bpy)₃²⁺ and bare gold screen-printed electrodes. Under these conditions, the system exhibited a strong and reproducible ECL signal, with a linear response to fentanyl concentration from 1 × 10⁻⁷ to 1 × 10⁻⁵ M and a limit of detection of 6.7 × 10⁻⁸ M. Notably, the proposed method does not require electrode surface modification, sample pretreatment or complex instrumentation, offering a rapid, sensitive, and cost-effective alternative for fentanyl detection. Furthermore, the storage of bare SPEs at room temperature in a dry place ensures their stability over months or even years, overcoming the limitations offered by ECL systems based on modifications of the working electrode with different nanomaterials. These findings highlight the potential of the proposed ECL approach as a robust and sensitive tool for the detection of synthetic opioids. Its simplicity, portability, and analytical performance make it particularly attractive for forensic and clinical applications where rapid and accurate opioid screening is essential. Full article

The classification of urban residential settlements through Machine Learning (ML) and Deep Learning (DL) remains a complex task due to the intrinsic heterogeneity of urban environments and the scarcity of large, accurately labeled training datasets. To overcome these limitations, this study introduces a novel GIS-based unsupervised classification framework that exploits Fuzzy C-Means (FCM) clustering for the detection and interpretation of urban morphologies. Compared to unsupervised classification approaches that rely on crisp-based clustering algorithms, the proposed FCM-based method more effectively captures heterogeneous urban fabrics where no clear predominance of specific building types exists. Specifically, the method applies fuzzy clustering to census units—considered the fundamental scale of urban analysis—based on construction techniques and building periods. By grouping census areas with similar structural features, the framework provides a flexible, data-driven approach to the characterization of urban settlements. The identification of cluster centroids’ dominant attributes enables a systematic interpretation of the spatial distribution of the built environment, while the subsequent mapping process assigns each cluster a descriptive label reflecting the prevailing building fabric. The generated thematic maps yield critical insights into urban morphology and facilitate evidence-based planning. The framework was validated across ten Italian cities selected for their diverse physical, morphological, and historical characteristics; comparisons with the results of urban zone classifications in these cities conducted by experts show that the proposed method provides accurate results, as the similarity to the classifications made by experts, measured by the use of the Adjusted Rand Index, is always higher than or equal to 0.93; furthermore, it is robust when applied in heterogeneous urban settlements. These results confirm the effectiveness of the method in delineating homogeneous urban areas, thereby offering decision makers a robust instrument to guide targeted interventions on existing building stocks. The proposed framework advances the capacity to analyze urban form, to strategically support renovation and urban regeneration policies, and demonstrates a strong potential for portability, as it can be applied to other cities for urban scale analyses. Full article

Surface-enhanced Raman spectroscopy (SERS) enables ultra-sensitive molecular detection and has broad analytical and biomedical applications; recent advances focus on high-performance substrates and innovative detection strategies. However, achieving controllable and reproducible substrate fabrication—particularly using natural templates such as hair—remains challenging, limiting SERS application in trace analysis and on-site detection. This study developed a single-hair in situ SERS platform using a natural hair template. Confinement within hair cuticle grooves and capillary-evaporation assembly enables dense arrangement of cetyltrimethylammonium bromide-coated Au nanorods and polyvinylpyrrolidone-coated Au nanoparticles, forming uniform plasmonic nanoarrays. Spectroscopy and microscopy analyses confirmed the regular alignment of nanostructures along the hair axis with denser packing at the edges. The platform detected crystal violet at 10⁻⁹ M, yielding clear signals, negligible background, and stable peaks after repeated washing. For p-phenylenediamine, enhancement was observed down to 10⁻⁶ M. On the platform, a concentration-dependent response appeared within 10⁻³–10⁻⁵ M, with spatial Raman imaging along the hair axis. Capillary-evaporation coupling and interfacial wettability facilitated solute enrichment from larger to smaller gap hotspots, improving signal-to-noise ratio and reproducibility. This portable, low-cost, and scalable method supports rapid on-site screening in complex matrixes, offering a general strategy for hotspot engineering and programmable assembly on natural templates. Full article

Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies. Full article

Entropy-based analyses have emerged as a powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG). In this regard, EEG-based lie detection offers the advantage of directly providing more objective and less susceptible-to-manipulation results compared to traditional polygraph methods. To this end, this study proposes a novel multi-scale entropy approach by fusing fuzzy entropy (FE), time-shifted multi-scale fuzzy entropy (TSMFE), and hierarchical multi-band fuzzy entropy (HMFE), which enables the multidimensional characterization of EEG signals. Subsequently, using machine learning classifiers, the fused feature vector is applied to lie detection, with a focus on channel selection to investigate distinguished neural signatures across brain regions. Experiments utilize a publicly benchmarked LieWaves dataset, and two parts are performed. One is a subject-dependent experiment to identify representative channels for lie detection. Another is a cross-subject experiment to assess the generalizability of the proposed approach. In the subject-dependent experiment, linear discriminant analysis (LDA) achieves impressive accuracies of 82.74% under leave-one-out cross-validation (LOOCV) and 82.00% under 10-fold cross-validation. The cross-subject experiment yields an accuracy of 64.07% using a radial basis function (RBF) kernel support vector machine (SVM) under leave-one-subject-out cross-validation (LOSOCV). Furthermore, regarding the channel selection results, PZ (parietal midline) and T7 (left temporal) are considered the representative channels for lie detection, as they exhibit the most prominent occurrences among subjects. These findings demonstrate that the PZ and T7 play vital roles in the cognitive processes associated with lying, offering a solution for designing portable EEG-based lie detection devices with fewer channels, which also provides insights into neural dynamics by analyzing variations in multi-scale entropy. Full article

Portable and wearable sensors have gained attention in recent years to perform measurements in many different applications. Sensors based on Electrical Impedance Spectroscopy (EIS) are particularly promising, because they can make accurate measurements with minimum perturbation to the sample under test. Electrochemical biosensors are devices that use electrochemical techniques to measure a target analyte. In the case of electrochemical biosensors based on EIS, the measured impedance spectrum is fitted to that of an equivalent electrical circuit, whose component values are then used to estimate the concentration of the target analyte. Fitting EIS data is usually carried out by sophisticated algorithms running on a PC. In this paper, we have evaluated the feasibility to perform EIS data fitting using simple Artificial Neural Networks (ANNs) that can be run on resource constrained microcontrollers, which are typically used for portable and wearable sensors. We considered a typical case of an impedance spectrum in the range 0.1 Hz–10 kHz, modeled by using the simplified Randles equivalent circuit. Our analyses have shown that simple ANNs can be a low power alternative to perform EIS data fitting on low-cost microcontrollers with a memory occupation in the order of kilo bytes and a measurement accuracy between 1% and 3%. Full article

Robotic surgery has become a cornerstone of modern urologic practice, with the da Vinci system maintaining dominance for over two decades. In recent years, however, a new generation of robotic platforms has emerged, introducing greater competition and innovation into the field. These systems aim to address unmet needs through features such as modular architectures, enhanced ergonomics, haptic feedback, and cost-containment strategies. Several platforms—including Hugo™ RAS, Versius™, Avatera™, REVO-I, Hinotori™, Senhance™, KangDuo, MicroHand S, Dexter™, and Toumai^®—have entered clinical use with early results demonstrating perioperative and short-term oncologic outcomes broadly comparable to those of established systems, particularly in procedures such as radical prostatectomy, partial nephrectomy, and radical cystectomy. At the same time, they introduce unique advantages in workflow flexibility, portability, and economic feasibility. Nevertheless, important challenges remain, including the need for rigorous comparative trials, standardized training curricula, and long-term cost-effectiveness analyses. The integration of artificial intelligence, augmented reality, and telesurgery holds the potential to further expand the role of robotics in urology, offering opportunities to enhance precision, improve accessibility, and redefine perioperative care models. This review summarizes the evolving landscape of robotic platforms in urology, highlights their clinical applications and limitations, and outlines future directions for research, training, and global implementation. Full article

Heavy metal contamination in soil poses significant ecological risks, particularly within agricultural and forest ecosystems. This study evaluates the bioaccumulation of heavy metals (Cr, Co, Ni, Cu, Zn, As, Mo, Pb) by the ground beetle Carabus coriaceus Linnaeus, 1758, across contrasting Croatian ecosystems, with a focus on the role of soil pH in shaping metal dynamics. Concentrations in soils (0–30 and 30–60 cm) and beetle tissues were measured using portable X-ray fluorescence (pXRF), which provides total concentrations; inferences on bioavailability were based on soil properties such as pH and organic matter. Orchard soils showed higher Cu (49.9 mg/kg), Mo (10.3 mg/kg), and Ni (32.5 mg/kg), whereas forest soils contained elevated Zn (105.6 mg/kg), Pb (84.5 mg/kg), As (29.7 mg/kg), and Co (16.3 mg/kg). Beetles accumulated up to 481.0 mg/kg Zn at the orchard and 90.0 mg/kg Cu at the forest site. Bioaccumulation factors exceeded 1.0 for Co, Cu, and Zn, with particularly high values for Zn (2.20–5.75) suggesting both site-specific availability and possible physiological regulation. Soil and beetle analyses were complementary rather than equivalent: soils indicated total load, while beetles reflected biologically relevant fractions. C. coriaceus, therefore, represents a sensitive bioindicator, suitable for biodiversity-based soil contamination monitoring. Full article

Cancer constitutes a category of diseases with high mortality rates, where early and precise detection plays a crucial role in diagnosis and treatment. Tumour markers are biomolecules produced during cancer progression, predominantly inert molecules that prove difficult to detect at low concentrations. Traditional detection methods, however, exhibit shortcomings in sensitivity and convenience. Biosensors, with their portability and high sensitivity, hold broad application prospects for detecting tumour markers. Nanomaterials, enhancing detection performance through signal amplification mechanisms, have increasingly become the primary choice for improving sensor analytical capabilities. This review retrieved 60 relevant publications from the Web of Science and PubMed databases (2018–2024) covering “nanomaterials, biosensors, tumour markers”, focusing on those employing signal amplification mechanisms and providing clinical sample validation. It summarises signal amplification mechanisms in nanomaterial-mediated electrochemical and optical biosensors, contrasting the differences between these two sensor types. This review focuses on the relationship between “nanomaterial functionality, signal amplification, and clinical application”. It systematises and presents the latest advances in nanomaterial-mediated biosensors for detecting tumour markers, analysing the challenges encountered in their clinical implementation. While providing guidance for the clinical translation of nanomaterial-mediated biosensors from laboratory research, their practical application still requires validation through further multicentre, large-scale studies. Full article