Search Results (78)

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals. Full article

Background: Axonal misdirection remains a major limitation in peripheral nerve repair. While nerve guidance conduits (NGCs) and nerve scaffolds (NSCs) have advanced structurally, it is unclear whether these designs effectively reduce misdirection compared to autografts (ANGs). This systematic review evaluates the impact of NGC and NSC structural features on axonal dispersion and reinnervation accuracy using retrograde tracing animal models. Methods: A systematic search was performed through Medline (PubMed), Scopus (EBSCOhost), and the Cochrane Library from inception to December 2024. Eligible studies included mammalian in vivo models of peripheral nerve transection repaired by direct coaptation, autografts, or artificial conduits and assessed with retrograde axonal tracing. Data on neurons labeling, innervation accuracy, and histomorphometric parameters were extracted, and misdirection rates were calculated. Risk of bias was assessed using the SYRCLE tool. Due to heterogeneity, data were synthesized narratively following the SWiM framework. Results: Out of 4043 records identified through database searching and 37 through citation searching, 19 studies (49 experimental groups) met the inclusion criteria. Motoneuron counts were consistently reported across all arms, but no outcome assessing axonal misdirection was reported in more than half. Structured designs resulted in outcomes more closely aligned with ANG repair, while unstructured generally underperformed, and certainty of evidence was very low. Discussion: The evidence in this study was limited by high risk of bias, substantial inconsistency across heterogeneous study designs and outcomes, and imprecision from small animal models with sparse outcome measures. Despite the trend for structured designs to improve over basic hollow designs, current evidence does not support any structure as superior. Future research should be more standardized to provide reliable knowledge translational into clinical practice. Full article

Wearable healthcare is shifting from passive tracking to active, closed-loop care by integrating polymeric three-dimensional (3D)-printed microneedle arrays (MNAs) with soft electronics and wireless modules. This review surveys the design, materials, and the manufacturing routes that enable skin-conformal MNA wearables for minimally invasive access to the interstitial fluid and precise but localized drug delivery. Looking ahead, the converging advances in multimaterial printing, nano/biofunctional coatings, and artificial intelligence (AI)-driven control are promising “wearable clinics” that can personalize monitoring and therapy in real time, thus accelerating the translation of MNA-integrated wearables from laboratory prototypes to clinically robust, patient-centric systems. Overall, this review identifies a clear transition from proof-of-concept MNA devices toward integrated, wearable, and closed-loop therapeutic platforms. Key challenges remain in scalable manufacturing, drug dose limitations, long-term stability, and regulatory translation. Addressing these gaps through advances in hollow MNA architectures, system integration, and standardized evaluation protocols is expected to accelerate clinical adoption. However, the realization of closed-loop wearable MNA-based systems remains constrained by challenges related to power consumption, real-time data latency, and the need for robust clinical validation. Full article

Widespread antibiotic residues in aquatic environments pose escalating threats to ecological stability and human health, highlighting the urgent demand for effective remediation strategies. In recent years, photocatalytic technology based on advanced nanomaterials has emerged as a sustainable and efficient strategy for antibiotic degradation, enabling the effective utilization of solar energy for environmental remediation. This review provides an in-depth discussion of six representative categories of photocatalytic nanomaterials that have demonstrated remarkable performance in antibiotic degradation, including metal oxide-based systems with defect engineering and hollow architectures, bismuth-based semiconductors with narrow band gaps and heterojunction designs, silver-based plasmonic composites with enhanced light harvesting, metal–organic frameworks (MOFs) featuring tunable porosity and hybrid interfaces, carbon-based materials such as g-C₃N₄ and biochar that facilitate charge transfer and adsorption, and emerging MXene–semiconductor hybrids exhibiting exceptional conductivity and interfacial activity. The photocatalytic performance of these nanomaterials is compared in terms of degradation efficiency, recyclability, and visible-light response to evaluate their suitability for antibiotic degradation. Beyond parent compound removal, we emphasize transformation products, mineralization, and post-treatment toxicity evolution as critical metrics for assessing true detoxification and environmental risk. In addition, the incorporation of artificial intelligence into photocatalyst design, mechanistic modeling, and process optimization is highlighted as a promising direction for accelerating material innovation and advancing toward scalable, safe, and sustainable photocatalytic applications. Full article

In the context of Industry 4.0, new methods of manufacturing, monitoring, and data generation related to industrial processes have emerged. Over the last decade, a new method of part manufacturing that has been revolutionizing the industry is Additive Manufacturing, which comes in various forms, including the more traditional Fusion Deposition Modeling (FDM) and the more innovative ones, such as Laser Metal Deposition (LMD) and Wire Arc Additive Manufacturing (WAAM). New technologies related to monitoring these processes are also emerging, such as Cyber-Physical Systems (CPSs) or Digital Twins (DTs), which can be used to enable Artificial Intelligence (AI)-powered analysis of generated big data. However, few works have dealt with a comprehensive data analysis, based on Digital Twin systems, to study quality levels of manufactured parts using 3D models. With this background in mind, this current project uses a Digital Twin-enabled dataflow to constitute a basis for a proposed data analysis pipeline. The pipeline consists of analyzing metal AM-manufactured parts’ surface roughness quality levels by the application of a Deep Neural Network (DNN) analytical model and enabling the assessment and tuning of deposition parameters by comparing AM-built models’ 3D representation, obtained by photogrammetry scanning, with the positional data acquired during the deposition process and stored in a cloud database. Stored and analyzed data may be further used to refine the manufacturing of parts, calibration of sensors and refining of the DT model. Also, this work presents a comprehensive study on experiments carried out using the CW-GTAW (Cold Wire Gas Tungsten Arc Welding) process as the means of depositing metal, resulting in hollow parts whose geometries were evaluated by means of both 3D scanned data, obtained via photogrammetry, and positional/deposition process parameters obtained from the Digital Twin architecture pipeline. Finally, an adapted PointNet DNN model was used to evaluate surface roughness quality levels of point clouds into 3 classes (good, fair, and poor), obtaining an overall accuracy of 75.64% on the evaluation of real deposited metal parts. Full article

Packaging materials made from polypropylene (PP) can be used to protect cultural heritage objects from damage ensuring their long-life preservation. This research work concerns the assessment of recycled polypropylene hollow chamber sheets as potential packaging materials for archival collections and cultural heritage objects. It was carried out through a multidisciplinary diagnostic methodology combining mechanical methods, non-destructive imaging techniques in visible light (VIS), and ultraviolet-induced visible luminescence (UVL), as well as handheld digital microscopy, colorimetry, glossimetry, and SEM microanalysis. The results showed that the condition and mechanical performance of the specimens are affected by the ageing process. Full article

The advent of sixth-generation (6G) communications envisions a paradigm of ubiquitous intelligence and seamless physical–digital fusion, demanding unprecedented performance from the optical transport infrastructure. Achieving terabit-per-second capacities, microsecond latency, and nanosecond synchronisation precision requires a convergent, flexible, open, and AI-native x-Haul architecture that integrates communication with distributed edge computing. This study conducts a systematic literature review of recent advances, challenges, and enabling optical technologies for intelligent and autonomous 6G networks. Using the PRISMA methodology, it analyses sources from IEEE, ACM, and major international conferences, complemented by standards from ITU-T, 3GPP, and O-RAN. The review examines key optical domains including Coherent PON (CPON), Spatial Division Multiplexing (SDM), Hollow-Core Fibre (HCF), Free-Space Optics (FSO), Photonic Integrated Circuits (PICs), and reconfigurable optical switching, together with intelligent management driven by SDN, NFV, and Artificial Intelligence/Machine Learning (AI/ML). The findings reveal that achieving 6G transport targets will require synergistic integration of multiple optical technologies, AI-based orchestration, and nanosecond-level synchronisation through Precision Time Protocol (PTP) over fibre. However, challenges persist regarding scalability, cost, energy efficiency, and global standardisation. Overcoming these barriers will demand strategic R&D investment, open and programmable architectures, early AI-native integration, and sustainability-oriented network design to make optical fibre a key enabler of the intelligent and autonomous 6G ecosystem. Full article

The construction industry faces urgent challenges in reducing its carbon footprint, particularly in geotechnical engineering where conventional methods often involve high-emission materials. Artificial Ground Freezing (AGF) presents a sustainable, material-saving alternative for stabilizing water-rich strata, but its efficiency relies on accurate characterization of frozen soil behavior under in situ conditions. This study advances the understanding of AGF’s sustainability by investigating the directional shear behavior of pressurized frozen saturated medium sand (Fujian ISO standard sand) at −10 °C using a novel hollow cylinder apparatus. Through systematic testing under varying mean principal stresses (p = 0.5–6 MPa) with fixed intermediate principal stress coefficient (b = 0.5) and principal stress direction (α = 30°), we demonstrate that pressurized freezing creates a fundamentally different soil–ice composite compared to conventional unpressurized freezing. Key findings reveal (1) a linear strength increase described by the failure criterion q_f = 1.17p + 3.77 (R² = 0.98) without pressure melting effects within the tested range; (2) a distinct brittle-to-ductile transition at p ≈ 4 MPa, with associated failure mode changes from localized shear bands to homogeneous plastic flow; (3) a stable peak stress ratio (q/p ≈ 1.8) for p ≥ 4 MPa. These findings enable more reliable and potentially less conservative frozen wall design, directly contributing to reduced energy consumption in AGF operations. The research provides mechanical insights and practical parameters that enhance AGF’s viability as a low-carbon ground stabilization technology, supporting the construction industry’s transition toward sustainable underground development. Full article

This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH₄) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R²) close to 0.999 and a root mean square error (RMSE) below 8 × 10⁻⁸ m³/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article

Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. Full article

Biosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose detection in sweat using machine learning. Briefly, hollow 3D-printed polylactic acid microneedles were constructed and loaded with chitosan nanoparticles encapsulating glucose oxidase, horseradish peroxidase, and the chromogenic substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), resulting in the formation of the chitosan nanoparticle−microneedle patches. Glucose detection was performed colorimetrically by first incubating the chitosan nanoparticle−microneedle patches with glucose samples of varying concentrations and then by using photographs of the top side of each microneedle and a color recognition application on a smartphone. The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. The developed biosensor system exhibited a good linear response range toward glucose (0.025−0.375 mM), a low limit of detection (0.023 mM), a limit of quantification (0.078 mM), high specificity, and recovery rates ranging between 86–112%. Lastly, the biosensor was applied to glucose detection in spiked artificial sweat samples, confirming the potential of the proposed methodology for glucose detection in real samples. Full article

A previously buried paleosol was found on the continental shelf during a study of sea floor scour, nucleated by large artificial reef structures such as vessel hulks, barges, train cars, military vehicles, etc., called “scour nuclei”. It is a relic paleo-land surface of sapling-sized tree stumps, root systems, and fossil animal bone exhumed by scour processes active adjacent to the artificial reef structure. Over the span of five research cruises to the site in 2022–2024, soil samples were taken using hand excavation, PONAR grab samplers, split spoon, hollow tube auger, and a modified Shelby-style push box. High-definition (HD) video was taken using a Remotely Operated Vehicle (ROV) and diver-held cameras. Radiocarbon dating of wood samples returned ages of 42,015–43,417 calibrated years before present (cal yrBP). Pollen studies, together with the recovered macrobotanical remains, support our interpretation of the site as a freshwater forested wetland whose keystone tree species was Taxodium distichum—bald cypress. The paleosol was identified as an Aquult, a sub-order of Ultisols where water tables are at or near the surface year-round. A deep (0.25 m+) argillic horizon comprised the bulk of the preserved soil. Comparable Ultisols found in Georgia wetlands include Typic Paleaquult (Grady and Bayboro series) soils. Full article

This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior. Full article

This study investigated the morphological dependence of ZnO nanostructures, specifically nanotube- and nanorod-based electrodes, on their electrochemical performance for the detection of lead ions (Pb²⁺) in aqueous solutions. The results demonstrate that ZnO nanotubes exhibit significantly enhanced sensitivity compared to nanorods during CV measurements. During SWV measurements, the sensitivity (116.79 mA·mM⁻¹) and a lower limit of detection of 0.0437 μM were determined. The hollow, high-aspect-ratio structure of nanotubes provides a larger active surface area and facilitates better ion accessibility, resulting in superior electron transfer efficiency and catalytic activity. These results underscore the critical role of morphology in optimizing ZnO-based sensors. Analysis of real water samples from various natural reservoirs revealed no detectable lead, while lead was identified exclusively in artificially prepared samples containing water exposed to lead hunting shot. Over a 30-day period, the sensor retained over 95% of its initial performance when stored under vacuum conditions, demonstrating minimal signal degradation. Under ambient conditions, stability loss was attributed to moisture adsorption on the porous nanostructure. The sensor also displayed outstanding reproducibility, with current response variations across multiple probes remaining within 4%. The cost-effective and simple fabrication process of ZnO nanostructures further highlights their potential for scalable production, environmental monitoring, and integration into portable sensing devices. Full article

This study delved into the integration of carbon nanotubes (CNTs) in Ultra-High Performance Concrete (UHPC), exploring aspects such as mechanical properties, microstructure analysis, accelerated chloride penetration, and life service prediction. A dispersed CNT solution (0.025 to 0.075 wt%) was employed, along with a superplasticizer, to ensure high flowability in the UHPC slurry. In addition, the combination of high-strength functional artificial lightweight aggregate (ALA) and micro hollow spheres (MHS) was utilized as a replacement for fine aggregate to not only reduce the weight of the concrete but also to increase its mechanical performance. Experimental findings unveiled that an increased concentration of CNT in CNT1 (0.025%) and CNT2 (0.05%) blends led to a marginal improvement in compressive strength compared to the control mix. Conversely, the CNT3 (0.075%) mixture exhibited a reduction in compressive strength with a rising CNT content as an admixture. SEM analysis depicted that the heightened concentration of CNTs as an admixture induced the formation of nanoscale bridges within the concrete matrix. Ponding test results indicated that, for all samples, the effective chloride transport coefficient remained below the standard limitation of 1.00 × 10⁻¹² m²/s, signifying acceptable performance in the ponding test for all samples. The life service prediction outcomes affirmed that, across various environmental scenarios, CNT1 and CNT2 mixtures consistently demonstrated superior performance compared to all other mixtures. Full article