Search Results (13,096)

Electroencephalography (EEG) has emerged as a promising modality for biometric user authentication due to its inherent uniqueness and resistance to spoofing attacks. Significant advances in brain wave signal analysis over recent years have reinforced its potential as a distinctive and reliable biometric trait. However, a comprehensive evaluation of the overall progress in this field remains limited. To address this gap, this paper presents an in-depth survey of EEG-based user authentication systems. The survey begins with a comprehensive overview of the human brain’s structure and functional organization, followed by a discussion of EEG signal acquisition principles and commonly used recording devices. It provides a detailed review of data acquisition protocols, publicly and proprietary available EEG databases, and essential preprocessing techniques required for effective signal refinement. The paper further examines feature extraction strategies and classification algorithms employed in EEG-based biometric authentication. In addition to reviewing existing methodologies, the survey identifies key challenges and future considerations in EEG biometrics, such as signal variability, age, mental health conditions, inter-session and inter-subject variability, etc, to establish stable and robust algorithms. This work serves as a foundational reference for researchers, outlining current progress and presenting a structured roadmap for future advancements in EEG-based biometric systems. Full article

To investigate the impact resistance of hybrid fiber-reinforced recycled aggregate concrete (RAC) under dry–wet cycles and sulfate attack, hybrid fiber-reinforced recycled aggregate concrete (RAC) was prepared. Dynamic impact compression experiments were conducted using an SHPB test device with a 50 mm diameter. The microstructure of recycled aggregate concrete (RAC) within dry–wet cycles and sulfate attack was examined using SEM. The results indicate that the dynamic compressive strength first rises and then declines with the rise in dry–wet cycles, and increases with the increase in the average strain rate. When the number of dry–wet cycles reaches 16, the dynamic compressive strength reaches its peak, with the B4S6 group achieving a maximum dynamic compressive strength of 59.02 MPa. The dynamic elastic modulus follows a good quadratic parabolic function distribution with respect to the number of dry–wet cycles. Both the incident energy and dissipated energy density initially rise and then reduce with increasing dry–wet cycles. The energy values of RAC with different fiber types follow the order: B4S6 > S6 > B4 > RAC. Under impact loading, the strain rate–strain time history curve of recycled aggregate concrete (RAC) exhibits the change of “increase–decrease–stable–decrease”. With increasing dry–wet cycles, the degree of fragmentation of recycled aggregate concrete (RAC) first increases and then decreases, the fractal dimension first decreases and then increases, and the average particle size first increases and then decreases. SEM results and microscopic reaction mechanisms reveal that in the early stage of dry–wet cycles, sulfate ions generate ettringite and gypsum within the recycled aggregate concrete (RAC), which fill internal cracks and pores, making the concrete denser and enhancing its mechanical properties. Towards the end of the dry–wet cycle, the amount of expansive ettringite and gypsum inside the recycled aggregate concrete (RAC) increases, leading to a sharp increase in pore wall stress, which induces new microcracks in the specimens, manifesting as a decline in mechanical properties at the macroscopic level. Full article

Time-resolved visualization of local structural dynamics driven by external fields is essential for understanding structure–property relationships in functional materials and devices. Conventional ultrafast methods primarily capture femtosecond-to-picosecond photoinduced dynamics, yet they lack real-space access to spatially inhomogeneous processes occurring at their intrinsic mesoscopic timescales that govern material and device performance—particularly electrically driven processes that closely mimic actual device operating conditions. Here, we report a multifunctional ultrafast transmission electron microscopy (UTEM) platform targeting reversible structural dynamics spanning nanoseconds to microseconds under stroboscopic multi-field excitation. Our system employs photoelectron pulses generated by nanosecond UV laser illumination as the probe, alongside optical and electric pulses as pump excitation. A unified electronic synchronization scheme based on a high-speed photodiode and a digital delay generator enables precise timing control among the optical pump, electrical pump, and photoelectron pulses across the nanosecond-to-microsecond range. Using vanadium dioxide (${\mathrm{VO}}_2$) as a model system, we demonstrate a combined spatiotemporal resolution with measurable signals on the order of 10 nm–10 ns, allowing real-space mapping of spatially inhomogeneous dynamics. Electrical-pump experiments further reveal Joule-heating-induced non-uniform structural phase transitions and thermal-shock-excited megahertz-range mechanical oscillations. These results establish the developed multi-field UTEM platform as a practical tool for probing local structural dynamics in functional materials under optical and electrical excitation. Full article

Decades of cultivation and the often exclusive use of mineral fertilisers as a substitute for organic inputs have reduced the soil organic carbon (SOC) content of agricultural soils, meaning they now represent a potential sink for carbon sequestration to mitigate climate change and improve soil function. As well as being a legacy of management, SOC will also be dependent on local scale climate, topography, and soil properties; accounting for this local context is important when benchmarking fields and quantifying the potential for additional carbon sequestration. We developed a landscape-scale methodology, using a handheld infrared device, for baselining SOC stocks in the top 30 cm across a 45,000 ha farm cluster in the UK. The cluster is exploring opportunities for landscape-scale environmental improvement with a focus on natural flood protection and water pollution reduction through conversion of arable land to permanent grassland. We used the baseline data to estimate additional benefits of arable reversion for soil carbon sequestration. Because all the farms in the cluster share the same pedoclimatic conditions, variance in SOC at the field scale could be confidently attributed to differences in soil type and land use. Average SOC stocks in arable and permanent pasture fields were 103.9 and 140.3 Mg C ha⁻¹, respectively. Variance in %SOC was modelled using soil series, sample depth, land use, and clay content, and fields were benchmarked based on deviation from the expected value. The fields with the largest SOC stocks were identified and used as references to predict future potential sequestration. The conversion of arable land to permanent pasture resulted in a predicted average uplift in SOC of 55.0 Mg C ha⁻¹. Our landscape-scale methodology provides robust evidence on current and future carbon stocks for public subsidy schemes and natural capital markets that account for local constraints and opportunities. Full article

Source-Seeking in unknown scalar fields is a fundamental problem in robotics with applications in environmental monitoring and disaster response. In this work, we present a source-seeking approach with non-reversing forward velocity regulation by fusing measurement data from multiple sensors within the Stochastic Extremum Seeking (SES) framework. Specifically, a device model with multiple sensors is first constructed, and then a velocity regulation scheme is designed by leveraging the boundedness of the hyperbolic tangent function and the non-negativity of the exponential function to guarantee strictly positive forward velocity. We then evaluate the algorithm both in simulation environments and on the real-world Two-Wheeled Differential Drive Robot platform. The experiments show that our approach not only ensures the forward velocity remains non-negative, aligning with the design expectation, but also accurately locates the source. This work provides new insights into the design of velocity regulation strategies within the SES framework. Full article

Power cycling reliability is one of the most widely used frameworks to evaluate the lifetimes of power semiconductor switching devices from a thermal stress perspective. Experimental tests can be used to predict their lifetimes under operating conditions. An estimation of the number of cycles to failure $N_f$ can also be given by several lifetime models, which express the number of cycles to end of life as a function of empirical coefficients. In the existing literature, these empirical coefficients are generally estimated using the classical least squares method (to find the best-fitting line through data points), where outliers are removed using the Random Sample Consensus algorithm. The aim of this paper is to present a general strategy for the calculation of empirical coefficients for different lifetime models, such as Coffin–Manson, Coffin–Manson–Arrhenius, Norris–Landzberg, and simplified Bayerer, aiming at minimizing the number of required experimental tests. The results show that the number of experimental trials required varies between two and four, depending on the number of empirical coefficients to be determined, which is specific to the lifetime model used. Furthermore, a limited number of experimental data points are selected to avoid any degradation in accuracy. The accuracy of coefficient estimation is significantly improved by excluding outliers: some relative errors decrease by 25%. Additionally, each empirical coefficient is determined under specific thermal stress conditions, such as a constant junction temperature swing $\mathsf{\Delta }{T}_j$, constant current per bond wire I, constant cycling frequency f, or constant mean junction temperature $T_{jm}$. Furthermore, a limited number of experimental data are selected to avoid any degradation in accuracy due to outliers. Moreover, this general method can be applied to all power devices, such as IGBTs or MOSFETs. Finally, the limitations of the analytical solution for the Scheuermann lifetime model are discussed. Full article

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Modern hands-free and wearable communication devices employ multiple microphones and loudspeakers, leading to the joint presence of acoustic echo, background noise, and desired speech signals. While acoustic echo cancellation (AEC) and beamforming are commonly combined to address this challenge, existing architectures face a trade-off between computational complexity, stability, and adaptability. In particular, adaptive beamforming approaches require repeated estimation and inversion of covariance matrices, incurring high computational cost and introducing potential sensitivity to time-varying conditions. Conversely, fixed beamformers reduce online complexity and improve stability, but their performance can degrade when the acoustic scene differs from the calibration condition. In this work, we investigate low-complexity AEC–beamforming architectures that combine fixed minimum-variance distortionless response (MVDR) beamforming with adaptive AEC. Since the ordering of these stages yields two inequivalent architectures, we evaluate two configurations: AEC followed by beamforming (AEC-BF) and beamforming followed by AEC (BF-AEC). To reduce dependence on a single head pose in wearable devices, we use an offline orientation-averaged calibration strategy in which the undesired-signal covariance matrix and, when required, the relative echo transfer functions (RETFs) are estimated from calibration measurements averaged across multiple head orientations. The proposed methods are evaluated using real-device recordings from a six-microphone wearable device. The results show a clear architectural asymmetry: the fixed BF-AEC configuration achieves the highest average echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ), with substantially lower online complexity than the fully adaptive baseline, whereas the fixed AEC-BF configuration provides a higher signal-to-distortion ratio (SDR) in the evaluated experiment. Additional calibration experiments show that orientation-averaged RETF calibration provides partial generalization across the measured head orientations, but also that the RETFs are not fully orientation-invariant. Overall, the results indicate that fixed BF-AEC provides a favorable trade-off between echo suppression, stability, and online complexity under the evaluated real-recording conditions. Full article

The development of biocompatible materials has gained traction due to the increasing clinical demands for customizable and functional medical devices. Chitosan, a deacetylated derivative of chitin, is a naturally occurring biopolymer with strong antimicrobial properties, immunocompatibility, and structural adaptability, making it a promising candidate for biomedical applications. Through mechanisms such as crosslinking, ionic bonding, gas formation, and UV radiation, the mechanical properties and stimulus responses of chitosan-based hydrogels can be tailored for drug delivery at specific sites or under specific pH, light, or electrical conditions. Beyond drug delivery, chitosan hydrogels have shown considerable potential for vascular tissue repair. The porous structure of chitosan allows patient specific vascular scaffolding to be created that promotes the recovery rate veins and stenting procedures. Thermally sensitive hydrogels can deliver drugs to target regions to further assist in vascular healing. Furthermore, recent developments with composite polymers and coatings engineered to self-assemble within veins provide scaffolds for vascular tissue growth. This manuscript reviews chitosan hydrogel fabrication methods and their corresponding materials properties, with particular emphasis on drug delivery to vascular tissues. Furthermore, relevant findings from clinical trials are summarized to support the potential of chitosan hydrogels for future clinical use. Challenges of chitosan hydrogels, such as insufficient mechanical strength, high degradation rates, and complex manufacturing, remain as areas for research break-through. Full article

Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development. Full article

We develop SAFIRE (Self-Attention Fuzzy-Inspired Rule Estimator), a differentiable fuzzy-inspired rule-scoring surrogate for binary medical tabular classification coupling multi-head self-attention, Gaussian membership functions, and Hard Concrete gates for continuous rule scoring. We position SAFIRE as a smooth surrogate of the discrete $L_0$-regularised rule-selection problem and establish five mathematical results and one complexity remark: (1) the relaxed objective is differentiable almost everywhere under positive Gaussian widths (enforced by a Softplus reparameterisation) and fixed batch-normalisation statistics; (2) the deterministic-inference active threshold is strictly stricter than the expected-nonzero training threshold, identifying Hard Concrete gates as continuous rule-scoring devices rather than automatic pruning mechanisms; (3) per-sample forward complexity identifies attention and rule layers as the dominant terms; (4) the Softplus–BatchNorm–linear rule operator violates all four triangular-norm axioms—with necessary and sufficient conditions per axiom and a no-finite-parameterisation impossibility result—while a Softplus reparameterisation restores coordinate-wise monotonicity; (5) a margin-based upper bound characterises disagreement between the full classifier and a top-k rule-only surrogate; and (6) the Softplus-reparameterised constrained variant is provably coordinate-wise monotone with explicit asymptotic regimes. Evaluated on four University of California, Irvine (UCI), medical binary tabular benchmarks under repeated stratified cross-validation, SAFIRE-Prog is statistically competitive with strong interpretable, modern, and gradient-boosting baselines, with one Bonferroni-significant gain over RuleFit on the Diabetic Retinopathy Debrecen corpus. The 48-configuration Hard Concrete sweep, constrained-variant comparison, and a top-k fidelity analysis (per-fold range 0.73–0.95) provide quantitative companion measurements for the mathematical framework. A supplementary large-scale hospital electronic health record (EHR) benchmark (Diabetes 130-US Hospitals, $n=101,766$) shows the rule-scoring mechanism scales to ∼${10}^5$ records and, under severe class imbalance, statistically matches gradient boosting on accuracy while significantly exceeding it on macro-F1. The results offer a mathematically auditable pathway towards interpretable, auditable rule scoring for medical tabular classification, with rule signatures defined in a projected latent space rather than over raw clinical variables. Full article

Arthrogenic muscle inhibition (AMI), neurophysiological suppression of voluntary quadriceps activation triggered by joint effusion and inflammation, is consistently initiated within hours of any form of knee surgery. If not actively counteracted during the first two postoperative weeks, AMI may drive a cascade of neuromuscular, morphological, and biomechanical deficits that can persist for years, substantially increasing the risk of post-traumatic osteoarthritis, reinjury, and long-term functional disability. Emerging evidence indicates that preoperative patient-related factors, including baseline quadriceps strength, age, body mass index, and physical fitness, further modulate the rehabilitation response and should be considered in planning early postoperative protocols. This narrative review, which was not designed as a systematic review or meta-analysis and therefore does not include formal quality assessment or pooled statistical analysis, evaluates evidence for seven early-phase (0–2 weeks postoperative) knee muscle activation interventions: neuromuscular electrical stimulation (NMES), isometric quadriceps exercise, blood flow restriction (BFR) training, electromyographic (EMG) biofeedback, open and closed kinetic chain (OKC/CKC) exercise, cryotherapy, and continuous passive motion (CPM). Findings are synthesized against six clinically relevant dimensions, safety in the 0–2 week window, home-based usability, capacity to overcome AMI, requirement for volitional effort, objective monitoring capability, and progressive resistance, to characterize a consistent pattern: no single existing modality simultaneously meets all combined requirements for home deployment, volitional engagement, objective monitoring, and progressive resistance from postoperative day one. This collective unmet need provides direction for future device development and clinical research. Full article

Organ failure remains one of the foremost medical and socioeconomic challenges of the twenty-first century, with global transplant waiting lists far exceeding the supply of donor organs. Chronic supportive therapies sustain life but do not restore organ function, underscoring an urgent need for curative alternatives. Bioartificial organs represent a major frontier in organ replacement, driven by converging advances in cell biology, biomaterials science, and bioengineering. By integrating living cells or biologically derived matrices with engineered devices or scaffolds, these systems aim to restore functions that purely mechanical supports cannot reproduce. This review examines the principal technological platforms underpinning the field, including cell encapsulation, decellularization and recellularization, three-dimensional bioprinting, organoids, organ-on-chip systems, and xenotransplantation, and discusses their application to kidney, liver, heart, pancreas, and lung replacement. Across organ systems, progress is advancing from experimental proof-of-concept toward modular and increasingly translational platforms, although whole-organ bioengineering remains largely preclinical for the most structurally complex targets. The major unresolved barriers include vascularization, immune compatibility, scalable cell manufacturing, durable function, and stable integration between biological and engineered components. Overall, bioartificial organ engineering is evolving toward clinically relevant therapeutic strategies capable of complementing, bridging, or eventually reducing dependence on donor-organ transplantation. Full article

Induced-junction silicon photodiodes based on SiO₂/SiN_x surface passivation are attractive for high-accuracy radiometry, but their use in the deep ultraviolet is limited by UV-induced degradation of the dielectric stack. In this work, we investigate the degradation of SiO₂/SiN_x-passivated p-type silicon photodiodes under UV irradiation and evaluate strategies for improving stability through shallow implanted junctions and oxide processing. Capacitance–voltage measurements on MIS capacitors and lifetime measurements on symmetrically passivated wafers show that UV exposure causes a rapid reduction in effective dielectric charge and carrier lifetime, followed by saturation at higher dose, consistent with filling of a finite population of electrically active trap states. Induced-junction photodiodes exhibit rapid photocurrent loss at 222 nm and, in some cases, eventual collapse, indicating that the remaining effective dielectric charge is insufficient to sustain the induced junction. To maintain junction functionality after UV exposure, shallow As- and Sb-implanted junctions are employed, resulting in an initial reduction during 222 nm exposure followed by stabilization at around 80–85% of the initial value up to the highest tested dose of 200 J/cm². Further improvement is achieved by stripping and regrowing the implanted screen oxide before SiN_x deposition, yielding nearly unchanged photocurrent after prolonged 222 nm exposure up to ca. 500 J/cm². These results show that UV stability can be substantially improved by reducing device dependence on dielectric-induced inversion and by improving post-implantation interfacial oxide quality. Full article

Blood-based biomarkers offer a promising “biochemical imaging” approach for acute ischemic stroke (AIS) management, providing objective and accessible tools to complement conventional neuroimaging. This narrative review synthesizes recent advances in biomarkers derived from multiple neurovascular unit (NVU) compartments, including glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), neuron-specific enolase (NSE), neurofilament light chain (NfL), matrix metalloproteinase-9 (MMP-9), Claudin-5, Occludin, brain-derived neurotrophic factor (BDNF), interleukin-33 (IL-33), tumor necrosis factor-alpha (TNF-alpha), PARK7/DJ-1, glycogen phosphorylase BB (GP-BB), and circulating microRNAs. We focus on their stage-specific clinical utility across three scenarios: (1) ultra-early differentiation between ischemic stroke and intracerebral hemorrhage in prehospital and emergency settings; (2) dynamic prediction and monitoring of hemorrhagic transformation after reperfusion therapies; and (3) assessment of infarct burden, neurorepair potential, and long-term functional outcomes. Despite their promise, clinical translation remains hindered by assay platform heterogeneity, lack of standardized cut-off values, limited cost-effectiveness data, and insufficient prospective validation adjusted for key covariates such as age and renal function. We further discuss multi-marker panel construction, including strategies to address biomarker collinearity and overfitting. Future directions emphasize stage-specific panels, point-of-care testing devices, and artificial intelligence algorithms to advance precision medicine in stroke care. Full article