Search Results (876)

Food waste valorization represents a critical challenge and opportunity for sustainable food systems. This study investigated the reuse of sauce production by-products through two approaches: (i) solvent-free recovery of an oil-rich fraction and (ii) development of polymeric films for potential edible or biodegradable packaging. Centrifugation recovered approximately 10 g per 100 g of by-product. The recovered oil was characterized for total polyphenols and fatty acid composition, showing a profile consistent with vegetable oils (mainly olive oil), with minor contributions attributable to cheese and meat components. A full factorial design was used to prepare and test films and to study the effects of the three ingredients used, namely pectin, carvacrol, and sauce by-products, on their mechanical, surface, and antibacterial properties. Chemometric analysis based on principal component analysis (PCA) and regression-based modeling (multiple linear regression and response surface analysis) was applied to identify the relationships among the responses and the most influential factors. Among the tested formulations, N3 (low pectin and by-product; high carvacrol) showed the most favorable overall balance, combining the strongest antibacterial activity (mean inhibition halo diameter of 14.8 mm and 17.8 mm against Escherichia coli ATCC 11229 and Staphylococcus aureus ATCC 6538, respectively) with favorable mechanical performance, including the highest maximum force (0.53 ± 0.01 MPa) and elastic modulus, (6.8 ± 0.01 MPa) and intermediate elongation (12 ± 3%) and work at maximum force (11.9 ± 0.9 N mm). Full article

Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the United States (U.S.) and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 milligrams per deciliter (mg/dL), but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless—meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum to concurrently monitor alcohol and expired carbon dioxide (CO₂) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0–220 mg/dL. These results indicate that incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving. Full article

The production of fiber-reinforced polymer composites using 3D printing technology offers significant potential and opportunities for industrial applications. However, current dimensional limitations in 3D printing necessitate the use of joining techniques to obtain larger components. Recently, innovative strategies such as friction stir spot welding (FSSW) have attracted considerable attention for joining polymer composites due to their ability to produce strong joints with relatively low heat input (solid-state welding). Nevertheless, it is important to understand how the fibers present in fiber-reinforced polymer composites influence material flow and welding performance during the FSSW process. In this study, glass fiber-reinforced polylactic acid (PLA-GF) composite samples produced using a 3D printer were joined by means of FSSW. Five different tool rotational speeds (900, 1200, 1500, 1800, and 2100 rpm) and three different plunge rates (10, 20, and 30 mm/min) were employed during the welding process. Mechanical tests were performed on the welded joints to investigate the relationship between the welding parameters and the resulting mechanical properties. In addition, microstructural analyses were conducted to examine the formation of welding defects. The results revealed that three distinct zones were formed in the material after the FSSW process: the stir zone, mixed zone, and shoulder zone. Defects were observed in the mixed zone of the samples exhibiting relatively lower mechanical properties. The highest tensile force was achieved at a plunge rate of 20 mm/min and a rotational speed of 900 rpm. The highest bending force, on the other hand, was obtained at a plunge rate of 30 mm/min and a tool rotational speed of 2100 rpm. Full article

Machine learning interatomic potentials (MLIPs) are typically developed for globally ordered homogeneous systems (GOHomS), which exhibit only minor local deviations from equilibrium configurations. Consequently, most existing MLIPs trained on GOHomS often perform inadequately when applied to locally ordered heterogeneous systems (LOHetS), e.g., substitutional alloying elements in multicomponent alloys. To describe doping alloy systems, we develop a fine-tuned MLIP based on the MACE foundation model, specifically tailored for Mo-based dilute alloys containing one or two out of 20 substitutional elements: Cr, Fe, Mn, Nb, Re, Ta, Ti, V, W, Y, Zr, Al, Zn, Cu, Ag, Au, Hg, Co, Ni, and Hf. The model is built on more than 7000 equilibrium and non-equilibrium structures derived from first-principles density functional theory (DFT) calculations. The optimized large-scale fine-tuned model attains state-of-the-art accuracy, with a mean absolute error (MAE) and root-mean-square error (RMSE) of 2.27 meV/atom and 3.79 meV/atom for energy predictions, and 13.83 meV/Å and 24.26 meV/Å for force predictions, respectively. Systematic evaluation under different data-splitting protocols shows that unknown element extrapolation remains challenging under strict dopant hold-out, whereas substantially improved accuracy can be achieved in partial-exposure transfer settings. The fine-tuned models reduce the MAE by approximately 7–10 times compared to models trained from scratch, and by 10–20 times relative to zero-shot foundation models. This performance gain remains consistent across varying dataset sizes (equilibrium vs. non-equilibrium structures) and model scales. Our work illustrates the efficacy of transfer learning from globally ordered homogeneous systems to locally ordered heterogeneous multicomponent alloy environments. However, direct transfer to entirely unknown elements remains challenging, especially when proxy embeddings are employed without fine-tuning. Thus, to achieve high accuracy without incurring additional cost, it is essential to include unknown elements in the training dataset while minimizing the number of configurations containing known elements. Moreover, the current findings are primarily validated for dilute Mo-based alloy systems. Extending this approach to more compositionally complex alloy spaces may necessitate additional data and further fine-tuning. Full article

Ankle–foot orthoses (AFOs) are widely used in the rehabilitation of patients with neurological or musculoskeletal disorders. However, treatment outcomes may be influenced by incorrect use of the device or by inappropriate orthosis selection. Since many types of AFOs are available, differing in materials, stiffness, and geometry, an objective evaluation tool can support clinical decision-making. This work presents the design, development, and characterization of an instrumented AFO able to quantify relevant gait parameters in an objective way. The proposed device integrates three measurement modalities in a compact wearable structure. Two longitudinal strain gauges estimate ankle plantar- and dorsiflexion angles. Two force-sensitive elements detect foot–ground contact and allow identification of stance and swing phases of the gait cycle. A single inertial measurement unit (IMU) is used to measure lateral shank inclination. The strain-gauge-based angle estimation was validated against a gold-standard motion capture system, achieving a root mean square error of approximately 1.6 degrees and showing higher accuracy than the IMU for plantar/dorsiflexion measurement, while maintaining a simple electronic architecture. The force sensors were validated using a force platform and demonstrated reliable detection of loading and unloading events. Monitoring lateral inclination through the single IMU provides additional information related to balance and potential fall risk. Data are transmitted via Bluetooth Low Energy (BLE) to a custom Python-based application for real-time visualization and recording. Overall, the results validate the electronic instrumentation and demonstrate reliable system performance, indicating that the proposed instrumented AFO represents a promising platform for objective gait assessment and future clinical applications. Full article

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

Piezoelectric thin films have been widely used in micro-electromechanical systems (MEMSs), such as sensors, actuators, and resonant devices. Electromechanically driven fractures can severely degrade device performance and reliability. In this work, a phase-field damage model is developed for MEMS piezoelectric thin films under coupled electromechanical loading, incorporating pre-existing defects via an equivalent local fracture toughness. Microcracks and micro-voids arising from manufacturing defects are integrated into the model through an effective local fracture toughness, enabling a unified description of their roles in crack initiation and propagation. The proposed model is implemented in ABAQUS by means of a user-defined element (UEL) subroutine and solved using a staggered scheme. Numerical results show that the level of pre-existing defects, the applied electric potential, and the polarization direction all exert significant effects on fracture behavior. As the defect parameter D_c increases from 0 to 0.10, the reaction force decreases from 87.8 N to 86.3 N, indicating reduced fracture resistance due to manufacturing-induced defects. In addition, the reaction force changes from 90.3 N at −500 V to 86.3 N at +500 V, while it decreases from 102.9 N to 87.1 N as the polarization angle β increases from 0° to 90°. These results demonstrate that pre-existing defects and electromechanical loading jointly govern crack evolution in MEMS piezoelectric thin films. The present study provides a useful numerical tool for fracture analysis, reliability assessment, and structural design of MEMS piezoelectric devices containing manufacturing defects. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Background/Objectives: Dravet Syndrome (DS) is a severe form of epilepsy that typically manifests in the first year of life and often requires polytherapy with two or more antiseizure medications (ASMs) to achieve adequate seizure control. Whereas the combination of stiripentol (STP) and cannabidiol (CBD) has demonstrated clinical efficacy, it presents significant formulation challenges due to the low aqueous solubility and poor oral bioavailability of both compounds. Furthermore, the high daily dosages of STP (approximately 50 mg/kg/day or higher) and the oily nature of conventional CBD formulations often hinder patient compliance, as pediatric patients frequently reject these treatments due to unfavorable organoleptic properties. Methods: Nanostructured lipid carriers (NLCs) containing STP and CBD suspended in an aqueous medium were developed. The formulation was optimized using Response Surface Methodology (RSM) and subjected to comprehensive in vitro and in vivo characterization. Results: The optimized formulation exhibited a mean particle size of 175.3 nm, a polydispersity index (PDI) of 0.232, a zeta potential of −8.35 mV, and an encapsulation efficiency greater than 99% for both drugs. Physicochemical characterization via atomic force microscopy, differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy revealed spherical nanoparticles without aggregation, with the drugs molecularly dispersed within the lipid matrix. Both STP and CBD showed sustained release profiles and demonstrated oral pharmacokinetic profiles that were comparable or superior to current commercial products. Conclusions: This novel formulation represents a promising therapeutic alternative for DS, enabling the co-administration of STP and CBD while potentially enhancing CBD bioavailability and treatment adherence in pediatric populations. Full article

Accurate needle insertion during epidural anesthesia is challenging due to strong dependence on clinician experience and the limited integration of guidance modalities that simultaneously provide visual feedback and physical motion constraints. Current approaches, including ultrasound guidance and augmented reality visualization, mainly offer passive assistance and do not actively regulate insertion trajectory and depth, which may lead to variability in accuracy and increased risk of complications. This work presents a multimodal human–machine assistance system that combines augmented reality guidance with virtual fixtures to support lumbar epidural needle placement. A Tuohy needle is coupled to a haptic device interacting with a patient-specific L3–L4 lumbar phantom fabricated using 3D printing and ballistic gel. A model-based force profile reproduces the mechanical response of anatomical layers during insertion. Three experimental conditions are evaluated: freehand execution, augmented reality guidance with trajectory and depth visualization, and cooperative guidance using virtual fixtures defined by a cylindrical corridor and a depth-limiting plane. Results show a progressive reduction in mean depth error from 6.82 ± 3.46 mm (freehand) to 4.96 ± 2.41 mm (augmented reality) and 2.21 ± 1.73 mm (virtual fixtures). These findings indicate that the integration of visual and haptic guidance significantly enhances insertion precision and control. The proposed approach highlights the potential of multimodal human–machine cooperation for safer training and assisted interventions. Full article

Asymptotic solutions are derived to model the development of atmospheric internal gravity waves generated by latent heating in a two-dimensional configuration involving a vertically-sheared background flow. The mathematical model comprises nonlinear partial differential equations derived from the conservation laws of fluid dynamics under the anelastic approximation where the background density and temperature vary with altitude. The latent heating is represented by a horizontally-periodic but vertically-localized nonhomogeneous forcing term in the energy conservation equation. This generates gravity waves that are considered as perturbations to the background flow and are expressed as perturbation series, with the leading-order contributions being the solutions of linearized equations. Taking into account the nonlinear terms at the next order gives expressions for the effects of the waves on the background mean flow. Due to the vertical shear, there is a critical level where momentum and energy are transferred from the wave modes to the mean flow. The asymptotic solutions show that the wave–mean-flow interaction is nonlocal and occurs over the range of altitudes from the thermal forcing level up the critical level. This is in contrast to what occurs in the case of waves forced by an oscillatory lower boundary, where the interaction is typically localized around the critical level. It is found that the wave drag is negative above the thermal forcing level, making the mean flow velocity more negative, but it becomes positive as the waves approach the critical level, indicating wave absorption in this region. There is wave transmission through the critical level, as well as absorption, and the extent of transmission depends on the depth of the latent heating profile. The mean potential temperature is reduced above the thermal forcing level and enhanced at the critical level, a situation that could ultimately lead to the development of convective instabilities. Full article

In this paper, the circular Sitnikov three-body problem is studied under the combined influence of radiation pressure and albedo. The model consists of two equal-mass primaries moving in circular orbits about their center of mass and an infinitesimal body constrained to oscillate along the perpendicular axis. The radiative emission from one primary and the reflected radiation from the other are incorporated into the effective potential through radiation and reflectivity parameters. Using the Jacobi integral, we determine the energetically admissible region for vertical motion and examine how radiative effects modify the accessible phase space. The study shows that the system admits a single vertical equilibrium point at the origin, which remains linearly stable within the physically admissible parameter range. Radiation and albedo reduce the effective restoring force and increase the oscillation period, producing a measurable rescaling of the physical time without altering the geometrical structure of the phase trajectories. The phase-space dynamics are further explored by means of Poincare (first-return) maps obtained from numerical integration of the nonlinear equation of motion. The resulting invariant curves confirm that the motion remains regular and bounded, while their progressive contraction reflects the reduction in the oscillation amplitude with increasing radiative effects. Overall, the results show that albedo acts as a quantitative modifier of the vertical Sitnikov dynamics by changing the effective potential, the admissible energy domain, and the observable time scale, without generating new qualitative phase-space structures. Full article

Gentleness, defined as the ability to handle tissues delicately while minimizing unnecessary force, is a critical indicator of surgical proficiency. Objective and real-time assessment of gentleness in virtual reality (VR)-based training can improve the understanding of both psychomotor and cognitive components of surgical skill. This study evaluates and classifies participants’ gentleness during VR-based laparoscopic simulations using fNIRS-derived hemodynamic features. Twenty-three volunteers with no prior laparoscopic experience performed a VR-based double-grasper task while hemodynamic activity over frontal and motor cortical regions was recorded using eighteen fNIRS channels. In parallel, subjective workload (NASA-TLX), error counts, and gentleness performance score (GPS) were collected. Temporal features, including slope, root mean square, and standard deviation, were extracted from the fNIRS signals and used to train multiple machine learning classifiers. Performance labels were binarized into low and high groups using median splits of the gentleness performance score. Models were evaluated using stratified 5-fold cross-validation. Results revealed stronger right-frontal HbO activity and increased left-motor HbR responses in the low-performance group, suggesting higher cognitive effort and less efficient motor strategies. Across classifiers, slope-based features consistently outperformed variability- and amplitude-based metrics. The highest classification performance was achieved using HbR slope features with Random Forest classifiers (accuracy ≈ 0.85, AUC up to 0.93). These findings highlight the potential of fNIRS-based metrics for automated performance assessment in VR surgical training. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article