Search Results (5,319)

This study describes the development of an electrochemical sensor for quantitatively measuring acetaminophen (ACOP) in drug tablets. The sensor design is based on the modification of glassy carbon electrode (GCE) using zinc oxide nanoparticles (ZnONPs) embedded in a naturally occurring clay matrix (Sa) functionalized with phenylalanine (Phe). To ensure that the ZnONPs are homogeneously dispersed on the clay surface, the nanocomposite was synthesized using an impregnation approach and low-temperature heat treatment. The amino acid promotes specific interactions with ACOP through hydrogen bonding and π-π stacking, acting as both a stabilizing agent and a molecular recognition moiety. FTIR, UV-Vis, XRD, and FESEM/EDX mapping were employed to fully characterize the developed material (ZnONPs-Sa/Phe). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the electrochemical determination of ACOP using the modified electrode GCE/ZnONPs-Sa/Phe. Parameters susceptible to affecting the sensitivity of the developed sensor were optimized, revealing that 5 µL of the suspension ZnONPs-Sa/Phe immobilized on GCE was ideal for the sensing of ACOP in a phosphate buffer solution at pH 2.0. The calibration curve obtained by plotting peak current intensity against ACOP concentration exhibited linear behavior within the concentration range between 0.02 µM and 0.28 µM, enabling determination of the limits of detection (LOD) and quantitation (LOQ) at 8.54 × 10⁻⁹ M and 2.84 × 10⁻⁸ M, respectively. The reproducibility, stability, and selectivity of the sensor were evaluated, followed by its application to the nano-sensing of ACOP in Africure and Doliprane tablets, yielding satisfactory results. The simplicity, affordability, and high analytical sensitivity of the developed sensor make this sensing platform a promising tool for pharmaceutical quality control applications. Full article

To investigate the distribution pattern and magnitude of residual stress at the weld toe of pipe-sphere connections in welded hollow sphere joints, the blind-hole method was employed to measure welding residual stress at both the spherical weld toe and pipe weld toe, yielding the magnitude and distribution characteristics of the residual stress. Additionally, the VISUAL-Environment finite element simulation software was utilized to obtain the temperature field, residual stress field, and residual stress distribution curves at the weld seam. The results indicate that welding residual stress in the entire welded hollow sphere joint exhibits both periodic and stochastic characteristics, with these two patterns showing good consistency. Meanwhile, finite element analysis results reveal the presence of both residual tensile and compressive stresses on the spherical surface and the connected pipe, with the welding residual stress maintaining a self-equilibrated state across the entire specimen. This study provides a foundational basis for subsequent research on the fatigue performance of grid structures with welded hollow sphere joints. Full article

Leakage flow interferes with the main flow movement and has a close relationship with the rotational stall phenomenon. To study the rotational stall characteristics of mixed-flow pumps under different tip clearances (rim clearances), numerical simulations of the internal flow field of the mixed-flow pump were carried out based on the SST k-ω turbulence model and hexahedral structured meshes, with the tip clearances set to 0.2 mm, 0.5 mm, and 0.8 mm respectively. The external characteristics, internal flow field under stall conditions, impeller surface pressure, and internal vorticity distribution of the mixed-flow pump were compared among the three different tip clearances. The research results show that when the tip clearance is 0.5 mm, the numerical simulation results are in good agreement with the experimental results, indicating the high reliability of the simulation. Under the three different tip clearances, the near-stall and deep-stall operating points on the external characteristic curves are consistent. When the tip clearance is 0.8 mm, the positive slope characteristic of the flow rate–head curve of the mixed-flow pump is the most obvious. From the small flow rate condition to the large flow rate condition, the influence of the tip clearance on the efficiency of the mixed-flow pump gradually increases. Under deep-stall conditions, with increasing tip clearance, the stall vortex at the flow passage outlet causes more intense disturbances to the inlet of the downstream flow passage and induces the formation of new stall vortices at the downstream passage inlet, thereby increasing internal flow losses. The increase in the tip clearance leads to changes in the morphology of the leakage vortex, a decrease in the impeller surface pressure, intensification of flow disorder, and enhancement of the leakage vortex intensity. Moreover, compared with the rated condition, the leakage vortex of the mixed-flow pump under stall conditions is more affected by the tip clearance. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

Surface roughness is a key tribological property commonly characterized by the power spectral density (PSD) of surface topography. However, the recent Surface Topography Challenge demonstrated that measurements of identical surfaces may yield PSD curves differing by several orders of magnitude depending on the laboratory and measurement method. Such discrepancies can arise from measurement artifacts, including spike-like outliers and macroscopic surface curvature. In this work, we analyze these effects and propose a correction procedure for recovering the intrinsic roughness spectrum. The method combines nonlinear median filtering for artifact detection with robust PSD reconstruction based on multiple one-dimensional surface sections. Outliers are removed in real space, the macroscopic shape is eliminated by detrending, and the PSD is obtained as the median of spectra from individual line scans. Tests on synthetic surfaces with known roughness spectra contaminated by curvature and artificial spikes demonstrate that the method reliably recovers the original spectrum even when artifacts dominate the raw data. Application to experimentally measured surfaces further indicates that some apparent roughness features may originate from measurement noise and stitching artifacts rather than the true surface structure. Full article

Despite their excellent torsional and bending strength, the economical production of hypotrochoidal profiles (H-profiles) remains an obstacle to their use. Due to the tool clearance angle, the commercially available twin-spindle turning process has limited ability to manufacture many of the profiles standardized according to DIN 3689 (Deutsches Institut für Normung). On the other hand, the manufacturing of cycloidal as a non-involute special geometry using generating processes (hobbing or continuous generating grinding) depends critically on the accuracy of the tool geometry—whether a hobbing cutter or a grinding worm. Conventional tool design methods—based on approximations, involute-derived profiles, or iterative trial-and-error corrections—face fundamental limitations: unpredictable cutting force variations, elevated surface roughness, and limited process capability. However, if the exact tool geometry has been determined analytically, the same machine achieves significantly better performance. In this work, the exact tool geometry conjugated to the H-profile for profile manufacturing is determined based on the gearing law. This provides modular H-profile manufacturing without deviations. Consequently, a design concept that enables the implementation of all existing rolling processes—including gear hobbing, gear shaping, gear planning, and other variants such as gear grinding—is presented. For profile shaping of hollow contours, the transfer ratio is considered and a curve conjugated to the profile contour is determined for the tool. A CAD-based simulation shows very good consistency with the analytically determined tool geometry. Full article

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates $C^2$-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

We report a curious numerical observation: If atomic nuclei are modelled as connect-sums of threefoil knots with alternating chirality, the ropelength of the composite knot—a purely geometric quantity requiring no quantum mechanics—tracks the experimental binding-energy curve from hydrogen to uranium. A two-parameter fit to 50 nuclei gives $R^2=0.9998$ (coefficient of determination; 1 = perfect fit) and $\mathrm{RMS}=6.9\mathrm{MeV}$ (root-mean-square deviation between model and experiment), comparable to the five-parameter Bethe–Weizsäcker formula ($\mathrm{RMS}=8.3\mathrm{MeV}$) at less than half the parameter count. Out-of-sample predictions for ${}^{244}\mathrm{Pu}$ and ${}^{252}\mathrm{Cf}$, not used in the fit, are accurate to $0.4\mathrm{MeV}$ and $8.4\mathrm{MeV}$, respectively. What makes the observation worth reporting is not the fit itself, but the range of nuclear phenomenology that emerges uninstructed from the topology: saturation, surface energy, isospin pairing, odd-even staggering, and geometric analogues of nuclear isomers all appear as consequences of the connect-sum construction, without additional assumptions. We catalogue these correspondences, assess which are structural and which may be coincidental, and identify concrete numerical tests that would distinguish the two possibilities. Full article

During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain burning surface area, which severely constrains efficiency. To address this challenge, this study presents a neural network-enhanced rapid performance prediction and matching optimization framework for solid rocket motors (NN-SRM). In NN-SRM, neural networks are employed to simulate the evolution of key parameters during grain combustion, including burning surface area, grain volume, and moment of inertia. The zero-dimensional internal ballistics equations coupled with one-dimensional steady isentropic flow relations are incorporated into the framework to rapidly obtain thrust curves. A discrete–continuous mixed differential evolution algorithm is further employed to identify the optimal grain configuration that satisfies specific thrust requirements. Results demonstrate that, as for cylindrical, star, and finocyl grains, the neural network achieves R² exceeding 0.95. Finally, thrust matching optimization is conducted on three grains and achieves promising thrust solutions for the conditions of large thrust with short time and small thrust with long time, which demonstrates the effectiveness and practicality of the constructed NN-SRM. Full article

Background/Objectives: Ogilvie’s syndrome, or acute colonic pseudo-obstruction (ACPO), is defined by acute colonic dilatation without mechanical obstruction and carries a risk of ischemia and perforation if not promptly managed. Treatment strategies include conservative supportive care, neostigmine administration, colonoscopic decompression, and combinations of these approaches. The aim of this study was to compare the relative effectiveness of these interventions for symptom resolution using a network meta-analysis. Methods: A systematic search of PubMed, Embase, Scopus, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and grey literature was conducted from inception in February 2025 to November 2025. Prospective cohort and case–control studies evaluating conservative supportive care, neostigmine, colonoscopic decompression, or their combinations were included. A random-effects network meta-analysis was performed using odds ratios (ORs) with 95% confidence intervals (CIs). Treatment ranking was assessed using surface under the cumulative ranking curve (SUCRA) values. Risk of bias was evaluated using the Newcastle–Ottawa Scale (NOS). Results: Four studies comprising 172 patients were included. Compared with supportive care alone, supportive care combined with neostigmine was associated with higher odds of symptom resolution (network OR 13.86, 95% CI 3.06–62.83). Supportive care combined with colonoscopic decompression demonstrated an even greater effect (network OR 65.65, 95% CI 11.70–368.50). Colonoscopic decompression versus neostigmine yielded a network OR of 4.74 (95% CI 1.17–19.25). SUCRA rankings indicated that colonoscopic decompression combined with supportive care had the highest probability of being the most effective strategy. Conclusions: Active interventions, particularly colonoscopic decompression or neostigmine combined with supportive care, were associated with a higher incidence of symptom resolution compared to supportive care alone. Larger comparative studies are needed to confirm these findings and refine treatment selection. Full article

This study re-examines two geologically and geotechnically similar geosynthetic-reinforced soil walls with modular block facings (GRS-MBs) that exhibited markedly different seismic performances during the 1999 Chi-Chi earthquake (M_L = 7.3). Integrating a multi-wedge failure mechanism that captures soil–facing–reinforcement interactions with a nonlinear hyperbolic soil model representing shear stress–displacement behavior along the slip surface, the Force–equilibrium-based Finite Displacement Method (FFDM) provides consistent and robust displacement evaluations over a wide range of input seismic inertial forces. A systematic sensitivity investigation confirms that the FFDM framework responds to parameter variations in a physically meaningful manner, and that displacement predictions remain stable with respect to reasonable uncertainties in soil, reinforcement, and facing properties. The analysis clarifies why two similar GRS-MBs responded so differently during strong shaking and demonstrates the broader applicability of FFDM for displacement-based seismic assessment, including under shaking levels (e.g., k_h ≈ 0.3) that would drive conventional limit–equilibrium calculations to F_s < 1.0, a physically impossible state requiring shear resistance greater than the soil’s ultimate strength. A comparative evaluation of seismic displacement predictions using the Newmark method and FFDM shows that FFDM successfully generates displacement-based seismic resisting curves and reproduces field-observed displacements. In contrast, the Newmark method yields order-of-magnitude variability in predicted movements and may be unsuitable for displacement-sensitive engineered slopes where deformations on the order of several 10⁻³–10⁻² m are practically significant. For interaction-rich GRS-MBs with high values of k_hc, beyond the predictive capability of Newmark’s equation, FFDM offers a practical and physically grounded tool for seismic displacement assessment of reinforced soil structures. Full article

In this work, the tribological and corrosion behavior of commercially pure titanium—Ti-Gr2 with coatings obtained by mechanized contactless local electrospark deposition (LESD) with low pulse energy and a rotating electrode of TiB₂-TiAl reinforced with ZrO₂ and NbC nanoparticles was investigated. The current research is driven by the need for improved corrosion and abrasion resistance of titanium surfaces in automotive components, shipbuilding, aerospace, petrochemical and many other industrial and domestic areas. This work is a continuation of our previous study, in which the dependences of the relief, roughness, thickness, microhardness, composition and structure of the coatings obtained with this electrode on the electrical parameters of the LESD mode were studied and analyzed. In this work, the influence of the pulse parameters of the LESD process (respectively, roughness, thickness, composition and structure of the coatings) on the tribological and corrosion characteristics of the coatings has been investigated and the possibility of simultaneous protection of titanium surfaces from wear and corrosion has been demonstrated. Coatings containing nanocrystalline and amorphous-like structures have been formed, with synthesized new compounds and phases, and with increased hardness up to 13 GPa, low roughness Ra = 1.5–3 μm, thickness 8–20 μm and minimal structural defects. By comparing the potentiodynamic polarization curves, polarization resistance, electrochemical impedance and tribological characteristics of the coated surfaces, it has been established that their corrosion resistance increases by more than 1–2 orders of magnitude and their wear resistance during friction increases by 4–5 times compared to those of the substrate. Appropriate values of the electrical parameters of the LESD mode are presented, which allow obtaining uniform coatings with reduced roughness and structural defects, with predictable thickness, roughness and hardness, and with maximized corrosion and abrasive wear resistance to allow for uniform coatings with reduced roughness and structural defects, with predictable thickness, roughness and hardness, and with maximized corrosion and abrasive wear resistance. Full article

Objectives: Neonatal hyperbilirubinemia is a common disease in the neonatal period. In this meta-analysis, we aim to evaluate the efficacy of adjuvant drugs combined with phototherapy in the treatment of neonatal hyperbilirubinemia. Methods: Randomized controlled trials (RCTs) published before September 2025 were searched from PubMed, Embase, Web of Science, and the Cochrane Library. A Bayesian random-effects network meta-analysis was performed to calculate mean differences and 95% confidence intervals. Interventions were ranked using the surface under the cumulative ranking curve (SUCRA) and probability of being the best treatment (PbBT). Results: Thirty-five RCTs involving 4060 neonates were included. Compared with phototherapy alone, clofibrate, ursodeoxycholic acid, fenofibrate, and calcium phosphate significantly reduced bilirubin levels and shortened admission duration. Clofibrate showed the greatest efficacy in bilirubin reduction within 48 h (SUCRA = 0.91, PbBT = 60.9%) and in shortening hospitalization (SUCRA = 0.84, PbBT = 40.83%). Probiotics, zinc, and agar exhibited relatively modest effects, while phenobarbital showed no significant benefit. Conclusions: Adjunctive therapies were associated with greater reductions in bilirubin levels compared with phototherapy alone. Future high-quality RCTs are needed to confirm the long-term efficacy and safety of these adjuvant therapies. Full article

In-pipe robots must navigate narrow, curved passages where rigid mechanisms often require bulky steering units. Soft crawlers offer better compliance but typically rely on multiple actuators or reconfigurable contacts to achieve multi-directional motion. Drawing inspiration from biological soft crawlers that exploit directional friction and coordinated anchor–slip patterns, this study focuses on locomotion principles observed in caterpillars, water boatmen, and whirligig beetles. Based on these bioinspired concepts, we present a tendon-driven soft in-pipe robot that combines continuum bending–twisting deformation with modular anisotropic friction pads (AFPs), enabling three locomotion modes using only two motors. AFP inclination, curvature, and ridge geometry were optimized through friction tests, constant-curvature modeling, and finite element analysis to enhance directional adhesion on flat and curved surfaces. A deformation-based locomotion framework was developed to couple tendon actuation with friction orientation, achieving longitudinal crawling, transverse translation, in-place rotation, and smooth transitions via programmed twisting. Driving experiments demonstrated repeatable anchor–slip locomotion with average speeds of 28.6 mm/s, 15.7 mm/s, and 11.5°/s for the three modes. Pipe tests in straight, curved, and T-junction sections further validated stable contact and reliable gait transitions. These findings highlight the potential of friction-programmed continuum robots as compact, bioinspired platforms for advanced in-pipe inspection and diagnostic tasks. Full article