The deflection of fatigued neck.

The human neck is a unique mechanical structure, highly flexible but fatigue prone. The rising prevalence of neck pain and chronic injuries has been attributed to increasing exposure to fatigue loading in activities such as prolonged sedentary work and overuse of electronic devices. However, a causal relationship between fatigue and musculoskeletal mechanical changes remains elusive. This work aimed to establish this relationship through a unique experiment design, inspired by a cantilever beam mechanical model of the neck, and an orchestrated deployment of advanced motion-force measurement technologies including dynamic stereo-radiographic imaging. As a group of 24 subjects performed sustained-till-exhaustion neck exertions in varied positions-neutral, extended, and flexed, their cervical spine musculoskeletal responses were measured. Data verified the occurrence of fatigue and revealed fatigue-induced neck deflection which increased cervical lordosis or kyphosis by 4-5° to 11°, depending on the neck position. This finding and its interpretations render a renewed understanding of muscle fatigue from a more unified motor control perspective as well as profound implications on neck pain and injury prevention.

Crack-Deflecting Lattice Metamaterials Inspired by Precipitation Hardening.

Lattice structures, comprising nodes and struts arranged in an array, are renowned for their lightweight and unique mechanical deformation characteristics. Previous studies on lattice structures have revealed that failure often originates from stress concentration points and spreads throughout the material. This results in collapse failure, similar to the accumulation of damage at defects in metallic crystals. Here the precipitation hardening mechanism found in crystalline materials is employed to deflect the initial failure path, through the strategic placement of strengthening units at stress concentration points using the finite element method. Both the mesostructure, inspired by the arrangement of crystals, and the inherent microstructure of the base materials have played crucial roles in shaping the mechanical properties of the macro-lattices. As a result, a groundbreaking multiscale hierarchical design methodology, offering a spectrum of design concepts for engineering materials with desired properties is introduced.

A Numerical Study of Crack Penetration and Deflection at the Interface Between Peritubular and Intertubular Dentin.

Dentin is a biological composite exhibiting multilevel hierarchical structure, which confers excellent damage tolerance to this tissue. Despite the progress in characterization of fracture behavior of dentin, the contribution of composite structure consisting of peritubular dentin (PTD), intertubular dentin (ITD) and tubules to fracture resistance remains elusive. In this study, calculations are carried out for energy release rate associated with crack propagation in the microstructure of dentin. Crack penetration and deflection at the PTD-ITD interface are accounted for in the numerical analyses. It is found that high stiffness of the PTD plays a role in increasing crack driving force, promoting crack growth in the microstructure of dentin. For crack penetration across the PTD-ITD interface, the crack driving force increases with increasing tubule radius; and thick PTD generates amplified crack driving force, thereby leading to weak fracture resistance. The driving force for crack deflection increases with the increase in tubule radius in the case of short cracks, while for long cracks, there is a decrease in driving force with increasing tubule radius. Furthermore, we show that the competition between crack penetration and deflection at the PTD-ITD interface is controlled by the ratio of PTD to ITD elastic modulus, tubule radius and thickness of PTD. High PTD stiffness can increase the propensity of crack deflection. The microstructure of dentin with large tubule radius favors crack deflection and thick PTD is beneficial for crack penetration.

Highly motile nanoscale magnetic artificial cilia.

Among the many complex bioactuators functioning at different scales, the organelle cilium represents a fundamental actuating unit in cellular biology. Producing motions at submicrometer scales, dominated by viscous forces, cilia drive a number of crucial bioprocesses in all vertebrate and many invertebrate organisms before and after their birth. Artificially mimicking motile cilia has been a long-standing challenge while inspiring the development of new materials and methods. The use of magnetic materials has been an effective approach for realizing microscopic artificial cilia; however, the physical and magnetic properties of the magnetic material constituents and fabrication processes utilized have almost exclusively only enabled the realization of highly motile artificial cilia with dimensions orders of magnitude larger than their biological counterparts. This has hindered the development and study of model systems and devices with inherent size-dependent aspects, as well as their application at submicrometer scales. In this work, we report a magnetic elastomer preparation process coupled with a tailored molding process for the successful fabrication of artificial cilia with submicrometer dimensions showing unprecedented deflection capabilities, enabling the design of artificial cilia with high motility and at sizes equal to those of their smallest biological counterparts. The reported work crosses the barrier of nanoscale motile cilia fabrication, paving the way for maximum control and manipulation of structures and processes at micro- and nanoscales.

The bevel effect: a prospective, randomized investigation into needle design in dental intraligamentary anesthesia.

OBJECTIVES: This in vivo study aimed to assess the impact of needle bevel design on puncture pain, anesthetic success, and mechanical deformations in intraligamentary injection (ILA) cases, comparing a short triple facet cut (STF) to a triple lancet cut (TL) after single or repetitive use. MATERIALS AND METHODS: In a prospective single-blind trial, 200 ILA needles (STF, n = 100; TL, n = 100) were randomly assigned for dental procedures. Patients received ILA either with STF or TL needles, used once (group A; n = 50 each) or repeatedly (group B; n = 50). Puncture pain was assessed using a numerical rating scale (NRS). Anesthetic success was determined via cold spray (yes/no), and scanning electron microscopy (SEM) analyzed needle tip deformations. RESULTS: Puncture pain did not significantly differ between STF and TL, regardless of needle use or injection area. Success rates were comparable in single use (82% STF vs. 79% TL; p > 0.05). For repetitive use, STF exhibited a significantly higher success rate (80% vs. 69%; p = 0.012). Mechanical deformations were prevalent in 97.5% of needles, with TL showing greater deformations than STF after single and repeated uses. Barbs were more common in TL (90/100) than STF (84/100), with a higher relative risk for barbed-like deformation in TL (RR single use: 1.26; p < 0.001; multiple use: 7.87, p < 0.001). CONCLUSIONS: The short triple facet-designed bevel demonstrated significantly less mechanical deformation, suggesting potential advantages in maintaining needle lumen patency. CLINICAL RELEVANCE: The intraligamentary needle bevel design is linked to mechanical deformation and anesthetic success after repetitive use, but not to puncture pain.

Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization-Variational Mode Decomposition Method.

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization-variational mode decomposition (PSO-VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO-VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm.

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.

A Theoretical Study on Static Gas Pressure Measurement via Circular Non-Touch Mode Capacitive Pressure Sensor.

A circular non-touch mode capacitive pressure sensor can operate in both transverse and normal uniform loading modes, but the elastic behavior of its movable electrode plate is different under the two different loading modes, making its input-output analytical relationships between pressure and capacitance different. This suggests that when such a sensor operates, respectively, in transverse and normal uniform loading modes, the theory of its numerical design and calibration is different, in other words, the theory for the transverse uniform loading mode (available in the literature) cannot be used as the theory for the normal uniform loading mode (not yet available in the literature). In this paper, a circular non-touch mode capacitive pressure sensor operating in normal uniform loading mode is considered. The elastic behavior of the movable electrode plate of the sensor under normal uniform loading is analytically solved with the improved governing equations, and the improved analytical solution obtained can be used to mathematically describe the movable electrode plate with larger elastic deflections, in comparison with the existing two analytical solutions in the literature. This provides a larger technical space for developing the circular non-touch mode capacitive pressure sensors used for measuring the static gas pressure (belonging to normal uniform loading).

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection.

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

3D-Printed Conformal Meta-Lens with Multiple Beam-Shaping Functionalities for Mm-Wave Sensing Applications.

In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna's beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing's low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements.

Sensor-Enhanced Thick Laminated Composite Beams: Manufacturing, Testing, and Numerical Analysis.

This study investigates the manufacturing, testing, and analysis of ultra-thick laminated polymer matrix composite (PMC) beams with the aim of developing high-performance PMC leaf springs for automotive applications. An innovative aspect of this study is the integration of Fiber Bragg Grating (FBG) sensors and thermocouples (TCs) to monitor residual strain and exothermic reactions in composite structures during curing and post-curing manufacturing cycles. Additionally, the Calibration Coefficients (CCs) are calculated using Strain Gauge measurement results under static three-point bending tests. A major part of the study focuses on developing a properly correlated Finite Element (FE) model with large deflection (LD) effects using geometrical nonlinear analysis (GNA) to understand the deformation behavior of ultra thick composite beam (ComBeam) samples, advancing the understanding of large deformation behavior and filling critical research gaps in composite materials. This model will help assess the internal strain distribution, which is verified by correlating data from FBG sensors, Strain Gauges (SGs), and FE analysis. In addition, this research focuses on the application of FBG sensors in structural health monitoring (SHM) in fatigue tests under three-point bending with the support of load-deflection sensors: a new approach for composites at this scale. This study revealed that the fatigue performance of ComBeam samples drastically decreased with increasing displacement ranges, even at the same maximum level, underscoring the potential of FBG sensors to enhance SHM capabilities linked to smart maintenance.

Exploring the influence of flexural rigidity variability of aquatic plants on stem deflection geometry and flow characteristics.

The existing assumption of a constant flexural rigidity (EI) for the entire height of aquatic plants in the analysis of flow past flexible vegetation may not translate in to real scenario as EI of a flexible plant stem varies along the height due to variation in tissue structure and cross section. This variation is therefore needed to be effectively quantified for better analysis of flow-vegetation interaction. In this regard, the own-weight cantilever method was used to represent the variability of EI along the stem height of three plants: water lily, water chestnut, and lotus. Analytical models are proposed to predict stem deflection geometry and vertical velocity distribution across channel depth incorporating EI as a function of stem height. Analytical and experimental data, obtained from open-channel flume tests, significantly support the developed models and confirm the assumption of variable flexural rigidity. Further investigations show that the flexural rigidity, transcends being a mere constant numeric value; it profoundly influences various aspects of the fluid-vegetation interaction such as: the extent of deflection geometry, velocity reduction, Reynold stress variation, and penetration length. Therefore, this study is crucial for better understanding of the physics of sediment transport, pollutant mixing, and wetland management and restoration.

Catalase Detection via Membrane-Based Pressure Sensors.

Membrane-based sensors (MePSs) exhibit remarkable precision and sensitivity in detecting pressure changes. MePSs are commonly used to monitor catalytic reactions in solution, generating gas products crucial for signal amplification in bioassays. They also allow for catalyst quantification by indirectly measuring the pressure generated by the gaseous products. This is particularly interesting for detecting enzymes in biofluids associated with disease onset. To enhance the performance of a MePS, various structural factors influence membrane flexibility and response time, ultimately dictating the device's pressure sensitivity. In this study, we fabricated MePSs using polydimethylsiloxane (PDMS) and investigated how structural modifications affect the Young's modulus (E) and residual stress (σ0) of the membranes. These modifications have a direct impact on the sensors' sensitivity to pressure variations, observed as a function of the volume of the chamber (Σ) or of the mechanical properties of the membrane itself (S). MePSs exhibiting the highest sensitivities were then employed to detect catalyst quantities inducing the dismutation of hydrogen peroxide, producing dioxygen as a gaseous product. As a result, a catalase enzyme was successfully detected using these optimized MePSs, achieving a remarkable sensitivity of (22.7 ± 1.2) µm/nM and a limit of detection (LoD) of 396 pM.

Balancing Data Privacy With Access to Health Services and Research: Facilitating Confidential Information Sharing in US Multisystem Collaborations.

This article addresses data privacy issues as they relate to multisystem collaborations for prearrest deflection into treatment and services for those suffering from a substance use disorder. The authors explore how the US data privacy regulations pose barriers to collaboration and care coordination and how data privacy regulations affect researchers' ability to evaluate the impact of interventions intentioned to facilitate access to care. Fortunately, this regulatory landscape is evolving to strike a balance between protecting health information and sharing it for research, evaluation, and operations, including comments on the newly proposed federal administrative rule that will shape the future of deflection and health access in the United States.

Atypical wound trajectory after a tangential pistol shot.

Three intermediate-range shots from a Browning, model 1955, 7.65 mm caliber, pistol were fired from the driver's seat of a car at a woman in the passenger seat. She sustained three wounds: An, ultimately fatal, penetrating head wound, a graze wound across her forehead, and a tangential, perforating, wound, with bullet entry over the medial sternum and exit through the right flank. Neither postmortem CT nor forensic autopsy discovered bony thoracic injuries or perforations of the thoracoabdominal cavities. There was pulmonary contusion in the medial lobe of the right lung and hemorrhage in the adipose tissue around the right kidney. The tangential bullet had left an almost 40-cm-long wound channel through a pronounced layer of subcutaneous fat. Based on 3D reconstructed CT-data determinations, a straight bullet trajectory between entry and exit wounds would have traversed the abdominothoracic cavities, right lung, and liver. The actual trajectory, however, described a prominent curve, without signs of deflection by bone. Postulated explanations for this unusual bullet track are that the woman was twisting her body in a dynamic scene when the bullet struck; further, due to its shallow angle of incidence on the skin, the bullet was deflected to an intracutaneous path. Additionally, soft tissue resistance may have caused the bullet to yaw. Caution should, thus, be exercised when reconstructing bullet trajectories solely from entry and exist wounds, also for bullet wounds through basically homogenous soft tissues.

An Aggregate Sternal Force-Deflection Model.

Understanding the force-deflection behavior of the sternum is an important element in designing devices for implants for chest wall deformity repair. Human growth and variability makes a single measure of the stiffness difficult to determine. This work takes empirical data from the literature to develop aggregate sternal force-deflection models. Statistical methods were used to determine possible groupings based on patient age and the effect of gender. It was found that three age groups could be used, representing childhood (4-10 years), adolescence (11-19 years), and adulthood (26-53 years). Gender was found to have a statistical p-value of 0.068, 0.0611, and 0.012, respectively, in the proposed age groups. Jittering of the data was used to account for human variability and assumptions made in data comparisons. The jittered results followed that of the initial dataset. Childhood force-deflection behavior follows a relatively constant stiffness, adolescence experiences a growth period of increasing stiffness, and adulthood stiffnesses again begin to stabilize around a relatively constant value.

Gaze deflection reveals how gaze cueing is tuned to extract the mind behind the eyes.

Suppose you are surreptitiously looking at someone, and then when they catch you staring at them, you immediately turn away. This is a social phenomenon that almost everyone experiences occasionally. In such experiences-which we will call gaze deflection-the "deflected" gaze is not directed at anything in particular but simply away from the other person. As such, this is a rare instance where we may turn to look in a direction without intending to look there specifically. Here we show that gaze cues are markedly less effective at orienting an observer's attention when they are seen as deflected in this way-even controlling for low-level visual properties. We conclude that gaze cueing is a sophisticated mental phenomenon: It is not merely driven by perceived eye or head motions but is rather well tuned to extract the "mind" behind the eyes.

Phase Deflectometry for Defect Detection of High Reflection Objects.

A method for detecting the surface defects of high reflection objects using phase deflection is proposed. The abrupt change in the surface gradient at the defect leads to the change in the fringe phase. Therefore, Gray code combined with a four-step phase-shift method was employed to obtain the surface gradients to characterize the defects. Then, through the double surface illumination model, the relationship between illumination intensity and phase was established. The causes of periodic error interference were analyzed, and the method of adjusting the fringe width to eliminate it was proposed. Finally, experimental results showed the effectiveness of the proposed method.

Technique-Dependent Relationship between Local Ski Bending Curvature, Roll Angle and Radial Force in Alpine Skiing.

Skiing technique, and performance are impacted by the interplay between ski and snow. The resulting deformation characteristics of the ski, both temporally and segmentally, are indicative of the unique multi-faceted nature of this process. Recently, a PyzoFlex® ski prototype was presented for measuring the local ski curvature (w″), demonstrating high reliability and validity. The value of w″ increases as a result of enlargement of the roll angle (RA) and the radial force (RF) and consequently minimizes the radius of the turn, preventing skidding. This study aims to analyze segmental w″ differences along the ski, as well as to investigate the relationship among segmental w″, RA, and RF for both the inner and outer skis and for different skiing techniques (carving and parallel ski steering). A skier performed 24 carving and 24 parallel ski steering turns, during which a sensor insole was placed in the boot to determine RA and RF, and six PyzoFlex® sensors were used to measure the w″ progression along the left ski (w1-6″). All data were time normalized over a left-right turn combination. Correlation analysis using Pearson's correlation coefficient (r) was conducted on the mean values of RA, RF, and segmental w1-6″ for different turn phases [initiation, center of mass direction change I (COM DC I), center of mass direction change II (COM DC II), completion]. The results of the study indicate that, regardless of the skiing technique, the correlation between the two rear sensors (L2 vs. L3) and the three front sensors (L4 vs. L5, L4 vs. L6, L5 vs. L6) was mostly high (r > 0.50) to very high (r > 0.70). During carving turns, the correlation between w″ of the rear (w1-3″) and that of front sensors (w4-6″) of the outer ski was low (ranging between -0.21 and 0.22) with the exception of high correlations during COM DC II (r = 0.51-0.54). In contrast, for parallel ski steering, the r between the w″ of the front and rear sensors was mostly high to very high, especially for COM DC I and II (r = 0.48-0.85). Further, a high to very high correlation (r ranging between 0.55 and 0.83) among RF, RA, and w″ of the two sensors located behind the binding (w2″,w3″) in COM DC I and II for the outer ski during carving was found. However, the values of r were low to moderate (r = 0.04-0.47) during parallel ski steering. It can be concluded that homogeneous ski deflection along the ski is an oversimplified picture, as the w″ pattern differs not only temporally but also segmentally, depending on the employed technique and turn phase. In carving, the rear segment of the outer ski is considered to have a pivotal role for creating a clean and precise turn on the edge.

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling.

Bone drilling is a common procedure in orthopedic surgery and is frequently attempted using robot-assisted techniques. However, drilling on rigid, slippery, and steep cortical surfaces, which are frequently encountered in robot-assisted operations due to limited workspace, can lead to tool path deviation. Path deviation can have significant impacts on positioning accuracy, hole quality, and surgical safety. In this paper, we consider the deformation of the tool and the robot as the main factors contributing to path deviation. To address this issue, we establish a multi-stage mechanistic model of tool-bone interaction and develop a stiffness model of the robot. Additionally, a joint stiffness identification method is proposed. To compensate for path deviation in robot-assisted bone drilling, a force-position hybrid compensation control framework is proposed based on the derived models and a compensation strategy of path prediction. Our experimental results validate the effectiveness of the proposed compensation control method. Specifically, the path deviation is significantly reduced by 56.6%, the force of the tool is reduced by 38.5%, and the hole quality is substantially improved. The proposed compensation control method based on a multi-stage mechanistic model and joint stiffness identification method can significantly improve the accuracy and safety of robot-assisted bone drilling.