Search Results (651)

Meissner’s corpuscles are essential mechanoreceptors that detect low-frequency vibrations. However, the internal fluid dynamic processes that convert directional mechanical stimuli into neural signals are not yet fully understood. This study aims to clarify the direction-specific sensing mechanism by analyzing internal fluid flow and shear stress distribution under different vibration modes. A biomimetic microfluidic platform was developed and coupled with a dynamic mesh computational fluid dynamics (CFD) model to simulate the response of the corpuscle to 20 Hz normal and tangential vibrations. The simulation results showed clear differences in fluid behavior. Normal vibration produced localized vortices and peak wall shear stress greater than 0.0054 Pa along the short axis. In contrast, tangential vibration generated stable laminar flow with a lower average shear stress of about 0.0012 Pa along the long axis. These results suggest that the internal structure of the Meissner corpuscle is important for converting mechanical inputs from different directions into specific fluid patterns. This study provides a physical foundation for understanding mechanotransduction and supports the design of biomimetic sensors with improved directional sensitivity for use in smart skin and soft robotic systems. Full article

Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging capabilities. However, recent advances in spectral computed tomography (CT), particularly dual-energy CT (DECT), have introduced new diagnostic opportunities, offering improved soft tissue characterization. Objective: To evaluate the diagnostic performance of DECT in detecting and grading lumbar disk herniations using dedicated color-coded fat maps. Materials and Methods: A total of 205 intervertebral levels from 41 consecutive patients with lumbar symptoms were prospectively analyzed. All patients underwent both DECT and MRI within 3 days. Three radiologists with varying years of experience independently assessed DECT images using color-coded reconstructions. A five-point grading score was attributed to each lumbar level: 1 = normal disk, 2 = bulging/protrusion, 3 = focal herniation, 4 = extruded herniation, and 5 = migrated fragment. The statistical analysis included Pearson’s correlation for score consistency, Cohen’s Kappa for interobserver agreement, generalized estimating equations for a cluster-robust analysis, and an ROC curve analysis. The DECT diagnostic accuracy was assessed in a dichotomized model (grades 1–2 = no herniation; 3–5 = herniation), using MRI as reference. Results: A strong correlation was observed between DECT and MRI scores across all readers (mean Pearson’s r = 0.826, p < 0.001). The average exact agreement between DECT and MRI was 79.4%, with the highest concordance at L1–L2 (86.7%) and L5–S1 (80.4%). The interobserver agreement was substantial (mean Cohen’s κ = 0.765), with a near-perfect agreement between the two most experienced readers (κ = 0.822). The intraclass correlation coefficient was 0.906 (95% CI: 0.893–0.918). The ROC analysis showed excellent performance (AUC range: 0.953–0.986). In the dichotomous model, DECT demonstrated a markedly higher sensitivity than conventional CT (95.1% vs. 57.2%), with a comparable specificity (DECT: 99.0%; CT: 96.5%) and improved overall accuracy (98.4% vs. 90.0%). Subgroup analyses by age and disk location revealed no statistically significant differences. Conclusions: The use of DECT dedicated color-coded fat map reconstructions showed high diagnostic performance in the assessment of lumbar disk herniations compared to MRI. These findings support the development of dedicated post-processing tools, facilitating the broader clinical adoption of spectral CT, especially in cases where MRI is contraindicated or less accessible. Full article

This study evaluated the impact of biochar on the growth of strawberry plants, combining visual and proximal sensing monitoring. The plants were rooted in soil enriched with biochar, derived from pyrolysis of soft wood at 550 °C and applied in two doses (2 and 15 g/L), and after physical activation with CO₂ at 900 °C; there was also a treatment with no biochar (unaltered). Visual monitoring was based on data logging twice per week of plants’ height and number of flowers and ripe fruits. Proximal sensing monitoring involved a system including a low-cost multispectral camera and a Raspberry Pi 4. The camera acquired nadiral images hourly in three spectral bands (550, 660, and 850 nm), allowing calculation of the normalized difference vegetation index (NDVI). After three months, control plants reached a height of 12.3 ± 0.4 cm, while those treated with biochar and activated biochar grew to 18.03 ± 1.0 cm and 17.93 ± 1.2 cm, respectively. NDVI values were 0.15 ± 0.11 for control plants, increasing to 0.26 ± 0.03 (+78%) with biochar and to 0.28 ± 0.03 (+90%) with activated biochar. In conclusion, biochar application was beneficial for strawberry plants’ growth according to both visual and proximal-sensed measures. Further research is needed to optimize the integration of visual and proximal sensing monitoring, also enhancing the measured parameters. Full article

Polyvinyl chloride (PVC)-dibutyl adipate (DBA) gels are a fascinating dielectric elastomer actuator showing promise in soft robotics. When actuated with high voltages, the gel deforms towards the anode. A recent application of PVC gels in electrohydraulic actuators motivates elastic and hyperelastic constitutive relationships for tensile loading modes. PVC gels with plasticizer-to-polymer weight ratios of 2:1, 4:1, 6:1, and 8:1 w/w were evaluated. PVC gels exhibit a linear elastic region up to 25% strain. The elastic modulus decreased with increasing plasticizer content from 288.8 kPa, 56.1 kPa, 24.7 kPa, to 11 kPa. Poisson’s ratio also decreased with increasing plasticizer content from 0.42, 0.43, 0.39, to 0.35. We suggest that the decrease in polymer concentration facilitates a weakly interconnected polymer network susceptible to chain slippage that hinders the network response, thus lowering Poisson’s ratio. Our work suggests that PVC gels can be treated as isotropic and incompressible for large strains and hyperelastic modeling; however, highly plasticized gels tend to act less incompressible at small strains. The power scaling law between the elastic modulus and plasticizer weight ratio showed high agreement, making the elastic modulus deterministic for any plasticizer content. The Neo–Hookean, Mooney–Rivlin, Yeoh, Gent, Ogden, and extended tube hyperelastic constitutive models are investigated. The Yeoh model shows the highest feasibility when evaluated up to 3.5 stretch, showing a maximum normalized root-mean-square-error of 6.85%. Together, these findings establish a constitutive basis for PVC-DBA gels, incorporating small strain elasticity, large strain non-linear behavior, and network analysis while providing suggestive insight into the network structure required for accurately modeling the EPIC. Full article

This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. Full article

Current ringing in dual-active bridge (DAB) converters significantly degrades efficiency and reliability, particularly due to resonant interactions in the magnetic tank impedance network. We propose a novel impedance resonant channel shaping technique to suppress the ringing by systematically modifying the converter’s equivalent impedance model. The method begins with establishing a high-fidelity network representation of the magnetic tank, incorporating transformer parasitics, external inductors, and distributed capacitances, where secondary-side components are referred to the primary via the turns ratio squared. Critical damping is achieved through a rank-one modification of the coupling denominator, which is analytically normalized to a second-order form with explicit expressions for resonant frequency and damping ratio. The optimal series–RC damping network parameters are derived as functions of leakage inductance and winding capacitance, enabling precise control over the effective damping factor while accounting for core loss effects. Furthermore, the integrated network with the damping network dynamically shapes the impedance response, thereby attenuating ringing currents without compromising converter dynamics. Experimental validation confirms that the proposed approach reduces peak ringing amplitude by over 60% compared to the conventional snubber-based methods, while maintaining full soft-switching capability. Full article

This study investigates the acoustic performance of Ecoflex™ 00-35, a highly flexible silicone rubber, for use in soft and adaptable vibration and noise control systems. Under normal conditions, Ecoflex™ 00-35 consists of two components—Part A and Part B—which are mixed and cured at room temperature to form an elastomer. In this study, curing parameters such as the A/B mixing ratio, thinning agent addition, and curing pressure were varied to examine their effects on acoustic behavior. The microstructure of the prepared samples was analyzed using scanning electron microscopy (SEM), while sound absorption properties were measured using impedance tubes. Test results demonstrated that modifying curing parameters, applying vacuum, and incorporating a thinning agent increased the average cell diameter, leading to the fabrication of a moderate sound absorber with a sound absorption coefficient ranging from 0.35 to 0.60 in the low- to mid-frequency ranges. Further enhancement in low-frequency absorption was achieved by applying low pressure for a short duration, allowing cell expansion. In contrast, the addition of a thinning agent significantly improved absorption at higher frequencies. These findings highlight the influence of processing conditions on the acoustic behavior of soft silicone elastomers and provide valuable insights into their structure–property relationships. Ultimately, this study contributes to the development of advanced materials for acoustic damping and noise control applications. Full article

Background: Systemic therapy for soft tissue sarcoma (STS) provides modest survival benefit but carries clinically relevant toxicity. Published trials report adverse events (AEs) of varying quality and extension. Poor toxicity reporting hampers balanced risk–benefit appraisal. Methods: A PRISMA-2020 systematic review was registered in PROSPERO CRD420251087366. PubMed, CENTRAL, and Google Scholar were searched from 16 December 2024 to 16 April 2025 for randomized controlled trials (RCTs) evaluating chemotherapy, kinase inhibitors, or immune checkpoint inhibitors in STS. AE terms were harmonized to CTCAE v5.0; event rates were normalized to patients evaluable for safety. Pooled proportions used DerSimonian–Laird random-effects models; between-group comparisons employed unpaired t-tests. Risk of bias (RoB 2) was assessed with the Cochrane RoB 2 tool. Results: Ten RCTs (1079 treated, 979 control patients; 1994–2024) met the inclusion criteria, although two lacked sufficient presentation of toxicity data and seven failed to report parallel control-arm AEs. Pooled normalized incidences for treated patients were as follows: grade ≥ 3 hematological AEs, 17% (95% CI 14–20); severe gastrointestinal AEs, 9% (8–11); and grade 4 AEs, ≤6%. Anthracycline-based and kinase-inhibitor regimens displayed comparable composite grade ≥ 3 burdens (58% vs. 84%, p = 0.64). Between-study heterogeneity was considerable for gastrointestinal and hematological events (I² > 60%), driven by differing AE scales and denominators. Late-effect toxicities (cardiac, hepatic, neurological, and nephrological) were rarely reported, occurring in <1% of the patients. Across the three RCTs with control-arm data, experimental therapy increased common grade 3 AEs by 4–12 percentage points (p = 0.001). RoB 2 flagged serious concerns in 4/10 trials. Conclusions: Severe AEs in STS systemic therapy are moderately frequent; while the toxicity spectrum differs across drug classes (e.g., hematological for anthracyclines vs. neuropathic or fatigue-related for agents such as eribulin), the aggregate burden of severe AEs has not been lower for newer agents. Confidence in these estimates is limited by incomplete and non-standardized AE reporting. Future sarcoma trials must adopt CTCAE v5.0, specify explicit safety denominators, and publish full AE matrices to enable high-certainty risk–benefit assessment. Full article

Industrial process monitoring increasingly relies on sensor-generated time-series data, yet the lack of labels, high variability, and operational noise make it difficult to extract meaningful patterns using conventional methods. Existing clustering techniques either rely on fixed distance metrics or deep models designed for static data, limiting their ability to handle dynamic, unstructured industrial sequences. Addressing this gap, this paper proposes a novel framework for unsupervised discovery of operational modes in univariate time-series data using image-based convolutional clustering with composite internal evaluation. The proposed framework improves upon existing approaches in three ways: (1) raw time-series sequences are transformed into grayscale matrix representations via overlapping sliding windows, allowing effective feature extraction using a deep convolutional autoencoder; (2) the framework integrates both soft and hard clustering outputs and refines the selection through a two-stage strategy; and (3) clustering performance is objectively evaluated by a newly developed composite score, $S_{eva}$, which combines normalized Silhouette, Calinski–Harabasz, and Davies–Bouldin indices. Applied to over 3900 furnace melting operations from a Nordic foundry, the method identifies seven explainable operational patterns, revealing significant differences in energy consumption, thermal dynamics, and production duration. Compared to classical and deep clustering baselines, the proposed approach achieves superior overall performance, greater robustness, and domain-aligned explainability. The framework addresses key challenges in unsupervised time-series analysis, such as sequence irregularity, overlapping modes, and metric inconsistency, and provides a generalizable solution for data-driven diagnostics and energy optimization in industrial systems. Full article

The COVID-19 pandemic has accelerated the development of virtual internationalization (VI) in higher education institutions (HEIs). Yet, how it becomes normalized and how digital governance factors collectively address the challenges arising from its implementation remain underexplored, particularly in non-Anglophone contexts. This qualitative study draws on eight focus group discussions involving 46 participants from Austria, Belgium, China, Portugal, Poland, and Türkiye. Guided by Actor-Network Theory (ANT), the study reveals that VI has been widely normalized through pandemic-driven adaptations. While its conceptual boundaries remain contested and continually negotiated amid rapid technological advancement, the findings illuminate VI’s expansive international outreach and potential for building institutional global visibility and national soft power. Despite the normalization of VI, its implementation is constrained by pedagogical, technological, and cross-cultural factors, alongside governance and management complexities. An interdependent system of digital governance factors has also been identified across strategic, operational, human, and collaborative dimensions. This study concludes that effective VI implementation does not arise from static policies but from the continuous negotiation and coordinated alignment of these digital governance factors. Full article

Considering that soft airbag sensors made from soft materials are limited to detecting only normal forces, a novel PEBA–silicone composite magneto-sensitive airbag sensor is proposed for simultaneously detecting normal contact force and horizontal motion during human–robot interaction. In terms of structural design, the PEBA–silicone composite airbag is manufactured using fused deposition modeling, 3D printing, and silicone casting, achieving a balance between high airtightness and adjustable stiffness. Beneath the airbag, a magneto-sensitive substrate with several NdFeB magnets is embedded, while a fixed Hall sensor detects spatially varying magnetic fields to determine horizontal displacements without contact. The results of contact-force and motion experiments show that the proposed sensor achieves a force resolution of 20 g, a force range of 0 to 1100 g, a fitting sensitivity of 7.54 N/Pa, an average static stiffness of 4.82 N/mm, and a horizontal motion detection range of 0.125 to 1 cm/s. In addition, the prototype of the sensor is lightweight (with the complete assembly weighing 81.25 g and the sensing part weighing 56.13 g) and low-cost, giving it potential application value in exoskeletons and industrial grippers. Full article

Background and Clinical Significance: Tooth impaction is a developmental anomaly characterized by the inability of a tooth to emerge into its predetermined anatomical position within the oral cavity during the normal eruption period. Impaction of the mandibular second premolar is an uncommon condition and poses a heightened risk of neurosensory injury when the tooth is adjacent to the mental foramen. Early diagnosis and precise planning are therefore essential. Case Presentation: This case report presents a rare instance of an asymptomatic impacted mandibular second premolar located in close proximity to the mental foramen in a 44-year-old female patient. The impaction was discovered incidentally on an orthopantomogram, and Cone-Beam Computed Tomography (CBCT) confirmed intimate contact between the root of the impacted second premolar and the mental nerve. Surgical removal was performed under local anesthesia via a conservative triangular flap and a corticotomy window. Platelet-Rich Fibrin (PRF) generated from autologous blood was placed in the socket to foster healing. The proximity of the mental foramen dictated minimal bone removal and atraumatic luxation to avoid nerve stretch or compression. PRF was selected as an effective biomaterial shown to accelerate soft tissue healing and moderate postoperative discomfort, potentially reducing the likelihood of neurosensory disturbance. The socket presented satisfactory healing, and neurosensory function was normal at the first week follow-up and remained normal at 7 months postoperatively (longest follow-up), and no complications were reported by the patient. Conclusions: CBCT-guided planning, meticulous surgical techniques, and adjunctive PRF allowed for safe extraction without post-operative paraesthesia. Timely identification of such rare impactions broadens treatment options and minimizes complications. Full article

The large size and metal-enclosed casings of oil-immersed power transformers present significant challenges for patrol robots attempting to accurately locate their position within the transformer. Therefore, this paper proposes a three-dimensional spatial localization method for transformer patrol robots using a nine-element ultrasonic array. This method is based on wavelet decomposition and weighted filter beamforming (WD-WFB) algorithms. To address the issue of strong noise interference in the field, the ultrasonic localization signals are adaptively decomposed into wavelet coefficients at different frequencies and scales. An improved semi-soft thresholding function is applied to the decomposed wavelet coefficients to reduce noise and reconstruct the localization signals, resulting in localization signals with low distortion and a high signal-to-noise ratio(SNR). To overcome the limitations of traditional beamforming algorithms regarding interference resistance and signal resolution, this paper presents an improved WFB algorithm. By obtaining the energy distribution of the scanning area and determining the position of the maximum energy point, the spatial position of the transformer patrol robot can be determined. The test results show that the proposed improved semi-soft threshold function demonstrates superior denoising performance compared to traditional threshold functions. When compared to the soft threshold function, it achieves improvements of 15.32% in SNR and 15.57% in normalized correlation coefficient (NCC), along with a 48.91% reduction in root mean square error (RMSE). Compared with the hard threshold function, the improvement is even more significant: the SNR is improved by 60.55%, the NCC is improved by 24.90%, and the RMSE is reduced by 58.77%. The denoising effect was significantly improved compared to the traditional threshold function. In a 1200 mm × 1000 mm × 1000 mm transformer test box, the improved WFB algorithm in this paper was used to perform multiple localizations of the transformer patrol robot at different positions after denoising the field signals using the semi-soft threshold function. The maximum relative localization error was 3.47%, and the absolute error was within 2.6 cm, meeting engineering application requirements. Full article

Arch structure is a crucial interface between the human body and the ground during landing tasks, but the biomechanical effects of arch support stiffness remain insufficiently explored. This study examines the effects of arch supports with different stiffnesses on lower-limb biomechanics during landing. Twelve male participants (six normal arches, six flat feet) performed a single-leg drop landing from a 45 cm height under four arch support conditions: no arch support pad (NAP), soft-stiffness arch support pad (SAP), medium-stiffness arch support pad (MAP), and high-stiffness arch support pad (HAP). Dominant lower-limb joint angles and moments in the sagittal plane and vertical ground reaction force (vGRF)-related parameters—time to peak vGRF, peak vGRF, and max loading rate—were recorded using a motion capture system and force plate. Data were analyzed using one-way repeated measures analysis of variance (ANOVA). Arch pad stiffness significantly affected ankle and knee kinematics. The NAP condition exhibited significantly higher ankle plantarflexion at initial contact (p ≤ 0.01), as well as larger range of motion (ROM) of the knee (p = 0.03) and hip (p < 0.01), compared to the use of a SAP or MAP. The use of a HAP resulted in a significantly lower peak ankle dorsiflexion moment and larger peak knee flexion angle than the other conditions (p ≤ 0.04). The peak knee extension moment was the highest when using a NAP, and was significantly higher than that shown with the use of a MAP or HAP (p ≤ 0.02). No significant differences were observed in hip joint moments or vGRF-related parameters across conditions (p ≥ 0.52). These results indicate that hard-stiffness arch support pads modulate lower-limb mechanics during landing, potentially enhancing shock absorption and reducing knee loading. Full article

Objectives: The presence of gingival buccal recession is a frequent problem especially in the canine area. The cortical buccal bone may be absent in presence of health normal lingual/palatal bone and of other periodontal pockets. The present case report describes a minimally invasive approach in a 76-year-old patient with previously endodontically treated lower canine affected by root fracture and by a serious chronic buccal recession. Methods: The tooth was characterized by a deep vestibular bone defect, lack of buccal bone and acute periapical lesion. After extraction, Maryland bridge was positioned on the edentulous area. A two-piece convergent neck transmucosal implant was inserted with a flapless approach after 6 months. Maryland bridge was left in place for additional 3 months. After this time, digital impressions were taken, and a customized abutment was positioned. A provisional crown was designed according to Biologically Oriented Preparation Technique (BOPT) concept and maintained for 6 months. A zirconia definitive crown was digitally designed and cemented with a polycarboxylate-based cement. The Pink Esthetic Score (PES) was used as an index to assess peri-implant soft tissue stability over time (preoperatively, at 9 months, at 12 months and 72 months). Results: The patient was followed for 6 years under a conventional hygienic recall program. No complications occurred, and the PES improved from 4 preoperatively to 8 at 9 months, 10 at 12 months and 13 at 72 months. Conclusions: The use of Maryland bridge prevented occlusal trauma on healing tissues and appeared to support bone and soft tissue healing for transmucosal implant placement. A stable aesthetic rehabilitation was achieved up to 6 years. Full article