Strength Data Research Articles

Polyaxial failure criteria for in situ stress analysis using borehole breakouts: Review of existing methods and development of an empirical alternative

Analysis of compressive wellbore failure, or breakouts, is one of the primary methods of constraining the maximum horizontal stress in deep boreholes. To estimate stress using the observation of breakouts, one needs to measure the breakout width from image logs and use a failure theory to predict the stress that led to the development of the measured breakout. Most commonly, Mohr–Coulomb failure criterion has been used which disregards the influence of intermediate stress on strength. Hence, various polyaxial criteria have been proposed to include this effect. Here, we first review some selected polyaxial criteria: Drucker–Prager, Mogi, Modified Wiebols–Cook, and Modified Lade, and we conclude that their application in breakout analysis may be cumbersome and often unreliable. One reason for these problems is that the criteria are defined using stress invariants, while the stress estimation is most easily performed and analyzed in the principal stress space. Therefore, an alternative is to define the polyaxial criterion as a simple relation between maximum and intermediate stresses. We propose to define such an empirical criterion as a second order polynomial which fits trends observed in polyaxial laboratory strength data. Such approach allows to limit strength overestimation, often associated with the use of previous polyaxial criteria, and to easily relate uncertainties in strength estimation to uncertainty in maximum horizontal stress prediction.

Effect of phosphoric acid etching and blasting with aluminum oxide on the enamel topography and adhesion of resin composite to intact or abraded enamel

ObjectivesTo evaluate the effects of the phosphoric acid (PA) etching, self-etching technique (SE) and blasting with Al2O3 particles (BL) on the bonding of a dental adhesive to intact (INT) or abraded (ABR) enamel. MethodsEnamel surfaces were treated as follows: 1- ABR-PA: INT was abraded with SiC paper and etched with PA (20 s) before Clearfil Universal Bond Quick adhesive application; 2- ABR-SE: ABR was SiC and adhesive applied in SE mode; 3- INT-PA: INT was etched with PA and adhesive applied; 4- INT-SE: the adhesive (SE mode) was applied to INT; 5- INT-BL: INT was BL and the adhesive was applied (SE mode), and 6- INT-BA: INT was BL, etched with PA and adhesive applied (SE mode). The enamel surface treated was examined with scanning electron microscopy (SEM) (n = 3) and Al2O3 particles were characterized using SEM and EDX. The enamel bond strength was measured by microtensile test (24 h and 1 year) (n = 8) and the morphology of enamel-adhesive interfaces were analyzed by SEM (n = 3). Bond strength data were analyzed by two-way ANOVA and Tukey’s test (α = 0.05). ResultsAl2O3 particles had an irregular shape, their length varied (50–20 µm) and the perimeter mean was 38.8 µm. The enamel morphology significantly influenced the enamel bond strength. ABR-PA, INT-BL, and INT-BA provided greater and stable enamel-dentin interaction and bond strength. SignificanceThe enamel morphology significantly influenced the enamel bond strength. Using the adhesive in etch-and-rinse mode, enamel must be abraded before etching and must be Al2O3-blasted when used in SE mode.

Clinical Outcome of Prefabricated Zirconia Crowns Cemented with Self-Adhesive Resin and Pure Glass Ionomer on Primary Teeth: A Retrospective Cohort Study.

Retentive strength data are critical to predicting the long-term clinical performance of zirconia crowns for primary teeth. Objectives: This research assessed the clinical outcome of prefabricated zirconia crowns (PZCs) cemented with either self-adhesive resin cement or pure glass ionomer cement in the primary teeth of children. Method: In the present research, a sample of 162 prefabricated zirconia crowns were collected through convenience sampling. A follow-up examination was conducted at 12 and 24 months to assess the clinical outcomes of PZCs post cementation. Results: Zirconia crowns cemented with self-adhesive resin showed high clinical outcomes at both 12 and 24 months, with 95.1% of crowns retained. In contrast, crowns cemented with glass ionomer cement had slightly lower clinical outcomes, with 91.4% retained at 12 months and 84.0% retained at 24 months, indicating a significant difference (p-value). Long-term follow-up is crucial for the optimal maintenance of crown stability. Conclusions: Self-adhesive resin cement is a viable option for cementing PZCs in pediatric dentistry, demonstrating satisfactory clinical performance over both 12 and 24 months. Future studies comparing different types of cement are recommended for further validation of these results.

Optimization of a coal mine roof characterization model using machine learning

Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.This paper provides a case study in machine learning using Artificial Neural Network (ANN) and geophysical log data information from an underground coal mine located in the Bowen Basin (Australia), whereby a suite of parameters is considered pertinent to excavation stability. This machine learning predictive model analyses key geotechnical parameters including field and mining-induced stress, rock mass strength, clay/shale content, and strata monitoring data to identify strata hazards. Finally, the classification model can indicate the risk of roof displacement and observed roof deformation as communicated through a contoured ‘geotechnical hazard map’. This hazard map can assist site engineers understand roof conditions in underground coal mining and thus put in place operational mitigation processes to reduce geotechnical risk on mining. This could potentially improve the identification of high-risk areas and inform future bolting requirements to maintain a safe working environment and minimise production interruptions.

Analysis of thermally-induced fracture of Callovo-Oxfordian claystone: From lab tests to field scale

Argillaceous rocks are a suitable host rock for the deep geological disposal of exothermic High-Level Radioactive Waste (HLW) and Spent Fuel (SF). Excess pore pressures develop in this type of rocks when subject to increased temperatures that, in some circumstances, may lead to the fracturing of the rock. The paper explores this phenomenon by means of coupled numerical analyses carried out within a fully coupled thermo-hydro-mechanical (THM) framework. The general THM formulation is described as well as the anisotropic elastic and anisotropic elastoviscoplastic constitutive laws employed. Thermal extension triaxial tests are simulated as a check on the performance of the numerical formulation and to provide calibration data for the tensile strength of the material. Selected results from a comprehensive set of three-dimensional analyses of a large-scale field heating test, designed to study the possibility of thermal-induced failure in the rock, are presented and discussed. The analyses reproduce satisfactorily the observed patterns of behaviour. The effects of the constitutive law, material parameters and the presence of the excavation damage zone (EDZ) around the main drift and around the heater boreholes are studied. In particular, their effects on the state of the stress in the heated area are examined in the context of the potential for thermal fracturing of the rock.

Normative Data of Grip Strength and Pinch Strength in the Indian Population.

Background There is no normative study of transregional grip strength data available from India. Hence, a multicenter study is designed to obtain reference value. Materials and Methods This is a prospective observational study conducted as a part of the Indian normative data project of the Indian Society for Surgery of the Hand. The study included three participating centers: one from the northern part and two from the southern part. Certified calibrated Jamar dynamometer and Jamar pinch gauge were used to measure the grip strength, key pinch strength, tip pinch strength, and tripod pinch strength as per the guidelines of the American Society of Hand Therapists. Results A total of 1,019 volunteers in the age group of 18 to 60 years were studied. The mean grip strength in males and females was 38.18 and 24.06 kg, respectively. The mean key pinch strength was 8.52 kg in males and 5.97 kg in females. The mean tip pinch strength was 4.86 kg in males and 3.59 kg in females. The mean tripod pinch strength was 5.41 kg in males and 4.16 kg in females. Conclusions All four strengths were lower in value when compared with American and other populations. The men had more strength than women. There was no relation to hand dominance. There was a correlation for age and height but no correlation with body mass index (BMI).

Material testing and low cycle fatigue life analysis of supercharger gasket

Abstract Aiming at the cracking problem of the supercharger metal gasket in the development stage of an engine, an analysis method is established. Firstly, the gasket is assumed to be a spring unit, and transient three-dimensional analysis is carried out to obtain the gap movement of the gasket during the whole cycle process. Then, a detailed two-dimensional model of the gasket is established to calculate the low-cycle fatigue life of the gasket structure through loading temperature and gap movement. The data of tensile strength and elastic modulus at different temperatures were obtained by testing the base material of the gasket. The results indicate that the material performance of the gasket drops sharply when it is higher than 400°C to 500°C, and the temperature of the gasket near the supercharger is in this interval. From the life results, it can be seen that the two layers of the gasket have cracks in half of the test, and the cracking problem can be solved by adjusting the material of the two layers.

Automatic detection and prediction of signal strength degradation in urban areas using data-driven machine learning

Signal strength degradation represents a variation in the coverage area for radio networks. Building maps that represent this degradation requires collecting information about signal coverage in scattered locations, which can be done conventionally by measurement methods such as the manual drive test. Nevertheless, as this process is large-scale, time-consuming, and costly, several methods for the minimization of drive tests have been introduced. In this study, our methodology first consisted of dividing the study area into several zones, and each zone into several sectors without considering the position of the existing broadcast base station. Then, we developed a custom mobile application to collect the signal strength data and the location coordinates of the concerned area. For data collection, we deployed the mobile application on more than 10 users' phones, who navigated in different areas using their cars. We applied the gradient-boosted trees algorithm to predict signal strength degradation in different areas. Our model has shown some interesting results after studying and analyzing the collected data, based on data mining algorithms. We also evaluated our model's ability to predict the zone's structure according to the strength of the degradation signal.

AI-based rock strength assessment from tunnel face images using hybrid neural networks

In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Evaluation of Patellar Tendon Structural Changes following Biological Treatments: Secondary Analysis of Double-Blinded Clinical Trial of Bone Marrow Mesenchymal Stromal Cells and Leukocyte-Poor Platelet-Rich Plasma.

Objective quantification of tendon structural changes through imaging is only achieved by evaluating tendon structure using ultrasound tissue characterization (UTC) technology. This study compares the effects of bone marrow mesenchymal stromal cells (BM-MSC) and leukocyte-poor platelet-rich plasma (Lp-PRP) on tendon structure and clinical outcomes in male patients with patellar tendinopathy measured with UTC at 3, 6, and 12 months after treatment. This is a double-blinded clinical trial with a randomized active control study with 20 male patients diagnosed with patellar tendinopathy who underwent BM-MSC and Lp-PRP. Bilateral ultrasound tissue characterization scans of the patellar tendon were carried out after 3, 6, and 12 months, as well as tests for strength and pain. UTC patellar tendon was analyzed at the insertion, proximal, and mid-tendon. BM-MSC showed a greater capacity to promote further positive changes than Lp-PRP. Lp-PRP presented higher disorganized echo-type II in the mid-tendon (p = 0.04; ES = 1.06) and III (p = 0.02; ES = -1.47) after 3 months in the Lp-PRP group. Similar results were seen after 6 and 12 months. Pain and strength data show improvement in the treated tendon. BM-MSC treatment demonstrates a superior capacity to promote tendon regeneration and organization, restore strength, and reduce pain compared to Lp-PRP, after 3, 6, and 12 months in male patients with patellar tendinopathy.

MatDEM-based study of disc cutter force model in open TBM tunnels: Incorporating installation radius and synergistic effects

The prediction of rock cutting force is critical for tunnel boring machine performance and cutterhead design. This paper presents a novel model for rock cutting force prediction based on the Colorado School of Mines (CSM) model, which incorporates the installation position of disc cutters by introducing installation radius and synergistic effect factors. Linear cutting tests in the laboratory and large-scale rotary cutting simulations in MatDEM software were conducted to examine the impact of these factors. Results indicate that the normal and rolling forces increase and stabilize as the installation radius increases. The synergistic effect produces three force modes in a cutting circle, with mode α having the largest cutting force, mode β having a smaller force, and mode γ having the smallest force. The impact of installation radius and synergistic effect varies with rock-cutter parameters. Multiple regression analysis was used to determine the introduced factors. The proposed model was validated with rock strength and operation data from the Irtysh River conveyance project. The results demonstrate that the proposed model outperforms the CSM model in predicting cutting force in field conditions.

Electrospun PAN/PANI/CNT scaffolds and electrical pulses: a pathway to stem cell-derived nerve regeneration

Biocompatible polymer-based scaffolds hold great promise for neural repair, especially when they are coupled with electrostimulation to induce neural differentiation. In this study, a combination of polyacrylonitrile/polyaniline (PAN/PANI) and Carbon Nanotubes (CNTs) were used to fabricate three different biomimetic electrospun scaffolds (samples 1, 2 and 3 containing 0.26 wt%, 1 wt% and 2 wt% of CNTs, respectively). These scaffolds underwent thorough characterization for assessing electroconductivity, tensile strength, wettability, degradability, swelling, XRD, and FTIR data. Notably, scanning electron microscopy (SEM) images revealed a three-dimensional scaffold morphology with aligned fibers ranging from 60 nm to 292 nm in diameter. To comprehensively investigate the impact of electrical stimulation on the nervous differentiation of the stem cells seeded on these scaffolds, cell morphology and adhesion were assessed based on SEM images. Additionally, scaffold biocompatibility was studied through MTT assay. Importantly, Real-Time PCR results indicated the expression of neural markers—Nestin, β-tubulin III, and MAP2—by the cells cultured on these samples. In comparison with the control group, samples 1 and 2 exhibited significant increases in Nestin marker expression, indicating early stages of neuronal differentiation, while β-tubulin III expression was significantly reduced and MAP2 expression remained statistically unchanged. In contrast, sample 3 did not display a statistically significant upturn in Nestin maker expression, while showcasing remarkable increases in the expression of both MAP2 and β-tubulin III, as markers of the end stages of differentiation, leading to postmitotic neurons. These results could be attributed to the higher electroconductivity of S3 compared to other samples. Our findings highlight the biomimetic potential of the prepared scaffolds for neural repair, illustrating their effectiveness in guiding stem cell differentiation toward a neural lineage.

A simulation approach for quantifying ballistic impact damage in ultra-high-performance concrete

In this work, we provide a hydrocode simulation model for high-velocity projectile impact against ultra-high-performance concrete targets and establish a methodology to extract damage quantities from the simulation results. In the parameter derivation process, published and own data stemming from material experiments, such as uniaxial, triaxial, and planar plate impact tests, are used as a starting point. To fill the systematic gaps of strength data for pressures of 1 GPa to 5 GPa and for fractured concretes, residual velocities of projectiles and qualitative target damage information from ballistic experiments with high-hard steel spheres are additionally used as a reference in parametric simulations. All criteria from the comparatively broad data basis are successfully reproduced by the simulation model simultaneously. The simulated damage quantities derived by the proposed extraction procedure are reasonable counterparts to the corresponding experimental measures from earlier published works, allowing a new quality of comparison between both worlds.

Efficient machine learning for strength prediction of ready-mix concrete production (prolonged mixing)

Purpose This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction. Design/methodology/approach This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength. Findings The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC. Research limitations/implications Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter. Practical implications The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants. Originality/value Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.

Dose-exposure-efficacy response of intravenous immunoglobulin G 10% in multifocal motor neuropathy.

Multifocal motor neuropathy is a rare chronic immune-mediated neuropathy with impaired grip strength representing a common symptom. While intravenous immunoglobulin G is an effective treatment for the disease, significant variation in treatment response has been observed but not well understood. This analysis characterized dose-exposure-response relationships in multifocal motor neuropathy, using grip strength as a clinical efficacy measure. Serum immunoglobulin G trough concentrations and grip strength data for the more affected hand from a Phase 3, randomized, double-blind, placebo-controlled, crossover trial of intravenous immunoglobulin 10% in 44 patients with multifocal motor neuropathy (NCT00666263) were used to develop a population pharmacokinetic-pharmacodynamic model. The model adequately described the observed pharmacokinetic and pharmacodynamic data and relationships between intravenous immunoglobulin 10% dose, serum immunoglobulin G trough levels, grip strength, and inter-patient variabilities in multifocal motor neuropathy. Model-based simulations for various dosing regimens (0.4-2.0 g/kg every 2-4 weeks) indicated that ≥1.6 g/kg/month would achieve clinically meaningful improvements in grip strength (≥4 kg) in ≥70% of patients. More frequent dosing at an equivalent monthly dose led to a more consistent response in grip strength. Furthermore, splitting the dose over multiple days for high doses (>1 g/kg) did not impact grip strength. These findings suggest that the majority of patients with multifocal motor neuropathy would respond rapidly to intravenous immunoglobulin 10% with a range of dosing regimens. Shorter dosing intervals may avoid the diminishing response seen with longer dosing intervals. Dose-splitting provided similar outcomes while offering flexibility and convenience.

Adhesive Performance of Pit and Fissure Sealants on Deproteinized Enamel with Different Proteolytic Agents: In Vitro Study.

The objective of this work was to assess the efficacy of different proteolytic agents on the bond strength of pit and fissure sealants to bovine enamel. Eighty-four bovine enamel specimens were randomly assigned in groups according to the pit and fissure sealant applied (HelioSeal F or Dyad Flow). Then, the specimens were subdivided according to the proteolytic agent used (n = 7): Group 1, distilled water (control); Group 2, 10 wt.% Tergazyme®; Group 3, 10 wt.% ZYME®; Group 4, 10% papain gel; Group 5, 10% bromelain gel; and Group 6, 5.25 wt.% sodium hypochlorite. The cell viability of the proteolytic solutions was assessed through the MTT assay. The proteolytic agents were applied on the enamel surface prior to the acid-etching procedure; then, the pit and fissure sealants were placed. The micro-shear bond strength was evaluated after 24 h or 6 months of water storing at 37 °C. Representative SEM images were taken for each experimental group. The bond strength data were statistically analyzed by a three-way ANOVA test using a significance level of α = 0.05. Bromelain and papain proteolytic solutions did not exert any cytotoxic effect on the human dental pulp cells. After 24 h and 6 months of aging, for both pit and fissure sealants, sodium hypochlorite, papain, bromelain, and Tergazyme® achieved statistically significant higher bond strength values (p < 0.05). Irrespective of the deproteinizing agent used, Dyad Flow resulted in a better bond strength after 6 months of aging. The type 1 etching pattern was identified for sodium hypochlorite, papain, and bromelain. Tergazyme®, papain, and bromelain demonstrated efficacy in deproteinizing enamel surfaces prior to acid etching, leading to the improved bond strength of pit and fissure sealants. Clinically, this suggests that these proteolytic agents can be considered viable alternatives to traditional methods for enhancing sealant retention and longevity. Utilizing these agents in dental practice could potentially reduce sealant failures.

Investigation of an Indian Site with Mafic Rock for Carbon Sequestration.

The increasing extent of greenhouse gas emissions has necessitated the development of techniques for atmospheric carbon dioxide removal and storage. Various techniques are being explored for carbon storage including geological sequestration. The geological sequestration has various avenues such as depleted oil and gas reservoirs, coal-bed methane reservoirs, and mafic and ultramafic rocks. Different trapping mechanisms are in play in these subsurface storage systems. In these sequestration sites, the mafic and ultramafic rocks are best suited for long-term and effective sequestration as they comprise minerals, conducive for chemical alteration, forming stable carbonates. However, these sites often suffer from distinct disadvantages of injectivity issues due to their low permeability and porosity. This study investigates the potential of sequestration in the rock samples obtained from one such site located in India. The rock samples are first characterized using various techniques including X-ray fluorescence, X-ray diffraction, Raman spectroscopy, and field emission scanning electron microscopy (FESEM). The mineralogical characterization shows that the rock sample contains approximately 10% of diopside. The samples were put in the reactor chamber comprising CO2, which were then investigated using FESEM analysis. Additionally, a reservoir block simulation using commercial software was conducted with the representative minerals in the sample to evaluate the CO2 sequestration potential. The simulation result suggests the formation of magnesite which corresponds to a major part of CO2 mineral trapping. The reduced injectivity due to low porosity and permeability in this rock can be addressed using hydraulic fracturing. The geomechanical behavior of the rock sample for hydraulic fracturing is studied using the Brazilian disc test. The Monte Carlo-based uncertainty analysis was conducted using the tensile strength data of the sample. Results suggest that the most likely fracturing pressure is 2100 psi for this rock sample.

Analysis of Quadriceps Strength and Knee Pain

ObjectiveTo investigate the association of quadriceps strength with the presence of knee pain. DesignThis cross-sectional study was based on data from the 1999-2000 to 2001-2002 National Health and Nutrition Examination Survey. SettingThis was a community-based study. ParticipantsThis study included 2619 adults with complete data for knee pain, quadriceps strength, and covariates. InterventionNot applicable. Main Outcome MeasuresSelf-reported knee pain. ResultsThis study included 2619 individuals, 1287 (52.66%) of whom were women and 1543 (81.66%) identified as Non-Hispanic White. The mean ±standard deviation age was 62.48±9.71 years. After adjusting for covariates, the odds of knee pain decreased with every 20 N/m increase in quadriceps strength (odds ratio, 0.87; 95% confidence interval, 0.81-0.94). Individuals in the upper quartile of quadriceps strength had lower odds of knee pain than those in the lower quartile (Q4 vs Q1 [reference]: odds ratio, 0.28, 95% confidence interval, 0.15-0.52; Ptrend=.006). Nonlinear analyses indicated L-shaped associations for knee pain. The subgroup analyses showed no significant interactions, except for sex (Pinteraction=.046). The significance of the sex interaction indicated a correlation exclusively in women. ConclusionsThe results demonstrated an inverse association between quadriceps strength and the presence of knee pain. The subgroup analysis by sex showed that this inverse relationship was statistically significant in the women but not in the men subgroup.

Water Temperature Effect on Flexural Strength of Posterior Ankle Splint: An Experimental Study.

Plaster of Paris splints are commonly utilised for foot and ankle injuries. However, during follow-ups, some of these splints were found to be broken. Various methods, including splint form or augmentation changes, have been explored to enhance flexural strength. However, the impact of water temperature on the splint's flexural strength still needs to be studied. This research aimed to investigate the effect of water temperature on the flexural strength of the Plaster of Paris splint. Three groups were set up based on different water temperatures: cold, hot, and room temperature. Posterior ankle splints were created and immersed in water at these varying temperatures, with five pieces tested per group. The splints were then allowed to harden fully over three days. Each splint underwent a tensile strength test using an axial pressure machine, which recorded their flexural strength data. There were no statistically significant differences in the general characteristics of the splints. The flexural strengths of the three splint groups (pre-cooled, pre-heated, and room temperature) were 182.6N, 162.45N, and 228.91N, respectively. Statistical analysis revealed that room-temperature splints demonstrated a statistically significant increase in flexural strength compared to pre-heated splints (p<0.05). However, they did not differ significantly from pre-cooled splints. The highest flexural strength was observed in splints immersed in room-temperature water.