Strength Index Research Articles

Dietary intake, energy availability, and power in men collegiate gymnasts.

The purpose was to examine the prevalence of low energy availability (LEA), explore dietary behaviors in men collegiate gymnasts (n = 14), and investigate the relationships between energy availability (EA), body composition, and plyometric performance. Body composition was measured using air displacement plethysmography. Lower- and upper-body peak power (PWRpeak) and modified reactive strength index (RSImod) were calculated from countermovement jump (CMJ) and plyometric push-up (PP) assessments. Energy expenditure was tracked over 3 days, while daily energy and macronutrient intake were recorded. EA was calculated and used to categorize athletes into LEA and non-LEA groups. Pearson correlation coefficients were used to examine relationships between EA, body composition, and performance metrics. 85.7% of athletes (n = 12) exhibited LEA (20.98 ± 5.2 kcals/kg FFM), with non-LEA athletes (n = 2) marginally surpassing the <30 kcal/kg of fat-free mass (FFM) threshold (30.58 ± 0.2 kcals/kg FFM). The cohort (n = 14) consumed insufficient energy (30.5 ± 4.5 kcal/kg/day) and carbohydrates (3.7 ± 1.1 g/kg/day), resulting in LEA (22.36 ± 5.9 kcal/kg/FFM). EA was not correlated with body composition or performance metrics. A high prevalence of LEA may exist in men gymnasts, largely due to a low relative energy and carbohydrate intake.

Comparative Evaluation of Orthodontic Bonding Systems for Ceramic Brackets on Zirconia Crowns: A Shear Bond Strength and Adhesive Remnant Index Study

Background The purpose of this research was to compare the adhesive residual score/adhesive remnant index (ARI) and shear bond endurance between the two orthodontic bonding systems for zirconia surface bonding: Self-etch (Scotchbond Universal) and total-etch (Transbond XT). Materials and Methods Color-coded acrylic blocks embedded with zirconia slabs were prepared, and the orthodontic brackets (ceramic) were bonded using either the total-etch or self-etch system. Group 1, represented by pink-colored blocks ( N = 12), accommodated 12 zirconia surfaces using the total-etch system, while Group 2, represented purple-colored blocks ( N = 12), also housed zirconia surfaces using the self-etch system. The shear bond strength was evaluated using an Instron Universal Testing Machine, and ARI scoring was done upon debonding. The statistical software SPSS was used for the analyses. Results The average bonding strength for the Scotchbond Universal etch system was 6.21 ± 0.76 MPa, while for the total-etch system, it was 5.59 ± 1.37 MPa. The ARI scores revealed that 83% of surfaces bonded with the self-etch system had no adhesive residue (ARI score 0), compared to 66% for the total-etch system. Conclusion The self-etch bonding agent demonstrated superior bond strength (6.2 MPa) compared to the total-etch system (5.6 MPa). Furthermore, it exhibited better ARI scores, indicating minimal adhesive residue. These findings suggest that the self-etch system is preferable for bonding ceramic brackets to zirconia crowns, offering improved bonding efficacy and ease of clean-up.

Advancements in Structural Integrity: Enhancing Frame Strength and Compression Index Through Innovative Material Composites

Advancements in Structural Integrity: Enhancing Frame Strength and Compression Index Through Innovative Material Composites

Post-activation performance enhancement of flywheel and traditional squats on vertical jump under individualized recovery time.

To explore the post-activation performance enhancement (PAPE) of flywheel and traditional squats on a series of vertical jumps, the loads of the two protocols were matched based on their linear velocities. In addition, we attempted to validate the effectiveness of determining individualized recovery time (IRT) between conditioning activities and explosive movements. Sixteen trained players participated in three main experiments: first, one-repetition maximum (1RM) assessment and intensity matching test; second, the weighted jump squat (WJS) test at baseline and at 2, 4, 6, 8, and 10min after flywheel and traditional protocols; and third, squat jump (SJ), countermovement jump (CMJ), and approach jump (AJ) tests incorporating IRT determined in the WJS sessions into both protocols. These protocols were standardized to 8 repetitions at 80% 1RM with equivalent concentric speed matched by a linear position transducer and conducted in a random order on separate days. In the WJS tests, both protocols exhibited significant increases on jump height (JH), peak force (PF), and peak power (PP) after 2 to 6min (all p < 0.05), and the time courses of changes in performance were in a similar trend. In the SJ, CMJ, and AJ tests, both protocols demonstrated highly significant increases on JH, PP, and reactive strength index (RSI) after incorporating IRT (all p < 0.01), with all participants exhibiting diverse improvement above the baseline levels. The potentiation percentages of the flywheel protocol on JH, PP, and RSI were higher than those of the traditional protocol across four jumping types (JH: 5.35%-9.79% vs. 4.13%-8.46%; PP: 4.16%-6.13% vs. 3.23%-4.77%; and RSI: 7.27% vs. 7.04%). High-intensity flywheel squats can produce jumping potentiation in neuromechanical factors comparable to, or even surpassing, those observed in traditional squats, potentially making them a more effective option for inducing PAPE. Additionally, incorporating IRT into potentiation protocols could further optimize the PAPE effects.

Investigation of oscillator strength, absorption coefficients and refractive index changes of ZnSe/ZnS non-concentric core–shell quantum dot

Investigation of oscillator strength, absorption coefficients and refractive index changes of ZnSe/ZnS non-concentric core–shell quantum dot

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

Optimized nitrogen and potassium fertilizers application increases stem lodging resistance and grain yield of oil flax by enhancing lignin biosynthesis

The lodging issue is a significant factor that hinders the enhancement of oil flax production efficiency in northern China. Crop lodging or not, and lignin content in the stems are closely related, and how nitrogen fertilizer and potassium fertilization interaction regulate lignin biosynthesis in the stems of oil flax requires further in-depth study. Therefore, the research aims to enhance the lodging resistance and facilitate an increased yield of oil flax. We are examined the interaction of different nitrogen fertilizers (75, 150, and 225 kg N ha-1) and potassium fertilizers (60 and 90 kg K2O ha-1) on oil flax lignin metabolism, lodging resistance, and grain yield in 2022 and 2023 growing seasons. The results indicated that nitrogen and potassium fertilizer levels and their interaction treatments are facilitated lignin accumulation, achievement of lodging resistance and high yield. Compared to CK, the 75-150 kg N ha-1 in combination with 60 kg K2O ha-1 treatments significantly enhanced the enzyme activities (TAL, PAL, POD, and CAD) and the gene expressions (4CL1 and F5H3) for lignin syntheses, lignin content dramatically increased 29.63-43.30%, improved stem bending strength and lodging resistance index, and grain yield increased 23.27-32.34%. Correlation analysis indicated that nitrogen and potassium fertilizer positively regulated the relative enzyme activities and genes expression for lignin to facilitated lignin biosynthesis and accumulation, and enhanced stem bending strength and lodging resistance index. Positive correlations were found among the relative enzyme activities and gene expressions for lignin, lodging resistance and grain yield. To summarize, 75-150 kg N ha-1 in conjunction with 60 kg K2O ha-1 treatment was promoted to lignin biosynthesis and accumulation, enhanced the lodging resistance and grain yield of oil flax in the dryland farming region of central Gansu. Furthermore, it served as a technical guide for cultivating stress tolerance and high-yield oil flax in the dryland farming region.

Dentist: Wrist Position, Handgrip Strength, and Body Mass Index Associated with Carpal Tunnel Syndrome in Daily Practice

Dentist: Wrist Position, Handgrip Strength, and Body Mass Index Associated with Carpal Tunnel Syndrome in Daily Practice

Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches

This investigation introduces the optimal performance models for predicting the compressive strength (CS) and pozzolanic activity index (PAI) by comparing the machine learning models. The machine learning models, i.e., multilinear regression (MLR), support vector machine (SVM), gaussian process regression (GPR), decision tree (DT), random forest (RF), and gene expression programming (GEP) have been trained (TRN) and tested (TST) by 28 and 7 data points. For the first time, the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, reactive SiO2, Blaine specific surface area, and specific gravity have been used as input variables to compute the CS, and 28 days PAI (28PAI), and 90 days PAI (90PAI) of the natural pozzolans. The multicollinearity analysis showed the SiO2, Al2O3, Fe2O3, SiO2 +Al2O3 +Fe2O3, reactive SiO2, and specific gravity have problematic multicollinearity (variance inflation factor – VIF > 10). Therefore, the root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), performance index (PI), and variance accounted for (VAF) metrics have been implemented to evaluate the model's performance and multicollinearity impact. From the comparison of models, it has been recorded that model GPR outperformed the MLR, SVM, DT, RF, and GEP models in predicting CS (PI = 1.29, VAF = 71.31, R = 0.8473, MAE = 0.9390 MPa), 28PAI (PI = 1.87, VAF = 94.88, R = 0.9744, MAE = 0.7295 %), and 90PAI (PI = 1.72, VAF = 88.11, R = 0.9393, MAE = 1.2444 %) in the TST phase, close to ideal values. The score, generalizability. Wilcoxon test, uncertainty analysis, Anderson-daring test, and accuracy metrics have confirmed the superiority of GPR models in predicting CS, 28PAI, and 90PAI of natural pozzolans.

The influence of muscle strength and aerobic fitness on functional recovery in elite female soccer players.

We examined the influence of muscle strength and aerobic fitness on wellbeing and muscle function recovery in female soccer players. Sixteen players from the English Women's Super League (age: 26±4 yrs; height, 1.70±0.07 m; mass, 67.9±6.4 kg) completed an isometric mid-thigh pull test (IMTP) and maximal aerobic speed test (MAST). Using a median split, players were divided by strength into high (H-IMTP) and low (L-IMPT) groups and by fitness into high (H-MAST) and low (L-MAST) groups. Countermovement jump height (CMJ), Reactive Strength Index (RSI) and wellbeing were measured 2 days prematch, then 3 subsequent days post-match (M+1, M+2, M+3). In H-IMTP, CMJ, RSI and wellbeing were higher at M+1-M+3 (P<0.05). At M+3, CMJ in H-IMTP was 30.3±3.5 cm vs. 26.2±3.4 in L-IMTP. CMJ and wellbeing were unaffected by fitness, but RSI was lower at M+1-M+3 in the L-IMTP group (P<0.05). In conclusion, isometrically stronger but not aerobically fitter female soccer players recover quicker after matches.

Effectiveness of an Individualized Training Based on Dynamic Strength Index on Sprinting, Jumping and Change of Direction Performance in Basketball Players: A Randomized Controlled Trial.

The dynamic strength index (DSI) is calculated as the ratio between countermovement jump (CMJ) peak force and isometric mid-thigh pull (IMTP) peak force and is said to inform whether ballistic or strength training is warranted for a given athlete. This study assessed the impact of an individualized in-season resistance training program, guided by DSI on basketball players' physical performance. Forty-three elite players (19.4 ± 2.9 years; 1.97 ± 0.08 cm; 89.1 ± 9.5 kg) were divided into an intervention group (IG) (27 players) and a control group (CG) (16 players). The IG was further split based on DSI into a ballistic group (DSI ≤ 0.90, 11 players) and a strength group (DSI > 0.90, 16 players). Over five weeks, participants underwent two weekly resistance sessions, with the IG following a DSI-based program and the CG a standard program. Performance was measured pre- and post-intervention through 20-m sprints, 505 change of direction test, CMJ, and IMTP. There were statistically significant improvements in the IG, notably in sprint times (η2 = 0.12-0.21, p < 0.05) and 505 test (η2 = 0.15-0.16, p < 0.05), predominantly in the strength group. The CG's performance was either unchanged or declined for different variables. Our results suggest that DSI-guided training effectively enhances basketball players' physical performance within a competitive season.

Axial compressive capacity prediction and optimal design of circular UHPC-filled steel tube based on Hybrid Symbolic Regression - Neural Network model

This study presents two methodologies for estimating the ultimate bearing capacity of ultra-high-performance concrete-filled circular steel tube (UHPCFST) columns and offers design optimization guidance: one employing a Symbolic Regression algorithm and the other a Hybrid Symbolic Regression - Neural Network (SR-NN) model. The models were trained, tested, and validated using experimental data from 498 samples sourced from existing literature. The optimal model, SR-NN_14_6, was identified from a pool of 480 models using a trial-and-error approach. The formula and model exhibited enhanced stability and precision, as indicated by their mean absolute error of 8.601 % and 8.051 %, respectively, marking an improvement exceeding 30 % relative to the best existing AIJ code. Using the validated model, a parametric analysis was conducted to assess the influence of the confinement effect coefficient (ξ) and steel ratio (α) on the strength index (SI) of UHPCFST members, leading to the identification of two optimal parameter ranges. The optimal ranges of ξ and α were determined to be 0.84 to 1.29 % and 20 % to 30 %, respectively, offering valuable guidelines for the design of UHPCFST members.

Emerging eco-friendly fiber-reinforced concrete with shaped synthetic aggregates using Taguchi grey relational analysis and utility concept

The present study assessed the applicability of employing Taguchi-based Grey relational analysis (T-GRA) and the Taguchi approach combined with the utility concept (T-UC) for enhancing the mix design parameters of fiber-reinforced synthetic aggregate concrete (FRSAC). Cement was supplemented with binders such as ground granulated blast furnace slag (GGBFS), metakaolin (MK), and lime powder (LP). The optimized mix proportion of FRSAC was determined to enhance the fresh and mechanical performance. Furthermore, this investigation used two weighting approaches: equally weighted and maximum deviation methods. An established L16 (45) Taguchi orthogonal array was used with five different factors, namely, cement replacement (40 %, 50 %, 60 % and 70 %), binder content (B1, B2, B3, B4), water-to-cement ratio (0.31, 0.32, 0.33, 0.34), fiber dosage (0.1 %, 0.2 %, 0.3 % and 0.4 %), and synthetic aggregate replacement (25 %, 50 %, 75 %, and 100 %) were employed to design the experiments. Then, T-GRA and T-UC were used to determine the optimum mix proportions for five response parameters: workability, compressive strength, split tensile strength, elastic modulus, and impact ductility index. Results indicated that the two methods, GRA and UC, can be successfully integrated with the Taguchi method to derive the optimized FRSAC mixture. Confirmation experiments were performed after determining the optimum mix design parameters, including cement replacement of 60 %, binder content of B4, the water-to-cement ratio of 0.34, fiber dosage of 0.1 %, and synthetic aggregate replacement of 75 %. The optimum FRSAC mix was examined for its fresh (workability) and hardened (compressive strength, split tensile strength, elastic modulus, impact ductility index) properties.

Multi-Objective Optimization and Mechanical Properties Analysis of Steel-PVA Hybrid Fiber-Reinforced Cementitious Composites.

When steel fiber and PVA fiber produced in China and PVA fiber made in Japan are prepared according to the appropriate proportions, the mechanical properties of hybrid fiber-reinforced cementitious composites (HFRCC) are better, which is beneficial to cost control and has wide application prospects. The effects of the volume content of steel fibers and the volume substitution rate of PVA fibers on the tensile strength, compressive strength, and flexural strength of HFRCC were analyzed using the factor optimization method and principal component analysis (PCA). Through the principal component analysis of HFRCC, a mathematical model for comprehensive performance evaluation was established, and a multi-objective optimization was carried out. The results show that compared with the matrix, the tensile strength, compressive strength, and flexural strength of concrete increase significantly when the volume content of steel fibers is 0.2-0.4% and the volume substitution rate of domestically produced PVA fibers in China or PVA fibers produced in Japan is 50-100%. The maximum cost reduction is 88.25%, and the strength index of HFRCC can reach the optimum; the weights of each factor on the performance of HFRCC were obtained through mathematical statistics. Combined with a variable correlation analysis, these indicators should be noted when optimizing the performance of HFRCC. The research results can provide a basis for the preparation of HFRCC.

Analysis on slope stability by anchorage based on soil creep with flood effect

In order to analyze the adverse effect of flood affection on slope stability, the analytical expressions of buoyancy force and capillary force, hydrodynamic pressure and impact force, and scour erosion were proposed based on the aging characteristics of soil shear strength and limit equilibrium theory. According to the load combination and flood action, shear failure occurs preferentially at the foot of slope. Then, the plastic zone continues to extend upward to produce traction landslide disaster mode. Furthermore, the power function relation between shear strength index and time was established. The nonlinear accelerated creep model was also obtained. At the same time, the safety factor formula for flood loading effect slope aging stability, the time-varying characteristic value of anchor force and the compensation value of anchor force were also obtained and used to research sliding mechanism. In addition, the numerical calculation example shows that the slope safety factor decreases by more than 20 % considering the effect of flood ascending scour and impact, and the compensation value of anchorage force increases obviously with time increasing. Simultaneously, the change rate of compensation value of anchorage force increases nonlinearly with the increase of design safety factor.

Sex differences in reduction of trunk muscle mass related to falls and cognitive function during the COVID-19 pandemic in older adults.

This study aimed to examine the influence of the COVID-19 pandemic on physical components and activity, and its relationship to physical performance in older adults. Sixty-seven participants aged 75 and older (81 ± 2 years, female: 66%), who underwent medical checkups continuously from 2018 to 2022 in one clinic, were enrolled. Muscle mass was assessed by the biometrical impedance analysis method, which was adjusted by height squared. Physical, oral, and cognitive performance data were obtained from Japanese standard questionnaires at medical checkups. Values obtained in 2018 and 2019 were defined as pre-pandemic, and those in 2021 and 2022 were defined as during the pandemic. Body weight, grip strength, and skeletal mass index did not change from 2018 to 2022, but trunk muscle mass index decreased significantly. A difference in the trunk muscle mass index (TMI) was observed between 2022 and 2018/2019 in men; however, a significant difference was found between 2021 and 2022 in women. Compared with the pre-pandemic period, TMI during the pandemic decreased only in men. The difference in TMI between the pre-pandemic period and during the pandemic (ΔTMI) was significantly decreased in participants with low physical activity and poor oral performance before the pandemic, and in those with falls and poor cognitive function during the pandemic only in men. Reduction of trunk muscle mass was related to falls and poor cognitive function during the COVID-19 pandemic in older male adults. These data suggest that the difference in response to the COVID-19 pandemic between men and women necessitates different types of support for older adults. Geriatr Gerontol Int 2024; 24: 1060-1066.

A data-driven approach to quantify track buckling strength through the development and application of a Track Strength Index (TSI)

Railroads are increasingly adopting advanced technologies to enhance safety and state of good repair of their track infrastructure, some of which employ artificial intelligence-based inspection methods. The objectivity of these inspection techniques, along with their detailed measuring capabilities, has created opportunities for railroads to improve both inspection and operational efficiency. This is achieved through more frequent and precise track data collections and technical data aggregations. This paper explores the potential of these track inspection methods to address one of the few drawbacks of continuous welded rail (CWR): track buckling. While track buckling mechanics and prevention have been the subject of numerous studies, the need for a practical and objective method to assess resistance to buckling remains. Numerous track health indices for geometry parameters and some for railway components have been developed and utilized in various applications. Yet, the opportunity exists to develop a metric that combines geometric parameters with the condition levels of railway components, particularly designed to quantify the resistance of track to buckling. Such a holistic view of the track and network-wide time series analyses of the proposed metric demonstrate whether the buckling resistance improves, remains in a steady state, or declines. In this study, a sensitivity analysis conducted using CWR-Risk software identified misalignment amplitude, track curvature, lateral resistance, torsional resistance, and longitudinal resistance as the main factors contributing to buckling resistance. Based on the sensitivity study and with a focus on the capacity side of the track buckling equation, a methodology was proposed for converting inspection data into 10-point scales that were combined through weighted averaging into a single Track Strength Index (TSI) to quantitatively assess the resistance to buckling at a track-system level. The influence of ballast condition on TSI output was evaluated using 15 ballast configuration scenarios, ensuring that the index accurately reflects ballast deficiencies in a proportionate manner. Lastly, the proposed metric was tested leveraging revenue service data collected from a Class I railroad mainline in the United States.

Fabrication and Characterization of Carrageenan‐Biopolymer Composite Microneedles for Interstitial Fluid Collection

AbstractIdentification of suitable polymeric materials to fabricate microneedles (MNs) for the collection of interstitial fluid (ISF) is a challenge. Here, characterization of different carrageenan‐biopolymer composites for MN patch fabrication intended for ISF collection is reported. Systematic oscillatory rheological studies of composites containing iota‐carrageenan mixed with alginate, gelatin, or pectin are performed to determine the linear viscoelastic region, gel point, tan delta, complex viscosity, and flow transition index. A polynomial equation is derived by relating flow transition index of biopolymer composites and compression strength of fabricated MNs. The biopolymer composite of iota‐carrageenan and gelatin at 2% and 14%, respectively, and CaCl2 crosslinker (80 mm) shows the greatest compression strength sufficient for MNs insertion into the excised porcine skin. MNs swell up on application in an agarose gel model and the ex vivo excised porcine skin model to collect 36 ± 5 and 14 ± 1 µL of fluid within 10 min, respectively. Taken together, it is demonstrated that rheological analysis can be performed to select suitable polymer composites that possess sufficient strength for the skin insertion and swellability for ISF collection.

A Randomized Study of Myostaal® Liniment as an Add-On Therapy for Muscle Strengthening in Cases of Knee Osteoarthritis.

Knee osteoarthritis (OA) is a prevalent degenerative musculoskeletal condition, affecting approximately 277 million people worldwide, with significant impacts on mobility, especially in women and obese patients, and an increasing incidence among Indians aged 30 to 50 years. The primary objective was to evaluate the knee muscle-strengthening effect of Myostaal® liniment (Solumiks Herbaceuticals Limited, Mumbai, India) as an add-on to physiotherapy for 90 days compared to physiotherapy alone in participants with knee OA. Secondary objectives included assessing changes in the total Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)score, WOMAC Subscale scores, Six-Minute Walk Test(6MWT) distance, Single Leg Stance Test (SLST) duration, Visual Analogue Scale (VAS) score, and the number of adverse events from baseline to Day 90 between the two groups. Seventy participants were randomly allocated to Group A (Myostaal® liniment plus physiotherapy) or Group B (physiotherapy alone) for 90 days, with Myostaal® liniment applied twice daily in Group A. Data were recorded in Case Report Forms (CRFs) and analyzed using parametric tests for within-group comparisons (one-way ANOVA or Friedman test) and non-parametric tests (Mann-Whitney test) for between-group comparisons, with significance set at p<0.05. The knee muscle strength (index knee) in Group A (test medication group) was significantly greater compared to Group B (standard treatment group) at Visit 3 (p<0.05; Day 60±3) and Visit 4 (p<0.001; Day 90±3). For the non-index (other) knee, a statistically significant increase in knee muscle strength was observed (p<0.001 at Day 90±3) solely in Group A. A notable reduction in total WOMAC score was seen in Group A from Visit 2 (p<0.01; Day 30±3) onward, compared to Visit 1 (Day 0). The scores at Visit 3 (p<0.001; Day 60±3) and Visit 4 (p<0.001; Day 90±3) were significantly lower than those at Visit 2 (Day 30±3). The local application of Myostaal® liniment through massage as an adjunct to a physiotherapy regimen, improved knee muscle strength in participants with knee OA, leading to an enhancement in joint functionality. Additionally, Myostaal® linimentprovided superior pain relief as an add-on therapy.

Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm

Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index (RSI) covering multiple time resolutions as a factor, as momentum is often used by investors and speculators in their trading decisions, and in contrast to volatility, RSI can distinguish between bull and bear markets. To the best of our knowledge, prior studies have not included RSI as a predictive factor in modeling IV. Instead of using a simple linear regression as in previous studies, we use a machine learning regression algorithm, namely random forest, to model a nonlinear IV. Previous studies apply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation. Our dataset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building. Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model’s accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to prevailing option pricing models that employ stochastic variables, our DIVF model does not include stochastic factors but exhibits reasonably good performance. It is also easy to compute due to the availability of real-time RSIs. Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature.