Modeling Procedure Research Articles

Compliant toggle-linkage mechanisms: A new type of displacement/force amplification principle

Abstract A compliant toggle-linkage mechanism with the functions of displacement amplification/reduction is proposed. A series of compliant amplification mechanisms are examined by permuting flexure toggle linkages, showcasing a flexible design by combining flexure toggle-linkage building blocks. A compliant toggle-lever mechanism is emphatically investigated as both displacement amplifiers and reducers for piezoelectric actuators by exchanging the input and output ports. Its kinetostatics and dynamics are captured by an improved dynamic stiffness modeling procedure with high prediction accuracy, which shifts the nodes of all adjacent flexure members to being coincident to deal with irregular connection of flexure building blocks. It is theoretically and numerically demonstrated that the forward and reverse motion directions can be simultaneously achieved based on the proposed compliant toggle-lever mechanism just by changing its structural parameters. At last, the relationship between the ratios of displacement amplification and reduction in-between input and output ports is clarified, analyzed and summarized based on the theoretical model, finite element simulation and experimental testing.

Improving the Use of Lifting Velocity to Predict Repetitions to Failure: A Systematic Review.

A recent advancement in velocity-based training involves estimating the maximum number of repetitions to failure (RTF) by analyzing the fastest velocity recorded within a set. A systematic review examining the fundamental characteristics of the RTF-velocity relationship is still lacking. This study aimed to (1)determine the basic properties of the RTF-velocity relationships (goodness of fit, reliability, and accuracy) and (2)offer guidance on implementing various methodological factors that can impact the RTF accuracy prediction. Data were sourced from 3 databases: PubMed, SPORTDiscus, and Scopus. Studies werequalified for inclusion if they involved at least 2 sets performed to failure with different loads, utilized multijoint weight-lifting exercises, and monitored the RTF and fastest velocity for each set. Six studies demonstrated (1)robust goodness of fit, (2)acceptable to high between-sessions reliability for the velocities associated to each RTF (1-15 RTF), and (3)acceptable RTF prediction accuracy during fatigue-free sessions (long interset rest), but, when fatigued (ie,short interset rest) the accuracy was compromised except for athletes with high training experience (eg,>2 y training-to-failure experience). The relationship properties remain unaffected regardless of the exercise (upper- vs lower-body), equipment (Smith- vs free-weight), velocity variable (mean and peak velocity), and resting time (from 5 to 10min). However, the modeling procedure used (multiple- vs 2-point) did alter the accuracy. The individualized RTF-velocity relationships can be constructed through a linear regression model, but the failure experience seems to be a critical factor to increase its accuracy.

How Does Inward FDI Lead to Trade Performance and Sustainable Growth In India?

Objectives: The purpose of this study is to examines the relationship between Foreign Direct Investment and trade performance (Export and Import) in India from 1991-2022. The purpose of the study is Causality analysis also, between FDI and trade with other considered variables. Methods: This study utilizes annual data from 1991 to 2022 from the World Bank (2022) and the Reserve Bank of India (2022). Data on GDP, Export, Inflation and interest rate, and FDI are collected from the World Bank. Autoregressive Distributive lag Model procedure has been used for the study in order to establish relationship between trade performance and FDI. Results and discussion: The Auto-regressive distributive lag-bound-co-integration (ARDL) method confirms a long-run association between FDI and export. Coefficient indicates significant and negative relation establishes between FDI and Export but the coefficient between FDI and import represent weak relation. Causality analysis reveals unidirectional relation between FDI and Export, and the direction of causality is from export to FDI. It means export causes FDI, FDI does not causes Export. A negative coefficient indicates that there is a need for efficiency-seeking and export-oriented FDI but negative relations support that Foreign Direct investors prefer market seeking FDI and they focused more on the domestic market opportunity of India. Research Implication: This result supports the market-seeking approach and FDI trade substitute theorem which confirms that the inflow of FDI in India is only for utilizing domestic market opportunity instead of exporting goods to other countries. Therefore, there is a need for efficiency-seeking or export-oriented FDI. The government should facilitate and accordingly create their policy to increase export-oriented FDI to improve technology and productivity of India as well as increases foreign exchange and infrastructure dvelopment in order to sustain growth of india. Originality: India’s FDI situation is presently seeing a growth with liberalized reforms over the recent two decades, several changes like ease of doing, infrastructural development, increased the limit of FDI in several sectors create alluring investment atmosphere having a beneficial outcome of the inflows. Result contributed towards trade and FDI relation that FDI comes from the other countries in india to capture our market potenials so government should try to enhance export oriented FDI in order to know the techniques and improves the export potential as well. Investment and export both simultaneously create a path for sustainable development.

Omnibus diagnostic procedures for vector multiplicative errors models

We suggest specification tests for the conditional mean function in vector multiplicative error models. The test statistics are easy to compute given a suitable estimator of the model parameters. Consistency of the test statistic is proved, the asymptotic distribution of the test under the null hypothesis is studied, while a bootstrap resampling is used in order to approximate critical points and actually carry out the test. Finite-sample results are presented as well as applications of the proposed procedures to real data from the financial markets.

Novel Optimal Staggered Grid Finite Difference Scheme Based on Gram–Schmidt Procedure for Acoustic Wave Modelling

Novel Optimal Staggered Grid Finite Difference Scheme Based on Gram–Schmidt Procedure for Acoustic Wave Modelling

Unboxing the Antecedents of Sustainable Employee Performance in China’s Hotel Industry

ABSTRACT Many hospitality firms face multiple obstacles in their attempts to enhance and sustain the performance and dedication of their personnel. This paper investigates the link between team climate, cultural diversity, organizational learning, and sustainable employee performance (SEP) in China’s hotels. Using purposive sampling, data were collected from hotel employees through a survey. A total of 452 questionnaires were distributed, and 380 were confirmed for analysis. Following a Structural Equation Modelling (SEM) procedure, the salient findings show that managers believe organizational learning is essential to boosting the hotel’s SEP. In contrast to management’s belief, team climate and cultural diversity toward organizational learning are the missing links in improving the hotel’s SEP. Finally, a proactive personality significantly strengthens the moderating effect between organizational learning and SEP. The study illuminates the importance of SEP in the hotel sector while offering fresh insights into its shortcomings and weaknesses. The practical implications of this research can empower hotel management with actionable strategies that promote sustainable employee performance, thereby enhancing their preparedness and effectiveness in this area.

The missing links of value congruence: evidence from the Thai healthcare workforce.

This study aims to investigate possible factors, such as trust in management and shared vision, that influence value congruence and its mediating effect on work engagement. It also explores how resilience, functioning as a moderator, could change the nature of the links between value congruence and its determinants. Data were collected through an online survey from 301 healthcare employees in Thailand. Hypotheses were tested and analyzed using confirmatory factor analysis, structural equation modeling and bootstrapping procedures. The results reveal that value congruence mediates the positive impacts of trust in management and shared vision on work engagement. Trust in management and shared vision translate into higher levels of value congruence, more so when employees are highly resilient. The findings suggest that healthcare administrators should design interventions to cultivate trustworthy leadership behaviors in daily operations while improving communication of the organization's overarching vision and objectives to help employees internalize and seamlessly integrate its core values into their own professional identities. Despite the extensive research on value congruence and its outcomes, little is known about its development. This study makes a valuable contribution by addressing these missing links, particularly in hospital settings. It also highlights how resource management explains the ways in which resilience in practice influences employees' value congruence at work.

Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete

Foamed concrete (FC) is increasingly used in modern construction due to its lightweight nature, superior thermal insulation, and sustainable properties. However, accurately predicting its compressive strength remains a challenge due to the complex interactions of its components. This study addresses this gap by employing advanced machine learning tools, including decision tree (DT), bagging, and AdaBoost, to develop predictive models for FC strength. The results provide a significant improvement in prediction accuracy, offering a reliable tool for optimizing FC design in construction applications. This research aims to streamline the sample creation process in the laboratory, minimize the waiting time for sample testing, and reduce the project's overall cost for researchers. A total of 149 data points were used from the literature to prepare a proper data set for modelling purposes. The modelling procedure used Python code via the Anaconda Navigator software. The statistical evaluation of the metrics, such as R2, MAE, and RMSE, along with the sensitivity analysis to check the impact of inputs and the 10-fold cross-validation method to validate the performance, were part of the presented research. Compared to the DT and bagging models, the results demonstrate that the AdaBoost model forecasts FC's compressive strength (CS) more accurately. The AdaBoost model gives the R2 value equal to 0.97, while DT and bagging show 0.86 and 0.94, respectively. The lower error result for the AdaBoost model and higher for both DT and bagging indicates the superior precision level of the AdaBoost approach. Finally, the graphical user interface (GUI) was designed utilizing the implemented models, which indicates the additional positive aspect of the presented study.

Design for shear at closely offset planted columns

Transfer slabs are commonly used in multi-storey buildings to accommodate changes in column position between floors. Despite this, the design of transfer slabs with closely offset columns for shear has received little attention from researchers. The paper summarises the results of an experimental campaign in which eight slabs were tested to investigate the punching resistance of transfer slabs with closely offset columns. The tested slabs were centrally supported and simultaneously loaded with a pair of point loads simulating closely offset planted columns and around their perimeter to simulate “field” shear from uniform loading on the transfer slab. The test results are shown to compare well with the predictions of a previously calibrated nonlinear finite element modelling procedure using 3D solid elements. The NLFEA modelling procedure is subsequently used to model a series of subassemblies extracted from a full scale five storey building with transfer slab at first floor level. The parametric study considers the influences on the upper column failure load of the gap between the planted and lower columns, the ratio between the upper column load and field shear, concrete strength, and shear reinforcement. A strut and tie model (STM) is developed for the calculation of planted column failure load. The STM is used to model the subassemblies previously analysed with NLFEA and the results of the two approaches are shown to compare well.

Assessing the Impact of Non-Pharmaceutical Interventions During the COVID-19 Pandemic on RSV Seasonality in Europe.

During the COVID-19 pandemic, atypical respiratory syncytial virus (RSV) circulation patterns emerged, with the occurrence of RSV activity outside the typical winter season. This study investigates the impact of COVID-19 and associated non-pharmaceutical interventions (NPIs) on RSV seasonality. The onset, offset and peak of RSV epidemics from 2018 to 2022 across 12 European countries were determined using the 3% positivity threshold method. A multilevel longitudinal logit regression model for proportions assessed the associations between five NPIs (school closures, mask use, workplace measures, public gathering restrictions and closure of public spaces) and RSV, utilising RSV surveillance data, two NPI databases (ECDC-JRC and Oxford) and COVID-19 surveillance data. Before 2020, consistent RSV seasonality patterns were observed, but the seasonal increase of RSV-positive cases in winter remained absent during the COVID-19 pandemic (2020-2022). Analysis revealed inconsistent associations between individual NPIs and RSV. The associations differed depending on the data source used (ECDC-JRC or Oxford), not only in magnitude but also in the direction of the coefficients. Public gathering restrictions and closure of public spaces exhibited significant negative associations with RSV incidence. However, this was only observed when using surveillance data for the entire epidemiological year and not when only examining weeks with increased RSV activity. This study highlights the need for standardised international data collection and procedures for infectious disease modelling, as varying NPI implementations, NPI registration and RSV surveillance across countries complicate the understanding of RSV dynamics during the pandemic. Caution is recommended when interpreting the effects of NPIs on RSV circulation.

Fractal Performance Under Magnetization Procedures of Fractional Memristive Wilson Neuron Dynamical Model

ABSTRACTThe non‐integer neuron dynamical models are feasible for accurate prediction and perfect estimation of magnetization and de‐magnetization in complicated physiological environments within reliable fractal‐fractional neuronal modeling. The memristive Wilson neuron model is proposed under the comparative performance of two types of fractal‐fractional differentials with two different types of kernels based on two different memories. The non‐classical memristive Wilson neuron model with and without magnetization is simulated for numerical schemes by means of linear multi‐step integration method. The numerical simulations are traced out by discretizing continuum processes of spatial and time domains for the sake of perfect approximations under singular and non‐singular kernel versus local and non‐local kernel. By applying the powerful methodology of fractal‐fractional differential and integral operators on the memristive Wilson neuron model, the antimonotonicity phenomenon and asymmetric coexisting electrical activities have been explored intensively to widen the neuron‐based engineering applications. Remarkably, our results based on magnetization and de‐magnetization procedures of Wilson neuron model have imitated the neuron activities under electrophysiological environment.

Prediction of Snatch and Clean and Jerk Performance From Physical Performance Measures in Elite Male Weightlifters.

Sandau, I and Kipp, K. Prediction of snatch and clean and jerk performance from physical performance measures in elite male weightlifters. J Strength Cond Res 39(1): 33-40, 2025-This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors.

Designing Local Folktales Based English Grammar Instructional Materials with the PACE Model Procedure

Designing Local Folktales Based English Grammar Instructional Materials with the PACE Model Procedure

Modulating Role of Vernonia amygdalina Leaf Meal on Spermatogenic and Steroidogenic Functions of West African Dwarf Bucks Testis

Vernonia amygdalina has a unique nutritional and phytochemical property with numerous physiological, biochemical and morphological benefits due to its anti-oxidative and anti-helminthic properties. This study evaluated the effects of Vernonia amygdalina leaf meal (VALM) on follicle stimulating (FSH), luteinizing hormones (LH) and testosterone regulation of seminal characteristics of West African dwarf (WAD) bucks. Twenty-four (24) WAD bucks were randomly assigned to four treatments (A, B, C and D) in a completely randomized design for a nutritional trial of eight weeks. Animals were given diets containing 0, 20, 30 and 40g VALM/kg feed. Animals were weighed and blood samples were collected at the beginning of the experiment and subsequently forth-nightly before the end of the experiment for hormonal assay. Semen volume, semen color, sperm concentration, sperm motility and sperm morphology were determined from the semen samples obtained forth-nightly by electro-ejaculation. All data collected were analyzed using the General Linear Model procedure of SAS, (2002). Means were separated using Duncan’s Multiple Range Test of the same software. There were significant differences (p<0.05) in most of the hormones studied except testosterone which was not significantly affected by VALM. Sperm concentration, motility and morphology increased significantly (P<0.05) and was highest at 40 g of VALM/kg feed. The color of the semen from all the treatments was whitish grey. It was therefore concluded that VALM improved the FSH and LH production at 40 g of VALM/kg feed with improved sperm quality in WAD bucks, thereby enhancing the reproductive performance of the goats. Keywords: Vernonia amygdalina, semen quality, hormone, goats

A Probabilistic Model-Based Framework for Damage Detection in Beam-Like Structures Considering Temperature Effects

To improve the assessment outcomes of structural health monitoring systems, it has been acknowledged that the damage diagnostics process is associated with a considerable amount of uncertainties from operational and environmental sources such as temperature. This study presents a probabilistic framework for structural health monitoring aimed at identifying common defects such as cracks and corrosion in beam-like structures under varying temperature conditions. The framework utilizes optimization methods integrated with a probabilistic approach, comprising two levels of damage identification: first, determining the optimal probability mass function for the damage location ratio, followed by the optimal conditional for the damage depth ratio. A simply supported beam divided into four segments was modeled to evaluate the effectiveness of the proposed algorithm across various damage scenarios using frequency response signals. Structural damage was simulated by manipulating the length and depth ratios of specific beam segments. The modeling procedure for defects in the simply supported beam was validated through comparison with experimental measurements, achieving a maximum difference of less than 1%. The proposed algorithm consistently yielded improved damage detail outcomes in the second level, characterized by higher probabilities and lower uncertainties for both damage location and depth ratios. Identifying cracks and corrosion with a 0.1 depth ratio at low temperatures proved challenging, but the algorithm effectively addressed such scenarios, achieving identification probabilities exceeding 90%. The analysis outcomes demonstrate the advantages of the proposed framework for structural health monitoring that accounts for temperature effects, thereby improving the fidelity of damage assessment in real-world conditions.

A GIS-based multiscale mapping framework to assess and visualize complex and polygenetic geomorphic systems – A novel ‘Geomorphic Entities’ approach

ABSTRACT This paper presents a novel GIS-based methodological framework for multiscale digital mapping and database implementation in sediment dynamics assessments. It relies on the concept of ‘Geomorphic Entities’ (GEs), which are polygon objects representing complex, polygenetic geomorphic systems. GEs allow mapping ‘focal landforms’ as elementary mapping units, while the database stores hierarchical information on sediment sources/sinks classification, process composition, activity, and morphodynamics. We tested this approach in the upper Val d’Arda (N-Apennines, Italy), integrating pre-existing geomorphological datasets with field assessments, photointerpretation, terrain analysis, semi-automated landform classification, and manual digital mapping to produce an Inventory Map of GEs. The primary result is a WebGIS application, allowing end-users to consult and query geospatial data and associated attributes interactively. By combining geomorphological and GIS expertise, this framework addresses the limitations of classical symbol-oriented maps, supporting scientific and applied purposes. It complements traditional mapping methods by structuring complex geomorphological data for use in integrated modelling procedures.

Adaptation and Growth Performance of F1 Progeny of Crossbred Sheep in Bangladesh

The research was conducted to assess the growth performance of different crossbred sheep at Bangladesh Livestock Research Institute (BLRI), Savar, Dhaka. In the crossbreeding program, Perendale, Dorper and Damara breed were considered as sire and BLRI improved native sheep (BNS) was used as dam. The production performance of assorted F1 progeny were evaluated and compared with BLRI improved native sheep. Data analysis was carried out using Generalized Linear Model (GLM) procedure of Statistical Package for the Social Sciences (SPSS) version 25.0. Each crossbred genotype outperformed native sheep in terms of live weights and Average Daily gain (ADG). The live weights (p<0.001 and p<0.01) and ADG (p<0.001) significantly influenced by genotype except the 6 months live weight. Seasonal effects were found non-significant on live weights and ADG except birth weight (p<0.05). Among the crossbreds, the highest birth weight was found in Dorper crossbred (2.37±0.13 kg) while 12 months live weight was found higher in Perendale crossbred (22.33±0.99 kg), respectively. In case of cumulative growth performance of male, the highest value was found in Damara crossbred while, Perendale crossbred female was found better compared to other crossbred. Major disease frequently occurred in crossbred sheep was diarrhea. The survivability rate (%) of crossbred sheep at lamb (0-3 months of age) and grower (3-8 months of age) stage were 92.55 and 90.8, respectively indicates positive influence on the crossbreeding program. Though, this is very first work regarding crossbred sheep, further research is needed in corporation with other economic trait associated with growth and reproduction to evaluate all the crossbred genotype as well as select a suitable crossbred for the production of commercial market lamb in Bangladesh.

Design of nonlinear rubber vibration isolators with low resonant frequency and high load bearing capacity

Designing a rubber vibration isolator to simultaneously meet the requirements on both low-frequency vibration isolation and high load carrying capacity has always been a major challenge. To tackle this problem, in this paper, a design strategy capable of effectively balancing these two effects to guide the design of such isolators is proposed. The rubber isolator static and dynamic behavior prediction models suitable for engineering applications are first established, where Yeoh model is adopted for rubber strong nonlinear feature prediction. Experiments are carried out to determine the material constants in Yeoh model and dynamic-to-static ratio of the rubber material. Combing with Multi-Objective Genetic Algorithm, a design strategy is proposed. The conducted experiments demonstrate that based on the proposed numerical model and design procedure, rubber isolators with enough load bearing capability and low-frequency vibration isolation performance can be successfully designed.

Extended Reality Gaming for Exercise and Mindfulness Throughout Pediatric Cancer Rehabilitation: Protocol for a Randomized Controlled Trial.

Pediatric patients with cancer have limited options to self-manage their health while they are undergoing treatments in the hospital and after they are discharged to their homes. Extended reality (ER) using head-mounted displays has emerged as an immersive method of improving pain and mental health and promoting health-enhancing physical activity among a variety of clinical groups, but there is currently no established protocol for improving both physical and mental health in pediatric cancer rehabilitation. This phase I, pilot, feasibility randomized controlled trial aims to investigate the potential effects of a 14-week ER program on physical activity participation and indicators of health among pediatric patients with cancer who undergo bone marrow transplantation. An ancillary aim is to evaluate the feasibility of the program through participant engagement. This study includes a 2-arm parallel group design with a 1-group crossover (the control group will start the intervention after a waiting period). Overall, 16 pediatric patients with cancer undergoing rehabilitation (aged ≥8 years) at a children's hospital will be randomly allocated into one of two groups: (1) an immediate start group that undergoes an ER program in the hospital until discharge and then for 8 weeks at home (total duration of approximately 14 weeks), and (2) a waitlist control group that undergoes usual care in the hospital and for 8 weeks at home, before receiving the 8-week home ER program. The program will include active video gaming with rhythmic music exercises as well as mindfulness-based practices using a high-quality app. Home-based programming will include behavioral coaching calls. Physical activity will be measured daily through step counts using a tri-axial accelerometer. Health outcomes will be measured across time and include global health, measured by the National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Global Health Scale Short Form 7+2, and lung function, measured by a forced expiratory volume using a peak flow meter. Feasibility will be evaluated through participant engagement metrics, such as enrollment, dropout, adverse events, and attendance rates. Descriptive statistics will be obtained for all study variables. Outcomes will be modeled using mixed modeling procedures, and changes in means will be estimated with CIs. The study was funded in February 2024. Recruitment procedures started on June 27, 2024. All data are anticipated to be collected by February 2026. Full trial results are anticipated to be analyzed and submitted for publication by March 2026. The study's anticipated end date is March 31, 2026. This trial tests an accessible remote program for improving both physical and mental health among pediatric patients with cancer. The knowledge obtained from this study will inform the development of a larger trial. ClinicalTrials.gov NCT06298357; https://clinicaltrials.gov/study/NCT06298357. DERR1-10.2196/64879.

Satellite-based mapping of canopy fuels at the pan-European scale

ABSTRACT Canopy base height (CBH) and canopy bulk density (CBD) are forest canopy fuel parameters that are key for modeling the behavior of crown wildfires. In this work, we map them at a pan-European scale for the year 2020, producing a new dataset consisting of two raster layers containing both variables at an approximate resolution of 100 m. Spatial data from Earth observation missions and derived down-stream products were retrieved and processed using artificial intelligence to first estimate a map of aboveground biomass (AGB). Allometric models were then used to estimate the spatial distribution of CBH using the canopy height values as explanatory variables and CBD using AGB values. Ad-hoc allometric models were defined for this study. Data provided by FIRE-RES project partners and acquired through field inventories was used for validating the final products using an independent dataset of 804 ground-truth sample plots. The CBH and CBD raster maps have, respectively, the following accuracy regarding specific metrics reported from the modeling procedures: (i) coefficient of correlation (R) of 0.445 and 0.330 (p-value < 0.001); (ii) root mean square of error (RMSE) of 3.9 m and 0.099 kg m−3; and (iii) a mean absolute percentage error (MAPE) of 61% and 76%. Regarding CBD, the accuracy metrics improved in closed canopies (canopy cover > 80%) to R = 0.457, RMSE = 0.085, and MAPE = 59%. In short, we believe that the degree of accuracy is reasonable in the resulting maps, producing CBH and CBD datasets at the pan-European scale to support fire mitigation and crown fire simulations.