Measurement Points Research Articles

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

Non-linear association and benchmark dose of blood pressure on carotid artery intima-media thickening in a general population of southern China

Background and aimsThis study aimed to evaluate whether there is a J-curve association between blood pressure (BP) and carotid artery intima-media thickening (CAIT) and estimate the effect of the turning point of BP on CAIT.Methods and resultsData from 111,494 regular physical examinations conducted on workers and retirees (aged 18 years or older) between January 2011 and December 2016, exported from the hospital information system, were analyzed. Restricted cubic splines (RCS) logistic regression was employed to access the association of BP with CAIT, and Bayesian benchmark dose methods were used to estimate the benchmark dose as the departure point of BP measurements. All the pnon-linear values of BP measurements were less than 0.05 in the RCS logistic regression models. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) had J-curve associations with the risk of CAIT at a turning point around 120/70 mmHg in the RCS. The benchmark dose for a 1% change in CAIT risk was estimated to be 120.64 mmHg for SBP and 72.46 mmHg for DBP.ConclusionThe J-curve associations between SBP and DBP and the risk of CAIT were observed in the general population in southern China, and the turning point of blood pressure for significantly reducing the risk of CAIT was estimated to be 120.64/72.46 mmHg for SBP/DBP.

Method for estimating management points for dimensional quality management in shipbuilding and offshore plant construction using terrestrial laser scanning and a computer-aided design model

This study proposes a method for estimating management points for dimensional quality management in shipbuilding and offshore plant construction using terrestrial laser scanning. The dimensional quality is managed by calculating errors between quality management points in a 3-dimensional (3D) computer-aided design (CAD) model and their corresponding measurement points in a fabricated block. The proposed method utilizes a point cloud of the fabricated block together with a 3D CAD model of the block to estimate the measurement points. To achieve this, global registration, extraction of the region of interest, point segmentation in the region of interest, plane estimation of the segmented points, and estimation of measurement points are performed in an automated manner. To verify the proposed method, it is implemented and experimented with actual ship blocks. The experimental results reveal that the estimated measurement points are within the allowable tolerance of 7 mm.

Remote Measurement of Tide and Surge Using a Deep Learning System with Surveillance Camera Images

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models

The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points.

Identification of Ground Vulnerability Based on Kg, PGA, and GSS Assessment by Single Station Microtremor in Bendosari Hamlet, Kalasan Village, Yogyakarta

Bendosari Hamlet, Kalasan, Yogyakarta is a research area that is one of the areas affected by the earthquake that occurred on May 27, 2006. The earthquake with a magnitude of 6.4 caused quite severe damage to buildings around the research area. Based on the phenomenon of earthquakes that have occurred, there needs to be further study related to vulnerable areas in the event of another earthquake. Research data is primary data taken using a single statiun microtremor tool with an overall acquisition point of 50 measurement points. This study aims to find out the value of seismic vulnerability index (Kg), Peak Ground Acceleration (PGA), and Ground Shear Strain (GSS). Using the Horizontal to Vertical Spectral Ratio (HVSR) method, the dominant frequency result (f0) and amplification factor value (A0) from data that has been recorded through the microtremor tool. Based on research that has been done, the results are obtained in the f0rm of seismic vulnerability index values that are in the range of 0.49 - 30.18, the value of the acceleration of ground movement is in the range of 0.21 - 0.61 Gal, and the ground shear strain value is in the range of 4 x 10-6 to 38 x 10-5. Point K26 which is the original documentation of buildings affected by the earthquake, analyzed that the value of Vs of 858.112 m / s on the vs ground profile indicates that there is hard soil at a depth after 27.2 m. This shows that the thickness of subsurface sedimentary rocks at point K26 is quite thick. Keywords: Bendosari Hamlet; Mikrotremor; GSS; HVSR; PGA; Seismic Vulnerability

Fast and accurate Zero-defect manufacturing using collaborative real-time photogrammetry

Abstract. For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

Study on Temperature Cascade ELM Inversion Method for 110 kV Single-Core Cable Intermediate Joints

The accurate calculation of the hotspot temperature of the cable intermediate joint can effectively guarantee the safe operation of the transmission and distribution network. This paper addresses the limitations of the current method of estimating hotspot temperature solely from surface temperature measurements. Specifically, we focus on a 110 kV single-core cable as our subject of study. We started by establishing a simulation model for the temperature field at the intermediate joint to generate data samples. Subsequently, the NCA (neighborhood component analysis) algorithm was employed to select the optimal measurement points on the cable’s surface. This allowed determination of the quantity and location of characteristic points. Finally, we developed a cascading inversion model, which consists of a radial inversion model and an axial inversion model, based on the extreme learning machine algorithm. The example results show that the mean squared error of hotspot temperature obtained by cascade inversion and direct inversion is 6.95 and 24.71, respectively, indicating that cascade inversion can effectively improve the inversion accuracy.

The diabetes body project: Study protocol of a multi-site trial of a virtually delivered eating disorder prevention program for young women with type 1 diabetes.

Young women with type 1 diabetes are a high-risk population for eating disorders (ED). Prevention programs are lacking. In young women without diabetes, the Body Project has produced reductions in ED risk factors, ED symptoms and future ED onset. Therefore, the Body Project was adapted to type 1 diabetes, the Diabetes Body Project (DBP). In this protocol, we describe the multi-site randomized controlled trial (RCT) to evaluate efficacy of the DBP. This is an ongoing RCT with four sites (Europe and US). In total 280, 14-35 year-old, women with type 1 diabetes ≥1 year and some level of body image concerns will be recruited in 2023. Participants are randomized to either virtual DBP groups or an educational control. The study constitutes 5 measurement points distributed over 2 years. The primary aim is to test if DBP will produce significantly greater reductions in ED behaviours, ED symptoms and future ED onset. The secondary aim is to test if DBP has significantly greater improvements in diabetes distress, quality of life and glycaemic outcomes. We expect that DBP will be more efficacious in reducing ED behaviours, ED symptoms and ED onset compared to the control condition. Additionally, we hope to gain new knowledge on how DBP may affect diabetes-related outcomes. If DBP proves efficacious, it has potential for immediate, clinical implementation at low-cost and may contribute to broad prevention of future ED onset among young women with type 1 diabetes. NCT05399446.

Effects of train speed and passenger capacity on ground vibration of underground suburban railways

This study aims to explore the optimal driving speed for ground vibration in suburban railway underground sections. We focused on the ground surface of suburban railway underground sections and developed a 3D finite element dynamic coupling model for the tunnel–soil system. Subsequently, considering factors such as train speed and passenger load, we analyzed the propagation characteristics of ground vibration responses in urban railway underground sections. The research results indicate a significant amplification phenomenon in the peak power spectrum of measurement points near the tunnels in underground sections. The high-frequency components of the power spectrum between measurement points are noticeably higher between the two tunnels. Furthermore, as the train speed increases, this amplification phenomenon becomes more pronounced, and the power spectrum of each measurement point mainly concentrates on several frequency bands, with the amplitude of the power spectrum near the prominent frequencies also increasing. However, when the train speed is between 100 and 120 km/h, the impact on the amplitude of the power spectrum at measurement points above the running tunnel is minimal. Additionally, the amplitude of the middle-to-high frequency components in the power spectrum increases with the increase in passenger numbers. The impact on the peak acceleration amplitude at each measurement point is minimal when the train speed is 80 km/h or below. However, once the train speed exceeds 80 km/h, the peak acceleration amplitude above the running tunnel rapidly increases, reaching its maximum value at 113 km/h, and then gradually decreasing.

Experimental comparison of cylindrical and plane parallel ionization chambers for reference dosimetry in continuous and pulsed scanned proton beams

Objective. In this experimental work we compared the determination of absorbed dose to water using four ionization chambers (ICs), a PTW-34045 Advanced Markus, a PTW-34001 Roos, an IBA-PPC05 and a PTW-30012 Farmer, irradiated under the same conditions in one continuous- and in two pulsed-scanned proton beams. Approach. The ICs were positioned at 2 cm depth in a water phantom in four square-field single-energy scanned-proton beams with nominal energies between 80 and 220 MeV and in the middle of 10 × 10 × 10 cm3 dose cubes centered at 10 cm or 12.5 cm depth in water. The water-equivalent thickness (WET) of the entrance window and the effective point of measurement was considered when positioning the plane parallel (PP) ICs and the cylindrical ICs, respectively. To reduce uncertainties, all ICs were calibrated at the same primary standards laboratory. We used the beam quality (k Q ) correction factors for the ICs under investigation from IAEA TRS-398, the newly calculated Monte Carlo (MC) values and the anticipated IAEA TRS-398 updated recommendations. Main results. Dose differences among the four ICs ranged between 1.5% and 3.7% using both the TRS-398 and the newly recommended k Q values. The spread among the chambers is reduced with the newly k Q values. The largest differences were observed between the rest of the ICs and the IBA-PPC05 IC, obtaining lower dose with the IBA-PPC05. Significance. We provide experimental data comparing different types of chambers in different proton beam qualities. The observed dose differences between the ICs appear to be related to inconsistencies in the determination of the k Q values. For PP ICs, MC studies account for the physical thickness of the entrance window rather than the WET. The additional energy loss that the wall material invokes is not negligible for the IBA-PPC05 and might partially explain the low k Q values determined for this IC. To resolve this inconsistency and to benchmark MC values, k Q values measured using calorimetry are needed.

Detection method for structural defects of U-shaped hanging ring for power fittings based on magnetic leakage principle

Abstract The U-shaped hanging ring of electric power fittings is usually installed outdoors and operates in harsh environments. Under the combined effect of the weight of the conductor and wind load, the bending part at the bottom of the U-shaped hanging ring often experiences wear and cracks due to friction contact, which can easily lead to accidents such as the detachment of transmission lines. Traditional methods for detecting corrosion defects in ferromagnetic materials, such as ultrasonic testing, eddy current testing, and manual testing, are affected by environmental factors, have a shallow measurement depth, and are difficult to quantify. In order to solve the above problems, this paper proposes a measurement method for detecting corrosion defects in the U-shaped hanging ring of electric power fittings based on the principle of magnetic leakage. The magnetic leakage signal characteristics of U-shaped hanging ring defects under different excitation structural parameters and different corrosion types are analyzed. The method of arranging magnetic leakage signal measurement points is studied. The detection of defect parameters is achieved based on the characteristic quantity of the magnetic leakage signal under different corrosion conditions. Finally, the method is analyzed and verified through examples. The results show that this method can achieve quantitative detection of defect parameters in the U-shaped hanging ring under uniform corrosion and crack corrosion, and has the advantages of high measurement accuracy and small error. The research results of this paper can provide a scientific theoretical basis for the inspection and replacement of transmission line fittings in engineering, which is of great engineering significance for ensuring the safe and stable operation of the power grid.

Exploratory investigation of virtual lesions in gastrointestinal endoscopy using a novel phase‐shift method for three‐dimensional shape measurement

AbstractAccurate measurement of the size of lesions or distances between any two points during endoscopic examination of the gastrointestinal tract is difficult owing to the fisheye lens used in endoscopy. To overcome this issue, we developed a phase‐shift method to measure three‐dimensional (3D) data on a curved surface, which we present herein. Our system allows the creation of 3D shapes on a curved surface by the phase‐shift method using a stripe pattern projected from a small projecting device to an object. For evaluation, 88 measurement points were inserted in porcine stomach tissue, attached to a half‐pipe jig, with an inner radius of 21 mm. The accuracy and precision of the measurement data for our shape measurement system were compared with the data obtained using an Olympus STM6 measurement microscope. The accuracy of the path length of a simulated protruded lesion was evaluated using a plaster model of the curved stomach and graph paper. The difference in height measures between the measurement microscope and measurement system data was 0.24 mm for the 88 measurement points on the curved surface of the porcine stomach. The error in the path length measurement for a lesion on an underlying curved surface was <1% for a 10‐mm lesion. The software was developed for the automated calculation of the major and minor diameters of each lesion. The accuracy of our measurement system could improve the accuracy of determining the size of lesions, whether protruded or depressed, regardless of the curvature of the underlying surface.

Effect of digital game intervention on cognitive functions in older adults: a multiple baseline single case experimental design study

BackgroundResidents in nursing homes are prone to cognitive decline affecting memory, visuospatial cognition, and executive functions. Cognitive decline can lead to dementia, necessitating prioritized intervention.MethodsThe current study aimed to investigate whether an intervention using a digital game was effective for preserving and improving the cognitive function of residents in nursing homes. An intervention study was conducted using a single-case AB design with multiple baselines. The participants in the study were five older adults aged 65 and over who do not play digital games regularly. The study ran for 15 weeks, including a baseline (phase A) and an intervention phase (phase B). Phase A had five baselines (5 to 9 weeks) with random participant assignment. In phase B, participants engaged in a digital game (Space Invaders) individually. Cognitive function was assessed as the outcome, measured using the Brain Assessment (performed on a tablet through the Internet) at 16 measurement points. Four of five participants (two female and two male) were included in the analysis, using visual inspection and Bayesian statistics with multi-level modeling.ResultsVisual inspection of the graphs revealed cognitive function score improvements after the intervention for most layers in terms of memory of numbers, memory of words, mental rotation test (visuospatial ability), and total scores in the Brain Assessment. These effects were also significant in the analysis by multi-level modeling.ConclusionsThe results suggest that the use of digital games may be effective for preserving and improving cognitive function among residents of nursing home.Trial registrationThis study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000048677; public title: Effect of a Digital Game Intervention for Cognitive Functions in Older People; registration date: August 30, 2022).

Strengthening collaborative learning in secondary school: Development and evaluation of a lesson-integrated training approach for transactive communication

BackgroundTransactive communication in collaborative learning environments occurs when students refer to their learning partners' ideas, build on them, and transform them into more elaborate ideas. This learning activity is essential for students to be able to benefit from collaboration, but they need scaffolding to produce transactive statements. From the teachers’ perspective, this intervention should take little effort. AimsWe developed and evaluated a lesson-integrated training in transactive communication for secondary school students, including instructions, exercises, and feedback on transactive communication, materials and tasks on curriculum-related content, and working with various cooperative learning methods. SampleIn a quasi-experimental pre-test post-test design, 594 ninth-grade students in 23 classes received training in transactive communication or presentation skills. A complete set of data exists for a parallelized sample of 82 students in each condition. MethodsAt both points of measurement, students worked in dyads, and their communication was audiotaped, transcribed, and their transactive statements and non-transactive, content-related externalizations were coded. Furthermore, students completed knowledge tests about the topic of partner work and reported on their experiences with collaboration and motivation for group work (trait). ResultsAnalyses revealed positive effects of the training in transactivity on transactive statements and experiences with collaboration. Students with the training in transactive communication also produced more externalizations. However, no differences were found for students’ knowledge acquisition and motivation for group work. ConclusionThe training in transactive communication was effective for the collaborative working process, but transfer effects need to be further investigated.

Signal-based fault location method using time and frequency domain

This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

Numerical Simulation and Deformation Prediction of Deep Pit Based on PSO-BP Neural Network Inversion of Soil Parameters

The finite element numerical simulation results of deep pit deformation are greatly influenced by soil layer parameters, which are crucial in determining the accuracy of deformation prediction results. This study employs the orthogonal experimental design to determine the combinations of various soil layer parameters in deep pits. Displacement values at specific measurement points were calculated using PLAXIS 3D under these varying parameter combinations to generate training samples. The nonlinear mapping ability of the Back Propagation (BP) neural network and Particle Swarm Optimization (PSO) were used for sample global optimization. Combining these with actual onsite measurements, we inversely calculate soil layer parameter values to update the input parameters for PLAXIS 3D. This allows us to conduct dynamic deformation prediction studies throughout the entire excavation process of deep pits. The results indicate that the use of the PSO-BP neural network for inverting soil layer parameters effectively enhances the convergence speed of the BP neural network model and avoids the issue of easily falling into local optimal solutions. The use of PLAXIS 3D to simulate the excavation process of the pit accurately reflects the dynamic changes in the displacement of the retaining structure, and the numerical simulation results show good agreement with the measured values. By updating the model parameters in real-time and calculating the pile displacement under different working conditions, the absolute errors between the measured and simulated values of pile top vertical displacement and pile body maximum horizontal displacement can be effectively reduced. This suggests that inverting soil layer parameters using measured values from working conditions is a feasible method for dynamically predicting the excavation process of the pit. The research results have some reference value for the selection of soil layer parameters in similar areas.

UNIFAC PREDICTION OF VAPOR LIQUID EQUILIBRIA INVOLVING GAMMA-VALEROLACTONE DERIVATIVE SYSTEMS

Researchers have studied the hydrogenation of Gamma-Valerolactone (GVL) over Ru/C to produce 2-methyltetrahydrofuran (2-MTHF), a biomass-based platform chemical with potential as a biofuel and green solvent. Other byproducts of this reaction include 2-butanol (2-BuOH), 2-pentanol (2-PeOH), and 1,4-pentanediol (1,4-PDO). In this study, UNIFAC activity coefficient models were used to predict the vapor-liquid equilibrium of several systems included in the process to explain the phase behavior which is important in purification process. The accuracy of the UNIFAC model is tested by comparing the experimental boiling points for the binary system of 2-propanol + 1-butanol and the vapor-liquid equi-librium of the GVL + 2-MTHF system. From this comparison, the Root Mean Square Deviation (RMSD) values for the boiling point measurements are obtained to be 3.153%, and for the liquid and vapor phases in the vapor-liquid equilibrium measurements, the RMSD values are 1.934% and 0.298% respectively. These RMSD values indicate the level of accuracy of the UNIFAC model in representing the experimental data for both boiling point measurements and vapor-liquid equilibrium phase behavior. The prediction results of vapor-liquid equilibrium data for GVL derivative systems showed that the 2-BuOH + 2-MTHF system does form an azeotrope when the mol fraction of 2-BuOH is 0.0031 at 79.780C. The calculation was performed using ChemCAD commercial software for chemical process modeling and simulation

The contribution and interplay of implicit and explicit processes on physical activity behavior: empirical testing of the physical activity adoption and maintenance (PAAM) model

The adoption and maintenance of physical activity (PA) is an important health behavior. This paper presents the first comprehensive empirical test of the Physical Activity Adoption and Maintenance (PAAM) model, which proposes that a combination of explicit (e.g., intention) and implicit (e.g., habit,, affect) self-regulatory processes is involved in PA adoption and maintenance. Data were collected via online questionnaires in English, German, and Italian at two measurement points four weeks apart. The study included 422 participants (Mage= 25.3, SDage= 10.1; 74.2% women) from Germany, Switzerland, Italy, Canada, and the U.S. The study results largely supported the assumptions of the PAAM model, indicating that intentions and habits significantly mediate the effects of past PA on future PA. In addition, the effect of past PA on future PA was shown to be significant through a mediation chain involving affect and habit. Although the hypothesis that trait self-regulation moderates the intention-behavior relationship was not supported, a significant moderating effect of affect on the same relationship was observed. The results suggest that interventions targeting both explicit and implicit processes may be effective in promoting PA adoption and maintenance.

The roots of errors in adaptive performance: clustering behavioral patterns after the introduction of a change

PurposeThe purpose of this study was to investigate work-related adaptive performance from a longitudinal process perspective. This paper clustered specific behavioral patterns following the introduction of a change and related them to retentivity as an individual cognitive ability. In addition, this paper investigated whether the occurrence of adaptation errors varied depending on the type of change content.Design/methodology/approachData from 35 participants collected in the simulated manufacturing environment of a Research and Application Center Industry 4.0 (RACI) were analyzed. The participants were required to learn and train a manufacturing process in the RACI and through an online training program. At a second measurement point in the RACI, specific manufacturing steps were subject to change and participants had to adapt their task execution. Adaptive performance was evaluated by counting the adaptation errors.FindingsThe participants showed one of the following behavioral patterns: (1) no adaptation errors, (2) few adaptation errors, (3) repeated adaptation errors regarding the same actions, or (4) many adaptation errors distributed over many different actions. The latter ones had a very low retentivity compared to the other groups. Most of the adaptation errors were made when new actions were added to the manufacturing process.Originality/valueOur study adds empirical research on adaptive performance and its underlying processes. It contributes to a detailed understanding of different behaviors in change situations and derives implications for organizational change management.