Manual dexterity of older Mexican American adults: a cross-sectional pilot experimental investigation

This paper reports results from an experimental pilot study performed to quantify the manual dexterity of older Mexican American adults. The Purdue pegboard test, a two-arm coordination test, and a hand-tool dexterity test were used in this study. To enable cross-sectional comparisons of manual dexterity measures of older Mexican American adults with young Mexican American adults, these tests were administered to 18 older Mexican American adults aged 63–85 (mean age: 71.3 years, SD: 7.0 years), recruited from senior recreation centers in El Paso, and eighteen young adults aged 21–32 (mean age: 25.6 years, SD: 3.8 years) recruited from the student body at the University of Texas at El Paso. For the Purdue pegboard test, the number of pegs placed in 30 s using the preferred hand, the non-preferred hand, and both hands were first individually monitored. Then, scores on an assembly task using the Purdue pegboard were obtained. For the two-arm coordination test, participants were required to trace a star pattern with a stylus using both hands. The time for task completion and the number of errors made during task performance were monitored. For the hand-tool dexterity test, participants were required to use common hand tools and remove nuts and bolts from one side of a wooden upright, and to assemble nuts and bolts in the corresponding holes on the other side of the upright. The time taken for task completion was recorded. Since modified Levene's test showed equality of variances, two sample t-tests, comparing the mean responses of older adults with the mean responses of young adults for each individual test, were conducted. Results indicate that responses for the older adults were statistically significant different (p<0.001) from young adults for all Purdue pegboard tasks. On the average, older adults performed significantly slower (p<0.001) than young adults on the two-arm coordination test, and committed more errors before task completion (p<0.05). Older adults also took longer to complete the hand-tool dexterity task compared to their younger counterparts (p<0.05). In addition to the t-tests, manual dexterity performance measures from older adults were regressed with age to determine the cross-sectional age effects on manual dexterity measures. Results indicate that all Purdue pegboard performance measures were significantly affected by age (p<0.05). The time to complete the two-arm coordination test was significantly linearly related to age (p<0.05). However, the number of errors committed by older adults in the performance of the two-arm coordination test was not significantly linearly related to age. Time to complete the hand-tool dexterity was also significantly linearly related to age (p<0.05). Accommodating age-related changes in manual dexterity is important for job design in industry, especially in industries employing older adults requiring significant assembly and hand-tool use.

Relevance to industry

Given the aging of the industrial workforce, it is important to understand how manual dexterity is affected by age, so that jobs requiring significant manual dexterity for task initiation, task performance and task completion are designed to fit older adult dexterity levels.     

Novel optimized tree-based stack-type architecture for 2n-bit comparator at nanoscale with energy dissipation analysis

Comparator is an essential building block in many digital circuits such as biometric authentication, data sorting, and exponents comparison in floating-point architectures among others. Quantum-dot Cellular Automata (QCA) is a latest nanotechnology that overcomes the drawbacks of Complementary Metal Oxide Semiconductor (CMOS) technology. In this paper, novel area optimized 2n-bit comparator architecture is proposed. To achieve the objective, 1-bit stack-type and 4-bit tree-based stack-type (TB-ST) comparators are proposed using QCA. Then, two tree-based architectures of 4-bit comparators are arranged in two layers to optimize the number of quantum cells and area of an 8-bit comparator. Thus, this design can be extended to any 2n-bit comparator. Simulation results of 4-bit and 8-bit comparators using QCADesigner 2.0.3 show that there is a significant improvement in the number of quantum cells and area occupancy. The proposed TB-ST 8-bit comparator uses 2.5 clock cycles and 622 quantum cells with area occupancy of 0.49 µm² which is an improvement by 10.5% and 38%, respectively, compared to existing designs. Scaling it to a 32-bit comparator, the proposed architecture requires only 2675 quantum cells in an area of 2.05 µm² with a delay of 3.5 clock cycles, indicating 9.35% and 28.8% improvements, respectively, demonstrating the merit of the proposed architecture. Besides, energy dissipation analysis of the proposed TB-ST 8-bit comparator is simulated on QCADesigner-E tool, indicating average energy dissipation reduction of 17.3% compared to existing works.

     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.     

Secure and Light Weight Elliptic Curve Cipher Suites in SSL/TLS

In the current circumstance, e-commerce through an online banking system plays a significant role. Customers may either buy goods from E-Commerce websites or use online banking to move money to other accounts. When a user participates in these types of behaviors, their sensitive information is sent to an untrustworthy network. As a consequence, when transmitting data from an internal browser to an external E-commerce web server using the cryptographic protocol SSL/TLS, the E-commerce web server ensures the security of the user’s data. The user should be pleased with the confidentiality, authentication, and authenticity properties of the SSL/TLS on both the user’s web browser and the remote E-commerce web server. E-Commerce web servers should choose the best SSL/TLS cipher suites for negotiating the user in order to attain such optimistic scenarios, as the cipher suite used in SSL/TLS plays an important role in securing E-Commerce web servers. The paper primarily focuses on analyzing the SSL/TLS cipher and elliptic curves. The paper also recommends the best elliptic curve cipher suites for E-Commerce and online banking servers, based on their power consumption, handshake execution time, and key exchange and signature verification time.     

Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm

Applied Intelligence - Microscopic Image segmentation has a crucial role in detecting and diagnosing numerous critical diseases like Alzheimer’s disease, Kidney disease, Cancer, many...     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit

This paper presents a study highlighting the predictive performance of a radial basis function (RBF) network in estimating the grade of an offshore placer gold deposit. In applying the radial basis function network to grade estimation of the deposit, several pertinent issues regarding RBF model construction are addressed in this study. One of the issues is the selection of the RBF network along with its center and width parameters. Selection was done by an evolutionary algorithm that utilizes the concept of cooperative coevolutions of the RBFs and the associated network. Furthermore, the problem of data division, which arose during the creation of the training, calibration and validation of data sets for the RBF model development, was resolved with the help of an integrated approach of data segmentation and genetic algorithms (GA). A simulation study conducted showed that nearly 27% of the time, a bad data division would result if random data divisions were adopted in this study. In addition, the efficacy of the RBF network was tested against a feed-forward network and geostatistical techniques. The outcome of this comparative study indicated that the RBF model performed decisively better than the feed-forward network and the ordinary kriging (OK).