Investigation of common insertion hand postures and directions and determination of the voluntarily maximal and preferred insertion forces in automotive assembly tasks

The aim of this study was to investigate common insertion hand postures and directions (study 1) and determine the maximal and preferred insertion forces (study 2) for assembly workers. Video analyses were conducted to investigate the insertion hand posture and direction according to assembly materials. The results of the video analyses revealed that the left and right hands used five and six types of hand postures, respectively, with the maximum frequency, and this accounted for 86.7% and 81.4% of the postures, respectively. Forty male subjects participated in a psychophysical methodology test in which they were asked to perform insertion tasks by selecting the magnitudes such that the maximal acceptable forces (MAFs) and preferred forces (PFs) could be determined. The results indicated that hand posture, insertion direction, and age significantly affected MAFs. Furthermore, hand posture, insertion direction, and age significantly affected PFs. The insertion force should be considered in the task design phase, especially hand posture, insertion direction, and age.     

A new mathematical model of the spine and its relationship to spinal loading in the workplace

A knowledge of the loads imposed on the spine during manual work and of the postures in which the spine is least stressed while performing a particular task are of considerable importance for the design of a safe and comfortable working environment. Spinal loads are intrinsically very difficult to measure and so are generally calculated from mathematical models of the spine. These models are based on assumptions of how the spine functions mechanically, and the forces calculated are dependent on the particular model used. A new model, which assumes that the spine functions in a similar way to an arch, is discussed. This model shows that spinal stresses are not as great as previously calculated using the traditional cantilever model and that, even when no external loads are being carried, the stress is strongly dependent on posture. The arch model re-interprets the role of intra-abdominal pressure and shows that it acts together with the lumbar lordosis to strengten the spine. Maintaining a lumbar lordosis is then important when lifting heavy loads.     

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

A statistical framework to minimise and predict the range values of quantisation errors in fixed-point FIR filters architectures

Filters datapath signals and coefficients are quantised when implemented in hardware to limit and reduce excessive hardware requirements. In this paper, we formulate quantisation errors (noises) in fixed-point arithmetic using a novel analytical model. The latter extends a conventional signal quantisation statistical model by assuming a Gaussian distribution noise. The paper gives the mathematical expressions to compute the statistical parameters and range values of the quantisation errors at any point in a multistage FIR filters structure depending on the wordlengths fractional precisions. Three case studies are included to vindicate the model?s validity and accuracy in predicting the quantisation error parameters in the absence of filter coefficients quantisation. To counter the effects of the latter, we present a novel approach, called errors cancellation. The approach tends to represent the filter coefficients using different wordlengths to minimise the dynamic of the error filter?s output. This allows limiting the quantisation effects to the signals quantisation only, which is statistically accurately modelled. The validity and the efficiency of the approach along with our analytical model are shown using two further case studies. Through our errors cancellation approach and analytical model, a hardware designer can now minimise the effects of the filter coefficients quantisation and predict subsequently the range values of the computation errors depending on the fractional precision used. He can also preset the latter to achieve the sought computations accuracy.     

A toolbox to find the best mechanistic model to predict the behavior of environmental systems

Reliable prediction of the long-term behavior of environmental systems such as Municipal Solid Waste (MSW) landfills is challenging. While many driving forces influence this behavior, characterization of them is limited by measurement techniques. Therefore, a model structure for reliable prediction needs to optimally combine all measured information with suitable mechanistic information from literature. How to get such an optimal model structure? This study presents a toolbox to find and build the model structure that describes an environmental system as close as possible. The toolbox combines environmental frameworks to include all suitable mechanistic information; it fully couples kinetic and equilibrium reactions and contains multiple resources to obtain biogeochemical parameters. Several possible optimal model structures are quickly built and evaluated with objective statistical performance criteria obtained via Bayesian inference. By applying the novel methodology, we select the best model structure for anaerobic digestion of MSW in full scale landfills.     

A model to predict the survivability of cancer comorbidity through ensemble learning approach

Cancer is one of the most common death causes worldwide. Breast and genital cancers in women and prostate cancer in men constitute three of the most common cancers. Detection and prevention of these types of cancers are critical objectives. Recent findings indicate that some patients suffer from cancer comorbidity. The probability of survival among patients with comorbid condition is lower than those with only one type of cancer. The importance of concomitant chronic illnesses during cancer treatment through the SEER data is assessed through many machine‐learning approaches. In order to improve the accuracy of prediction of survival rates in patients with cancer and comorbidity of cancers, the gradient boosting ensemble method is adopted for feature selection and modelling. This proposed method increases the accuracy rate and reduces the error rate, and exhibits a significant predictive improvement of survival rates in comorbid cancer compared with the previous proposed models.     

A modified version of the three-compartment model to predict fatigue during submaximal tasks with complex force-time histories

The three-compartment model (3CM) was validated previously for prediction of endurance times by modifying its fatigue and recovery rates. However, endurance times do not typically represent work demands, and it is unknown if the current version of the 3CM is applicable for ergonomics analysis of all occupational tasks. The purpose of this study was to add biological fidelity to the 3CM, and validate the model against a series of submaximal force plateaus. The fatigue and recovery rates were modified to represent graded physiological motor unit characteristics (termed 3CM_GMU). In nine experiments of submaximal efforts, the 3CM_GMU produced a root-mean squared difference (RMSD) of 4.1 ± 0.5% MVC over experiments with an average strength loss (i.e. fatigue) of 31.0 ± 1.1% MVC. The 3CM_GMU model performed poorly for endurance tasks. The 3CM_GMU model is an improvement for evaluating submaximal force patterns consisting of intermittent muscle contractions of the hand and forearm.

Practitioner Summary: We modified an existing fatigue model using known physiological properties in order to predict fatigue during nine different submaximal force profiles; consistent with efforts seen in industrial work. We expect this model to be included in digital human modelling software, for the assessment of repetitive work and muscle fatigue in repetitive tasks.

Social Media Summary: The proposed model has applications for estimating task fatigue in proactive ergonomic analyses of complex force patterns using digital human models.     

Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism implemented in a causal probabilistic network. It handles data on insulin dose, carbohydrate intake and blood glucose concentration to predict hourly blood glucose concentrations and thus provide an indication of blood glucose values between home blood tests. DIAS was used to predict blood glucose profiles in eight patients with well-controlled insulin-dependent diabetes, who are at increased risk of hypoglycaemia (abnormally low blood glucose levels). DIAS predicted nocturnal hypoglycaemia in six patients and daytime hypoglycaemia in one patient. The occurrence of nocturnal hypoglycaemia was not recognised by the patient or suspected by their doctor but was subsequently confirmed by blood testing in five patients. It is known that unrecognised nocturnal hypoglycaemia is common in patients with well-controlled diabetes. The ability of DIAS to identify such periods of hypoglycaemia with reasonable accuracy illustrates how the advanced technology it employs may provide reliable decision support to clinicians.     

A statistic approach to the detection of human faces in color nature scene

In this paper, a novel algorithm for oriental face detection is presented to locate multiple faces in color scenery images. A binary skin color map is first obtained by applying the skin/non-skin color classification algorithm. Then, color regions corresponding to the facial and non-facial areas in the color map are separated with a clustering-based splitting algorithm. Thereafter, an elliptic face model is devised to crop the real human faces through the shape location procedure. Last, local thresholding technique and a statistic-based verification procedure are utilized to confirm the human faces. The proposed detection algorithm combines both the color and shape properties of faces. In this work, the color span of human face can be expanded as wilder as possible to cover different faces by using the clustering-based splitting algorithm. Experimental results reveal the feasibility of our proposed approach in solving face detection problem.     

Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.     

Development of a kinematic model to predict finger flexor tendon and subsynovial connective tissue displacement in the carpal tunnel

Finger flexor tendinopathies and carpal tunnel syndrome are histologically characterised by non-inflammatory fibrosis of the subsynovial connective tissue (SSCT) in the carpal tunnel, which is indicative of excessive and repetitive shear forces between the finger flexor tendons and SSCT. We assessed flexor digitorum superficialis (FDS) tendon and adjacent SSCT displacements with colour Doppler ultrasound as 16 healthy participants completed long finger flexion/extension movements captured by a motion capture system. FDS tendon displacements fit a second-order regression model based on metacarpophalangeal and proximal interphalangeal joint flexion angles (R² = 0.92 ± 0.01). SSCT displacements were 33.6 ± 1.7% smaller than FDS tendon displacements and also fit a second-order regression model (R² = 0.89 ± 0.01). FDS tendon and SSCT displacement both correlated with finger joint thickness, enabling participant-specific anthropometric scaling. We propose the current regression models as an ergonomic method to determine relative displacements between the finger flexor tendons and SSCT.     

A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for B2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm.     

Using a three-dimensional particle-tracking model to estimate the residence time and age of water in a tidal estuary

A three-dimensional hydrodynamic model that includes a Lagrangian particle-tracking simulation was applied to the Danshuei River estuarine system in northern Taiwan. The model's accuracy was validated with data from 1999; the results from the model agreed well with empirical observations of water surface elevation, tidal currents, and salinity. The validated model was then used to investigate the residence time and water age in response to different levels of freshwater discharge. A regression analysis of the model results revealed that an exponential equation best explained the correlation between residence time and freshwater input. We found that the residence times during the low and high freshwater discharge episodes were 4.4 and 2.5 days, respectively. The water age during the low-flow periods was greater than that during the high-flow periods. The modelled residence time and water age values without density-induced circulation were higher than those with density-induced circulation, which indicates that density-induced estuarine circulation may play a significant role in the estuary.     

The development of a model to predict the effects of worker and task factors on foot placements in manual material handling tasks

Accurate prediction of foot placements in relation to hand locations during manual materials handling tasks is critical for prospective biomechanical analysis. To address this need, the effects of lifting task conditions and anthropometric variables on foot placements were studied in a laboratory experiment. In total, 20 men and women performed two-handed object transfers that required them to walk to a shelf, lift an object from the shelf at waist height and carry the object to a variety of locations. Five different changes in the direction of progression following the object pickup were used, ranging from 45° to 180° relative to the approach direction. Object weights of 1.0 kg, 4.5 kg, 13.6 kg were used. Whole-body motions were recorded using a 3-D optical retro-reflective marker-based camera system. A new parametric system for describing foot placements, the Quantitative Transition Classification System, was developed to facilitate the parameterisation of foot placement data. Foot placements chosen by the subjects during the transfer tasks appeared to facilitate a change in the whole-body direction of progression, in addition to aiding in performing the lift. Further analysis revealed that five different stepping behaviours accounted for 71% of the stepping patterns observed. More specifically, the most frequently observed behaviour revealed that the orientation of the lead foot during the actual lifting task was primarily affected by the amount of turn angle required after the lift (R ² = 0.53). One surprising result was that the object mass (scaled by participant body mass) was not found to significantly affect any of the individual step placement parameters. Regression models were developed to predict the most prevalent step placements and are included in this paper to facilitate more accurate human motion simulations and ergonomics analyses of manual material lifting tasks.

Statement of Relevance: This study proposes a method for parameterising the steps (foot placements) associated with manual material handling tasks. The influence of task conditions and subject anthropometry on the foot placements of the most frequently observed stepping pattern during a laboratory study is discussed. For prospective postural analyses conducted using digital human models, accurate prediction of the foot placements is critical to realistic postural analyses and improved biomechanical job evaluations.     

A statistical study employing agent-based modeling to estimate the effects of different warehouse parameters on the distance traveled in warehouses

In manually operated warehouses, the travel distance of the order picker has profound effects on the warehouse cost and efficiency. Estimating this distance is difficult because the warehouse environment is a stochastic one, affected by a great number of parameters. Therefore, we present a comprehensive statistical study to assess how the different warehouse parameters and their interactions affect the travel distance. To estimate the travel distance, we simulate the different designs using agent-based modeling (ABM). Having 324 different designs, ABM has enabled us to build one computer model to simulate all the cases. The study shows that having one cross aisle only and using a class-based storage policy decreases the travel distance. Moreover, the results obtained show that choosing the best routing policy depends on the warehouse layout, which proves the importance of considering the interactions among the different parameters.     

A study of words relating to youth and old age in the plays of euripides and its special implications for Euripides'' Suppliant Women

This study focuses on the imagery of youth and old age in the plays of Euripides, especially the Suppliant Women, considering frequently used words in each play according to a formula developed by Guiraud. The study identifies a motif, the rejuvenation theme, an elaborate interaction between young and old, in the Suppliant Women and in: Alcestis, Heraclidae, Andromache, Hecabe, and Heracles. The difference between the use of neos (young, new) in the Suppliant Women and in the other plays is statistically significant. This word helps Euripides contrast two different kinds of youth: the fearful, rash, and animalistic (Theban); and that which has been properly schooled and led (Athenian). The greatest ground in the Suppliant Women for praising Athens is in her treatment of the young as a politically valuable force.Eva M. Thury is associate professor of Humanities and Communications at Drexel University in Philadelphia, Pa. She is currently working with computers to produce a concordance of the Latin prose work of John Milton. She has described that process in the Winter 1987–88 issue of Academic Computing (volume 2.4, pages 6–9, 57–61). She has also recently published in Rheinisches Museum für Philologie, Arethusa and American Journal of Philology.     

Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model

Use of the PharmaCloud can improve the quality of healthcare, but improvements are likely to be thwarted if physicians resist using the system. This study uses the dual-factor model to explain physicians’ resistance behaviors to system usage. The results of a field survey conducted in Taiwan showed that physicians’ resistance to using the PharmaCloud stemmed from regret avoidance, inertia, perceived value, and perceived threat. These results also indicate that system, information, and service qualities are the key determinants of the behavioral intention to use. This research advances the theoretical understanding of user acceptance and resistance to technology post-implementation and offers practical implications.     

A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model

Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank’s internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.