Osmotic and activity coefficients in the solutions of 1- and 2-naphthol in methanol and ethanol at 298.15 K

The osmotic coefficient of the solution, activity of the solvent and activity coefficient of the solute in solutions of 1- and 2-naphthol in methanol and ethanol have been measured by the isopiestic method. The experimental osmotic coefficient data were correlated using the Non-Random Two Liquid model (NRTL), the Non-Random Factor model (NRF), the two suffix Margules equation and a polynomial in terms of the molality of the solute. The models give reliable results for the correlation of the osmotic coefficient data. The activity coefficients of 1- and 2-naphthol were evaluated according to the considered methods using the parameters obtained by the correlation of osmotic coefficients.     

Learning to transform time series with a few examples

We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.     

Analyzing CAD competence with univariate and multivariate learning curve models

Understanding how learning occurs, and what improves or impedes the learning process is of importance to academicians and practitioners; however, empirical research on validating learning curves is sparse. This paper contributes to this line of research by collecting and analyzing CAD (computer-aided design) procedural and cognitive performance data for novice trainees during 16-weeks of training. The declarative performance is measured by time, and the procedural performance by the number of features used to construct a design part. These data were analyzed using declarative or procedural performance separately as predictors (univariate), or a combination of declarative or procedural predictors (multivariate). Furthermore, a method to separate the declarative and procedural components from learning curve data is suggested.     

Variant versus invariant time to total forgetting: the learn–forget curve model revisited

Understanding and quantifying the learning–forgetting process helps predict the performance of an individual (or a group of individuals), estimate labor costs, bid on new and repeated orders, estimate costs of strikes, schedule production, develop training programs, set time standards, and improve work methods [IIE Trans. 29 (1997) 759]. Although there is agreement that the form of the learning curve is as presented by [J. Aeronaut. Sci. 3 (1936) 122], scientists and practitioners have not yet developed a full understanding of the behavior and factors affecting the forgetting process. The paucity of research on forgetting curves has been attributed to the practical difficulties involved in obtaining data concerning the level of forgetting as a function of time [IIE Transactions 21 (1989) 376]. The learn–forget curve model (LFCM) was shown to have many advantages over other theoretical models that capture the learning–forgetting relationship. However, the deficiency of the LFCM is in the assumption that the time for total forgetting is invariant of the experience gained prior to interruption. This paper attempts to correct this deficiency by incorporating the findings of [Int. J. Ind. Ergon. 10 (1992) 217] into the LFCM. Numerical examples are used to illustrate the behavior of the modified LFCM (MLFCM) and compare results to those of the LFCM.     

Design of automated hybrid work stations: An evaluation of robot sensory systems for safety

Hybrid robotic systems necessitate a new integrated approach to the design of tasks and the performance requirements for human operators and robots. The presence of operators in hybrid work stations adds to the complexity and unpredictability of such design requirements. An important component of the hybrid system design is the integration of both human and robot sensory capabilities for task completion. A model for the integration of human and robot sensory information collection, processing, and action is presented. Robot sensory systems are evaluated with respect to the safety of operators within a hybrid work station. Four sensory technologies of optical (vision), sonar, capacitance, and infrared are compared. Optically-based and infrared sensors appear to be the most promising in terms of the safety and efficiency of hybrid work stations.     

Human perception of robot safe speed and idle time

Operators and users of robotic systems perform tasks which require close proximity to dangerous moving parts. Two experiments were performed to assess human perception of safe robot arm speed and idling times. Experiment 1 was designed to determine the maximum safe speed of robots. Subjects were asked to adjust the robot speeds. Perceived safe speeds were indicated for two different types of robots. Experiment 2 was designed to determine safe programmed idle time of robots. Subjects were asked to enter the robot work envelope when a programmed idle was perceived to be caused by a malfunction. Safe idle times were reported for two different robot speeds during operational cycles.     

Plastic limit load of cylindrical shells with cutouts subject to pure bending moment

In this paper, the results of limit analyses of thin-walled cylindrical shells with a circular hole under the action of a pure bending moment are presented in dimensionless form for a wide range of geometric parameters. Analytical estimation of lower bound limit load is carried out using the feasible sequential quadratic programming (FSQP) technique. The finite element calculations of limit load consist of elastic–plastic and lower and upper bound predictions by elastic compensation methods. A testing device was made to perform experiments to obtain limit bending moment of cylinders with circular openings. The analytical and finite element calculations are compared with experimental results and their correlation is discussed. The finite element calculation results were found to be in good agreement with lower bound estimations by the nonlinear mathematical programming (FSQP) method and the formula proposed by Shu.     

Microwave-assisted encapsulation of citric acid using hydrocolloids

This study elucidates the capability of a novel technique for producing microcapsules at an enormously short time and low cost. This technique is based on the difference between dielectric constants of core and coat materials. Edible citric acid was mixed with various biomacromolecules at ratios of 1:5, 1:10, and 1:100. Each mixture was treated up to 600 s at various powers (120–1200 W) in a microwave oven. Subsequently, the microcapsules were separated by distinct sieves, and their apparent structure and quality were evaluated using binoculars, and photographs were taken for visual comparisons. Our observations showed that only five hydrocolloids were able to produce high-quality and efficient encapsulation [casein > inulin > carboxymethylcellulose (CMC) > low methoxyl (LM) pectin (9/5%) > sorbitol]. Moreover, the highest coating efficiency was seen at highest intensity (1200 W) at a mixing ratio of 1:10. Furthermore, the optimum treating time periods for those five efficient coating materials were about 400, 75, 400, 100, and 100 s.     

Conductive Recyclable Organogel Composites

As the use of automation in industry accelerates, the development of flexible, electrically conducting materials with the requisite environmental resilience for impact‐resistant sensors, foldable electronics, and electrostatic shielding are needed; simultaneously, recyclability for these materials remains a crucial attribute. Traditional conductive stretchable materials, such as rubbers, are not recyclable, and hydrogel materials have limited applications due to water evaporation and operating temperature range. Comparatively, organogels can be formulated with enhanced tunability, matrix recyclability, and the ability to support many conductive fillers. Here, rheology, mechanical testing, and electrochemical impedance spectroscopy (EIS) characterize the nanoscale interactions between carbon fillers, the liquid phase, and the network matrix in hemiaminal dynamic covalent network (HDCN) organogels. HDCN chemical equilibria are shown to strongly influence macroscopic gel properties, while HDCN composites exhibit very high conductivities up to 9.95 mS cm^?1 appropriate for sensing applications, demonstrating promise as recyclable alternatives for conductive stretchable materials.