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This paper follows the implementation of a proof-of-concept localization system for GNSS-denied environments. WALLSY (Wireless Ad-hoc Low-power Localization SYstem) is a portable and modular Ultra Wide-Band (UWB) and Smart Mesh IP (SMIP) hybrid. WALLSY uses UWB two way ranging (TWR) to measure distances, which are then sent via the low-power SMIP backbone network to a central hub for calculating coordinates of tracked objects. The system is highly flexible and requires no external infrastructure or prior knowledge of the installation site. It uses a completely nomadic topology and delivers high localization accuracy with all modules being battery powered. It achieves this by using a custom time-slotting protocol which maximizes deep-sleep mode for UWB. Battery life can be further improved by activating inertial measurement unit (IMU) filtering. Visualization of tracked objects and system reconfiguration can be executed on-the-fly and are both accessible to end users through a simple graphical user interface (GUI). Results demonstrate that WALLSY can achieve more than ten times longer battery lifetime compared to competing solutions (localizing every 30 seconds). It provides 3D coordinates with an average spatial error of 60.5cm and an average standard deviation of 15cm. The system also provides support for up to 20 tags.

Background In Czechia, alcohol-induced deaths account for a significant portion of preventable mortality. As inequalities in health are both socially and spatially determined, the paper aims at the detailed examination of socio-geographic inequalities of this phenomenon. Methods The 2011-2015 annual data on both ICD-10 cause-specific deaths (K70; F10; X45/64; Y15) and mid-year population were obtained from the official Czech registries - the data were cross-classified by gender, 5-year age-groups, and permanent residence (N = 6,302 small area spatial units). The selected socio-demographic indicators (education, unemployment, religious population) from the Czech 2011 Census were spatially merged to the mortality dataset. From the data on education and unemployment, composite deprivation index (DI) was derived. In the adult population aged 25+, the age-standardised mortality ratios (SMR) were computed for each of the spatial units, separately by genders. The SMRs were spatially modelled by the Besag-York-Mollié (BYM) autoregressive approach, applying a fully bayesian framework integrated within the INLA R-package. The study applied cross-sectional design and employed ecological regression conducted on observational data. Results Compared to the Czech average, the highest SMRs were located in the historical regions of Moravia [SMR=1.15; 95%CI: 1.11-1.19] and Silesia [SMR=1.59; 95%CI: 1.52-1.66]. The SMRs were significantly correlated with DI among males [Rel.Risk=1.15; 95%CI: 1.11-1.19], and with religiousness rate among females [Rel.Risk=0.83; 95%CI: 0.77-0.90]. Conclusions Significant socio-geographic inequalities were detected, particularly with respect to the Czech historical regions. Among males, higher mortality was associated with a structural deprivation. Among females, protective effect of religiousness rate was found to be significant. The results highlight an importance of both socially and spatially integrated efforts for public health promotion. Key messages The inequalities in health are both socially and spatially contextualised. The paper presents robust empirical evidence in favour of the proposition, as examined on alcohol-related mortality data. The health determinants may be gender sensitive. Males might be more responsive to a structural disadvantage. Among females, cultural factors related to a local community might be more relevant.

This study examined factors that might affect researchers' willingness to collaborate with a specific researcher and the priorities given to those factors. In addition, it investigated how researchers determined the ownership of collaborative project data and how they determined the order of authorship on collaborative publications in condensed matter physics. In general, researchers rated their intrinsic motivations the highest, such as the quality of ideas a potential collaborator might have and their satisfaction with a past collaboration, followed by their extrinsic motivations, such as the complementary knowledge, skills, or resources the collaborator could provide. In addition, researchers who had a greater number of collaborative projects and researchers who had served as a project PI or co-PI valued the deep-level, personality-related characteristics of a collaborator higher than did those who had not. Younger researchers were more risk averse and more concerned with a collaborator's reputation and the possible cost of a collaboration decision. Additionally, younger researchers indicated more often than older researchers that they did not know whether their project teams followed any rules or norms or engaged in negotiation to determine the order of authorship on collaborative publications.

An auxiliary process controller was designed, implemented, and validated for on-line process and quality optimization. The objective function included terms related to the process variation, model uncertainty, and control energy. The controller architecture relied on characterized models including both process transfer functions and principal components analysis to perform on-line optimization in parallel with the physical molding process. New process and quality observations were input to the controller to update the models and provided new settings for the machine controller. Experimentation included characterization with a D-optimal design of experiments followed by a validation to measure the controller's performance with respect to controller stability, extrinsic material variation, cycle time reduction, and other common manufacturing goals. In every case, the controller was able to reduce the value of the objective function while also improving the part dimensions relative to tight tolerance specifications. While characterization experiments could be costly, the use of the resulting process models greatly speeds convergence and facilitates the consideration of various cost and quality terms in the objective function. POLYM. ENG. SCI., 55:2743–2750, 2015. © 2015 Society of Plastics Engineers

The articles in this special section focus on the deployment of mechatronics for intelligent manufacturing. The articles are dedicated to new advances enabled by mechatronics in modeling, analysis, sensing and control methods, implementation, and validation for advanced manufacturing.

MANUFACTURING, Edited by Galip Ulsoy Manufacturing Systems and Their Design Principles, M.G. Mehrabi, A.G. Ulsoy, and Y.Koren Computer-Aided Process Planning For Machining, Derek Yip-Hoi Discrete Event Control of Manufacturing Systems, D.M. Tilbury and P.P. Khargonekar Machine Tool Vibrations, Yusuf Altintas Machine Tool Monitoring and Control, Kourosh Danai Process Monitoring and Control of Machining Operations, Robert G. Landers, A. Galip Ulso, and Richard J. Furness Forming Processes: Monitoring and Control, David E. Hardt Assembly and Welding Processes and their Monitoring and Control, S. Jack Hu and Elijah Kannatey-Asibu, Jr. Control of Polymer Processing, David Kazmer and Kourosh Danai Precision Manufacturing, Thomas R. Kurfess VIBRATION CONTROL, Edited by Nejat Olgac Active Damping of Large Trusses, Preumont, F. Bossens, and N. Loix Semi-Active Suspension Systems, Nader Jalili Semi-Active Suspension Systems II, Michael Valasek and Willi Kortum Active Vibration and Absorption and Delayed Feedback Tuning, Nejat Olgac and Martin Hosek Vibration Suppression Utilizing Piezoelectric Networks, K.W. Wang Vibration Reduction via the Boundary Control Method, Siddharth P. Nagarkatti and Darren M. Dawson DYNAMICS AND CONTROL OF AEROSPACE SYSTEMS: AN INTRODUCTION TO TENSEGRITY STRUCTURES, Edited by Robert E. Skelton An Introduction to the Mechanics of Tensegrity Structures, Robert E. Skelton, J. William Helton, Rajesh Adhikari, Jean-Paul P. Pinaud, and Waileung Chan The Dynamics of the Class 1 Shell Tensegrity Structure, Robert E. Skelton and Jean-Paul Pinaud, D.L. Mingori ROBOTICS, Edited by Miomir Vukobratovic Robot Kinematics, Bruno Siciliano Robot Dynamics ,Miomir Vukobratovic and V. Potkonjak Actuators and Computer-Aided Design of Robots, Miomir Vukobratovic, V. Potkonjak, Masaharu Takano, and Kenji Inoue Control of Robots, Miomir Vukobratovic and D. Stokic Control of Robotic Systems in Contact Tasks, Miomir Vukobratovic andD. Surdilovic Intelligent Soft-Computing Techniques in Robotics, D. Katic and B. Karan Teleoperation, Telerobotics, A. Bejczy Mobile Robotic Systems, Nenad M. Kircanski Humanoid Robots, Miomir Vukobratovic , Branislav Borovac, D. Stokic, and D. Surdilovic Present State and Future Trends in Robot Applications, M. Hagele and Dieter Schraft

Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.

Over the past decade, Learning through Service (LTS) has proliferated in higher education as an effective teaching and learning method. LTS is an umbrella term that includes both curricular and extracurricular activities, recognizing that there are many models that exist currently for how faculty members use opportunities for students to learn while providing service to a community. Reflection by the students on their service activity provides rich opportunities for students to add meaning to their learning through engagement with community. While, many colleges and universities in the United States have increased the use of LTS in engineering programs, there has been limited study to evaluate engineering faculty perception of the purpose of reflection in support of facilitating and assessing the expected learning outcomes. In this research, twenty-six interviews were conducted with engineering LTS practitioners to explore how and why engineering faculty incorporate reflection in LTS efforts. The findings reveal that majority of engineering LTS faculty practitioners engage students in reflection to enhance the professional development skills of their students, with fewer of the faculty using reflection to develop students’ personal skills.

Manufacturing is now a national strategy for many countries to combat slow economic growth, and positively viewed with the current trend of onshoring foreign manufacturing operations. We develop a cross-country regression model that predicts manufacturing employment as a function of population growth, foreign direct investment, and purchasing power parity. Results through the year 2100 suggest that manufacturing is trending toward a global equilibrium with higher levels of manufacturing outputs but much lower levels of manufacturing employment. The reason is that countries tend to evolve from having little manufacturing to commodity manufacturing at large scale and low wages. As infrastructure and human capital develop, there is the tendency to pursue advanced manufacturing in support of higher valued goods. The manufacture of commodity products is then outsourced to those countries with lower costs justified by their less-developed infrastructure and human capital, and so the virtuous cycle continues. While this model suggests that current efforts in revitalization of domestic manufacturing would lead to an increase in wealth in the United States, the bad news is that these gains are unlikely to be sustainable in the long term. However, the good news is that manufacturing acts as a rising tide that raises all nations and our global quality of life.

To be effective and at the same time sustainable, a community data curation model needs to be aligned with the community's current data practices, including research project activities, data types, and perceptions of data quality. Based on a survey of members of the condensed matter physics (CMP) community gathered around the National High Magnetic Field Laboratory, a large national laboratory, this article defines a model of CMP research project tasks consisting of 10 task constructs. In addition, the study develops a model of data quality perceptions by CMP scientists consisting of four data quality constructs. The paper also discusses relationships among the data quality perceptions, project roles, and demographic characteristics of CMP scientists. The findings of the study can inform the design of a CMP data curation model that is aligned and harmonized with the community's research work structure and data practices.

Amyotrophic lateral sclerosis (ALS) is a progressively debilitating neurodegenerative condition that occurs in adulthood and targets the motor neurons. Social support is crucial to the well-being and quality of life of people with unpredictable and incurable diseases such as ALS. Members of the PatientsLikeMe (PLM) ALS online support community share social support but also exchange and build distributed knowledge within their discussion forum. This qualitative analysis of 1,000 posts from the PLM ALS online discussion examines the social support within the PLM ALS online community and explores ways community members share and build knowledge. The analysis responds to 3 research questions: RQ1: How and why is knowledge shared among the distributed participants in the PLM-ALS threaded discussion forum?; RQ2: How do the participants in the PLM-ALS threaded discussion forum work together to discover knowledge about treatments and to keep knowledge discovered over time?; and RQ3: How do participants in the PLM-ALS forum co-create and treat authoritative knowledge from multiple sources including the medical literature, healthcare professionals, lived experiences of patients and "other" sources of information such as lay literature and alternative health providers? The findings have implications for supporting knowledge sharing and discovery in addition to social support for patients. © 2014 ASIS&T.

With the increased focus on community engagement in higher education, engineering faculty members are using Learning through Service (LTS) as a viable way of providing a platform for interactive participatory student learning and involvement in the community. By engaging students to learn in the community, the creation of knowledge shifts from the classroom to the community. This paper explores how LTS engineering practitioners from a diverse range of institutions use LTS to engage community partners in the co-creation of knowledge. Twenty-seven interviews were conducted with these faculty members to identify best practices for mutually beneficial partnership with the communities they serve. Analysis of the interview transcripts reveals six guiding principles of effective and sustainable community partnerships: 1) understand the needs of the community, 2) seek community feedback through direct interaction, 3) engage community partners from an asset-based viewpoint, 4) use cultural intermediaries in international LTS efforts, 5) deliver useful projects, and 6) sustain long-term relationships to improve project outcomes.

Both course-based service-learning (S-L) and extracurricular community service activities, together referred to as Learning Through Service, provide the opportunity for rich learning, personal growth, and tangible beneficial outcomes for students, community partners, and faculty. However, to fully realize this potential careful planning and design of the Learning Through Service (LTS) effort are required. This paper describes a framework with nine important elements to consider for LTS program design: (1) stakeholders, (2) value propositions, (3) relationships, (4) channels, (5) key activities, (6) resources, (7) partnerships, (8) value stream returns, and (9) value stream costs/outlays. This LTS development framework is based on the Business Canvas Model used in developing and evaluating the business viability of an innovative product or service. For LTS, the stated program design attributes were identified based on the personal LTS experience of the research team and input from additional experts. These nine elements can be effectively presented as a blueprint for an LTS program. Thoughtful planning in each of these areas will help ensure that the program goals are met and provide optimal benefits for all of the stakeholders. These elements also help to identify where management and assessment efforts are best targeted. Specific examples for each of the nine attributes from thirty-four different LTS programs are presented. Some of these LTS programs were in the planning phases and missing elements that could make the programs more meaningful, such as student reflection or engaging community partners as equals. Together, the model and examples provide guidance for others who are interested in designing a new LTS program, or improving an existing one.

Online monitoring of product quality is key to automated machine operation and process optimization. This paper demonstrates an in-process sensing technique for online product quality assessment, using injection moulding as an example. The system measures four parameters within the injection mould cavity, which are directly correlated with the part quality: melt pressure, temperature, velocity, and viscosity. Relationship between the sensor data and quality characteristics, e.g., thickness and width of the part, are established by incorporating governing physics for pressure–volume–temperature with other mechanistic models. Good agreement between sensor measurements and experimental results is confirmed from tests performed on a production-grade machine.

Common three-dimensional processes including stereolithography, selective laser sintering, fused deposition modeling, and polyjet modeling are described and benchmarked for production of a three-sided cube with protrusions and depressions of various sizes; metrology of 3D printed parts indicates that tolerances vary by process but are typically on the order of 0.2 mm with a surface roughness of 50 μm. Design guidelines are provided regarding geometric design and tolerances, mold design, and material properties; compared to conventional plastic part designs, 3D printing affords greater complexity without added cost. Manufacturing economics are also modeled to provide guidance to technology strategy, manufacturing strategy, and risk mitigation.

Metallic shielding of electromagnetic waves presents a roadblock for machine tool and process monitoring. Acoustic waves, alternatively, provide a viable mechanism, due to their inherent transmissibility in metals. This paper presents a quantitative analysis of factors affecting the performance of acoustic-based signal transmission including carrier frequency, bandwidth, signal-to-noise ratio, data bit-rate, etc. Signal attenuation and data loss due to wave diffraction and reflection is first numerically studied using finite element models of representative machine structures. Experimental evaluation of these models with operating production machinery quantifies the accuracy and reliability of in situ acoustic signal transmission methods for precision metrology in manufacturing.

Data fusion enables the integration of multiple sensing data associated with the same physical process for more comprehensive process representation, thereby improving quality control in manufacturing. Based on a correlation analysis of measurement data, the effectiveness of data fusion has been investigated, using precision injection moulding as an application context and the predicted weight of moulded parts as a criterion. The study systematically explains why a multivariate sensor that quantifies four parameters at the same sensor location has consistently outperformed multiple single-parameter commercial sensors under various operation conditions, thereby contributing to the theory of data fusion for measurement enhancement.

In the second half of the 20th century, scientific research in physics, chemistry, and engineering began to focus on the use of large government-funded laboratories. This shift toward so-called big science also brought about a concomitant change in scientific work itself, with a sustained trend toward the use of highly specialized scientific teams, elevating the role of team characteristics on scientific outputs. The actual impact of scientific knowledge is commonly measured by how often peer-reviewed publications are, in turn, cited by other researchers. Therefore, how characteristics such as author team seniority, affiliation diversity, and size affect the overall impact of team publications was examined. Citation information and author demographics were reviewed for 123 articles published in Physical Review Letters from 2004 to 2006 and written by 476 scientists who used the National High Magnetic Field Laboratory's facilities. Correlation analysis indicated that author teams that were more multi-institutional and had homogeneous seniority tended to have more senior scientists. In addition, the analysis suggests that more mixed seniority author teams were likely to be less institutionally dispersed. Quantile regression was used to examine the relationships between author-team characteristics and publication impact. The analysis indicated that both weighted average seniority and average seniority had a negative relationship with the number of citations the publication received. Furthermore, the analysis also showed a positive relationship between first-author seniority and the number of citations, and a negative relationship between the number of authors and the number of citations.

Online process metrology is critical to ensuring manufacturing quality and productivity. This paper presents the design and modelling of a multivariate sensor that enables the simultaneous measurement of multiple parameters from within an RF shielded environment, e.g. an injection mold. A coded wave modulation scheme is developed for wirelessly transmitting the parameters through the mold. The design of the modulator is optimized through a coupled field analysis for noise reduction. The effectiveness of the sensing method is demonstrated in the online measurement of melt pressure, temperature, viscosity, and velocity. This sensing method is applicable to various process monitoring scenarios.

This poster presents preliminary results from the second year of a three-year project designed to help elementary students learn about scientific inquiry and become active participants in their own science education. Florida State University has developed a set of technologies (an iPad application and website) integrated with a standards-based nature of science and scientific inquiry curriculum called Habitat Tracker. Preliminary analysis of the students' task engagement shows that this project has the potential to support student engagement during field trips to natural wildlife centers. Current results will inform future iterations of the project, and improve support for science teachers integrating museum field trips and classroom activities.

Library scholars and practitioners have frequently reflected on the various factors that in combination make up a hospitable library, but there has been little theoretical synthesis of the notion of the library as a place of hospitality. The hospitality industry provides a rich vein of theoretical material from which to draw definitions of hospitality, and an analysis of this material leads to a preliminary definition of library hospitality: the provision of library resources by a genuinely motivated employee to fulfill the library need of a patron in an environment conducive to the provision of those resources. Libraries have historically provided and today continue to provide services that can be examined under the six elements of this definition. Library hospitality is best seen as a framing device for the entire service approach of the institution as a whole, a way for libraries to make decisions about implementing particular initiatives.

Many libraries, archives, and museums provide their users with social computing environments that include the ability to tag collections, annotate objects, and otherwise contribute their thoughts to the knowledge base of the institution. Information professionals have responded to the world of user-created content by developing open source tools to coordinate these activities and researching the best ways to involve users in the co-creation of digital knowledge. This rapid influx of new technologies and new methods for interacting with users comes at a time when libraries, archives, and museums still struggle to share data across their own institutions, let alone between institutions of different types. Information professionals had barely begun to make progress developing crosswalks and data interoperability standards when, as social computing became the norm on the Web, providing the ability for users to manipulate data changed from a cool toy to a basic expectation. Moving forward—and keeping pace with user expectations— requires the coordination of many different users (in all their variety) as they contribute, participate, shape, and create all types of data in all types of contexts. This issue of Library Trends offers the chance to consider what social computing means for the future of libraries, archives, and museums, and to think carefully about the future trends and long-term implications of involving users in the co-construction of knowledge online. The authors of the following articles have thought broadly about the issues raised when we bring users into the mix in various ways and at various points in the information life cycle. Their efforts contribute to ongoing broad-based discussions about what happens when users are involved in shaping, guiding, and directing the development of online libraries, archives, and museums and their information resources.

The production of scientific knowledge has evolved from a process of inquiry largely based on the activities of individual scientists to one grounded in the collaborative efforts of specialized research teams. This shift brings to light a new question: how the composition of scientific teams affects their production of knowledge. This study employs data from 1,415 experiments conducted at the National High Magnetic Field Laboratory (NHMFL) between 2005 and 2008 to identify and select a sample of 89 teams and examine whether team diversity and network characteristics affect productivity. The study examines how the diversity of science teams along several variables affects overall team productivity. Results indicate several diversity measures associated with network position and team productivity. Teams with mixed institutional associations were more central to the overall network compared with teams that primarily comprised NHMFL's own scientists. Team cohesion was positively related to productivity. The study indicates that high productivity in teams is associated with high disciplinary diversity and low seniority diversity of team membership. Finally, an increase in the share of senior members negatively affects productivity, and teams with members in central structural positions perform better than other teams. © 2011 Wiley Periodicals, Inc.

The effectiveness of seven methods for controllingswitchover from the filling to packing stage wereinvestigated, including: (1) screw position, (2) injectiontime, (3) machine pressure, (4) nozzle pressure, (5) run-ner pressure near the sprue, (6) cavity pressure nearthe gate, and (7) cavity temperature at the end of flow.The activation threshold for each of the seven switch-over methods was iteratively determined so as to pro-duce similar part weights relative to a standard pro-cess. A design of experiments was implemented foreach of the seven switchover methods that perturbsthe process settings by an amount equal to six stand-ard deviations of the standard process so as to repli-cate the expected long-term process variation. Theresults suggest that conventional switchover methods(e.g., screw position) had lower short-term variation,but other methods were more robust with respect torejecting long-term process variation. The merits ofdifferent dimensional measurements for quality controlare also discussed relative to the society of the plas-tics industry (SPI) standard tolerances.

Manufacturing process automation is often impeded by limitations related to automatic quality assurance. Many plastics manufacturers use univariate statistical process control (SPC) for quality control by charting the critical process states relative to defined control limits. Alternatively, principal component analysis (PCA) and projection to latent stuctures (PLS) are multivariate methods that measure the process variance by the distance to the model (DModX) and the Hotelling t-squared (T2) values. A methodology for robust model development is described to perturb the manufacturing process for process characterization based on a design of experiments; best subset analysis is used to provide an optimal set of regressors for univariate SPC. Four different statistical models were derived from the same data set for a highly instrumented injection molding process. The performance of these models was then assessed with respect to fault diagnosis and defect identification when the molding process was subjected to twelve common process faults. Across two hundred molding cycles, the univariate SPC models correctly diagnosed five of the twelve process faults with one false positive, detecting only eighteen of twenty four defective products while indicating two false positives. With the same molding cycles, PCA and PLS provided nearly identical performance by correctly diagnosing ten of the twelve process faults and detecting twenty three of the twenty four defective products; PCA indicated two false positives while PLS indicated only one false positive.

An on-line model development and quality control methodology is presented for manufacturers in the process industries with the goal of enabling automated quality assurance. Given appropriate process instrumentation, the methodology starts with the characterization of statistical variation for the process while operating in steady state. Significant process conditions are then perturbed by six standard deviations to bound the expected long-term process variation including lot-to-lot variability of feedstock materials. If the process is found to be robust, acquired process data is used to model the process behavior using principle components analysis (PCA). The PCA model is then used to accept and reject manufactured parts given real-time process data. This methodology is applied to an instrumented injection molding process that is subjected to 12 common process faults. The results indicate that the methodology was able to detect every one of 33 defective molding cycles caused by eight of the imposed faults as well as two additional faults that did not result in observable defective products. The quality controller did not detect the two remaining imposed process faults that did not produce defects and also rejected three molding cycles (2% of the total) that appeared to produce acceptable products.

This methodological paper addresses practical strategies, implications, benefits and drawbacks of collecting qualitative semi-structured interview data about Internet-based research topics using four different interaction systems: face to face; telephone; email; and instant messaging. The discussion presented here is based on a review of the literature and reflection on the experiences of the authors in performing completed research that used those four interaction systems. The focus is on functional effects (e.g. scheduling and other logistics, data transcription and data management), as well as methodological effects (e.g. ability to probe, collecting affective data, and data representation). The authors found that all four methods of data collection produced viable data for the projects they completed, but that some additional issues arose. Five themes emerged that form the organization of the paper: (1) interview scheduling and participant retention; (2) recording and transcribing; (3) data cleaning a...

On-line monitoring of polymer melt state is critical to ensuring part quality in injection molding. This paper presents a novel, dual-parameter sensing method for simultaneous measurement of pressure and temperature variations within the mold cavity through a modulator circuit. Pressure variation during the molding cycle, which is proportional to the electrical charge output of a piezoceramic stack, is discretized into acoustic pulses that are subsequently frequency-modulated by a temperature-sensitive oscillator (TSO). The ability to measure two parameters using one sensor package without batteries and cables for data transmission provides a new platform for monitoring injection molding processes.

People who work, learn, or play in online social worlds must sometimes leave those social worlds. Such departures may happen for many reasons. Often they are anticipated departures because the social world was meant from the start to be temporary. Most people do not yet have much practice at leaving an online social world, nor do we have a good model of the process. Activities that people undertake while disengaging from transient online social worlds affect them personally, as well as their future personal and professional relationships with one another. For this research, 30 students near the time of graduating from an online learning master’s degree program participated in semi-structured interviews exploring their activities and emotions related to disengaging. The result is a model of the disengaging process encompassing 12 dimensions.

Most plastic processing set points are variables that need to be defined for each sample point of the cycle. However, in the absence of on-line measures of part quality, the set points cannot be defined by feedback and need to be prescribed a priori for the entire cycle. In conventional practice, the shape of each set-point profile is defined either heuristically, according to qualitative experience, or mechanistically, to enforce a predefined trajectory for a simulated internal process state that is used as a surrogate measure of part quality (e.g., the velocity profile defined to dictate a constant melt front velocity). The purpose of this study is twofold: (i) to evaluate the efficacy of using a single internal state as the surrogate of part quality, and (ii) to explore the feasibility of devising a multivariate profiling approach, where indices of multiple process states act as surrogates of part quality. For this study, an injection-compression molding process used for production of digital video disks was considered as the development domain, and a pseudo-optimal cycle of the process was found by reinforcement learning to provide a basis for evaluating the ideal behavior of the process states. Compared to conventional molding, the results indicate that the asymmetric process capability index, CPK, was increased by ∼50% with velocity profile optimization and to 120% with both velocity profile and pressure profile optimization. Two general conclusions result. First, velocity and pressure profiling provide important degrees of freedom for optimizing process control and maximizing part quality. Second, estimators for unobservable process states, in this case birefringence and warpage, can be used to develop different machine profiles to selectively trade off multiple quality attributes according to user preferences.

The energy efficiency of a self-powered wireless sensing system for pressure monitoring in injection molding is analyzed using Bond graph models. The sensing system, located within the mold cavity, consists of an energy converter, an energy modulator, and a ultrasonic signal transmitter. Pressure variation in the mold cavity is extracted by the energy converter and transmitted through the mold steel to a signal receiver located outside of the mold, in the form of ultrasound pulse trains. Through Bond graph models, the energy efficiency of the sensing system is characterized as a function of the configuration of a piezoceramic stack within the energy converter, the pulsing cycle of the energy modulator, and the thicknesses of the various layers that make up the ultrasonic signal transmitter. The obtained energy models are subsequently utilized to identify the minimum level of signal intensity required to ensure successful detection of the ultrasound pulse trains by the signal receiver. The Bond graph models established have shown to be useful in optimizing the design of the various constituent components within the sensing system to achieve high energy conversion efficiency under a compact size, which are critical to successful embedment within the mold structure.

This paper describes a wireless data transmission technique using acoustic waves as the information carrier for on-line injection molding process measurements, including melt temperature, pressure, velocity and viscosity. A design scheme of the optimization of the input electrical impedance matching with the transmission system of the multivariate sensor based on the bond graph modeling approach is proposed. Bond graph models of each constituent component within the transmission system are established, where parameters regarding the properties of the piezo-material, layer thickness, and input electrical impedance could be manipulated easily. The effects of different combinations of capacitor and inductor that constitute the input electrical circuit are investigated. An optimal combination for electrical impedance matching is determined through bond graph simulation. This optimal solution improves the design of sensor circuits with the capability of generating the highest output gain or increasing the transmission distance under the same electrical excitation, meanwhile ensuring that the received signal has high signal-to-noise-ratio (SNR) for further parameter retraction. Bond graphing has been shown as an effective approach for guiding the design of complex, cross-domain, and embedded sensing systems.

One goal of library and information science (LIS) education is for graduates to engage with the profession and interact with one another throughout their careers. The increase over the last decade in online distance learning options for LIS students presents a challenge to educators with respect to this goal: are students who earn the degree online sufficiently connected with one another to encourage the growth of lasting and productive professional networks? This article examines this question by looking at creation and loss of sociotechnical capital among LIS distance education graduates. The findings indicate that LIS educators should consider sociotechnical capital issues at the individual, interpersonal, and cohort level to support shared, lasting professional contributions.

Polymer processing has been limited by the lack of direct flow and pressure control of the polymer melt at multiple points in space and time. Improved axial and radial valve designs are discussed that require negligible actuation force to control the flow of the pressurized melt. The forces resulting from pressure loads and shear stresses are first analyzed for an axial valve pin. Subsequently, a radial valve design is implemented and experimentally characterized using neat polycarbonate. A sigmoidal response surface is fit to the experimental data and found to very well model the observed pressure drop as a function of flow rate and valve pin position. The juncture loss at the valve port is then characterized by estimating and removing the pressure drops in the circular flow segments of the valve. Analysis indicated that the juncture loss is inversely proportional to the exposed area, or vesica piscis, formed between the circular flow channel in the valve body and the flow port on the moving valve pin. While applicable to many different polymer processing operations, the validated models are used to show the dynamic valve pin position as a function of the desired flow rate and desired cavity pressure in the hot runner of an injection mold. Finally, the impact of the designs on lower power, more compact mechanical and control system designs are discussed.

A wireless pressure sensor is described for use in a high-pressure manufacturing process with three major subsystems: energy conversion by a stack of piezoelectric disks, energy measurement and control by a threshold modulator, and ultrasonic signal transmission by a piezoelectric transmitter. The second system, the threshold modulator, is the focus of this paper. The charge, proportional to pressure, on a capacitive element is measured and controlled through the use of a two-transistor modulator. Standard NPN and PNP transistors are used to passively control the flow of charge between a piezoelectric stack and an ultrasonic transmitter. The basis for the design is discussed, from which a simulation is developed and compared to a bench top prototype. The results of this comparison indicate the appropriateness of the assumptions used to produce an analytical model of the design and the limiting conditions under which the modulator will effectively measure charge. Finally, the prototype device is optimized with respect to sensitivity, gain, and operating range for use in real-time process monitoring and control.

This paper describes a new mechanical wireless data transmission technique using ultrasonic waves as the information carrier for on-line injection mold cavity pressure measurement. Ultrasonic transmitters with specific frequency characteristics were designed, modeled, simulated, and prototyped for pressure data retrieval from an enclosed machine environment, as well as for sensor identification in a sensor matrix configuration. The effects of the front layer and bonding layer of the transmitter on the overall sensor frequency characteristics were investigated, using an equivalent circuit model. The optimal layer thickness was determined for the design of transmitters with specific dominant resonant frequency and narrow bandwidth. Experimental results were in good agreement with the analysis, thus confirming the design approach.

A new signal processing technique has been developed for detecting and differentiating timely overlapped ultrasonic pulse trains that carry spatially distributed pressure information across an injection mold cavity. The new technique is based on wavelet transform using analytic wavelets. Compared to conventional wavelets that have a constant relative bandwidth at all the scales, the analytic wavelets investigated in this paper feature variable relative bandwidth, making it possible to simultaneously match the frequency characteristics of the ultrasonic pulse trains transmitted from the mold-embedded pressure sensors. As a result, more accurate detection and differentiation of the temporal and spectral information embedded within the ultrasonic pulse trains could be achieved. A theoretical framework for the analytic wavelet transform was established, and the performance of three analytic wavelets was comparatively studied. Subsequently, a multi-channel ultrasonic pulse detector based on the complex Morlet wavelet was designed and experimentally investigated. The results have confirmed the effectiveness of the new signal processing technique for on-line pressure sensing in injection molding process monitoring.

Online learners often stay located in, and tied to, their communities, kinship networks, households, and workplaces. Institutions providing online education can thus create ties to communities as students draw their learning into networks in which they are already embedded. Frequent interactions across multiple media that are afforded by information and communication technologies (ICT) allow students to create strong ties with their fellow students and instructors. Those relationships provide a network of weak ties that is indirectly available to friends, coworkers, and community members who live and work near the students. Community‐embedded learning that takes advantage of these strong and weak ties and is appropriate to ICT modes of delivery is important for two reasons. The various clienteles served by students while they earn their degrees will affect library and information science (LIS) education and outcomes, and LIS distance education offered via interactive ICT can directly affect the clienteles...

In online learning communities, graduations are both joyous and sad events when students must "disengage" from their shared experience. Using technologies to interact during online learning means that technologies have an effect on any social process, including disengaging. Even the simplest technology hurdles can affect people's departures from online learning communities. These effects of technology on disengaging have implications for the design of systems that support online communities. This paper explores the technology problems that students encounter in disengaging from online learning communities, and develops design implications for such communities. To set the stage, it provides a discussion of how online learners build and maintain their learning communities.

This paper advocates an approach to the design, use and impact of new learning technologies that incorporates the view from multiple perspectives. This includes considering the questions and goals of students, faculty, administrators and co-workers, as well as social dimensions of learning, professional socialization, family dynamics, career, community formation, and institutional change.