Dynamic optimisation of an industrial web process

An industrial web process has been studied and it is shown that the underlying physics of such processes governs by the Navier-Stokes partial differential equations with moving boundary conditions, which in turn have to be determined by the solution of the thermodynamics equations. The development of a two-dimensional continuous-discrete model structure based on this study is presented. Other models are constructed based on this model for better identification and optimisation purposes. The parameters of the proposed models are then estimated using real data obtained from the identification experiments with the process plant. Various simulation tests for validation are accompanied with the design, development and real-time industrial implementation of an optimal controller for dynamic optimisation of this web process. It is shown that in comparison with the traditional controller, the new controller resulted in a better performance, an improvement in film quality and saving in raw materials. This demonstrates the efficiency and validation of the developed models

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

Formulation Pre-screening of Inhalation Powders Using Computational Atom–Atom Systematic Search Method

The synthonic modeling approach provides a molecule-centered understanding of the surface properties of crystals. It has been applied extensively to understand crystallization processes. This study aimed to investigate the functional relevance of synthonic modeling to the formulation of inhalation powders by assessing cohesivity of three active pharmaceutical ingredients (APIs, fluticasone propionate (FP), budesonide (Bud), and salbutamol base (SB)) and the commonly used excipient, α-lactose monohydrate (LMH). It is found that FP (−11.5 kcal/mol) has a higher cohesive strength than Bud (−9.9 kcal/mol) or SB (−7.8 kcal/mol). The prediction correlated directly to cohesive strength measurements using laser diffraction, where the airflow pressure required for complete dispersion (CPP) was 3.5, 2.0, and 1.0 bar for FP, Bud, and SB, respectively. The highest cohesive strength was predicted for LMH (−15.9 kcal/mol), which did not correlate with the CPP value of 2.0 bar (i.e., ranking lower than FP). High FP–LMH adhesive forces (−11.7 kcal/mol) were predicted. However, aerosolization studies revealed that the FP–LMH blends consisted of agglomerated FP particles with a large median diameter (∼4–5 μm) that were not disrupted by LMH. Modeling of the crystal and surface chemistry of LMH identified high electrostatic and H-bond components of its cohesive energy due to the presence of water and hydroxyl groups in lactose, unlike the APIs. A direct comparison of the predicted and measured cohesive balance of LMH with APIs will require a more in-depth understanding of highly hydrogen-bonded systems with respect to the synthonic engineering modeling tool, as well as the influence of agglomerate structure on surface–surface contact geometry. Overall, this research has demonstrated the possible application and relevance of synthonic engineering tools for rapid pre-screening in drug formulation and design

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e. a controlling message) compared to no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly-internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared to the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly-internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing: Controlled motivation was associated with more defiance and less long-term behavioral intentions to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

On the use of inclusion structure in fuzzy clustering algorithm in case of Gaussian membership functions

This article addresses the problem of incorporating an inclusion structure in the general class of fuzzy c-means algorithms. Conventionally, all the classes of fuzzy clustering algorithms involve a distance structure as the main tool to compute the interaction between the expected class prototypes and all the patterns. However, as the inclusion violates the basic metric assumptions, thereby it cannot be directly substituted for regular distance structure. The approach, advocated in this paper, consists of supporting the distance structure by a semi definite matrix A, which preserves the inclusion constraint globally for each class. Particularly, a graded inclusion index is put forward that takes into account the rational requirements underlying the definition of the inclusion of two Gaussian membership functions. Behaviour and algebraic properties of the proposed methodology are investigated. The proposed approach is then incorporated into the general fuzzy c-mean scheme, where the corresponding optimization problem is solved. Using both synthetic and real datasets, some illustrations are carried out in order to highlight the performances of the constructed algorithm and their evaluations, which are also compared to standard fuzzy c-means algorithm