Processing Of Relevant Information Research Articles

Photodiode-based process monitoring for the ultrashort-pulsed laser structuring of the diffusion media for fuel cells

In recent years, the incorporation of process monitoring systems has become an integral part across several laser material processing applications. This is due to the feasibility of inline evaluations, which enable the elimination of downstream quality checks that are typically time-consuming and costly. Simultaneous to recent sensor developments, ultrashort-pulsed (USP) laser material processing has become a promising technology for creating complex microstructures in a wide variety of materials due to its high precision and negligible thermal input. So far, process monitoring has yet to be established in USP applications as it is unknown which sensors are suitable, which parameters can be observed, and which process-relevant information can be detected within the data. Within this work, a state-of-the-art photodiode-based process monitoring system was used to collect spectral data from the process zone and extract relevant information from the process. The laser structuring of the two-layered diffusion media used in polymer electrolyte membrane fuel cells was selected as the use case. This structuring process is challenging due to production-related fluctuations in the material thickness and the surface quality. Within this study, information was acquired from a sensor system employing three photodiodes. Through the application of explorative data analysis techniques and machine learning, AI models were created, focusing on determining the focus position of the laser beam and identifying the transition between the two layers. The developed models were capable of determining the initial focus position with varying accuracies. The best performance was achieved by a convolutional neural network using spectrograms as input data, while the worst accuracy was achieved by a ridge regression model using manually selected features. Furthermore, a clustering model was used, demonstrating the capability to detect the layer transitions within a specified tolerance.

Understanding followers’ processing and perceptions of social media influencer destination marketing: Differences between #Millennials and #GenZs

Social media influencer (SMI) marketing implemented by tourism and destination managers has attracted widespread academic attention in recent years. Efforts have been made to evaluate the effectiveness of SMI in destination marketing using source credibility, informativeness, and SMIs’ relationship with their followers. However, extant literature has paid limited attention to capturing and understanding the various mechanisms of persuasion of how SMI destination marketing audiences process relevant SMI information and how their processing of the information would influence their travel decision-making. This study bridges these research gaps with a proposed research framework by integrating the Elaboration Likelihood Model (ELM) with theories of self-congruity, parasocial interaction, and persuasion knowledge activated by advertisement disclosure. Subsequently, the study executed an audience segmentation of the SMI destination marketing between the Millennials and Generation Z. Adopting a quantitative approach (N = 501), results showed that Millennials and Generation Z audiences have distinct information processing preferences and attitudes toward advertisement disclosure of SMIs. Key implications for theory and practice were also discussed.

Hubungan Antara Short Term Memory dengan Kemampuan Sintaksis pada Anak Prasekolah Usia 3-5 Tahun di TK IV dan KB IV Saraswati Denpasar

Abstract Background: Short term memory is a memory that is responsible for holding and processing relevant information and at the same time, this will activate the repository language of lexical representations in long term memory. Syntax is an aspect of language that refers to grammatical rules, i.e., how words are combined into phrases and sentences correctly in grammar (White et al., 2017). Objectives: This study aims to determine the relationship between short term memory and the syntactic ability in preschool children aged 3-5 years at Kindergarten IV and Play Group IV Saraswati Denpasar. Methods: This research uses a quantitative method, an observational approach with a cross sectional study design. The population in this study were children 3-5 years old in Kindergarten IV and Play Group IV Saraswati Denpasar. The sampling technique used purposive sampling by taking 37 samples of children. Results: The results of the Spearman rank test show that short term memory ability and syntactic ability have a significance of 0.000 (ρ < 0.05) so that the alternative hypothesis (Ha) is accepted. The correlation test value (r) shows a figure of 0.938 and shows a positive correlation. Conclusion: There is a relationship between short term memory and the syntactic abilities of preschool children aged 3-5 years at Kindergarten IV and Play Group IV Saraswati Denpasar. Keywords: Short Term Memory, Syntax, Syntactic, Preschool Cognitive, Language

Design of Software Reliability Prediction using Radial Basis Function Networks with Nonlinear Analysis and Topological Considerations

This study presents a thorough methodology for predicting software reliability by employing Radial Basis Function Networks (RBFNs). By using machine learning methodologies such as Radial Basis Function Networks (RBFNs), software development teams are enabled to make well-informed decisions, optimize resource allocation, and proactively mitigate potential dependability concerns. Consequently, this results in improved software quality and heightened customer happiness. This methodology involves multiple stages, starting with data collection and preprocessing. We assemble a comprehensive dataset comprising historical software performance metrics, defect reports, and relevant development process information. After data cleansing and feature engineering, we split the dataset into training, validation, and testing subsets. The core of this approach lies in the construction and training of RBFNs. These neural networks consist of an input layer, a hidden layer with radial basis functions, and an output layer. The architecture parameters, particularly the number of hidden neurons and the spread parameter of the radial basis functions, are optimized through a systematic hyperparameter tuning process using the validation dataset. Upon achieving an optimal model configuration, we rigorously evaluate the RBFN predictive capabilities using the testing dataset.

A question of pattern recognition: investigating the impact of structure variation on students’ proficiency in deciding about resonance stabilization

The ability to reason with representations is pivotal for successful learning in Organic Chemistry and is closely linked to representational competence. Given the visual nature of this discipline, this comprises competency in extracting and processing relevant visual information. With regard to the resonance concept, proficiency in identifying whether electron delocalization applies in a molecule is an essential prerequisite to using this concept in problem-solving. However, prior research shows that students struggle to recognize whether molecules profit from electron delocalization, and seldom use this concept in problem-solving. As it remains unclear how the variation of structural features affects students’ consideration of resonance, this quantitative study seeks to identify characteristics regarding students’ perception of electron delocalization. To this end, undergraduate students enrolled in an Organic Chemistry I course (N = 699) completed an online survey in which they had to decide on resonance stabilization for molecular structures with varying structural features. K-means cluster analysis was performed to explore patterns in students’ proficiency in discerning resonance stabilization and how they relate to other performance variables (e.g., time-on-task). The results suggest pattern recognition approaches with students’ attention being guided by singular structural features or structures’ visual similarity to familiar patterns (i.e., allylic carbocations), with less attention to implicit features.

Relevant and Quasi-Relevant Management Accounting Information

This paper examines two criteria for relevance of management accounting information: the decision and the decision process. The study aims to show whether the information that is relevant to both the decision and the process of decision making (process-relevant) can lead to predictably different decisions compared to the information that is relevant to the decision but irrelevant to the decision process (process-quasi-relevant). Based on Drury (2018, p. 285) a field experiment was designed to test the hypothesis employing 352 students and PhD students. The results generally support the view that process relevance seems to be a superior to decision relevance as a qualitative characteristic of useful management accounting information. Fully rational agents would derive the same meaning, and thus the same utility, from both process-relevant information and process-quasi relevant information. Boundedly rational agents, however, as confirmed by the field experiment, derive different utility from the process-relevant information and the process-quasi-relevant information. If management accounting aims to partner managers in decision making, the focus of generating and communicating the information should be on the core driver of the better decisions, and this intuition suggests that the decision process should be a pivot for the management accounting information.

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Assessing drivers’ cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers’ natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

The effects of modular process models on gaze patterns - A follow-up investigation about modularization in process model literacy

Advanced organizational processes today require many process-relevant artifacts to be formally represented in process models. As these processes become more complex, process designers face the challenge of maintaining high comprehensibility while adequately visualizing process-relevant information. Modularization has been introduced to address the increasing complexity and information overload of process models, tackling this issue. While research acknowledges the importance of process model comprehensibility in order to benefit from the merits of such models, there still needs to be more clarity about the benefits of process model modularization. Recent research has focused on the human visual system and investigated visual behavior in the context of process model literacy. Continuing this branch of research, this paper analyses gaze behavior when comprehending modularized process models. A follow-up eye-tracking study was conducted to comprehend differently modularized BPMN 2.0 process models (i.e., vertical, horizontal, orthogonal). The results indicated that process model readers comprehend modularization approaches differently. Vertical modularization was perceived as the best comprehensible approach, whereas horizontal received the least acceptance. Considered eye movement performance measures confirm prior observation that attention is deployed more effectively in vertical modularization. Based upon the findings, the paper proposes a revised theoretical model from prior work that relates visual tasks to the execution of visual routines, explaining comprehension and information processing in modularized process models. The theoretical model elaborates how the visual system process and comprehends information obtained from modularized process models. Furthermore, the paper derived implications for modeling and practical applications, pointing out solid representational points and shortcomings of each modularization approach regarding how modularized process models are read and comprehended.

Planning and control of the ship curvilinear route

The possibility of applying the traditional navigation parameters used in pilotage (bearing and distance), as well as their sum and difference for planning and controlling the ship curvilinear route in coastal waters is being investigated. Mathematical apparatus is based on the classical navigation methods such as the theory of isolines, the lines of position, the principles of route planning, as well as the approximation of the curvilinear route. The mathematical justification of the circle arcs, ellipse and hyperbola used as the approximation lines of both the ship curvilinear route and the control isolines is given. The methods for estimating the vessel approaching to the point of the beginning and end of the turn and for controlling the ship movement along the planned path by measuring the navigational parameters are proposed. The problems of the physical and mathematical feasibility of the route arising at the junction points of the straight and curvilinear parts of the ship route are recommended to be resolved on the principles of transition curves (clothoids), considering the maneuvering characteristics of the vessel. The main theses of the research are supported by the graphical interpretation while the given expressions are ready for practical application. The formalization of the proposed methods in both automatic navigation systems and autonomous ship control systems will allow the navigator on the bases of the configuration of the relative position of landmarks and the ship curvilinear route to solve the problems of processing relevant navigation information in coastal and congested waters more efficiently in conjunction with the traditional pilotage methods, which is especially important for advanced autonomous navigation.

Mapping attention across multiple media tasks

ABSTRACT Paying attention to media requires continuously selecting and processing relevant information while filtering out numerous competing stimuli. Although the factors that drive attention toward or away from a single media task are relatively well characterized, there is a lack of understanding regarding how attention to media functions in the presence of multiple, concurrent tasks. In this manuscript, we report findings from four experiments investigating this question. Results indicate that, rather than attention being based on a strict hierarchy between “primary” and “secondary” tasks, attentional resources are distributed across concurrent media tasks based on the (relative) rewardingness and effortfulness of each task. More rewarding tasks elicited more attention, and the attention-capturing influence of rewarding “secondary” tasks was magnified when the “primary” task was more cognitively effortful. These results provide support for recent theoretical advancements in media psychology research and point to promising future directions using updated models of motivated attention to predict the allocation of attentional resources across multiple concurrent tasks.

Prediction of Surface Roughness in Functional Laser Surface Texturing Utilizing Machine Learning

Functional laser surface texturing (LST) arose in recent years as a very powerful tool for tailoring the surface properties of parts and components to their later application. As a result, self-cleaning surfaces with an improved wettability, efficient engine components with optimized tribological properties, and functional implants with increased biocompatibility can be achieved today. However, with increasing capabilities in functional LST, the prediction of resulting surface properties becomes more and more important in order to reduce the development time of those functionalities. Consequently, advanced approaches for the prediction of the properties of laser-processed surfaces—the so-called predictive modelling—are required. This work introduces the concept of predictive modelling with respect to LST by means of direct laser writing (DLW). Fundamental concepts for the prediction of surface properties are presented employing machine learning approaches, theoretical concepts, and statistical methods. The modelling takes into consideration the used laser parameters, the analysis of topographical, and other process-relevant information in order to predict the resulting surface roughness. For this purpose, two different algorithms, namely artificial neural network and random forest, were trained with experimental data for stainless steel and Stavax surfaces. Statistical results indicate that both models can predict the desired surface topography with high accuracy, despite the use of a small dataset for the training process. The approaches can be used to further optimize the laser process regarding the process efficiency, overall throughput, and other process outcomes.

How do foodservice companies organize for inbound open innovation? Empirical evidence from a Dutch organization

PurposeThe aim of the paper is to analyze how actors in foodservice companies organize for inbound open innovation (OI).Design/methodology/approachThis paper conducted a case analysis of a large and successful foodservice company operating in the Dutch market. Furthermore, drawing on 18 interviews and archive data, we identified the main organizational practices involved in the implementation of inbound innovation activities and the ways they are embraced are defined.FindingsThe results provide a holistic view of the main organizational practices a foodservice company implemented at different organizational levels, to exploit external knowledge coming from third parties and to promote the sharing and recombination of knowledge resources within the organization. The identified organizational practices reveal the main interaction patterns between relevant internal actors and other external parties in the company network, as well as between actors on different hierarchical organizational levels which allows processing relevant innovation information and make relevant decisions about it.Research limitations/implicationsImplications are provided in terms of both theory and practice. This paper helps foodservice companies to create an internal organizational environment that supports the exploitation of customer knowledge.Originality/valueThere are few studies on how companies organize themselves for OI in general, and especially in the foodservice sector.

Towards a Model-Based Systems Engineering Approach for Robotic Manufacturing Process Modelling with Automatic FMEA Generation

AbstractThe process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses.

A framework for the automatic description of healthcare processes in natural language: Application in an aortic stenosis integrated care process.

In this paper, we propose a framework for the automatic generation of natural language descriptions of healthcare processes using quantitative and qualitative data and medical expert knowledge. Inspired by the demand of novel ways of conveying process mining analysis results of healthcare processes (Rojas et al., 2016), our framework is based on the most widely used Data-To-Text (D2T) pipeline (Reiter, 2007) and on the usage of process mining techniques. Backed by a general model that handles process data, this framework is able to quantify attributes in time during a process life-span, recall temporal relations and waiting times between events and its possible causes and compare case (patient) attributes between groups, among other features. Through integrating fuzzy quantification techniques, our framework is able to represent relevant quantitative process information with some degree of uncertainty present on it and describe it in natural language involving uncertain terms. A real application over the Aortic Stenosis Integrated Care Process of the University Hospital of Santiago de Compostela is presented, showcasing the potential of our framework for providing natural language descriptions of healthcare processes addressed to medical experts. Following the standards of D2T systems, manual human validation was conducted for the generated natural language descriptions by fifteen medical experts in Cardiology. Validation results are very positive, since a global average of 4.07/5.00 was achieved for questions related to understandability, usefulness and impact of the natural language descriptions on the medical experts work. More precisely, results indicate i) that the modality which conveyed the information most efficiently was natural language ii) a very clear preference of texts over the usual graphic representation of process information as the way for conveying information to experts (4.28/5.00), and iii) natural language descriptions provide relevant and useful information about the process, allowing for its improvement.

Using Blur for Perceptual Investigation and Training in Sport? A Clear Picture of the Evidence and Implications for Future Research.

Dynamic, interactive sports require athletes to identify, pick-up and process relevant information in a very limited time, in order to then make an appropriate response. Perceptual-cognitive skills are, therefore, a key determinant of elite sporting performance. Recently, sport scientists have investigated ways to assess and train perceptual-cognitive skills, with one such method involving the use of blurred stimuli. Here, we describe the two main methods used to generate blur (i.e., dioptric and Gaussian) and then review the current findings in a sports context. Overall, it has been shown the use of blur can enhance performance and learning of sporting tasks in novice participants, especially when the blur is applied to peripheral stimuli. However, while intermediate and expert level participants are relatively impervious to the presence of blur, it remains to be determined if they are positive effects on learning. In a final section, we describe some of the methodological issues that limit the application of blur and then discuss the potential use of virtual reality to extend the current research base in sporting contexts.

Summaries of Articles

Summaries of Articles

The Brain Selectively Tunes to Unfamiliar Voices during Sleep

The brain continues to respond selectively to environmental stimuli during sleep. However, the functional role of such responses, and whether they reflect information processing or rather sensory inhibition, is not fully understood. Here, we present 17 human sleepers (14 females) with their own name and two unfamiliar first names, spoken by either a familiar voice (FV) or an unfamiliar voice (UFV), while recording polysomnography during a full night of sleep. We detect K-complexes, sleep spindles, and microarousals, and assess event-related and frequency responses as well as intertrial phase synchronization to the different stimuli presented during nonrapid eye movement (NREM) sleep. We show that UFVs evoke more K-complexes and microarousals than FVs. When both stimuli evoke a K-complex, we observe larger evoked potentials, more precise time-locking of brain responses in the delta band (1–4 Hz), and stronger activity in the high frequency (>16 Hz) range, in response to UFVs relative to FVs. Crucially, these differences in brain responses disappear completely when no K-complexes are evoked by the auditory stimuli. Our findings highlight discrepancies in brain responses to auditory stimuli based on their relevance to the sleeper and propose a key role for K-complexes in the modulation of sensory processing during sleep. We argue that such content-specific, dynamic reactivity to external sensory information enables the brain to enter a sentinel processing mode in which it engages in the important internal processes that are ongoing during sleep while still maintaining the ability to process vital external sensory information.SIGNIFICANCE STATEMENT Previous research has shown that sensory processing continues during sleep. Here, we studied the capacity of the sleeping brain to extract and process relevant sensory information. We presented sleepers with their own names and unfamiliar names spoken by either an FV or a UFV. During NREM sleep, UFVs elicited more K-complexes and microarousals than FVs. By contrasting stimuli that evoked K-complexes, we demonstrate that UFVs evoked larger, more synchronized brain responses as well as stronger power at high frequencies (>16 Hz) relative to FVs. These differences in brain responses disappeared when no K-complexes were evoked. Our results suggest a pivotal role for K-complexes in the selective processing of relevant information during NREM sleep.

A study of gender and employment in non- farm activities in Haryana and Punjab

The rural households of Punjab and Haryana were predominantly employed in the agriculture sector in the pre-reforms era. Later during the period of study (1993–94 to 2011–12), the examination of NSSO data reveals that there has been a major shift away from agriculture activities and that shift occurs towards non-farm employment (RNFE) in Punjab while towards RNFE and particularly regular salary earning in case of Haryana. The rural non-farm sector in recent times is considered to be an effective strategy for decentralization of economic activities to India and to check the migration of people to urban areas. Though employability is a function of a number of factors like level of education and type of degree, job seekers’ physical and mental status, mobility of labour, and availability of coordinating factors of production like capital, entrepreneurship and stage and rate of growth of the economy (Chhaya, 2016). Yet the significance of education/skills to increase access to rural non-farm employment cannot be undermined. Improvement in the level of education develops the human capital as well as capacity and hence the chances of employment in an RNF activity. It also leads to a change in priorities, which always shift away from agriculture (Vatta and Garg, 2008). Moreover, better-educated individuals are likely to possess skills that facilitate successful involvement in non-farm activities, including the ability to manage a business, process relevant information, adapt to changing demand patterns, and liaise with public and private service providers (Wandschneider, 2003).

Data driven logistics-oriented value stream mapping 4.0: A guideline for practitioners

The use of data-oriented approaches like data mining or machine learning has an increasing potential for application in the planning and control of production and logistics systems. The growing amount of digital process information helps to expand the existing process understanding in order to determine weaknesses in the process landscape. Due to the extensive complexity within production and logistics systems, a comprehensive approach is required to ensure a systematic analysis. This article presents an extension of the value stream method based on the existing approaches that is intended to support operators of logistics systems in the company. This methodology collects all relevant process information and validates the data maturity. Hence, indications for the use of data-oriented approaches can be given and potential machine learning-based analysis scenarios can be derived.

Research and development of needs organizations in information

In today's rapidly changing environment, the ability of organizations to sense these changes and respond appropriately to them in order to maintain successful functioning is relevant. An important role in any organization is played by the information management process, which provides the organization with timely and reliable data. The basic capabilities that support the organization's ability to search, acquire, process and use relevant information for the organization are investigated. The technology of development of the subprocess of managing the need for information and a set of proposals for their implementation in the organization are proposed.