Dual Processing Research Articles

Enhancing Consumers’ Repurchase Intention in Peer-to-Peer Accommodation Following a Dual Processing Model: A Principal–Agent Perspective

In peer-to-peer (P2P) accommodation, consumers face high levels of consumption uncertainty from both P2P sites and hosts. Our study applies principal–agent theory to develop a dual processing model in examining how to support consumers’ repurchase intention by reducing consumers’ uncertainty perceptions associated with booking and staying. We selected Airbnb as our context and collected survey data from American consumers. The results show that perceived uncertainty (hosts) has a direct effect on repurchase intention, whereas perceived uncertainty (site) has an indirect effect. Further, perceived utility and trust in the last host can help reduce fears of opportunism (hosts), whereas site quality can alleviate fears of opportunism (site). Our study contributes to the literature by developing a dual-processing model of repurchase intention to clarify how consumers’ repurchase intention can be enhanced. The results can provide important guidelines regarding how to support consumers’ repurchase intention. We also provide suggestions regarding how to deal with additional uncertainties post COVID-19.

Exploring randomness in autism.

The fast, intuitive and autonomous system 1 along with the slow, analytical and more logical system 2 constitute the dual system processing model of decision making. Whether acting independently or influencing each other both systems would, to an extent, rely on randomness in order to reach a decision. The role of randomness, however, would be more pronounced when arbitrary choices need to be made, typically engaging system 1. The present exploratory study aims to capture the expression of a possible innate randomness mechanism, as proposed by the authors, by trying to isolate system 1 and examine arbitrary decision making in autistic participants with high functioning Autism Spectrum Disorders (ASD). Autistic participants withhigh functioning ASD and an age and gender matched comparison group performed the random number generation task. The task was modified to limit the contribution of working memory and allow any innate randomness mechanisms expressed through system 1, to emerge. Utilizing a standard analyses approach, the random number sequences produced by autistic individuals and the comparison group did not differ in their randomness characteristics. No significant differences were identified when the sequences were examined using a moving window approach. When machine learning was used, random sequences' features could discriminate the groups with relatively high accuracy. Our findings indicate the possibility that individual patterns during random sequence production could be consistent enough between groups to allow for an accurate discrimination between the autistic and the comparison group. In order to draw firm conclusions around innate randomness and further validate our experiment, our findings need to be replicated in a bigger sample.

The effect of dual thermomechanical processing paths on resulting microstructure and quasi-static mechanical properties of Mg-1Zn-0.2Ca

In this work, cast Mg-1Zn-0.2Ca (wt%) was thermomechanically processed using a two-step procedure to identify the impact of both processing steps on the resulting microstructure and mechanical properties. Samples were first preprocessed via rolling or conventional extrusion (at 400 °C), followed by Equal Channel Angular Extrusion (ECAE), utilizing routes A or Bc. ECAE was performed at 300 °C for the first pass, followed by 275 °C, 250 °C, and 225 °C for passes 2–4. Subsequent microstructures, textures, and quasi-static tension test results are presented to illuminate the effect of preprocessing and ECAE route on grain size refinement and mechanical behavior. Preprocessing via rolling yielded a smaller, more homogeneous initial grain size than the conventionally extruded material. After the second processing step, ECAE, the microstructural differences due to the preprocessing methods are reduced, but not eliminated, and mechanical properties show little variation between rolling and extrusion. The ECAE route did not impact the microstructural refinement, however, the 4Bc route did lead to enhanced ductility (>36%) and higher average ultimate tensile strengths, compared to the 4A route, when tested in the normal direction (perpendicular to the ECAE extrusion direction). The relative importance of texture alteration versus grain refinement and homogeneity is highlighted herein. Lastly, we comment on the influence of processing routes on inclusions and voids.

Characterization of bioprocessed white and red sorghum flours: Anti-nutritional and bioactive compounds, functional properties, molecular, and morphological features.

In the current investigation, white and red sorghum grains were exposed to bioprocessing techniques, such as soaking, germination, fermentation, and dual processing (both germination and fermentation). Germination and fermentation resulted in improved bioactive profile attributing to better antioxidant activity, whereas a reduction in antinutrient components was observed. On the other hand, soaking had decreased phenolic components and anti-nutritional factors attributing to their leaching in the soaking water. A significant change in the functional properties and color profile was also observed on bioprocessing. It also caused alterations in the morphological structure of the starch-protein matrix and molecular interactions of certain functional groups that reveal the synthesis of certain new bioactive compounds in the flour. The alterations in the bioprocessed flours occurred due to the structural breakdown attributing to the activity of hydrolytic enzymes that were activated during the processing treatments. Bioprocessing was also responsible for the degradation of the starch granules and unfolding of the protein matrix, thus altering the in vitro nutrient digestibility of the flours. Principal component analysis was used to authenticate the differences between different treatments and observations recorded. These bioprocessed flours could be potential ingredients for several valorized cereal products.

Habits and Reflective Processes in COVID-19 Transmission-reducing Behaviors: Examining Theoretical Predictions in a Representative Sample of the Population of Scotland.

Based on theory, COVID-19 transmission-reducing behaviors (TRBs) should become habitual because of their frequent performance. Habits have been hypothesized to develop through reflective processes and, to act in conjunction with them. We investigated the existence, development, and consequences of TRB habits, for physical distancing, handwashing, and wearing face coverings. A representative sample of the Scottish population (N = 1,003) was interviewed by a commercial polling company in August-October 2020 and half were re-interviewed later. Measures included adherence, habit, personal routine tendency, reflective processes, and action control for three TRBs. Data were analyzed using general linear modeling, regression, and mediation analyses. Handwashing was most habitual; only face covering became more habitual over time. Routine tendencies predicted TRB habits, and adherence to handwashing and physical distancing. Those reporting greater habits reported better adherence, for physical distancing and handwashing, and this remained true after controlling for previous adherence. Reflective and habit processes independently predicted adherence for physical distancing and handwashing; only reflective processes were independently predictive for face covering. The relationship between planning and forgetting and adherence was partly direct, and partly mediated by habit. The results confirm hypotheses from habit theory including the role of repetition and of personal routine tendency in developing habits. They are consistent with dual processing theory in finding that both reflective and habit processes predict adherence to TRBs. Action planning partly mediated the relation between reflective processes and adherence. The COVID-19 pandemic has enabled the testing and confirmation of several theoretical hypotheses about habit processes in the enactment of TRBs.

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.

Hybrid Piezoelectric–Magnetic Self‐Sensing Actuator using Novel Dual‐Alignment Magnetic/Mechanical Processing for Vibration Control of Whole‐Body Vibrations

Multi‐stimuli‐responsive/‐functional polymeric materials can respond to numerous stimuli and execute multiple tasks, overcoming barriers faced by single‐stimuli materials. Herein, the development of hybrid piezoelectric–magnetic self‐sensing actuator (HPMSA) that can both sense and actuate is proposed. This iron oxide/functionalized carbon nanotube/polyvinylidene fluoride film optimizes both piezoelectric and magnetic properties through dual‐alignment fabrication, utilizing strong element bonds for simultaneous alignment. Magnetic nanoparticles are advantageous over nanorods due to latter's randomized shape anisotropy decreasing magnetization. The dual magnetic and mechanical processing increases polar β‐crystal content to 88%, where magnetic alignment alone increases degree of crystallinity to 66%. As a vibration damper, HPMSA operates within 40–600 Hz frequency, with a sensing sensitivity of 2.5 mV g−1 and 0.72 m s−2 weighted acceleration damping, lowering passenger health risks. Piezoelectric and magnetic relationship shows 0.19 V increase with 125 mT applied. The flexible HPMSA can integrate onto a curved surface and sense/dampen vibrations of an air motor, propeller drone, and simulated tremors. The HPMSA provides tremendous potential and understanding into multi‐stimuli‐responsive/functional materials, simultaneous alignment, and vibration control in the next generation of transportation vehicles for human safety.

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.

Coriolis mass flowmeter signal processing system based on time-varying signal model

In order to improve the signal processing effect and efficiency of Coriolis mass flowmeter, a signal processing system of Coriolis mass flowmeter based on time-varying signal model is designed. Based on the analysis of the structure and working principle of Coriolis mass flowmeter, the environmental factors affecting the performance of Coriolis mass flowmeter are discussed. Based on the above analysis, the signal processing environment is designed, including serial signal processing environment and dual core parallel signal processing environment. Then the effective signal processing of Coriolis mass flowmeter is completed from three aspects: initial processing, phase calculation and frequency tracking. The experimental results show that after the time-varying signal model is applied, a complete signal frame can be collected in less than 50 ms in the pre-processing process, and the signal processing frame can be completed in less than 80 ms in the post-processing process. This shows that the signal processing time of the system is short, and the phase difference detection accuracy of the signal processing system based on the time-varying signal model is high, which is of great value to further improve the application effect of Coriolis mass flowmeter.

Resource sharing in cognitive control: Behavioral evidence and neural substrates

Cognitive control is a capacity-limited function responsible for the resolution of conflict among competing cognitive processes. However, whether cognitive control handles multiple concurrent requests through a single bottleneck or a resource sharing mechanism remains elusive. In this functional magnetic resonance imaging study, we examined the effect of dual flanker conflict processing on behavioral performance and on activation in regions of the cognitive control network (CCN). In each trial, participants completed two flanker conflict tasks (T1 and T2) sequentially, with the stimulus onset asynchrony (SOA) varied as short (100 ms) and long (1000 ms). We found a significant conflict effect (indexed by the difference between incongruent and congruent flanker conditions) in reaction time (RT) for both T1 and T2, together with a significant interaction between SOA and T1-conflict on RT for T2 with an additive effect. Importantly, there was a small but significant SOA effect on T1 with a prolonged RT under the short SOA compared to the long SOA. Increased activation in the CCN was associated with conflict processing and the main effect of SOA. The anterior cingulate cortex and anterior insular cortex showed a significant interaction effect between SOA and T1-conflict in activation parallel with the behavioral results. The behavioral and brain activation patterns support a central resource sharing model, in which the core resources for cognitive control are shared when multiple simultaneous conflicting processes are required.

Exploring the moderating role of well-being on the adaptive link between self-assessment practices and learning achievement

Self-assessment practices can positively impact student learning and achievement. However, less is known about psychological moderators that can influence the impact that self-assessment practice may have on achievement. Integrating the dual processing self-regulation model and the self-assessment cycle framework, this study examined the role of students’ positive well-being in the link between self-assessment practices and domain-specific achievement scores. Hierarchical multiple regression analyses using data from 765 secondary school students revealed that self-assessment practices positively predicted achievement scores. Results also showed that positive well-being moderated the relationship between seeking internal feedback and self-reflection and achievement scores, in that the positive link between such self-assessment practices and achievement was stronger for students who had low or moderate well-being. These findings reinforce the impact of self-assessment on learning and add to the ecological validity of the dual processing self-regulation model. The discussion focuses on how these findings contribute to our understanding of how well-being could influence the adaptive role of internal and cognitive self-assessment practices in student learning. Limitations and future research directions are also discussed.

Dual processing approach to sedentary behavior and physical activity in the workplace.

Regular physical activity is an important health promoting behavior. Yet, many adults live sedentary lifestyles, especially during their workday. The current study applies an extended theory of planned behavior model, incorporating affective attitudes and instrumental attitudes, along with habit, to predict limiting sedentary behavior and physical activity within an office environment. Theory of planned behavior constructs and habit were assessed with an online survey on a sample of 180 full-time office workers, with self-reported behavior assessed 1 week later (Mage = 25.97, SDage = 10.24; 44 males, 134 females, and 2 nonbinary). Model fit was indicated by BRMSEA (M = 0.057, SD = 0.023), B γ^ (M = 0.984, SD = 0.010) and BCFI (M = 0.959, SD = 0.026), accounting for 46.1% of variance in intention, 21.6% of variance in sedentary behavior, and 17.4% of variance in physical activity behavior. A Bayesian structural equation model revealed direct effects of instrumental attitudes and perceived behavioral control on intention to limit sedentary behavior, direct effects of intention and perceived behavioral control on limiting sedentary behavior, and direct effects of perceived behavioral control and habit on engaging in physical activity. The current study indicates intentions to be active in the office are primarily driven by beliefs about the benefits of activity and individuals' perceived level of control, rather than normative or affective beliefs. As behavior was predicted by both intention and habit, findings also indicate office-based activity is likely not always a consciously driven decision. These findings may have implications for improving activity levels in this highly sedentary population.

Fabrication of grains from bioprocessed nutri‐cereal flours: assessment of cooking profile, phytochemical composition, secondary protein fractions and structural characteristics

SummaryThe current study aimed at bioprocessing i.e. germination, fermentation and dual processing of different nutri‐cereals (red sorghum, white sorghum, foxtail millet, proso millet and barnyard millet) followed by their use in the development of fabricated grain in equal proportion with and without steaming. The fabricated grains were studied for cooking quality, bioactive composition and associated antioxidant activity, molecular interactions, structural characterisation and organoleptic evaluation. Bioprocessed fabricated grains had significantly lower cooking time, water absorption, and volume expansion but enhanced gruel solid loss while pre‐gelatinisation resulted in decreased losses. Bioprocessed fabricated grains had enhanced bioactive profiles when compared to pre‐gelatinised and control fabricated grains. Pre‐gelatinizaion modified the starch‐protein matrix preventing the leaching of compounds into the water. The results suggest that bioprocessed grains can be potential ingredients for the development of pre‐gelatinised fabricated grains.

Data Storytelling to Communicate Big Data Internally – a Guide for Practical Usage

Purpose: Data is collected from all aspects of our lives. Yet, data alone is useless unless converted into information and, ultimately, knowledge. Since data analysts, in most cases, are not the ones in charge of making decisions based on their findings, communicating the results to stakeholders is crucial to passing on information of data-driven insights. That is where the discipline of data storytelling comes into play. Often, data storytelling is considered an effective data visualization. Creating data stories is a structured approach to communicating data insights as an interplay of the three elements data, visuals, and narrative. Sharing data-driven insights to support better business decisions require data storytellers skilled in the “art of storytelling”.
 Design/Method/Approach: In this paper, the authors discuss the use of data storytelling in business to communicate data to stakeholders for improving decision-making. The findings are derived from (1) an extensive literature review and (2) a qualitative analysis of 13 expert interviews with people incorporating data storytelling into their daily work within their jobs in international companies.
 Findings: These interviews revealed the importance of providing a flexible tool to support knowledge sharing for people communicating complex data to internal stakeholders. Combining literature with qualitative research enabled the authors to create the "data storytelling cheat sheet", a guide for practical data storytelling.
 Theoretical Implications: Theories like the Psychological distance or the idea of the theory of dual processing dual are used to base our research idea on. There was no new theory built in this paper.
 Practical Implications: One of the results is an implementation systematic cheat sheet that helps practitioners to implement data storytelling in their daily business.
 Originality/Value: The theory of data storytelling is overwhelming the first time to use and based on an empirical study with experts in the field a guideline for hands on use was developed under a based on a cleanly defined empirical study.
 Research Limitations/Future Research: The paper focus on internal data storytelling – maybe with external stakeholders it might be slightly different. The results the data communication part in any data analytics project.
 Paper Type: Empirical
 JEL Classification: D7, D8

Dynamic Face Perception: The Role of Expertise in Dual Processing of Features and Configuration

Face perception is the basis of many types of social information exchange, but there is controversy over its underlying mechanisms. Researchers have theorized two processing pathways underlying facial perception: configural processing and featural processing. Featural processing focuses on the individual features of a face, whereas configural processing focuses on the spatial relations of features. To resolve the debate on the relative contribution of the two pathways in face perception, researchers have proposed a dual processing model that the two pathways contribute to two different perceptions, detecting face-like patterns and identifying individual faces. The dual processing model is based on face perception experiments that primarily use static faces. As we mostly interact with dynamic faces in real life, the generalization of the model to dynamic faces will advance our understanding of how faces are perceived in real life. This paper proposes a refined dual processing model of dynamic face perception, in which expertise in dynamic face perception supports identifying individual faces, and it is a learned behaviour that develops with age. Specifically, facial motions account for the advantages of dynamic faces, compared to static faces. This paper highlights two intrinsic characteristics of facial motions that enable the advantages of dynamic faces in face perception. Firstly, facial motion provides facial information from various viewpoints, and thus supports the generalization of face perception to the unlearned view of faces. Secondly, distinctive motion patterns serve as a cue to the identity of the face.

Parallel Processing Method for Microseismic Signal Based on Deep Neural Network

The microseismic signals released by rock mass fracture can be captured via microseismic monitoring to evaluate the development of geological disasters. This is crucial for underground engineering construction, underground mining, and earthquake and geological disaster evaluation. However, extracting information effectively is difficult due to the low signal-to-noise ratio of microseismic signals caused by complex environmental factors. Therefore, denoising and detection (onset time picking) are essential to processing microseismic signals and extracting source information. To improve the efficiency and accuracy of microseismic signal processing, we propose a parallel dual-tasking network, which is an advanced deep learning model that can simultaneously perform microseismic denoising and detection tasks. The network, comprising one encoder and two parallel decoders, is customised to extract input data features, and two outputs can be simultaneously generated to denoise and detect microseismic signals. The model exhibits excellent denoising and detection performance for microseismic signals containing various types of noise. Compared with traditional methods, the signal-to-noise ratio of the denoised signal is greatly improved, and the waveform distortion of the denoised signal is small. Even when the signal-to-noise ratio is low, the proposed model can maintain good onset time pickup performance. This method obviates the need for different denoising methods for different types of noise and precludes setting thresholds artificially to improve the denoising effect and detection accuracy. Moreover, the dual processing characteristics of the model facilitate simultaneous denoising and detection, which improves the processing efficiency of microseismic data and meets the demand for automatically processing massive microseismic data. Therefore, this method has excellent data processing potential in exploration seismology, and earthquake and disaster assessment.

Sequence Order in the Range 1 to 19 by Chimpanzees on a Touchscreen Task: Processing Two-Digit Arabic Numerals

The sequence of Arabic numerals from 1 to 19 was taught to six chimpanzees, three pairs of mother and child. Each chimpanzee participant sat facing a touchscreen on which the numerals appeared in random positions within an imaginary 5-by-8 matrix. They had to touch the numerals in ascending order. Baseline training involved touching the adjacent numerals from 1 to X or from the numeral X to 19. Systematic tests revealed the following results: (1) The range 1 to 9 was easier than 1 to 19. (2) Adjacent numerals were easier than nonadjacent ones. (3) The "masking" (memory task) caused deterioration of performance. All these factors depended on the number of numerals simultaneously presented on the screen. A chimpanzee named Pal mastered the skill of ordering two-digit numerals with 100% accuracy. Human participants were tested in the same experiment with the same procedure. Both species showed relative difficulty in handling two-digit numerals. Global-local information processing is known to be different between humans and other primates. The assessment of chimpanzee performance and comparison with humans were discussed in terms of the possible difference in the global-local dual information processing of two-digit numerals.

Application of multi-beam bathymetry system in shallow water area

Multi-beam bathymetry technology plays an important role in ocean, river and lake surveying and mapping. According to the measurement principle and technical requirements of MS8200 multi-beam bathymetry system, this paper introduces the composition and working principle of the system, the installation and calibration of the system, and the data in the post-processing process, the sea area and terrain characteristics near Wenchang Qinglan Port are taken as the research object to discuss the dual multi-beam bathymetric technology and data processing process, and compare the data of single multi-beam in the same sea area. The research results show that the dual probe of multibeam bathymetry system can better reflect the topographic features of the seafloor compared with the single probe, and shorten the time of mapping, reduce the labor intensity and improve the measurement efficiency, and provide data reference for the stability and reliability evaluation study of the dual probe of multibeam bathymetry system.

Investigation on the effect of machining parameters on 42CrMo4 DPS steels

Machining 42CrMo4 martensite—ferrite dual phase steel is challenging due to its high hardness and it is essential to determine the favorable requirements for the optimum machining condition. The ability to alter the martensite quantity in dual phase structure of steel leading to variation in bulk hardness is the motivation for machinability investigation. In heavy duty machining, the determination of tool life and surface roughness at various conditions of machining plays an important role in the manufacturing industry. In the present work, machinability tests are carried out on 42CrMo4 martensite—ferrite dual phase steel to assess the tool life and surface roughness. Speed, feed, and depth of cut are varied in different levels. The tests are carried out as per the full factorial method. A microstructure study is performed to correlate the various mechanical properties with phase morphology. The main objective of this study is to obtain the optimized machining process parameters for the turning operation of 42CrMo4 martensite—ferrite dual phase steel. All cutting tests are carried out under dry conditions using a carbide insert. Microstructure and mechanical property analysis shows an increase in the martensite quantity with the increase in the dual phase processing temperature. From the ANOVA results, it is found that the depth of cut is the major contributing factor to the variation of tool life and surface roughness within the range of values considered for the study. Microstructure analysis revealed the distribution of ferrite and martensite phases evenly. The optimum combination of machining parameters is calculated for obtaining a superior combination of tool life and surface roughness. The 42CrMo4 DPS steel may be used for structural applications with wide variation in the property range.

Cross-Over from Dual Processing on Evaluative Decision-Making

Cross-Over from Dual Processing on Evaluative Decision-Making