Stages Of Process Research Articles

Application of an offline grey box method for predicting the manoeuvring performance

The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship.

Psychological mechanism of language cognition to "awaken" artificial intelligence.

The study of artificial intelligence needs tracing back to the working mechanism of the human brain, and based on simulating brain cognition activity, information technology is the most widely influential technology. Language cognition is the core and foundation of human cognition. Biolinguistics' core problem about brain mechanisms (a psychological mechanism) is related to the development of human language cognition in that the cognitive process of humans is from the construction of mediation and language is the very cognitive mediation. The logical law of human language cognition algorithm is about the process of human cognitive psychology that is from the neurological stage, the psychological perception stage, and to the cognitive stage of symbols, which makes psychological certainty account for the connection from mental content to language meaning. Language cognition is where the "awakening" of artificial intelligence begins. First of all, humans produce mental images based on sensory perception and categories. The referential functions of mental images extend the human cognitive scope. Then, categorization is realized through the mediation of mental images. At the time the category is formed, the symbol is required. Language is involved in the thinking process in the cognitive stage of symbols to realize the construction of the mediation to represent the category abstracted from the concrete things. Above all, human language metacognition is realized from the subject-predicate structure that is the grammatical structure on the surface but is throughout the whole process of human rational knowledge and configures the content of rational knowledge, which is the developmental goal of "awakening" artificial intelligence. The demonstration of this proposition is conducive to providing a theoretical basis for the research of the working mechanism of the human brain stimulated by artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Experimental research of novel true triaxial hydrothermal phase change impact fracturing

When extracting coal-bed methane (CBM) from deep ground, conventional hydraulic fracturing struggled to expand fractures due to both inadequate pressure and the water-lock phenomenon, while burgeoning fracturing techniques were unable to provide both high pressure and impact stress. The hydrothermal phase change impact fracturing technology can instantaneously discharge high-pressure thermal fluid and generate impact stress to volumetric fracturing. In order to research the fracturing mechanism of the new fracturing strategy, a separately designed experimental system was utilised for high-pressure thermohydraulic fracturing at different release pressures and different peripheral pressures. The experimental findings: (1) The pressurisation profile in the reactor was divided into five stages: accelerated reaction process, reaction homogeneity process, deceleration process, pressure relief shock stage and residual pressure seam formation process; (2) High-pressure hydrothermal fracture produces both radial and tearing cracks, with radial cracks being the main cracks; (3) The dimension of the fractal can be used as a damage variable to describe both the fracture morphology and the degree of damage.

FEATURES OF THE PROCESS OF LOADING AND TRANSPORTATION OF HOT ASPHALT CONCRETE MIXTURE. UKRAINIAN EXPERIENCE

Depicted in the Article are two stages of technological construction processes for asphalt concrete courses, namely: loading and transportation of asphalt concrete mix. As regards the loading, presented in the Article are two versions of discharging directly into dump body of dump truck or into storage hopper. Mentioned are the advantages of storage hopper usage, as well as the variety of such facilities and the storage time for asphalt concrete mix inside them. Described is the preparation procedure for a dump body of dump truck prior to loading (cleaning, lubrication). Explained are both the proper and improper schemes of loading, as well as the visual signs of asphalt concrete quality after discharging. Described is the right way to protect the asphalt concrete mix against cooling in dump body of dump truck, as well as the proper distance and allowed time-interval for transportation, possible problems occurring during transportation, expediency of using re-loaders for discharging the mix into the receiving hopper of asphalt paver

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

Attentional Control Setting: Type, Quantity and Processing Stage

The attentional control setting is a search template containing target features. Individuals can establish attentional control settings based on the color, size, semantics, joint location-feature of stimulus and even relationships between targets and non-targets. When multiple targets exist, participants may establish multiple attentional control settings or a control setting based on the feature singleton item. However, the processing stage of the attentional control setting is still controversial. Future studies should further analyze the time course of attention control setting in different experimental paradigms to provide solutions to existing controversies, while examining the quantitative limits of attention control setting and the influence of distractor inhibition as a control setting on visual search.

Test Input Prioritization for 3D Point Clouds

3D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud data and the vast number of possible test cases. Test input prioritization has emerged as a promising approach to enhance testing efficiency by prioritizing potentially misclassified test cases during the early stages of the testing process. Consequently, this enables the early labeling of critical inputs, leading to a reduction in the overall labeling cost. However, applying existing prioritization methods to 3D point cloud data is constrained by several factors: (1) inadequate consideration of crucial spatial information, and (2) susceptibility to noises inherent in 3D point cloud data. In this article, we propose PCPrior , the first test prioritization approach specifically designed for 3D point cloud test cases. The fundamental concept behind PCPrior is that test inputs closer to the decision boundary of the model are more likely to be predicted incorrectly. To capture the spatial relationship between a point cloud test and the decision boundary, we propose transforming each test (a point cloud) into a low-dimensional feature vector, toward indirectly revealing the underlying proximity between a test and the decision boundary. To achieve this, we carefully design a group of feature generation strategies, and for each test input, we generate four distinct types of features, namely spatial features, mutation features, prediction features, and uncertainty features. Through a concatenation of the four feature types, PCPrior assembles a final feature vector for each test. Subsequently, a ranking model is employed to estimate the probability of misclassification for each test based on its feature vector. Finally, PCPrior ranks all tests based on their misclassification probabilities. We conducted an extensive study based on 165 subjects to evaluate the performance of PCPrior, encompassing both natural and noisy datasets. The results demonstrate that PCPrior outperforms all of the compared test prioritization approaches, with an average improvement of 10.99% to 66.94% on natural datasets and 16.62% to 53% on noisy datasets.

Large language models present new questions for decision support

Large language models (LLMs) have proven capable of assisting with many aspects of organizational decision making, such as helping to collect information from databases and helping to brainstorm possible courses of action ahead of making a choice. We propose that broad adoption of these technologies introduces new questions in the study of decision support systems, which assist people with complex and open-ended choices in business. Where traditional study of decision support has focused on bespoke tools to solve narrow problems in specific domains, LLMs offer a general-purpose decision support technology which can be applied in many contexts. To organize the wealth of new questions which result from this shift, we turn to a classic framework from Herbert Simon, which proposes that decision making requires collecting evidence, considering alternatives, and finally making a choice. Working from Simon’s framework, we describe how LLMs introduce new questions at each stage of this decision-making process. We then group new questions into three overarching themes for future research, centered on how LLMs will change individual decision making, how LLMs will change organizational decision making, and how to design new decision support technologies which make use of the new capabilities of LLMs.

Contextual Knowledge Framework in CAD Education from the Perspectives of Practicing Engineers

The contemporary industry landscape requires engineering graduates to have expertise in utilizing advanced Computer-Aided Design (CAD) tools. However, prevailing practices reveal a lack of contextual knowledge among Mechanical Engineering undergraduates and fresh graduate engineers in effectively employing 3D CAD modelling software for optimal product design development. Consequently, this study was undertaken to address this issue, with a specific focus on the integration of contextual knowledge into 3D CAD modelling. The study aims to elucidate the fundamental elements of contextual knowledge regularly employed by practicing engineers in their daily design endeavours, specifically in 3D CAD modelling. A transcendental phenomenology approach was used, and four engineers from the engineering department of a shipbuilding company in Peninsular Malaysia were purposefully selected as respondents. The analysis yielded three recurring themes central to the application of contextual knowledge in visualizing and presenting models through 3D CAD modelling: Realization, Design Intention, and Normalization. These elements are pivotal in assisting engineers in contextualizing their design work during the modelling and presentation stages of the new product development process.

Synhospitality of eriophyoid mites (Acariformes, Eriophyoidea): taxonomic analysis of gall-forming mite species complexes on boreal woody dicotyledons

Microscopic mites of the Eriophyoidea superfamily are characterized by an ability to cause the growth of galls on plants. Synhospital complexes of gall-forming Eriophyoidea (SCGE) species are formed by several species of gall mites coexisting on the same host plant. To assess the phenomenon of synhospitality, we investigated the features of the taxonomic composition and diversity of galls formed by eriophyoid mites on boreal woody dicotyledons and carried out a statistical analysis of the host relationships of different gall mite genera from the Eriophyidae family. We found out that phylogenetically closely related hosts within biogeographically integral territories possess the similar SCGE. During colonization of plants, gall mites demonstrate a high ability to cryptic speciation, which leads to genesis of morphologically similar mite species that cause the formation of different types of galls. Host preferences of gall mites belonging to the different supergenera groups play an important role in the formation of the SCGE, which determines the differences in mite taxa spectra associated with hosts from different families. In general, the results of the work demonstrate that the synhospitality of Eriophyoidea is not a random process, but is the result of a long-term coadaptation in the “parasite–host” system. This is one of the phenomena reflecting the stages of the global historical process of the colonization of Embriophyta plants by mites of the Eriophyoidea superfamily.

Peer Instruction Integrated with Process Writing in EFL Writing Sessions: A Classroom Observation

This study investigated how peer instruction integrated with process writing influences EFL (English as a Foreign Language) students’ writing competence in a Chinese university. It randomly chose one intact class of non-English majors to implement the peer instruction and process writing approaches. The researcher divided the class into seven sub-groups to fulfill the writing tasks smoothly. Employing a qualitative research design, this study utilized observation methods to collect data. Three non-participant observers were invited with the researcher to observe the writing sessions among the sub-groups in the intact class. Thematic analysis of observation data showed that students effectively used peer instruction skills during all stages of process writing: planning, generating ideas, translating, editing, and revising. Integrating peer instruction with process writing served as a positive catalyst, motivating EFL students. Following Vygotsky’s Zone of Proximal Development (ZPD) principle, this integration significantly broadened students’ ZPD. By working within this zone, students unlocked their full developmental potential, surpassing their current abilities, leading to a more efficient and effective writing process.

Improving the Organization of Quasi-Professional Activities of Masters of Adaptive Physical Education as a Guarantee of Increasing Professional Competence

Introduction. The article discusses the problem: what are the effective forms and methods of work of developing professional competence among masters of the department of physical education in the field of training “Physical education for persons with health problems (adaptive physical education)” of the Sevastopol Institute of Economics and Humanities (branch) of the Crimean Federal University named after V. I. Vernadsky. The purpose of the study is to substantiate the importance of organizational and methodological improvement of the quasi-professional sector of the educational process. Methodology. Using scientific methods of pedagogical observation, questioning, studying the products of pedagogical activity and pedagogical documents, the authors substantiate the role of inclusive competence as an integral part of professional competence, as well as the possibility of its optimal improvement at the quasi-professional stage of the educational process using special pedagogical technologies and methods, including conducting sociocultural inclusive events. Results. It was revealed that masters of the Faculty of Physical Culture have, in general, sufficient and strong theoretical knowledge in the field of inclusion. At the same time, masters who carried out educational activities in traditional forms and in standard organizational and methodological conditions demonstrated unstable, weak skills in applied, practical activities related to inclusive competence. Conclusions. The main reserves for increasing the inclusive competence of master of physical education lie in the quasi-professional sector of the educational process, and the task of increasing the level of inclusive competence of master of physical education, and therefore increasing their overall professional level, can be solved by intensifying and optimizing educational and applied activities at the quasi-professional stage formation of inclusive competence.

Network Spreading from Network Dimension.

Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.

Study of the Lipophilicity and ADMET Parameters of New Anticancer Diquinothiazines with Pharmacophore Substituents.

Lipophilicity is one of the principal parameters that describe the pharmacokinetic behavior of a drug, including its absorption, distribution, metabolism, elimination, and toxicity. In this study, the lipophilicity and other physicochemical, pharmacokinetic, and toxicity properties that affect the bioavailability of newly synthesized dialkylaminoalkyldiquinothiazine hybrids as potential drug candidates are presented. The lipophilicity, as RM0, was determined experimentally by the RP-TLC method using RP18 plates and acetone-TRIS buffer (pH 7.4) as the mobile phase. The chromatographic parameters of lipophilicity were compared to computationally calculated partition coefficients obtained by various types of programs such as iLOGP, XLOGP3, WLOGP, MLOGP, SILCOS-IT, LogP, logP, and milogP. In addition, the selected ADMET parameters were determined in silico using the SwissADME and pkCSM platforms and correlated with the experimental lipophilicity descriptors. The results of the lipophilicity study confirm that the applied algorithms can be useful for the rapid prediction of logP values during the first stage of study of the examined drug candidates. Of all the algorithms used, the biggest similarity to the chromatographic value (RM0) for certain compounds was seen with iLogP. It was found that both the SwissADME and pkCSM web tools are good sources of a wide range of ADMET parameters that describe the pharmacokinetic profiles of the studied compounds and can be fast and low-cost tools in the evaluation of examined drug candidates during the early stages of the development process.

An Attractiveness Bias? How Women Entrepreneurs’ Physical Appearance Affects Men Investors

Women founders frequently appear to encounter varied and often negatively biased decisions from investors. Our study builds on the theoretical and practical interest in understanding whether and how biases against women entrepreneurs are attributed to men investors’ cognitive and physiological (i.e., hormonal) responses to the women’s physical appearance. Using a cross-sectional research design, we recruited 106 experienced investors and randomly assigned them to one of two versions of a prerecorded pitch to test the effect of a woman entrepreneur’s physical attractiveness. The versions were identical in content and form but were delivered by different actresses, one of whom was considered highly attractive. We asked participants to observe the pitch and answer questions on related topics, including how likely they believed the business described would successfully progress through the screening stage of the investment process. In contrast to our hypothesis, we find that a woman entrepreneur’s attractiveness positively influences men investors’ assessments of her competence. These competence assessments lead to evaluations that the entrepreneur’s proposal would progress through the investment screening stage. We also theorize and find that men investors have a marked increase in cortisol levels when presented with an attractive woman entrepreneur. This increased cortisol leads to evaluations that the entrepreneur’s proposal would progress through the screening stage. Identifying the role physiological mechanisms play in investment evaluations underscores the importance of adopting proactive measures to ensure equitable and fair investment practices alongside fostering introspection within the investment community.

Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing.

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple nonlinearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more nonlinear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity per se. We compared VGG-19 with AlexNet, which has much larger receptive fields in its lower layers. Whereas the best-performing layer of VGG-19 occurred after seven nonlinear steps, the first convolutional layer of AlexNet best predicted V1 responses. Although the predictive accuracy of VGG-19 was somewhat better than that of standard AlexNet, we found that a modified version of AlexNet could match the performance of VGG-19 after only a few nonlinear computations. Control analyses revealed that decreasing the size of the input images caused the best-performing layer of VGG-19 to shift to a lower layer, consistent with the hypothesis that the relationship between image size and receptive field size can strongly affect model performance. We conducted additional analyses using a Gabor pyramid model to test for nonlinear contributions of normalization and contrast saturation. Overall, our findings suggest that the feedforward responses of V1 neurons can be well explained by assuming only a few nonlinear processing stages.

Lexical pathway from L2 to L1 activation in intermediate proficient bilinguals: behavioral and ERP evidence.

Numerous studies have demonstrated that second language (L2) comprehension is often accompanied by activations in the first language (L1). Using both behavioral measurement and event-related potential (ERP), this study conducted two experiments to investigate whether a direct activation pathway exists from L2 lexical representation to L1 lexical representation (the lexical pathway) in intermediate proficient bilinguals. In Experiment 1, we designed a vowel letter search task on English word pairs, which enables bilinguals to prevent semantic priming in the first 300 ms processing stage after the words' onset. In Experiment 2, Mandarin-English bilinguals were recruited to complete this task on English word pairs with occasional first character repetition between the Chinese counterparts of a word pair. Results showed a significant main effect within both the P200 and N400 time windows, indicating the activation of bilinguals' L1 lexical representation during these intervals. However, the main effect of semantic relatedness was only significant in the N400 time window. These results suggest that bilinguals can activate their L1 lexical representation directly before engaging in conceptual representation. This finding supported a lexical pathway of activation from L2 lexical representation to L1 lexical representation during visual-word recognition in intermediate proficient bilinguals.

Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading.

Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research. The present study capitalizes on the coupling of fast periodic visual stimulation and MEG recordings to address this issue and investigate the role of different kinds of visual and linguistic units in the visual word identification system. We compared strings of pseudo-characters; strings of consonants (e.g., sfcl); readable, but unattested strings (e.g., amsi); frequent, but non-meaningful chunks (e.g., idge); suffixes (e.g., ment); and words (e.g., vibe); and looked for discrimination responses with a particular focus on the ventral, occipito-temporal regions. The results revealed sensitivity to alphabetic, readable, familiar, and lexical stimuli. Interestingly, there was no discrimination between suffixes and equally frequent, but meaningless endings, thus highlighting a lack of sensitivity to semantics. Taken together, the data suggest that the visual word identification system, at least in its early processing stages, is particularly tuned to form-based regularities, most likely reflecting its reliance on general-purpose, statistical learning mechanisms that are a core feature of the visual system as implemented in the ventral stream.

Three-Dimensional Printing in Breast Reconstruction: Current and Promising Applications.

Three-dimensional (3D) printing is dramatically improving breast reconstruction by offering customized and precise interventions at various stages of the surgical process. In preoperative planning, 3D imaging techniques, such as computer-aided design, allow the creation of detailed breast models for surgical simulation, optimizing surgical outcomes and reducing complications. During surgery, 3D printing makes it possible to customize implants and precisely shape autologous tissue flaps with customized molds and scaffolds. This not only improves the aesthetic appearance, but also conforms to the patient's natural anatomy. In addition, 3D printed scaffolds facilitate tissue engineering, potentially favoring the development and integration of autologous adipose tissue, thus avoiding implant-related complications. Postoperatively, 3D imaging allows an accurate assessment of breast volume and symmetry, which is crucial in assessing the success of reconstruction. The technology is also a key educational tool, enhancing surgeon training through realistic anatomical models and surgical simulations. As the field evolves, the integration of 3D printing with emerging technologies such as biodegradable materials and advanced imaging promises to further refine breast reconstruction techniques and outcomes. This study aims to explore the various applications of 3D printing in breast reconstruction, addressing current challenges and future opportunities.

Direct and indirect effects of degree of interdisciplinarity on firms' innovation performance: The moderating role of firms' capabilities

AbstractCombinations of heterogeneous knowledge from different scientific domains may drive highly innovative outcomes. Our study investigates whether firms can benefit from interdisciplinary research and development (R&D) activities by also reflecting on the potential negative consequences of increasing complexity of outcome validation activities at later stages of innovation processes. We explore which resources and organizational capabilities may influence the efficacy of interdisciplinary R&D activities. We examine whether the available financial slack, collaborations with universities, and a high betweenness centrality in scientific networks may promote the flexibility, scope, and efficiency of knowledge integration, and thus moderate the direct and mediated relationships between the degree of interdisciplinarity (DoI) of a firm's R&D activities and innovation performance. To test our hypotheses, we performed an analysis of medical technology firms that are highly dependent on interdisciplinary R&D. We collected a panel dataset of R&D activities and successful market launches of new products from 79 large medical technology firms between 1997 and 2021. The measurement of a firm's DoI is based on a novel approach involving neural networks in a bibliographic data graph. Our results suggest that firms can improve their innovation performance if they conduct interdisciplinary R&D activities. However, higher levels of DoI also increase the complexity of the required outcome validation, which reduces a firm's innovation performance. We also found that high levels of financial slack help to overcome barriers of implementing interdisciplinary R&D activities. Collaborations with universities, and good access to knowledge in scientific networks, further foster interdisciplinary knowledge application in new product development.