Increased Processing Costs Research Articles

Enhancing Concrete with Ultrafine Fly Ash: Mechanical Strength and Frost Resistance

Fly ash, a byproduct collected from the flue gas of coal combustion, serves as an effective additive in concrete. It offers environmental benefits by converting waste into a valuable resource, thus promoting energy conservation, emission reduction, and waste utilization. Additionally, fly ash enhances the mechanical properties and frost durability of concrete. Commonly used as a partial substitute for cement, it helps conserve resources. However, its use may compromise early strength and durability, which limits its applications. The transformation of raw fly ash into ultrafine fly ash through mechanical grinding increases its fineness and specific surface area, thereby boosting its reactivity and micro-aggregate effects. This process produces denser C-S-H gel and optimizes the concrete microstructure. Research shows that although ultrafine fly ash may slightly reduce early concrete strength, it significantly enhances long-term strength, with a 10% substitution improving early compressive strength by 20.2% and later strength by 13.7%. However, excessive fly ash content can reduce frost resistance, with an optimal substitution level at 25% significantly enhancing this property compared to standard concrete. Blending ultrafine fly ash with other mineral admixtures further boosts mechanical properties and frost durability. Challenges include increased processing costs and early strength deficiencies, along with the necessity to control heavy metal content for environmental and health safety. Future research should aim to optimize fly ash particle distribution and reactivity, develop cost-effective production methods, and blend fly ash with other minerals to improve concrete’s mechanical and frost resistance properties, thus expanding its potential applications.

E-TransUNet: TransUNet provides a strong spatial transformation for precise deforestation mapping

Forest loss significantly contributes to climate change. As a result, scientists aim to understand its causes and evaluate forest loss classification maps. While Vision Transformers (ViTs) have demonstrated superior performance compared to convolutional neural networks (CNNs) in computer vision applications, they face challenges in recognizing and analyzing remote sensing images. These challenges include computational complexity, which increases processing costs, and the requirement for more labeled input data compared to CNNs. To address these issues, this paper introduces enhanced transformer UNet (e-TransUNet), which integrates spatial transformation architecture to enhance feature description in the skip connection path of the attention TransUNet for precise deforestation mapping. The model is validated in two prominent South American forest biomes—the Amazon and Atlantic forests. Experimental results highlight the significant improvement achieved by e-TransUNet, particularly in the Atlantic rainforests, where it boosts recall, overall accuracy, F1-score, dice coefficient, and precision by approximately 2, 2, 3, 4, and 4 percentage points, respectively, compared to the base TransUNet segmentation architecture. The code will be made publicly available at https://github.com/aj1365/e-TransUNet.

The Shear-Accelerated II-I Phase Transition of Isotactic Poly(1-Butene).

The II-I phase transition of isotactic poly(1-butene) (iPBu) leads to improved mechanical performance. However, this will take several weeks and increase storage and processing costs. In this work, shear forces are introduced into the supercooled iPBu melt, and the effects of isothermal crystallization temperature (Tc) and shear temperature (Tshear) on crystallization and phase transition are explored. Shear-induced transcrystalline morphology of Form II with a significantly shortened crystallization induction period can beobserved at relatively high Tc (105 °C). Besides, the shear-induced Form II can transit to Form I faster than the unsheared one. In addition, the phase transition rate increases as the Tshear decreases, with the fastest rate occurring at Tshear of 120 °C. The half transition time (t1/2) is measured as 6.3 h when Tc = 105 °C, Tshear = 120 °C, which is much shorter than the 20.7 h required for unsheared samples. The accelerated phase transition of iPBu can be attributed to the stretching of molecular chains, resulting from shear treatment. This study provides a quantitative analysis of the influence of the shear treatment and the Tshear on the II-I phase transition rate. It also presents a cost-effective and straightforward approach for expediting the phase transition process.

Trends in selective hydrogenation

In spite of being commercialized for well over a century, hydrogenation is still a subject of intensive research and development. A patent landscape analysis showed high growth rates for topics related to climate change, bio-renewables, low waste and recyclability. The current paper describes trends, providing examples on the effect of structure sensitivity, on novel intermetallic active phases and on discoveries initiated by modelling. Due to increased processing costs and a negative environmental impact of undesirable by-products, the present focus favours high selectivity over activity. A relatively new class of materials showing an exponential growth in publications, these are the so-called intermetallics. These materials are defined as compounds formed by at least two metals, with – at least partly – ordered crystal structures which are different from the ones of the constituting elements. Non-metallic active phases formed by interaction of a metal ion and a (reducible) support may have a superior hydrogenation performance compared to the corresponding zero-valent metals. Aided by Density Functional Theory modelling (DFT) new, often intermetallic catalysts like Ni5Ga3 were identified which sparked a great number of follow-up studies.

Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue.

Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts-measured as breaks in the finger movement data before the correct gesture was carried out-when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.

Tribological behavior of polycrystalline diamond based on photo-Fenton reaction

Polycrystalline diamonds (PCD) has high thermal conductivity and as heat sinks in semiconductor power devices. However, PCD exhibits high hardness and strong chemical inertness lead to lower processing efficiency and increase processing costs. Hence, achieving efficiently obtain high-quality PCD surfaces is crucial. In this study, we investigated the tribological behavior of PCD in different environments (deionized water, H2O2 + UV, Fenton, and photo-Fenton) and under different photo-Fenton conditions (pH, light intensity, Fe3O4 concentration, and H2O2 concentration) via Si3N4 ball-disk friction and wear experiments. The photo-Fenton reaction improved the material removal rate of PCD, which was the highest (285.4 μm3/min) under optimized conditions of pH = 3, 100 mW/cm2 light intensity, 2 wt% Fe3O4 concentration, and 10 wt% H2O2 concentration. Based on the friction and wear experimental results and X-ray photoelectron spectroscopy, we propose a material removal mechanism for PCD. The hydroxyl radical (·OH) produced by the photo-Fenton reaction oxidize the PCD surface to form CO, CO, and C–OH groups, which are converted into C–O–Si bridge bonds on the PCD surface, resulting in material removal. Our study findings provide theoretical support for the photo-Fenton reaction -assisted CMP of PCD.

The Influence of Powder Particle Size Distributions on Mechanical Properties of Alloy 718 by Laser Powder Bed Fusion

Currently, metallic powders for laser powder bed fusion (LPBF) primarily come in two commercially available powder size distributions (PSDs): 15+/45− for non-reactive powders and 15+/63− for reactive powders. These powders are generally produced via gas atomization processes that create highly spherical particles with a Gaussian PSD. Because of the standard deviation within a Gaussian distribution, only small portions of the total product are used for LPBF applications. This screening process makes the other particle sizes a waste product and, thus, increases processing costs. The non-reactive 718 powder was printed with both the typical PSD of 15+/45− and a wider bimodal experimental PSD. Compared to conventional 718, the 718 alloys with bimodal PSD shows less than a 0.2% difference in density, and insignificant change in mechanical behavior. Electron backscattered diffraction studies revealed that grain sizes and morphology were similar between the two sample sets, but bimodal 718 alloy has a slightly greater degree of large grains. The study suggests that particles with wide or bimodal size distributions show promise in producing equivalent high-quality products without sacrificing mechanical properties.

Closed-loop recycling of valuable metals from spent LiCoO2 batteries through phosphate-chemistry-based process

The sustainable recycling of valuable metals from spent lithium-ion batteries (LIBs) has attracted an increasing attention. While the uncontrollable nature of reactants from traditional processes will result in increased process cost and environmental footprint. Herein, phosphate chemistry based was process proposed for the recovery of valuable metals from spent LIBs, in which H3PO4 was used as both leachant and precipitant for the selective leaching of Li and direct precipitating of transition metals (Me, Me = Ni, Co, Mn, Fe). In this work, Li will be converted into soluble phosphate (i.e. LiH2PO4) while transition metals tend to form insoluble phosphates of Me3(PO4)2 and MeHPO4 under the optimized conditions, indicating the possibility for priority extraction of Li and direct conversion of transition metals into products under controlled experimental condition. Then, LiH2PO4 can be further recovered as struvite (MgNH4PO4·6H2O, the upcycling of P, recovery rate > 99.97%) and Li2CO3 (purity > 99.99%). This phosphate-chemistry-based recycling process is a validated and promising candidate towards the closed-loop recycling of valuable metals from spent LIBs with minimized impacts on the environment.

Neurophysiological evidence for the first mention effect during pronominal reference resolution in German Sign Language

AbstractAnaphoric pronoun resolution in spoken language has been shown to be influenced by the first mention bias. While this bias has been well investigated in spoken languages, less is known about a similar bias in sign languages. In sign languages, pronominal pointing signs (index) are directed towards referential locations in the signing space typically associated with discourse referents. In German Sign Language (DGS), signers follow an ipsi-contralateral default pattern while tracking referents, i.e., the first referent is associated with the ipsilateral and the second referent with the contralateral area of the signing space. Hence, directing a pronoun to either the ipsi- or the contralateral side of the signing space refers to either the first or the second discourse referent. The present event-related potential study reanalyzes the data fromWienholz et al. (2018)and examines the first mention effect during pronoun resolution in ambiguous contexts in DGS. The original study presented participants with sentence sets containing two referents without overt localization in the first and a sentence-initial pronominalindexsign in the second sentence directed to either the ipsilateral or contralateral side of the signing space. Based on the direction of theindexsign, our analysis reveals an N400 for contralateralindexsigns suggesting increased processing costs triggered by a violation of the first mention effect. Thus, the current study provides first experimental evidence for a first mention effect in DGS and highlights the modality-independent nature of this effect.

Why do He and She Disagree: The Role of Binary Morphological Features in Grammatical Gender Agreement in German

In many languages, grammatical gender is an inherent property of nouns and, as such, forms a basis for agreement relations between nouns and their dependent elements (e.g., adjectives, determiners). Mental gender representation is traditionally assumed to be categorial, with categorial gender nodes corresponding to the given gender specifications in a certain language (e.g., [masculine], [feminine], [neuter] in German). In alternative models, inspired by accounts put forward in theoretical linguistics, it has been argued that mental gender representations consist of sets of binary features which might be fully specified (e.g., masc [+ m, − f], fem [− m, + f], neut [− m, − f]) or underspecified (e.g., masc [+ m], fem [+ f], neut [] or masc [+ m, − f], fem [], neut [− f]). We have conducted two experiments to test these controversial accounts. Native speakers of German were asked to decide on the (un-)grammaticality of gender agreement of visually presented combinations of I) definite determiners and nouns, and II) anaphoric personal pronouns and nouns in an implicit nominative singular setting. Overall, agreement violations with neuter das / es increased processing costs compared to violations with die / sie or der / er for masculine or feminine target nouns, respectively. The observed pattern poses a challenge for models involving categorial gender representation. Rather, it is consistent with feature-based representations of grammatical gender in the mental lexicon.

Transcriptome and metabolome analyses provide insights into the relevance of pericarp thickness variations in Camellia drupifera and Camellia oleifera

Camellia fruit is a woody edible oil source with a recalcitrant pericarp, which increases processing costs. However, the relevance of pericarp thickness variations in Camellia species remains unclear. Therefore, this study aimed to identify pericarp differences at the metabolic and transcription levels between thick-pericarp Camellia drupifera BG and thin-pericarp Camellia oleifera SG. Forty differentially accumulated metabolites were screened through non-targeted UHPLC-Q-TOF MS-based metabolite profiling. S-lignin was prominently upregulated in BG compared with SG, contributing to the thick pericarp of BG. KEGG enrichment and coexpression network analysis showed 29 differentially expressed genes associated with the lignin biosynthetic pathway, including 21 genes encoding catalysts and 8 encoding transcription factors. Nine upregulated genes encoding catalysts potentially led to S-lignin accumulation in BG pericarp, and transcription factors NAC and MYB were possibly involved in major transcriptional regulatory mechanisms. Conventional growth-related factors WRKYs and AP2/ERFs were positively associated while pathogenesis-related proteins MLP328 and NCS2 were negatively associated with S-lignin content. Thus, Camellia balances growth and defense possibly by altering lignin biosynthesis. The results of this study may guide the genetic modifications of C. drupifera to optimize its growth-defense balance and improve seed accessibility.

Field Assessment of Downed Timber Strength Deterioration Rate and Wood Quality Using Acoustic Technologies

Hurricane and tornado events cause significant damage to high-value timber in the United States each year. Forest managers and landowners are keenly interested in finding solutions to salvage and repurpose these downed timbers before they cause pest infestations and fire outbreaks, completely losing their value or increasing processing costs. To better understand the wood quality of the downed timber, we used acoustic waves techniques as a nondestructive testing approach to assess the wood degradation rate of downed trees and determine the extent of fracture and voids in the damaged regions. We periodically monitored the acoustic velocity of the downed trees for 12 consecutive months using a time of flight (TOF) acoustic method. Acoustic measurements were conducted using three different techniques—longitudinal, transverse, and off-set methods. Wood density, age, and the diameter at breast height (dbh) class measurement for southern timber (chip-n-saw for dbh 8″–11″ and sawtimber with dbh 12″ and up) were used as the predictive parameters of the downed trees. The results indicated positive relationships between dbh class, stand age, and acoustic velocity measurement (R2 > 65%). The TOF acoustic velocity was indicated to potentially separate higher-stiffness timber from lower-stiffness timber in a hurricane event for structural or non-structural applications. The regression coefficient from the repeated measurements indicated that both age and diameter class strongly impacted the acoustic properties of the downed trees (p-value ≤ 0.001). The sawtimber dbh class recorded a higher acoustic velocity compared to the chip-n-saw type. Fracture, voids, and massive decay in downed trees were detected beyond the visible inspection, features that often are identified by loggers in lower quality wood; however, TOF showed a weak response in picking up incremental deterioration due to changes in specific environmental factors that affected acoustic readings. This study showed that acoustic wave methods could potentially be used as a field evaluation tool for assessing the quality of downed trees.

The role of semantic transparency in the processing of spoken compound words.

The question of whether lexical decomposition is driven by semantic transparency in the lexical processing of morphologically complex words, such as compounds, remains controversial. Prior research on compound processing has predominantly examined visual processing. Focusing instead on spoken word word recognition, the present study examined the processing of auditorily presented English compounds that were semantically transparent (e.g., farmyard) or partially opaque with an opaque head (e.g., airline) or opaque modifier (e.g., pothole). Three auditory primed lexical decision experiments were run to examine to what extent constituent priming effects are affected by the semantic transparency of a compound and whether semantic transparency affects the processing of heads and modifiers equally. The results showed priming effects for both modifiers and heads regardless of their semantic transparency, indicating that individual constituents are accessed in transparent as well as opaque compounds. In addition, the results showed smaller priming effects for semantically opaque heads compared with matched transparent compounds with the same head. These findings suggest that semantically opaque heads induce an increased processing cost, which may result from the need to suppress the meaning of the head in favor of the meaning of the opaque compound. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Clearly, fame isn’t everything: Talker familiarity does not mitigate the perceptual consequences of talker variability

Familiarity with a talker’s voice provides numerous benefits to speech perception, including faster response times and improved intelligibility both in quiet and amidst competing talkers. However, it is unclear whether familiarity provides resilience against talker variability, or the increased processing costs attributed to hearing speech from multiple different talkers compared to a single talker. Here, listeners completed a speeded word recognition task where stimuli were the words “do” and “to” excised from political speeches, presented in either single- or multiple-talker blocks. Talkers were either famous (the last five U.S. Presidents) or non-famous (other male politicians of similar ages). No information about the talkers was provided. As predicted, listeners categorized words more quickly when spoken by famous talkers. However, talker familiarity did not provide any resiliency to talker variability; reaction times increased (from single- to multiple-talker blocks) by greater amounts for famous talkers than non-famous talkers. In a post-task questionnaire, participants recognized famous talkers better than non-famous talkers from the “do”/”to” target words, a full sentence, or when shown their names and asked if they knew who the talker was. Results were not correlated with self-rated political interest. Thus, familiarity might not alleviate the perceptual consequences of talker variability.

Epistemic reasoning in pragmatic inferencing by non-native speakers: The case of scalar implicatures

Scalar implicatures involve inferring the use of a less informative term (e.g. some) to mean the negation of a more informative term (e.g. not all). A growing body of recent research on the derivation of scalar implicatures by adult second language (L2) learners shows that while they are successful in acquiring the knowledge of scalar implicatures, a property at the semantics–pragmatics interface, it remains controversial as to which mechanism, default or non-default, could account for L2 learners’ derivation of scalar implicatures. The present study used an online self-paced reading task to address this issue by examining the role of the speaker’s knowledge state in the interpretation of the existential quantifier some by Chinese-speaking learners of English in incremental sentence processing. Results showed that both L2 and native participants demonstrated comparable online sensitivity to the speaker’s knowledge state. Critically, when the scalar implicature was computed in situations where the speaker was more likely to know whether the statement with the stronger alternative was true, it gave rise to measurable reading latency, indicative of increased processing costs. We conclude by arguing that our findings are compatible with the context-driven models within the Gricean tradition.

Enzyme modification using mutation site prediction method for enhancing the regioselectivity of substrate reaction sites

Enzymes with low regioselectivity of substrate reaction sites may produce multiple products from a single substrate. When a target product is produced industrially using these enzymes, the production of non-target products (byproducts) causes adverse effects such as increased processing costs for purification and the amount of raw material. Thus it is required the development of modified enzymes to reduce the amount of byproducts’ production. In this paper, we report a method called mutation site prediction for enhancing the regioselectivity of substrate reaction sites (MSPER). MSPER takes conformational data for docking poses of an enzyme and a substrate as input and automatically generates a ranked list of mutation sites to destabilize docking poses for byproducts while maintaining those for target products in silico. We applied MSPER to the enzyme cytochrome P450 CYP102A1 (BM3) and the two substrates to enhance the regioselectivity for four target products with different reaction sites. The 13 of the total 14 top-ranked mutation sites predicted by MSPER for the four target products succeeded in selectively enhancing the regioselectivity up to 6.4-fold. The results indicate that MSPER can distinguish differences of substrate structures and the reaction sites, and can accurately predict mutation sites to enhance regioselectivity without selection by directed evolution screening.

The use of deep learning techniques in acoustic tag signal identification

Acoustic telemetry is routinely employed to detect and quantify behaviors of aquatic animals. The ability to acoustically tag and release fish and aquatic animals allows researchers to monitor their presence/absence and fine-scale spatial movement. One method of identifying individual tags employs a period signal encoding (HTI signal) method, which utilizes the full transmitted acoustic energy for both detection and identification, providing significantly greater detection ranges than encoded BPSK signal types. However, identifying period encoded tags typically requires manual data review by trained human analysts, resulting in increased processing cost and a lack of scalability. Innovasea conducted a project to determine the efficacy of applying modern Deep Learning techniques to automatically identify animals implanted with period encoded acoustic tags. Specifically, the well-known U-Net model was adapted and trained to detect period encoded acoustic tag signals from raw acoustic telemetry data. The raw data is first transformed into a spectrogram-like image representation and the U-Net learns an image mask that separates actual tag detections from noise and interference. The trained U-Net achieved greater than 95% acoustic tag identification accuracy when compared to tag identification performed by human analysts on the same test data. Numerous examples of the U-Net performance will be presented.

Lexical-semantic processing costs are not inherent to intra-sentential code-switching: The role of switching habits

A bank of past electrophysiological research suggests that code-switching (i.e., the use of multiple languages during a single conversational event) is characterized by increased processing costs. However, the majority of these studies tested bilinguals who did not necessarily code-switch on a regular basis. In order to investigate the impact of switching habits on language processing, two groups of French-English bilinguals (habitual code-switchers and non-habitual code-switchers) read sentences containing unilingual and switched determiner phrases while their EEG was recorded. The findings indicate important processing differences between the groups. Notably, switched trials (particularly those with a French determiner) generated an increased negativity from 300 to 500 ms for non-habitual code-switchers, but not for habitual code-switchers. The veritable impact of switching habits were also supported by a correlation analysis. This finding suggests that code-switching is not inherently effortful to process, but rather, that comprehension costs are related to idiosyncratic factors, such as the extent of prior exposure to code-switched input. An LPC was also observed from 500 to 900 for habitual code-switchers; for non-habitual switchers, this effect may have been attenuated by the long-lasting N400 effect. We advance several possible interpretations for the late positivity. Altogether, the results from the current study support the claim that the code-switching cost observed in previous literature may be linked to the bilingual samples tested in those studies: habitual code-switchers may not find switches effortful to process, but not all bilinguals code-switch. We highlight the importance of experience and individual differences in the study of bilingual cognition.

Psycholinguistic mechanisms of classifier processing in sign language.

Nonsigners viewing sign language are sometimes able to guess the meaning of signs by relying on the overt connection between form and meaning, or iconicity (cf. Ortega, Özyürek, & Peeters, 2020; Strickland et al., 2015). One word class in sign languages that appears to be highly iconic is classifiers: verb-like signs that can refer to location change or handling. Classifier use and meaning are governed by linguistic rules, yet in comparison with lexical verb signs, classifiers are highly variable in their morpho-phonology (variety of potential handshapes and motion direction within the sign). These open-class linguistic items in sign languages prompt a question about the mechanisms of their processing: Are they part of a gestural-semiotic system (processed like the gestures of nonsigners), or are they processed as linguistic verbs? To examine the psychological mechanisms of classifier comprehension, we recorded the electroencephalogram (EEG) activity of signers who watched videos of signed sentences with classifiers. We manipulated the sentence word order of the stimuli (subject-object-verb [SOV] vs. object-subject-verb [OSV]), contrasting the two conditions, which, according to different processing hypotheses, should incur increased processing costs for OSV orders. As previously reported for lexical signs, we observed an N400 effect for OSV compared with SOV, reflecting increased cognitive load for linguistic processing. These findings support the hypothesis that classifiers are a linguistic part of speech in sign language, extending the current understanding of processing mechanisms at the interface of linguistic form and meaning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Definite Descriptions in the Light of the Comprehension vs. Acceptance Distinction: Comparing Self-Paced Reading with Eye-Tracking Measures

This paper presents two experiments on the processing of informative definite descriptions in plausible vs. implausible contexts. Experiment 1 is a self-paced reading task (with French native speakers,n= 69), with sentences containing a definite vs.indefinite NP, each preceded byplausibleorimplausiblecontexts. Our study replicated Singh and colleagues’ findings, namely that definite descriptions are significantly costlier when they occur inimplausiblecontexts. The translation of the original stimuli from English to French did not affect the results, suggesting that the phenomenon applies cross-linguistically. Experiment 2 consists in an eye-tracking task, designed to measure the participants’ (n= 44) gaze patterns on complete sentences with the same four conditions (definite vs.indefinite NP; implausible vs. implausible contexts). A mixed effect model analysis revealed that (a) thetotal gaze durationon target segments and (b) theprocessing of the complete sentencewere significantly longer in implausible conditions. These results show that implausible contexts predict a marked increase in the offline processing costs of definite descriptions. However, no significant difference was found for online processing measures (i.e.,first fixation duration, first-pass reading timeandregression path timemeasures) across all experimental conditions. These results suggest that it is only once the sentence is fully processed that implausible contexts increase processing costs. Furthermore, these results raise methodological issues related to the study of the online processing of definite descriptions, to the extent that self-paced reading and eye-tracking methods in the present study lead to incompatible results. With respect to the eye-tracking results, we suggest that the contrast between online and offline processing is likely to reflect the fact that participants first adopt a stance of trust to understand utterances before filtering the information through their epistemic vigilance module.