Automatic Processing Research Articles

Play contagion in African elephants: The closest, the better

Behavioural contagion is an automatic process through which a behaviour performed by an individual (trigger) is reproduced by an observer (responder) without necessarily replicating the exact motor sequence. It has been linked to inter-individual synchronisation and possibly emotional contagion. Play can convey emotions and enhance social bonding, although its contagiousness is understudied. To verify social play contagion presence and modulating factors, we gathered audio-video data on social play, distance and affiliation on a group of savannah African elephants (15 individuals) at Parque de la Naturaleza de Cabarceno (Cantabria, Spain). Social play was contagious as it was more likely started by uninvolved elephants (within 3-min) in Post-Play Condition (PP) - after that other elephants had started playing - than in Matched-control Condition (MC; no previous play). Social play contagion mostly occurred within 30 m – probably due to elephants’ limited visual acuity – and it was highest between individuals that affiliated the most, with the distance-affiliation interaction having no effect. The most prominent individuals in the social play network were also the most influential in the play contagion network (Eigenvector-centrality measure). Play contagion was socially modulated, thus suggesting it may extend from motor replication to the replication of the underlying affective state.

Ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data

Abstract. Ceilometers are used routinely at aerodromes worldwide to derive the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of meteorological aerodrome reports (METARs). Here, we present ampycloud, a new algorithm, and its associated Python package for automatic processing of ceilometer data with the aim of determining the sky coverage fraction and base height of cloud layers above aerodromes. The ampycloud algorithm was developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program to help in the fully automatic production of METARs at Swiss civil aerodromes. ampycloud is designed to work with no direct human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers from individual cloud base hits reported by ceilometers. The robustness of the ampycloud algorithm stems from the first processing step, which is simple and reliable. It constrains the two subsequent processing steps that are more sensitive but also better suited to handling complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponymous, pip-installable Python package developed on GitHub and made publicly accessible.

Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic

SUMMARY Seismic phase detection and classification using deep learning is so far poorly investigated for regional events since most studies focus on local events and short time windows as the input to the detection models. To evaluate deep learning on regional seismic records, we create a data set of events in Northern Europe and the European Arctic. This data set consists of about 151 000 three component event waveforms and corresponding phase arrival picks at stations in mainland Norway, Finland and Svalbard. We train several state-of-the-art and one newly developed deep learning model on this data set to pick P- and S-wave arrivals. The new method modifies the popular PhaseNet model with new convolutional blocks including transformers. This yields more accurate predictions on the long input time windows associated with regional events. Evaluated on event records not used for training, our new method improves the performance of the current state-of-the-art methods when it comes to recall, precision and pick time residuals. Finally, we test our new model for continuous mode processing on 4 d of single-station data from the ARCES array. Results show that our new method outperforms the existing array detector at ARCES. This opens up new opportunities to improve automatic array processing with deep learning detectors.

Automatic Extraction of Medication Data from Semi-Structured Prescriptions.

In many healthcare facilities, the prescription of drugs is done only in a semi-structured manner, using free-text fields where various information is often mixed. Therefore, automatic processing, especially for secondary use such as research purposes, is often challenging. This paper compares various approaches that identify and classify the various parts of these free-text fields in German language, namely simple Levenshtein-based, rule-based and CRF (conditional random field)-based approaches. Our results show that a F1-score >90% can be achieved with both the rule-based and the CRF-based approach, with the CRF-based approach even reaching nearly 95%.

Combining algorithm techniques with mechanical and acoustic profiles for the prediction of apples sensory attributes

The research work shows the potentiality of advanced linear and nonlinear learning algorithm techniques in the prediction of apples texture sensory attributes as “hardness”, “crunchiness”, “flouriness”, “fibrousness”, and “graininess”. Starting from the information contained in the entire mechanical and acoustic curves acquired during samples compression test, the prediction performances of five different statistical tools as Partial Least Squares regression (PLS), Multilayer Perceptron (MLP), Support Vector Regression (SVR) and Gaussian Process Regression (GPR) are shown and discussed.All Predictive models validations evidence best accuracies for texture sensory attributes “hardness” and “crunchiness” and in general for GPR learning algorithm. By combining mechanical and acoustic profiles, 5-fold cross validations produce values of coefficient of determination R2 up to 0.885 (GPR) and 0.840 (GPR), respectively for “hardness” and “crunchiness”. These results, comparable to those obtained by considering a large number of mechanical and acoustic parameters extracted from acquired profiles as predictive factors, evidence a new and reliable way for the prediction of texture sensory attributes of apples. The proposed approach can overcome the necessity to define, in advance, number and type of features to be calculated from instrumental texture profiles and can be easily implemented in an automatic process.

Comparative Analysis of Artificial Intelligence Algorithms for Breast Cancer Detection from Pathological Image in Burkina Faso.

Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep learning, enabling the development of algorithms for automatic processing of medical images from massive medical data. In Burkina Faso, early and accurate detection of breast cancer is a significant challenge due to limited resources and lack of specialized expertise. In this article, we examine the effectiveness of different artificial intelligence algorithms for breast cancer detection from pathological image.

Migraine versus tension-type headache in automatic emotional processing: A visual mismatch negativity study

ObjectiveIt is important to discriminate different headaches in clinical practice, and neurocognitive biomarkers may serve as objective tools. Several reports have suggested potential cognitive impairment for primary headaches, whereas cognitions within specific domains remain elusive, e.g., emotional processing. In this study, we aimed to characterize processing of facial expressions in migraine and tension-type headache (TTH) by analyzing expression-related visual mismatch negativity (EMMN) and explored whether their processing patterns were distinct. MethodsAltogether, 73 headache patients (20 migraine with aura (MA), 28 migraine without aura (MwoA), 25 TTH) and 27 age-matched healthy controls were recruited. After a battery of mood/neuropsychological evaluations, an expression-related oddball paradigm containing multiple models of neutral, happy and sad faces was used to investigate automatic emotional processing. ResultsWe observed cognitive impairment in all headache patients, especially in attention/execution subdomains, but no discrepancy existed among different headaches. Although analyses of P1/N170 did not reach significant levels, amplitude of early and late EMMN was markedly diminished in MA and MwoA compared with controls and TTH, regardless of happy or sad expression. Moreover, sad EMMN was larger (more negative) than happy EMMN only in controls, while not in all headache groups. ConclusionsOur findings implied that migraine, rather than TTH, might lead to more severe impairment of automatic emotional processing, which was manifested as no observable EMMN elicitation and disappearance of negative bias effect. The EMMN component could assist in discrimination of migraine from TTH and diagnosis of undefined headaches, and its availability needed further validations.

Eye-tracking contribution on processing of (implicit) reading comprehension

Reading comprehension is a fundamental skill to be developed from the early stages of reading acquisition and it is essential for both formative and personal learning. This study examines eye-tracking as a useful and complementary tool in the assessment and improvement of implicit reading comprehension. The aim is to understand the role of eye-tracking in implicit reading comprehension and, complementarily, in intervening skills such as vocabulary, rapid automatized naming, and processing speed. In a final sample of 7–8 year-old 67 students, tests for implicit reading comprehension (literal, inferential, and total), eye-tracking measures, vocabulary, rapid automatized naming, processing speed, as well as tests to control for intellectual and attentional levels were administered. The results of the correlational and regression analyses indicate the existence of predictive relationships between (i) implicit reading comprehension and eye-tracking measures, and additionally, (ii) between intervening skills (vocabulary, rapid automatized naming, processing speed) and eye-tracking measures, and (iii) between implicit reading comprehension and intervening skills (vocabulary, rapid automatized naming, processing speed). In summary, better performance in implicit reading comprehension (literal, inferential, and total) is related to and explained by shorter eye movement times in recognition and access to meaning, larger vocabulary, and faster rapid automatized naming and processing speed. Furthermore, eye-tracking measures are better predictors of implicit reading comprehension. These findings provide promising evidence for the contribution of eye-tracking to optimising the level of implicit reading comprehension, which is applied daily in classrooms.

Discrimination of doubled acoustic emission events using neural networks

In observatory seismology, the effective automatic processing of seismograms is a time-consuming task. A contemporary approach for seismogram processing is based on the Deep Neural Network formalism, which has been successfully applied in many fields. Here, we present a 4D network, based on U-net architecture, that simultaneously processes seismograms from an entire network. We also interpret Acoustic Emission data based on a laboratory loading experiment. The obtained data was a very good testing set, similar to real seismograms. Our Neural network is designed to detect multiple events. Input data are created by augmentation from previously interpreted single events. The advantage of the approach is that the positions of (multiple) events are exactly known, thus, the efficiency of detection can be evaluated. Even if the method reaches an average efficiency of only around 30% for the onset of individual tracks, average efficiency for the detection of double events was approximately 97% for a maximum target, with a prediction difference of 20 samples. Such is the main benefit of simultaneous network signal processing.

Method for vibration suppression in rotary machines using multiple automatic ball balancers

An automatic ball balancer (ABB) is a device that is used to automatically balance an eccentric rotor; these are typically used in rotary machines with unknown or variable imbalances. The typical construction of an ABB consists of several balls in a ring race. The balls can move freely inside the race, which changes the eccentricity radius and phase angles relative to the rotor. The automatic balancing process requires an ABB unit to be installed on an eccentric rotor's axis; thus, its application is quite limited and usually requires the redesign of a rotary machine. This paper presents a new method for eliminating vibrations in rotary machines by using multiple ABBs that are mounted away from the rotor's axis. In this application, ABBs can be considered to be unbalanced rotary masses with adjustable eccentricity radii and phases. Theoretically, this ability allows for the total reduction of the inertial force from an eccentric rotor; however, the parameters of the ABBs must be set correctly. The process of setting the eccentricity radius and phase in each ABB occurs due to a self-synchronization phenomenon and generally depends on the physical parameters of either a rotary machine or ABBs. Both analytical and numerical investigations that were based on the mathematical model of a system of a rotary machine with a pair of ABBs allowed us to formulate basic rules to apply such a system in existing rotary machines without interference on the rotors.

Implicit attitudes toward obesity-related cues and their relation to body mass index, psychosocial functioning, and health behavior.

Differences in automatic cognitive processes exist among individuals with overweight and obesity, thus there is a need to expand our conceptualization of overweight and obesity to emphasize the predictive utility of these automatic processes, rather than focusing solely on behavioral outputs. Implicit association tests (IATs) may afford a noninvasive method of examining automatic preferences that might contribute to overweight and obesity; namely, preferences for unhealthy foods and sedentary behavior versus healthy foods and physical activity. The purpose of the current study was to examine whether implicit attitudes toward foods and physical activity differed based on body mass index (BMI) status. The relationships between implicit attitudes and key psychosocial factors and health behaviors were also examined. Participants (N = 127) included undergraduate students with an average age of 19.05 years old (SD = 1.52). Average BMI of the sample was 24.20 (SD = 4.93); 33.8% met criteria for overweight or obesity. Participants completed an IAT and questionnaires. There were no differences in implicit preferences based on BMI or BMI status. Overall, the sample demonstrated implicit preferences for healthy foods and active words, and preferences were not linked to the corresponding behavioral outputs, though preferences were linked to various indices of emotion and emotion regulation. Future research should explore an extended model to examine how implicit preferences might impact intentions to engage in protective versus risky obesity-related health behaviors, and the various psychosocial factors that might impact the translation of those preferences and intentions in actual behavioral outputs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Attitude, Habit Strength, and the Relationship Between Intention and Leisure-Time Physical Activity Behavior Among College Students

ABSTRACT According to the dual-process theories, many scholars proposed that physical activity behavior tends to be governed by an interplay of intentional planning and habitual action. The major purpose of this study was to determine the moderation effects of habit strength on the relationship between leisure time moderate-to-vigorous physical activity (MVPA) intention and behavior and the impacts of cognitive and affective attitude on PA habit strength, utilizing a latent interaction modeling approach. A total of 1803 undergraduate students completed this study. All variables were measured using validated self-report questionnaires. Structural equation modeling and Hayes’ PROCESS program (model 1) were conducted to address the research questions. Habit strength significantly and positively moderated the effects of intention on leisure time MVPA behavior (β = .08, p < .01). The association between intention and PA behavior became stronger as habit strength increased. Affective attitude positively and significantly influenced habit strength (β = .71, p < .01). The effect of cognitive attitude on habit strength was significant and negative with a small effect size (β = –.08, p < .01). Findings indicated that habit strength positively moderated the relationship between PA intention and behavior, suggesting that stronger habit strength may amplify the association between PA intention and actual PA behavior. Affective attitude toward PA significantly influenced habit strength, whereas cognitive attitude exhibited minimal negative impact on habit strength. These findings contribute to our understanding of the interaction effects between deliberative and automatic processes in regulating PA behavior, as well as the nuanced relationship between attitude and habit development.

A Proven Translation from a UML State Machine Subset to Timed Automata

Although Unified Modeling Language (UML) state machines constitute a convenient modeling formalism that is widely used in many applications, the lack of formal semantics impedes carrying out automatic processing, such as formal verification. In this article, we aim to achieve a proven translation from a subset of UML state machines to timed automata. A generic abstract syntax is defined for state machines that allows us to specify state machines as a tree-like structure, explicitly illustrating the hierarchical relationships within the model. Based on this syntax, a formal asynchronous semantics for state machines and systems of state machines is established. Additionally, the semantics of timed automata is specified. Then, a translation relation from the considered set of state machines to timed automata is defined and a strong equivalence relation — namely, a timed bisimulation between the source and target models — is formally proven. The proof is carried out inductively while considering continuous (time) and discrete transitions separately. This proof allows us to demonstrate a strong similitude between these models.

Research on the Optimal Trajectory Planning Method for the Dual-Attitude Adjustment Mechanism Based on an Improved Multi-Objective Salp Swarm Algorithm

In this study, an optimization method for the motion trajectory of attitude actuators was investigated in order to improve assembly efficiency in the automatic docking process of large components. The self-developed dual-attitude adjustment mechanism (2-PPPR) is used as the research object, and the structure is symmetrical. Based on the modified Denavit–Hartenberg (MDH) parameter description method, a kinematic model of the attitude mechanism is established, and its end trajectory is parametrically expressed using a five-order B-spline curve. Based on the constraints of the dynamics and kinematics of the dual-posture mechanism, the total posturing time, the degree of urgency of each joint, and the degree of difficulty of the mechanism’s posturing are selected as the optimization objectives. The Lévy flight and Cauchy variation algorithms are introduced into the salp swarm algorithm (SSA) to solve the parameters of the multi-objective trajectory optimization model. By combining the evaluation method of the multi-objective average optimal solution, the optimal trajectory of the dual-tuning mechanism and the motion trajectory of each joint are obtained. The simulation and experiment results show that the trajectory planning method proposed in this paper is effective and feasible and can ensure that the large-part dual-posture mechanism can complete the automatic docking task smoothly and efficiently.

Automated Microstructural Analysis of Anodic Catalyst Layers in Proton Exchange Membrane Water Electrolyzers (PEMWEs) Using Python-Based Image Processing Framework

Adverse climatic conditions caused by greenhouse gas (GHG) emissions from non-renewables have led to increased focus on utilization of clean alternative fuels such as hydrogen (H2). Clean H2 can be obtained via electrochemical reaction of water splitting through proton exchange membrane water electrolyzers (PEMWEs). However, challenges to its widespread commercialization lie with the high H2 production cost, a large portion of which comes from use of expensive anodic catalyst materials. To improve anodic catalyst utilization to attain better performance at reduced cost, a thorough understanding of anodic catalyst layer microstructure is needed. In-depth characterization through scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS) can provide a comprehensive dataset, which can be processed further to extract structural parameters and correlate to electrochemical performance. However, extracting relevant information from these datasets can be very challenging and time-consuming.In this study, an automatic python-based image processing framework is implemented to quantify compositional and microstructural parameters for different IrO2 based catalyst inks and their corresponding catalyst layers. The code can provide areal density of catalyst materials, pore/agglomerate size distribution, connectivity of phases, coverage of catalyst particles by ionomer, and its content percentage and distribution. Quantified parameters are then correlated with the electrochemical performance of the PEMWEs. The results provide adequate pointers to not only help capturing microstructural details of catalyst layers but also provide a basis to predict electrochemical behavior and performance of the inks and catalyst layers being studied.

Automatic Morphological Processing in Middle School Students with and without Word Reading Difficulties

Morphological processing is the use of morphological structure during word reading. This study investigated whether middle school students applied morphological structure automatically when reading words. In addition, this study asked whether students with word reading difficulties (WRD) applied morphological structure in a way that differed from proficient word readers. Participants were seventh- and eighth-grade students (n = 80). Students were divided into two reading ability groups: proficient word readers (n = 55) and students with word reading difficulties (n = 25). Four computer-administered experimental tasks measured automaticity in reading morphologically complex words and morphologically simple words. A backward masking measure assessed whether students were applying morphological structure automatically to support task accuracy. Students were significantly more accurate in masked performance with morphologically complex words than with morphologically simple words on an oral word reading task. Students with WRD benefitted more from morphological structure on this task than proficient readers did. Findings suggest that proficient word readers and students with WRD automatically apply morphological structure when reading words aloud. In addition, middle school students with WRD may rely more on morphological structure than their proficient peers. However, there may be differences in morphological processing based on the nature of word reading tasks.

AI-based automatic identification and processing techniques for agricultural safety information

In the context of globalization, agricultural safety is directly linked to food security and human health. Modern agriculture’s challenge is effectively monitoring and processing vast agricultural safety information in the digital era. This study aims to achieve the automatic identification and intelligent processing of agricultural safety information within a digital media environment by applying artificial intelligence (AI) technologies. Initially, the background of AI applications in agriculture is explored, followed by an analysis of the urgent need for automated processing of agricultural safety information and an overview of current research in this field. It is demonstrated that, despite progress, existing methods still lack in-depth feature extraction and real-time capabilities of the data processing systems. A method based on multi-level feature fusion for identifying agricultural product safety is proposed to address these limitations alongside a decentralized AI Internet of Things (IoT) system for processing agricultural safety information. The multi-level feature fusion method can extract and integrate essential details on agricultural products from different dimensions and levels, thus enhancing the identification accuracy. Meanwhile, the decentralized AIoT system strengthens the efficiency and reliability of data processing, ensuring timely responses to agricultural safety information. Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment. Utilizing deep learning algorithms, the system could learn and understand the characteristics of different agricultural products and accurately identify potential safety issues, such as pests, diseases, or spoilage, providing timely monitoring and management for agricultural production. The innovation of this research lies in the comprehensive utilization of multi-level data features and advanced Internet of Things (IoT) technology, significantly improving the level of intelligence in agricultural safety supervision.

Emotions and emotion regulation in L2 classroom speaking tasks: A mixed‐methods study combining the idiodynamic and quantitative perspectives

AbstractThis study used idiodynamic methodology to investigate the dynamics of second language (L2) learners’ foreign language anxiety (FLA) and foreign language enjoyment (FLE), and the details of emotion regulation (ER) directed at managing these emotions, in pair‐ and group‐work speaking tasks performed by 10 advanced English‐as‐a‐foreign‐language (EFL) learners. L2 classroom tasks were video recorded and then while viewing them, participants registered their emotion ratings per second. In stimulated‐recall interviews, they revealed the causes of emotional intensity fluctuations and ER strategies used to manage the emotions. Additionally, the adapted Cognitive Emotion Regulation Questionnaire was used to collect quantitative data from a larger same‐population sample (N = 103). The major high ecological validity results concerned (a) the highly individual patterns of emotional intensity fluctuations, with FLA fluctuating more than FLE, (b) the common triggers and mitigators of FLA and FLE—including specific errors, performance deficits, and message‐conceptualization problems—and FLE triggers and mitigators related to its social aspect, (c) the varying degrees of the relationship between FLE and FLA, which depends on communication dynamics, and (d) a range of ER strategies, including their chains (sequences) and clusters (co‐occurrence), with a special focus on the most common category—namely, cognitive ER, some types of which emerged as automatic ER processes.

Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.

Medical staff inspect lumbar X-ray images to diagnose lumbar spine diseases, and the analysis process is currently automated using deep-learning techniques. The detection of landmarks is necessary in the automatic process of localizing the position and identifying the morphological features of the vertebrae. However, detection errors may occur owing to the noise and ambiguity of images, as well as individual variations in the shape of the lumbar vertebrae. This study proposes a method to improve the robustness of landmark detection results. This method assumes that landmarks are detected by a convolutional neural network-based two-step model consisting of Pose-Net and M-Net. The model generates a heatmap response to indicate the probable landmark positions. The proposed method then corrects the landmark positions using the heatmap response and active shape model, which employs statistical information on the landmark distribution. Experiments were conducted using 3600 lumbar X-ray images, and the results showed that the landmark detection error was reduced by the proposed method. The average value of maximum errors decreased by 5.58% after applying the proposed method, which combines the outstanding image analysis capabilities of deep learning with statistical shape constraints on landmark distribution. The proposed method could also be easily integrated with other techniques to increase the robustness of landmark detection results such as CoordConv layers and non-directional part affinity field. This resulted in a further enhancement in the landmark detection performance. These advantages can improve the reliability of automatic systems used to inspect lumbar X-ray images. This will benefit both patients and medical staff by reducing medical expenses and increasing diagnostic efficiency.

Perceptual anchoring: Children with dyslexia benefit less than controls from contextual repetitions in speech processing

ObjectivesIndividuals with dyslexia perceive and utilize statistical features in the auditory input deficiently. The present study investigates whether affected children also benefit less from repeating context tones as perceptual anchors for subsequent speech processing. MethodsIn an event-related potential study, eleven-year-old children with dyslexia (n = 21) and without dyslexia (n = 20) heard syllable pairs, with the first syllable either receiving a constant pitch (anchor) or variable pitch (no-anchor), while second syllables were identical across conditions. ResultsChildren with and without dyslexia showed smaller auditory P2 responses to constant-pitch versus variable-pitch first syllables, while only control children additionally showed smaller N1 and faster P1 responses. This suggests less automatic processing of anchor repetitions in dyslexia. For the second syllables, both groups showed faster P2 responses following anchor than no-anchor first syllables, but only controls additionally showed smaller P2 responses. ConclusionsChildren with and without dyslexia show differences in anchor effects. While both groups seem to allocate less attention to speech stimuli after contextual repetitions, children with dyslexia display less facilitation in speech processing from acoustic anchors. SignificanceAltered anchoring in the linguistic domain may contribute to the difficulties of individuals with dyslexia in establishing long-term representations of speech.