Repetitive Patterns Research Articles

A Qualitative Analysis of Unintentional Injuries in Autism Spectrum Disorder.

Prior research demonstrates that children with autism are more likely to experience unintentional injuries than the general population. Limited research exists on the symptoms or traits directly related to autism and this elevated injury rate, especially from the perspective of families with children with autism. This study used qualitative methodology to elucidate risk factors that may contribute to unintentional injuries in children with autism from the perspective of mothers raising children with autism. Participants included 15 mothers reporting on their children with autism. The mothers engaged in a semi-structured qualitative interview consisting of questions related to child characteristics, injury concerns and experiences, injury prevention strategies and resources, and safety behaviors. Interviews were transcribed and coded in NVivo following a systematic, deductive approach. Injury risk and concern among mothers was generally related to both the diagnostic and associated features of ASD as well as commonly co-occurring behaviors or disorders. Mothers reported that deficits in social communication and social interaction, plus restricted or repetitive patterns of behavior, contributed to increased unintentional injury risk. Additionally, mothers reported that general developmental differences and behavior during play or exploration increased risk of injury. By considering the lived experiences of families of children with autism, this study reveals that specific diagnostic features, associated features, and other behaviors often co-occurring with autism underlie parental perceptions of increased risk of and concern for unintentional injury in children with autism. These findings guide where intervention is needed and inform development of evidence-based, practical safety interventions.

Efficient and Effective Detection of Repeated Pattern from Fronto-Parallel Images with Unknown Visual Contents

The effective detection of repeated patterns from inputs of unknown fronto-parallel images is an important computer vision task that supports many real-world applications, such as image retrieval, synthesis, and texture analysis. A repeated pattern is defined as the smallest unit capable of tiling the entire image, representing its primary structural and visual information. In this paper, a hybrid method is proposed, overcoming the drawbacks of both traditional and existing deep learning-based approaches. The new method leverages deep features from a pre-trained Convolutional Neural Network (CNN) to estimate initial repeated pattern sizes and refines them using a dynamic autocorrelation algorithm. Comprehensive experiments are conducted on a new dataset of fronto-parallel textile images as well as another set of real-world non-textile images to demonstrate the superiority of the proposed method. The accuracy of the proposed method is 67.3%, which represents 20% higher than the baseline method, and the time cost is only 11% of the baseline. The proposed method has been applied and contributed to textile design, and it can be adapted to other applications.

Swin-transformer for weak feature matching

Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures. To address the issue of incorrect matches in scenes with repetitive patterns, we use a cross-attention and positional encoding mechanism to learn the correct matches of repetitive patterns in two scenes, achieving higher matching precision. We also introduce a matching optimization algorithm that calculates the spatial expected coordinates of local two-dimensional heat maps of correspondences to obtain the final sub-pixel level matches. Experiments indicate that, under identical training conditions, the SwinMatcher outperforms other standard methods in pose estimation, homography estimation, and visual localization. It exhibits strong robustness and superior matching in weakly textured areas, offering a new research direction for feature matching in weakly textured images.

Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children.

The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child's well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions.

Relationship between social anxiety and separation anxiety symptoms with insistence on sameness with the mediating role of sensory hypersensitivity

BackgroundAutism spectrum disorder (ASD) is characterized by impairments in social communication and interaction, restricted and repetitive patterns of behavior, and sensory processing abnormalities. These core features are often accompanied by comorbid anxiety disorders. However, the sequence and mechanisms of these associations warrant further investigation. Therefore, the aim of this study was to examine the relationship of social anxiety and separation anxiety on insistence on sameness, mediated by sensory hypersensitivity.MethodsThis was a cross-sectional study based on caregiver reports of 247 children, aged 7 ̶ 11 years. The parents completed the Autism Quotient-Children’s Version (AQ-C), the Repetitive Behavior Questionnaire-2 (RBQ-2), the Sensory Profile 2 (SP2), and the Spence Children’s Anxiety Scale-Parent Version (SCAS-P).ResultsThe path analysis revealed a significant indirect effect of social anxiety and separation anxiety on insistence on sameness via sensory hypersensitivity (p ≤ 0.05). However, neither social anxiety nor separation anxiety had a significant direct effect on insistence on sameness (p = 0.108 and p = 0.342, respectively).ConclusionsBased on the results, the mediated association of sensory hypersensitivity with the relationship between social anxiety, separation anxiety, and insistence on sameness was observed. The article discusses the theoretical explanations and implications of the results, as well as various manifestations of anxiety symptoms in children with ASD.

Chromosome-scale and haplotype-resolved genome assembly of Populus trichocarpa

Abstract Populus trichocarpa, a pivotal model organism for woody transgenic research, not only garners substantial scientific interest but plays an integral role in forestry economics. Previous genomic assemblies of P. trichocarpa predominantly treated its heterozygous genome as homozygous, thereby neglecting crucial haplotypic diversity. Leveraging the high fidelity (HiFi) sequencing capabilities of PacBio sequencing and the chromosome conformation capture insights provided by Illumina's Hi-C technique, this study is the first to achieve a near telomere-to-telomere assembly of both paternal and maternal haplotypes in P. trichocarpa. Comparative genomic analysis between these haplotypes has uncovered several allelic variants and pathways critical for trait determination through allele-specific expression. Furthermore, utilizing RNA-seq data from multiple tissues, this investigation has detailed the tissue-specific expression patterns of the leucine-rich repeat (LRR) gene family, which are essential in mediating plant signal transduction and developmental regulation. Our results not only illuminate the functional genomics landscape of P. trichocarpa but also provide invaluable theoretical underpinnings for the genetic improvement of woody plants and a robust framework for exploring genetic variability and allelic expression disparities in arboreal species.

Sound recurrence analysis for acoustic scene classification

In everyday life, people experience different soundscapes in which natural sounds, animal noises, and man-made sounds blend together. Although there have been several studies on the importance of recurring sound patterns in music and language, the relevance of this phenomenon in natural soundscapes is still largely unexplored. In this article, we study the repetition patterns of harmonic and transient sound events as potential cues for acoustic scene classification (ASC). In the first part of our study, our aim is to identify acoustic scene classes that exhibit characteristic sound repetition patterns concerning harmonic and transient sounds. We propose three metrics to measure the overall prevalence of sound repetitions as well as their repetition periods and temporal stability. In the second part, we evaluate three strategies to incorporate self-similarity matrices as an additional input feature to a convolutional neural network architecture for ASC. We observe the characteristic repetition of transient sounds in recordings of “park” and “street traffic” as well as harmonic sound repetitions in acoustic scene classes related to public transportation. In the ASC experiments, hybrid network architectures, which combine spectrogram features and features from sound recurrence analysis, show increased accuracy for those classes with prominent sound repetition patterns. Our findings provide additional perspective on the distinctions among acoustic scenes previously primarily ascribed in the literature to their spectral features.

Advances in the chromosome analysis of fennel Anethum piperitum and A. foeniculum

Fennel is a member of the Apiaceae family native to the Mediterranean area. The last taxonomic revision recognizes two species of fennel, Anethum foeniculum and Anethum piperitum corresponding, respectively, to the taxa previously classified as Foeniculum vulgare subsp. vulgare and Foeniculum vulgare subsp. piperitum. In recent years research has dedicated interest to fennel, considering both its agronomic and pharmaceutical aspects but largely overlooking its cytogenetic characteristics. To fill this gap, the chromosome structure of A. foeniculum and A. piperitum was examined by applying a combination of FISH (Fluorescence In Situ Hybridization), GISH (Genomic In Situ Hybridization) and DAPI (4′-6-diamidino-2-phenylindole) banding techniques. The FISH experiments revealed a similar distribution pattern of rDNA sites and telomeric repeats in both fennel species. An in-depth view of the chromosome structure of A. piperitum and A. foeniculum was obtained using the self-GISH procedure which revealed repetitive DNA sequences located in the centromeric and terminal regions of all chromosomes. The correspondence of s-GISH signals with DAPI banding suggests that these sequences are the principal component of the heterochromatin of fennel. These results could constitute the basic references for further molecular cytogenetic analyses to shed light on the evolutionary and taxonomic aspects of fennel.

Concrete Block Paving Tested as Articulated Slabs

This paper describes the experimental evaluation of articulated slabs composed of concrete block paving laid and compacted within a steel frame. By testing the paving in isolation from the bedding sand, basecourse and other elements of a complete block pavement it proved feasible to study the load spreading and other structural characteristics of the pavers themselves. This provided insights into the structural behaviour of concrete block paving under load. Factors studied in the project included the effects of paver shape, laying pattern, joint width, spacer nibs and load repetition. Each of these factors was shown to significantly influence the structural capacity of the paving course. By analysing the paver performance as a quasi-elastic slab it was possible to quantify the behaviour of block paving in a form suitable for inclusion in mechanistic pavement design and analysis. Where appropriate these measures were compared with data obtained both from Accelerated Trafficking Tests and Falling Weight Deflection studies of full scale block pavements.

FRACTAL GEOMETRIES DESIGN FOR THREE-DIMENSIONAL PRINTING: ANALYSIS OF PERFORMANCE AND VIBRATION RESPONSE FOR PRODUCT DESIGN

Additive Manufacturing (AM) has enhanced the development of complex designs and geometries that are difficult or impossible to achieve with conventional manufacturing methods. Therefore, research focused on the analysis of the functional properties related to the behavior conferred by the filling material and the internal structure became especially relevant. In this sense, fractal geometry is one of the most established theories to characterize complex geometries based on repetitive self-similar patterns that allow complex shapes to be described and relate them to mechanical, rheological and morphological properties. The design of fractal microstructures promises the development of advanced materials with improved mechanical properties and multiple functions related to vibration absorption, weight reduction, among others. In relation to the compatibility of elastic materials with three-dimensional printing processes, the study of fractal geometries in terms of geometric design and its correlation with improved mechanical behavior is established as an objective. Several TPU-based fractal samples were designed and tested using compression processes to characterize the behavior of elastic fractal geometries as a function of fractal dimension. Specifically, Sierpinski pyramids-based specimens were designed to explore the influence of fractal dimensions and iterations on the elasticity of the samples. Additionally, a case study was carried out to by using Finite Element Method (FEA) in order to analyze the stress distribution and vibrational behavior based on fractal order. The results demonstrated a notable variation in compression behavior and vibrational behavior depending on the fractal order, highlighting the potential of fractal geometries to enhance mechanical performance in AM applications. Keywords: Fractal Geometries, Product Design, Three-Dimensional Printing, Performance, Thermoplastic Polyurethane.

Unlocking the Mind: Using Word Frequency Analysis to Reveal Metaphor Patterns in Schizophrenia Patients' Poetry

Introduction: This study investigates the metaphorical language used in poetry by schizophrenia patients, aiming to reveal underlying cognitive and emotional patterns. Understanding these patterns can enhance therapeutic approaches and provide insights into the patients' mental states.Methods: A qualitative analysis was conducted on poetry written by a patient diagnosed with hebephrenic schizophrenia at Menur Mental Hospital in Surabaya, Indonesia. The analysis employed word frequency analysis (WFA) to identify recurring themes and metaphorical expressions, focusing on the emotional and cognitive implications of the language used.Results: The findings revealed a significant prevalence of metaphors related to pain, voice, and mind, indicating the patient's struggles with emotional distress and cognitive disorganization. The poetry exhibited patterns of repetition, particularly concerning the concept of illness, reflecting the patient's ongoing battle with their mental health condition. Additionally, the analysis highlighted the disorganized thought processes characteristic of schizophrenia, as evidenced by incoherent and fragmented expressions.Conclusions: The study underscores the therapeutic potential of poetry as a medium for self-expression among schizophrenia patients. By analyzing metaphorical language, clinicians can gain deeper insights into patients' emotional experiences, facilitating more empathetic and effective therapeutic interventions. Future research should explore the implications of these findings across diverse populations to further understand the intersection of mental health and creative expression.

GPT wars: Creating and detecting fake ophthalmology data sets with artificial intelligence

Aims/Purpose: Recently, our group demonstrated how the Large Language Model GPT‐4 can fabricate fake ophthalmology data sets, designed to support false scientific evidence. The objectives of this study were: (1) to identify peculiar statistical patterns of AI‐generated data sets, (2) to attempt at enhancing the quality of fabricated data for eliminating any discernible mark of non‐authenticity, and (3) to develop a custom GPT trained to detect signs of fabrication.Methods: Three fictional studies were designed to compare treatment outcomes for a specific ocular disease. First, prompts were submitted to the custom GPT “Data Analyst” to produce 12 “unrefined” fake data sets for the three studies. Then, the custom GPT “Fake Data Creator” was developed to generate 12 “refined” fake data sets, specifically designed to evade authenticity checks. Forensic analysis was performed on all data sets using IBM SPSS. Finally, the custom GPT “Fake Data Detector” (FDD) was developed to replicate the forensic analysis and assign 1 point for each sign of fabrication.Results: Forensic analysis of the unrefined fake data sets revealed numerous flaws, most notably: name/gender mismatch (n = 12), non‐uniform distribution (n = 11) and repetitive patterns of last digits (n = 7), absence of correlation between study variables (n = 12), and distribution shape anomalies (n = 11). In refined fake data sets, the forensic analysis did not reveal statistical flaws, except for distribution shape anomalies (n = 7). Overall, 5 refined fake data sets (41.7%) passed forensic analysis as authentic. The results of the statistical analyses performed by GPT FDD were identical to those obtained with IBM SPSS, with a mean of 9.67±2.13 (95% IC 8.46‐10.87) and 1.00±1.00 (95% IC 0.43‐1.57) points respectively allocated to unrefined and refined data sets.Conclusions: Sufficiently sophisticated custom GPTs can perform complex statistical tasks and may be abused to fabricate seemingly authentic ophthalmology data sets, passing forensic analysis.References Taloni A, Scorcia V, Giannaccare G. Large Language Model Advanced Data Analysis Abuse to Create a Fake Data Set in Medical Research. JAMA Ophthalmol. 2023; 141(12):1174–1175. doi:10.1001/jamaophthalmol.2023.5162

GPT wars: Creating and detecting fake ophthalmology data sets with artificial intelligence

Aims/Purpose: Recently, our group demonstrated how the Large Language Model GPT‐4 can fabricate fake ophthalmology data sets, designed to support false scientific evidence. The objectives of this study were: (1) to identify peculiar statistical patterns of AI‐generated data sets, (2) to attempt at enhancing the quality of fabricated data for eliminating any discernible mark of non‐authenticity, and (3) to develop a custom GPT trained to detect signs of fabrication.Methods: Three fictional studies were designed to compare treatment outcomes for a specific ocular disease. First, prompts were submitted to the custom GPT “Data Analyst” to produce 12 “unrefined” fake data sets for the three studies. Then, the custom GPT “Fake Data Creator” was developed to generate 12 “refined” fake data sets, specifically designed to evade authenticity checks. Forensic analysis was performed on all data sets using IBM SPSS. Finally, the custom GPT “Fake Data Detector” (FDD) was developed to replicate the forensic analysis and assign 1 point for each sign of fabrication.Results: Forensic analysis of the unrefined fake data sets revealed numerous flaws, most notably: name/gender mismatch (n = 12), non‐uniform distribution (n = 11) and repetitive patterns of last digits (n = 7), absence of correlation between study variables (n = 12), and distribution shape anomalies (n = 11). In refined fake data sets, the forensic analysis did not reveal statistical flaws, except for distribution shape anomalies (n = 7). Overall, 5 refined fake data sets (41.7%) passed forensic analysis as authentic. The results of the statistical analyses performed by GPT FDD were identical to those obtained with IBM SPSS, with a mean of 9.67 ± 2.13 (95% IC 8.46‐10.87) and 1.00 ± 1.00 (95% IC 0.43‐1.57) points respectively allocated to unrefined and refined data sets.Conclusions: Sufficiently sophisticated custom GPTs can perform complex statistical tasks and may be abused to fabricate seemingly authentic ophthalmology data sets, passing forensic analysis.References Taloni A, Scorcia V, Giannaccare G. Large Language Model Advanced Data Analysis Abuse to Create a Fake Data Set in Medical Research. JAMA Ophthalmol. 2023; 141(12): 1174–1175. doi: 10.1001/jamaophthalmol.2023.5162

The Pattern of Repetition in Teaching According to Deuteronomy 6:7-9

One of the problems in Christian families occurs when parents shift the responsibility of educating their children to educational institutions. Even if they finally realise the biblical guidance that parents are responsible for educating their children, they do not understand the method of teaching in the home. The biblical text, Deuteronomy 6:7-9, primarily addresses this issue. The purpose of this study is to show the pattern of repetition as a method of teaching in the household of God's people. The research was conducted by applying exegetical procedures to the target passage, aided by a literature review. Biblical commentaries and biblical journal articles were used to interpret the meaning of the passage. An exploration of the target passage shows that the household teaching method mandated in Deuteronomy 6:7-9 is based on love for God and has three important components: the discipline of repetition, fellowship with God, and social norms in the community. Ultimately, the simple repetition method is effective in cultivating children's spirituality.

High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition

Detection of high-impedance faults in direct current microgrid lines presents a challenge for most conventional protection schemes because the magnitude of the fault current is similar to other transients that occur during normal operation. However, the waveform of high-impedance faults differs from that of other transients as it is characterized by a repetitive and nonlinear pattern caused by current reignition. Various methods have been proposed to exploit fault response waveforms for detecting high-impedance faults, including those based on deep discriminative intelligent classification. Different from previous works that focus on closed-set classification, this study frames fault detection as an open-set recognition problem, employing a neural network as the classifier. The resulting approach enables the detection of high-impedance faults as outliers from the normal operating states of microgrid lines with passive constant impedance loads and requires only the Fourier transform of the current signal as input to the neural network. Remarkably, the proposed solution eliminates the need for hard-to-model high-impedance faults in the training dataset and hence is more generally applicable. The proposed method consistently outperforms commercially available high-impedance fault detection systems, achieving high accuracy in fault detection.

Behavioral therapies for the treatment of autism spectrum disorder: A systematic review.

Autism Spectrum Disorder (ASD) is a neurodevelopment spectrum characterized by persistent deficits in social communication and interpersonal interaction, along with restricted and repetitive patterns of behavior, interest, or activities. The appropriate screening and diagnosis must be realized to improve the children's prognosis. Moreover, appropriate treatments are necessary to promote better social integration and development. In this scenario, this systematic review aims to evaluate the impacts of behavioral therapies applied in healthcare settings for patients with ASD. This systematic review followed the PRISMA guidelines. The databases MEDLINE, Embase, CENTRAL (Cochrane), and Lilacs were accessed, and gray and manual searches were performed. The search strategy was created with terms referring to autism and behavioral therapy. The studies were assessed qualitatively. Randomized clinical trials and observational studies demonstrated improvements in cognitive and verbal components of patients who received behavioral therapies in therapeutic settings. These results indicate a positive impact of both cognitive-behavioral therapy and ESDM on the development of patients' skills. Among the cognitive-behavioral therapies, the one based on the MASSI protocol did not impact the reduction of anxious symptoms. As for cognitive-behavioral therapy, one study demonstrated that the Behavioral Intervention for Anxiety in Children with Autism (BIACA), when compared to the Coping Cat protocol, improves cognition and reduces anxiety symptoms. Despite these results, further randomized clinical trials comparing behavioral therapies with one another are needed. In the context of behavioral therapy within a healthcare setting, the Early Start Denver Model (ESDM) showed improvements in the cognitive, verbal, and social aspects of the evaluated patients. Improvement in scores sometimes is achieved independently of the group and related to the time of interventions.

A Robust Method for Validating Orientation Sensors Using a Robot Arm as a High-Precision Reference.

This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot's base frame and the sensor's reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions. This approach enables automated, high-precision, and comparative evaluation of various orientation sensing devices in a reproducible manner. Moreover, it facilitates efficient calibration and analysis of sensor errors, such as drift, noise, and response delays under various motion conditions. The method's effectiveness is demonstrated through experimental validation of an Inertial Navigation System module and the SLAM-IMU fusion capabilities of the HTC VIVE VR headset, highlighting its versatility and reliability in addressing the challenges associated with orientation sensor validation.

Identifying sequential differences between protein structural classes using network and statistical approaches

Protein sequence information is believed to embed the hint of their structures. To uncover the nature between protein sequence and their structures, this study motivates to inspect the dynamic interactions between various protein sequence features, and identify the sequential differences between the different protein structures. Protein sequence data from all structural classes in CATH and SCOP, and the structural disordered proteins from DisProt, as well as the structural motifs in PROSITE, are analyzed in this study. Betweenness and closeness centrality measures are employed to capture the topology of the networks constructed from amino acid feature interactions, while statistical tests are further implemented to compare the feature series distributions. Key findings suggest that in all structural classes, the features for Ala and α-helix and bend preference property, Ala and side-chain size, Ala and Gly, as well as Met and Leu attain significant interactions between each other, and the feature for Leu, Val, and Asn are acted as the critical sources of feature interactions, whereas Cys, His, Trp, and Met exhibit weak intra-type interactions with other features. These implicate that these feature interactions may have little impact in coding the structural differences. For the α structures, Glu, Pro and side-chain size, hydrophobicity properties exhibit high importance in feature interactions, whereas Gly, Thr and physical properties such as α-helix and bend preference, extended structural preference, pK-C value and surrounding hydrophobicity for β structures, show special high importance in β structures. Both α and β types of structures show Ser as the common sources of feature interactions, while the mixed α and β structures not only show common characters with the α and β types of structures, but also preferred interactions between Met, Lys and double-bend preference property, and between the sequence arrangements of Cys, His, Met, Tyr and amino acid composition features. The intrinsically disordered proteins (IDPs) present high frequency for the repetition patterns of certain amino acids, while the different structural motifs also show special characters. More sequential differences between the structures can also be identified from K-mers statistics and feature series distributions. The new discoveries reveal the nature of amino acid feature interaction mechanics, and show great importance of these interactions in coding the different types of protein structures. The results can not only contribute to future molecular design for protein-based vaccine or drug, but also enlighten the development for new protein structural classifiers.

Predicting freeway non-recurring congestion via a spatio-temporal deep learning approach

Freeway unexpected events, such as car crashes, result in non-recurring congestion, which does not follow repetitive patterns in space and/or time and increases the challenge of freeway management. Existing prediction approaches of Freeway Non-recurring Congestion (FNC) are either qualitative with information loss or single-step quantitative for a short period. To address these limitations, this paper develops a quantitative, interpretability-enhanced, and spatial-temporal approach using a 2-Encoder-Decoder Model with an Attention Layer (Att-2ED) for FNC prediction. The model leverages Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) as two encoders to process time-series speed profiles and static crash features independently. It also employs an LSTM-based decoder to generate a spatio-temporal sequence output and an attention mechanism to prioritise the weights of input sequences at each time step. The model has been trained and tested using real-world freeway data. The proposed model demonstrates superior performance in predicting traffic conditions across both spatial and temporal dimensions compared to existing benchmarks, achieving a Mean Absolute Error (MAE) of 3.74 for 15-minute-ahead predictions. Its effectiveness is further underscored through a comparative analysis of prediction accuracy across various congestion levels and crash severities. For instance, the MAE ranges from 2.77 to 7.00 for severe crashes and from 2.68 to 6.47 for peak-hour crashes, with prediction horizons spanning from 5 min to 60 min ahead. Moreover, the attention mechanisms incorporated in the model provide interpretability-enhanced predictions, highlighting the significance of the last 10-min input in the prediction.

Deficits in emotional cognition among individuals with conduct disorder: theoretical perspectives

Conduct disorder refers to a persistent and repetitive pattern of problematic behavior, and is usually accompanied by deficits in cognitive processing of emotional stimuli. Behavioral and physiological correlates of these deficits have been the subject of sufficiently many investigations. Meanwhile, several theories concerning conduct disorder have been developed. However, the validity of these theories to explain the emotional processing deficits among conduct-disordered individuals has not been tested. Here we summarized four relevant theories, including “social-cognitive theory and social-information-processing theory”, “violence inhibition model”, “optimal stimulation/arousal theory and stimulation/sensation seeking theory”, and “fearlessness theory”, and discussed their validity in predicting the emotional processing deficits among conduct disordered individuals. Future directions on the theories and evidence were proposed.