Processing Model Research Articles

Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models

Effective model evaluation is crucial for robust machine learning, and cross-validation techniques play a significant role. This study compares Repeated k-folds Cross Validation, k-folds Cross Validation, and Leave-One-Out Cross Validation (LOOCV) on imbalanced and balanced datasets across four models: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Bagging, both with and without parameter tuning. On imbalanced data without parameter tuning, Repeated k-folds cross-validation demonstrated strong performance for SVM with a sensitivity of 0.541 and balanced accuracy of 0.764. K-folds Cross Validation showed a higher sensitivity of 0.784 for RF and a balanced accuracy of 0.884. In contrast, LOOCV achieved notable sensitivity for RF and Bagging at 0.787 and 0.784, respectively, but at the cost of lower precision and higher variance, as detailed in Table 1. When parameter tuning was applied to balanced data, the performance metrics improved. Sensitivity for SVM reached 0.893 with LOOCV and balanced accuracy for Bagging increased to 0.895. Stratified k-folds provided enhanced precision and F1-Score for SVM and RF. Notably, processing times varied significantly, with k-folds being the most efficient with SVM taking 21.480 seconds and Repeated k-folds showing higher computational demands where RF took approximately 1986.570 seconds in model processing, as shown in Table 4. This analysis underscores that while k-folds and repeated k-folds are generally efficient, LOOCV and balanced approaches offer enhanced accuracy for specific models but require greater computational resources. The choice of cross-validation technique should thus be tailored to the dataset characteristics and computational constraints to ensure optimal model evaluation.

Adopting Theories Underlying Directed Technology Integration Strategies: Study Objectivist Learning Theories

This paper examines key theories underlying directed technology integration strategies in education, focusing on objectivist learning approaches. The study reviews and analyzes several influential theories, including B.F. Skinner's behaviorism, Atkinson and Shiffrin's information processing model, Robert Gagne's conditions of learning, and systems theory approaches to instructional design.The analysis reveals how these theories provide conceptual foundations for technology integration in education. Behaviorism emphasizes reinforcement and observable outcomes. Information processing theory explains memory and cognitive processes. Gagne's work highlights conditions for effective learning. Systems approaches offer frameworks for designing technology-enhanced instruction. While each theory offers valuable insights, an integrated approach incorporating behaviorist, cognitive, and constructivist perspectives may be most effective for technology-enhanced learning. Understanding these theoretical underpinnings can guide educators in developing more effective technology integration strategies and creating optimal learning conditions in digital environments. This paper synthesizes key objectivist learning theories relevant to educational technology, providing a theoretical foundation to inform technology integration practices in modern education.

The collapse of modern exposures in the recording of X-RAY images in medicine

This article reviewed the methods and algorithms for processing and diagnosing X-ray images that are widely used in medicine today. In order to improve the implementation of deep learning in modern medicine and further improve the widespread use of HMLs, the method, algorithm and model of X-ray image processing using the Raspberry Pi device were analyzed.

Predicting subjective ratings of affect and comprehensibility with text features: a reader response study of narrative poetry.

Literary reading is an interactive process between a reader and a text that depends on a balance between cognitive effort and emotional rewards. By studying both the crucial features of the text and of the subjective reader reception, a better understanding of this interactive process can be reached. In the present study, subjects (N=31) read and rated a work of narrative fiction that was written in a poetic style, thereby offering the readers two pathways to cognitive rewards: Aesthetic appreciation and narrative immersion. Using purely text-based quantitative descriptors, we were able to independently and accurately predict the subjective ratings in the dimensions comprehensibility, valence, arousal, and liking across roughly 140 pages of naturalistic text. The specific text features that were most important in predicting each rating dimension are discussed in detail. In addition, the implications of the findings are discussed more generally in the context of existing models of literary processing and future research avenues for empirical literary studies.

Imaging Mutant Huntingtin Aggregates using [18F]CHDI-650 PET in a preclinical model of Huntington’s disease

Huntington’s disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin gene (HTT) that encodes pathogenic mutant huntingtin (mHTT) protein. Therapeutic strategies aimed at lowering mHTT would benefit from non-invasive PET-based methods to evaluate treatment response. The CHDI foundation developed the first-generation PET ligands labelled with 11C, which advanced to clinical evaluation. Further optimization generated an 18F radioligand with improved metabolic stability, brain exposure and specificity, shown by PET imaging in wild-type mice and autoradiography in HD brain samples1,2. Here, we characterised the PET radioligand [18F]CHDI-650 in the R6/1 mouse model of HD to assess its sensitivity for monitoring treatment response of mHTT lowering therapies. [18F]CHDI-650 was produced according to an established method3 using a TRACERlab system. Radiochemical purity/molar activity of the final [18F]CHDI-650 product was detected by reverse-phase HPLC analysis. Dynamic PET imaging was conducted using R6/1 mice and wild-type littermates at various disease stages. Image processing and kinetic modelling (2TCM) analysis were performed using PMOD software. Ex vivo brain radioactivity was measured using gamma counting. Brains were cryo-sectioned and processed for immunofluorescence to identify HTT load. [18F]CHDI-650 was produced (1.3 ± 0.2 GBq) with an average radiochemical purity of 99.9 ± 0.1 % and a molar activity of 32.4 ± 5.2 GBq/µmol (n = 9). The radioligand was able to discriminate R6/1 HET from WT mice at an early presymptomatic age for both dynamic PET imaging (striatal) and ex vivo whole brain gamma. A well-defined age associated increase was also evident using both measures. Dual immunofluorescence spatially defined the level of aggregated HTT in striatal and hippocampal regions across the age groups. [18F]CHDI-650 PET shows great potential as a non-invasive tool for visualising the development of mHTT pathology and as a biomarker for preclinical efficacy studies. Please click on the 'PDF' for the full abstract!

Scalable incident detection via natural language processing and probabilistic language models

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risks under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It’s based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: (1) suicide attempt; (2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ~ 0.77 (95% CI 0.75–0.78) for suicide attempt and AUPR ~ 0.31 (95% CI 0.28–0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race differed across phenotypes. Scalable phenotyping models, like most healthcare AI, require algorithmovigilance and debiasing prior to implementation.

Assessing the use of unstructured electronic health record data to identify exposure to firearm violence.

Research on firearm violence is largely limited to people who experienced acute bodily trauma and death which is readily gathered from Inpatient and Emergency Department settings and mortality data. Exposures to firearm violence, such as witnessing firearm violence or losing a loved one to firearm violence, are not routinely collected in health care. As a result, the true public health burden of firearm violence is underestimated. Clinical notes from electronic health records (EHRs) are a promising source of data that may expand our understanding of the impact of firearm violence on health. Pilot work was conducted on a sample of clinical notes to assess how firearm terms present in unstructured clinical notes as part of a larger initiative to develop a natural language processing (NLP) model to identify firearm exposure and injury in ambulatory care data. We used EHR data from 2012 to 2022 from a large multistate network of primary care and behavioral health clinics. A text string search of broad, gun-only, and shooting terms was applied to 9,598 patients with either/both an ICD-10 or an OCHIN-developed structured data field indicating exposure to firearm violence. A sample of clinical notes from 90 patients was reviewed to ascertain the meaning of terms. Among the 90 clinical patient notes, 13 (14%) notes reflect documentation of exposure to firearm violence or injury from firearms. Results from this study identified refinements that should be considered for NLP text classification. Unstructured clinical notes from primary and behavioral health clinics have potential to expand understanding of firearm violence.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

FluencyBank Timestamped: An Updated Data Set for Disfluency Detection and Automatic Intended Speech Recognition.

This work introduces updated transcripts, disfluency annotations, and word timings for FluencyBank, which we refer to as FluencyBank Timestamped. This data set will enable the thorough analysis of how speech processing models (such as speech recognition and disfluency detection models) perform when evaluated with typical speech versus speech from people who stutter (PWS). We update the FluencyBank data set, which includes audio recordings from adults who stutter, to explore the robustness of speech processing models. Our update (semi-automated with manual review) includes new transcripts with timestamps and disfluency labels corresponding to each token in the transcript. Our disfluency labels capture typical disfluencies (filled pauses, repetitions, revisions, and partial words), and we explore how speech model performance compares for Switchboard (typical speech) and FluencyBank Timestamped. We present benchmarks for three speech tasks: intended speech recognition, text-based disfluency detection, and audio-based disfluency detection. For the first task, we evaluate how well Whisper performs for intended speech recognition (i.e., transcribing speech without disfluencies). For the next tasks, we evaluate how well a Bidirectional Embedding Representations from Transformers (BERT) text-based model and a Whisper audio-based model perform for disfluency detection. We select these models, BERT and Whisper, as they have shown high accuracies on a broad range of tasks in their language and audio domains, respectively. For the transcription task, we calculate an intended speech word error rate (isWER) between the model's output and the speaker's intended speech (i.e., speech without disfluencies). We find isWER is comparable between Switchboard and FluencyBank Timestamped, but that Whisper transcribes filled pauses and partial words at higher rates in the latter data set. Within FluencyBank Timestamped, isWER increases with stuttering severity. For the disfluency detection tasks, we find the models detect filled pauses, revisions, and partial words relatively well in FluencyBank Timestamped, but performance drops substantially for repetitions because the models are unable to generalize to the different types of repetitions (e.g., multiple repetitions and sound repetitions) from PWS. We hope that FluencyBank Timestamped will allow researchers to explore closing performance gaps between typical speech and speech from PWS. Our analysis shows that there are gaps in speech recognition and disfluency detection performance between typical speech and speech from PWS. We hope that FluencyBank Timestamped will contribute to more advancements in training robust speech processing models.

Exploring the Effectiveness of Advanced Chatbots in Educational Settings: A Mixed-Methods Study in Statistics

The Generative Pre-trained Transformer (GPT) is a highly advanced natural language processing model. This model can generate conversation-style responses to user input. The rapid rise of GPT has transformed academic domains, with studies exploring the potential of chatbots in education. This research investigates the effectiveness of ChatGPT 3.5, ChatGPT 4.0 by OpenAI, and Chatbot Bing by Microsoft in solving statistical exam-type problems in the educational setting. In addition to quantifying the errors made by these chatbots, this study seeks to understand the causes of these errors to provide recommendations. A mixed-methods approach was employed to achieve this goal, including quantitative and qualitative analyses (Grounded Theory with semi-structured interviews). The quantitative stage involves statistical problem-solving exercises for undergraduate engineering students, revealing error rates based on the reason for the error, statistical fields, sub-statistics fields, and exercise types. The quantitative analysis provided crucial information necessary to proceed with the qualitative study. The qualitative stage employs semi-structured interviews with selected chatbots; this includes confrontation between them that generates agreement, disagreement, and differing viewpoints. On some occasions, chatbots tend to maintain rigid positions, lacking the ability to adapt or acknowledge errors. This inflexibility may affect their effectiveness. The findings contribute to understanding the integration of AI tools in education, offering insights for future implementations and emphasizing the need for critical evaluation and responsible use.

Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture

India has a vast number of inhabitants and the main food source distribution is from agriculture. Agricultural lands are being demolished generally owing to plant and crop illnesses. The detection of plant diseases by using image processing models can aid agriculturalists in defending the farming area from damaging or affecting it. Paddy is the main harvest worldwide. Early recognition of the paddy diseases at dissimilar phases of development is very vital in paddy production. However, the present manual technique in identifying and classifying paddy diseases needs a very educated farmer and is time-consuming. Deep learning (DL) is an effectual research area in the classification of agriculture patterns where it can efficiently solve the problems of diseases identification. Therefore, the articles focus on the design and expansion of Deep Learning based Crested Porcupine Optimizer for the Detection and Classification of Paddy Leaf Diseases (DLCPO-DCPLD) method for Sustainable Agriculture. The main aim of the DLCPO-DCPLD method use DL method for the recognition and identification of rice plant leaf diseases. To accomplish this, the DLCPO-DCPLD technique performs the image pre-processing using Median Filtering (MF) to recover the excellence of the input frames. Next, the ConvNeXt-L method is applied for extraction of feature vectors from the pre-processed images. Also, the Conditional Variational Autoencoder (CVAE) model is utilized for the automated classification of Paddy Leaf diseases. Eventually, the hyperparameter tuning of the CVAE technique is accomplished by implementing the Crested Porcupine Optimizer (CPO) technique. To safeguard the enhanced predictive results of the DLCPO-DCPLD method, a sequence of experimentations is implemented on the benchmark dataset. The experimental validation of the DLCPO-DCPLD method portrayed a superior accuracy value of 99.12% over existing approaches.

A mismatch between striatal cholinergic pauses and dopaminergic reward prediction errors

Striatal acetylcholine and dopamine critically regulate movement, motivation, and reward-related learning. Pauses in cholinergic interneuron (CIN) firing are thought to coincide with dopamine pulses encoding reward prediction errors (RPE) to jointly enable synaptic plasticity. Here, we examine the firing of identified CINs during reward-guided decision-making in freely moving rats and compare this firing to dopamine release. Relationships between CINs, dopamine, and behavior varied strongly by subregion. In the dorsal-lateral striatum, a Go! cue evoked burst-pause CIN spiking, followed by a brief dopamine pulse that was unrelated to RPE. In the dorsal-medial striatum, this cue evoked only a CIN pause, that was curtailed by a movement-selective rebound in firing. Finally, in the ventral striatum, a reward cue evoked RPE-coding increases in both dopamine and CIN firing, without a consistent pause. Our results demonstrate a spatial and temporal dissociation between CIN pauses and dopamine RPE signals and will inform future models of striatal information processing under both normal and pathological conditions.

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations.

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

Truncated M13 phage for smart detection of E. coli under dark field

BackgroundThe urgent need for affordable and rapid detection methodologies for foodborne pathogens, particularly Escherichia coli (E. coli), highlights the importance of developing efficient and widely accessible diagnostic systems. Dark field microscopy, although effective, requires specific isolation of the target bacteria which can be hindered by the high cost of producing specialized antibodies. Alternatively, M13 bacteriophage, which naturally targets E. coli, offers a cost-efficient option with well-established techniques for its display and modification. Nevertheless, its filamentous structure with a large length-diameter ratio contributes to nonspecific binding and low separation efficiency, posing significant challenges. Consequently, refining M13 phage methodologies and their integration with advanced microscopy techniques stands as a critical pathway to improve detection specificity and efficiency in food safety diagnostics.MethodsWe employed a dual-plasmid strategy to generate a truncated M13 phage (tM13). This engineered tM13 incorporates two key genetic modifications: a partial mutation at the N-terminus of pIII and biotinylation at the hydrophobic end of pVIII. These alterations enable efficient attachment of tM13 to diverse E. coli strains, facilitating rapid magnetic separation. For detection, we additionally implemented a convolutional neural network (CNN)-based algorithm for precise identification and quantification of bacterial cells using dark field microscopy.ResultsThe results obtained from spike-in and clinical sample analyses demonstrated the accuracy, high sensitivity (with a detection limit of 10 CFU/μL), and time-saving nature (30 min) of our tM13-based immunomagnetic enrichment approach combined with AI-enabled analytics, thereby supporting its potential to facilitate the identification of diverse E. coli strains in complex samples.ConclusionThe study established a rapid and accurate detection strategy for E. coli utilizing truncated M13 phages as capture probes, along with a dark field microscopy detection platform that integrates an image processing model and convolutional neural network.Graphical

Lightweight medical image segmentation network with multi-scale feature-guided fusion

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image segmentation to limited computing resources. There is tremendous value in developing accurate, real-time vision processing models that require minimal computational resources. When building lightweight models, there is always a trade-off between computational cost and segmentation performance. Performance often suffers when applying models to meet resource-constrained scenarios characterized by computation, memory, or storage constraints. This remains an ongoing challenge. This paper proposes a lightweight network for medical image segmentation. It introduces a lightweight transformer, proposes a simplified core feature extraction network to capture more semantic information, and builds a multi-scale feature interaction guidance framework. The fusion module embedded in this framework is designed to address spatial and channel complexities. Through the multi-scale feature interaction guidance framework and fusion module, the proposed network achieves robust semantic information extraction from low-resolution feature maps and rich spatial information retrieval from high-resolution feature maps while ensuring segmentation performance. This significantly reduces the parameter requirements for maintaining deep features within the network, resulting in faster inference and reduced floating-point operations (FLOPs) and parameter counts. Experimental results on ISIC2017 and ISIC2018 datasets confirm the effectiveness of the proposed network in medical image segmentation tasks. For instance, on the ISIC2017 dataset, the proposed network achieved a segmentation accuracy of 82.33 % mIoU, and a speed of 71.26 FPS on 256 × 256 images using a GeForce GTX 3090 GPU. Furthermore, the proposed network is tremendously lightweight, containing only 0.524M parameters. The corresponding source codes are available at https://github.com/CurbUni/LMIS-lightweight-network.

Middle-aged dogs with low and high Aβ CSF concentrations show differences in energy and stress related metabolic profiles in CSF

BackgroundAmyloid beta (Aβ) accumulation in the brain is one of the earliest findings in Alzheimer's disease (AD). The dog is a natural animal model for amyloid processing and early brain amyloid pathology. The goal of this study is to examine which differences in metabolomic profiles in cerebrospinal fluid (CSF) could be detected in dogs with a difference in CSF Aβ concentrations before amyloid accumulation occurs. MethodMetabolic profiling was performed on CSF from 4 to 8 year old dogs with different CSF Aβ concentrations. ResultsMetabolomic profiling of CSF showed differences in brain energy metabolism. More specifically, increases in N-acetylation of amino acids and amino sugars, creatine and pentose metabolism, and a decrease in tricarboxylic acid (TCA) cycle were seen in dogs with a high CSF Aβ concentration. In addition, signs of elevated oxidative stress, higher methionine, lipid and nucleotide metabolism and increased levels of cysteine, myo-inositol and trimethylamine N-oxide were noted in these animals. ConclusionsDifferences in energy metabolism and stress mediated metabolic changes are seen in the brain of dogs with different CSF Aβ concentrations, before any amyloid deposition occurs. Similar metabolic changes, as in the high Aβ dogs, have been described in AD in humans and/or transgenic AD mice, some of them in very early phases. General significanceThe differences observed in metabolomic profiles could help in identifying potential biomarkers for an increased risk of developing amyloid pathology in the brain and open the door to the evaluation of preventive treatments for amyloid pathology in humans.

The Bilingual Dual-path model

Abstract Experimental research has yielded many important psycholinguistic findings in bi-/multilingualism. However, cognitive computational models of sentence processing were limited to the single-language case until recently. In this methodological review, we discuss cognitive modelling work that uses the Bilingual Dual-path model to simulate experimental research on bilingual sentence production, processing, and development. As a detailed example of such work, we then report on new simulations conducted with the model. Finally, we suggest directions for future cognitive modelling research using this model.

Thermal profile modeling and microstructural evolution in laser processing of Inconel 625 plates by comparison of analytical and numerical methods

Microstructural evolution of materials under specified process conditions and parameters can be predicted by thermal modeling of additive manufacturing laser processes. The objective of this study was to develop, analyze and compare two methods for prediction: an analytical method and a numerical method for laser processing of Inconel 625 material. These methods were compared with experimental results for thermal profiling, and the effect of thermal profiles on microstructure of the experimental samples was explored. Maximum temperature and cooling rate of the numerical method were shown in good agreement, while the analytical method proved more challenging when compared to the experimental results for three laser parameters. Cooling curves were correlated with microstructure in terms of grain size, morphology, and orientation, with findings trending with parameter adjustments. This research supports the numerical modeling approach as a method for examining optimal laser processing conditions for Inconel 625 that is ideally suited for complex fluid flow analyses.

Musical Sophistication and Multilingualism: Effects on Arcuate Fasciculus Characteristics.

The processing of auditory stimuli which are structured in time is thought to involve the arcuate fasciculus, the white matter tract which connects the temporal cortex and the inferior frontal gyrus. Research has indicated effects of both musical and language experience on the structural characteristics of the arcuate fasciculus. Here, we investigated in a sample of n = 84 young adults whether continuous conceptualizations of musical and multilingual experience related to structural characteristics of the arcuate fasciculus, measured using diffusion tensor imaging. Probabilistic tractography was used to identify the dorsal and ventral parts of the white matter tract. Linear regressions indicated that different aspects of musical sophistication related to the arcuate fasciculus' volume (emotional engagement with music), volumetric asymmetry (musical training and music perceptual abilities), and fractional anisotropy (music perceptual abilities). Our conceptualization of multilingual experience, accounting for participants' proficiency in reading, writing, understanding, and speaking different languages, was not related to the structural characteristics of the arcuate fasciculus. We discuss our results in the context of other research on hemispheric specializations and a dual-stream model of auditory processing.

Research on data processing of 3D modeling by images based on helicopter oblique photogrammetry

Abstract The use of tilting photogrammetry technology to build a refined urban 3D model has become the industry research hotspot with the rapid development of China’s “digital city.” To investigate and analyze the methodology of 3D data modeling using helicopter oblique photogrammetry, a 3D model was generated for a study area covering approximately 34 km2. This paper delves into the technical process of 3D modeling data processing utilizing helicopter oblique photogrammetry, and it examines the precision of the model results alongside a comprehensive technical analysis. The results show that the results of the process are consistent with the relevant specifications of the first-order 3D model landscape. Compared with the traditional 3D model, the method has obvious progress in efficiency and has a very wide application prospect.