Processing Mechanisms Research Articles

ТИП ФОРМИРОВКИ ЯБЛОНИ, ОПРЕДЕЛЯЮЩИЙ УРОЖАЙНОСТЬ И КАЧЕСТВО ПЛОДОВ СОРТА РЕНЕТ СИМИРЕНКО

The purpose of research is to study a new type of formation of a “fruit wall” in relation to the apple tree of the Renet Simirenko variety when cultivated in the conditions of the south of Russia. Objectives: to establish the growth characteristics of vertical conductors from the cordon when planting a tree at an angle of 45° to the lower trellis; to determine the productivity of trees and yield on different types of formations – a “fruit wall” and a “slender spindle”; to establish commercial qualities and features of the formation of the mineral composition of fruits according to developmental phases, determined by the type of crown. The objects of the study are different planting schemes for planting an apple orchard of the Renet Simirenko variety (4.0 × 1.5 m and 4.0 × 0.5 m) established on the basis of EkvatorAgro LLC in 2020, using a new approach to planting in the pilot version garden, where annual seedlings with a planting pattern of 4.0 × 1.5 m were planted at an angle of 45° at a distance of 1.5 m from each other to form the tree crown in the form of a “fruit wall”. After planting the tree, a permanent horizontal trunk of the seedling, the length of which is 1.2–1.5 m, is directed and secured with a garter along the first wire, from which renewable vertical conductors subsequently grow. The results of the experiments showed that the proposed technology for growing an apple orchard, with a 3-fold reduction in the consumption of planting material, ensured early fruiting and made it possible to double the productivity of the tree, receiving 18 t/ha in the second year after planting the orchard, which will further increase the productivity up to 75–80 t/ha. The formation of a “fruit wall” by reducing the potential of the tree aimed at the growth of wood and leaves, provides rapid access of nutrients to the fruits located on vertical conductors, which makes it possible to grow apples of high commercial quality while increasing the efficiency of harvesting, mechanization, and automation of technological processes.

Magnetic Pulse Powder Compaction

Powder metallurgy (PM) offers several advantages over conventional melt metallurgy, including improved homogeneity, fine grain size, and pseudo-alloying capabilities. Transitioning from conventional methods to PM can result in significant enhancements in material properties and production efficiency by eliminating unnecessary process steps. Dynamic compaction techniques, such as impulse and explosive compaction, aim to achieve higher powder density without requiring sintering, further improving PM efficiency. Among these techniques, magnetic pulse compaction (MPC) has gained notable interest due to its unique process mechanics and distinct advantages. MPC utilizes the rapid discharge of energy stored in capacitors to generate a pulsed electromagnetic field, which accelerates a tool to compress the powder. This high-speed process is particularly well-suited for compacting complex geometries and finds extensive application in industries such as powder metallurgy, welding, die forging, and advanced material manufacturing. This paper provides an overview of recent advancements and applications of MPC technology, highlighting its capabilities and potential for broader integration into modern manufacturing processes.

Zinc deficiency enhances salt preference through altered peripheral and central taste processing mechanisms.

Zinc deficiency enhances salt preference through altered peripheral and central taste processing mechanisms.

BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in Bangladesh.

Bananas are among the most widely consumed fruits globally due to their appealing flavor, high nutritional value, and ease of digestion. In Bangladesh, bananas hold significant agricultural importance, being one of the most extensively cultivated fruits in terms of land coverage and ranking third in production volume. The banana image dataset presented in this article includes clear and detailed images of four common banana varieties in Bangladesh: Sagor Kola (Musa acuminate), Shabri Kola (Musa sapientum), Bangla Kola (Musa sp.), and Champa Kola (Musa sapientum), as well as four key stages of banana ripeness: Green, Semi-ripe, Ripe, and Overripe. The bananas were collected from wholesale markets and retail fruit shops located in different places in Bangladesh. Overall, the dataset has 2471 original images of different varieties of bananas and 820 original images of varying ripeness stages of bananas. All the images were carefully captured using a high-quality smartphone camera. Later, each image was manually reviewed, maintaining the quality standard throughout the dataset. The augmented version of the banana variety classification dataset contains 7413 images and the augmented banana ripeness stages dataset contains 2457 images. The dataset possesses immense potential in driving innovation and development of automated and efficient processes and mechanisms in several fields, including precision agriculture, food processing, and supply chain management. Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. Such ML and DL models can be leveraged to develop automated systems to determine the optimal harvest time, establish standards for quality control of bananas, develop products and marketing strategies through analysis of consumer preferences for various banana varieties and ripeness levels, and streamline the banana supply chain through improvements in harvesting, sorting, packaging, and inventory management. Additionally, researchers aiming to contribute to developing Computer Vision technologies in food and agricultural sciences will find this dataset valuable in advancing precision farming and food processing mechanisms. Therefore, the dataset has a vast capacity for automating banana production and processing, minimizing the costs of manual labor, and improving overall efficiency.

Development and ergonomic evaluation of hand held vegetable seedling transplanter

Efficient vegetable production is essential to meeting growing food demands, requiring innovative solutions to reduce labor intensity and enhance productivity. Vegetable cultivation, especially for small farmers, demands innovative and affordable solutions to enhance productivity and reduce labor-intensive practices. The mechanization of agricultural processes has become increasingly important in reducing the physical strain and time-intensive nature of traditional farming operations, paving the way for innovative tools that enhance efficiency and user comfort. Transplanting of seedlings, a highly drudgery-oriented operation, is mainly performed by women. A hand held seedling transplanter has been designed and developed for transplanting vegetables seedlings based on the anthropometric dimensions of the user population. The number of seedlings transplanted per min using the handheld seedling transplanter ranged from 19 to 21, compared to 16 to18 using the conventional method. By using the hand held seedling transplanter, a significant reduction in the cost of operation was achieved, with 11.44 to 16.52% savings in cost reported. The average working heart rate while transplanting using the seedling transplanter was 103 bpm, compared to 120 bpm for conventional transplanting. The average energy expenditure while transplanting the seedlings using the seedling transplanter was 7.72 kJ min-1, compared to 10.94 kJ min-1. The performance evaluation of the designed transplanted indicted an increase in human productivity and a decrease in musculoskeletal discomfort during the transplanting process. The introduction of ergonomic and cost-effective solutions, such as the handheld seedling transplanter, not only improves productivity and reduces operational costs but also promotes sustainable practices while prioritizing the well-being of farmers.

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

The quest to understand relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how optimal and specific network structures form to efficiently solve distinct tasks using a framework of performance-dependent network evolution, leveraging reservoir computing principles. Our study demonstrates that task-specific minimal network structures obtained through this framework consistently outperform networks generated by alternative growth strategies and Erdős-Rényi random networks. Evolved networks exhibit unexpected sparsity and adhere to scaling laws in node-density space while showcasing a distinctive asymmetry in input and information readout node distribution. Consequently, we propose a heuristic for quantifying task complexity from performance-dependently evolved networks, offering valuable insights into the evolutionary dynamics of the network structure-function relationship. Our findings advance the fundamental understanding of process-specific network evolution and shed light on the design and optimization of complex information processing mechanisms, notably in machine learning. Published by the American Physical Society 2025

What do pseudowords tell us about word processing? An overview

This article provides an overview of the use of pseudowords—letter strings that resemble real words by adhering to phonotactic and orthotactic rules (e. g., fambo follows the rules of English phonology and orthography, but it does not have an actual meaning)—in written word processing research, with a focus on readers in alphabetic languages. We review how pseudowords have been used in research to isolate specific features of words to examine the cognitive mechanisms underlying various aspects of their processing, including orthographic, phonological decoding, lexical-semantic, and syntactic components, as well as to the way those empirical observations have shaped theories and models of word recognition. The overview also considers their broader applications, such as in studying non-alphabetic scripts, speech processing, and language disorders like dyslexia. By providing a focused synthesis of empirical findings, this article underscores the critical insights that research using pseudowords offers into the interconnected nature of cognitive mechanisms in language processing.

A Conceptual Framework for a Latest Information-Maintaining Method Using Retrieval-Augmented Generation and a Large Language Model in Smart Manufacturing: Theoretical Approach and Performance Analysis

In the modern manufacturing environment, the ability to collect and refine data in real time to deliver high-quality data is increasingly important in maintaining a competitive advantage and operational efficiency. This paper proposes a conceptual architectural framework for the continuous knowledge updating of Retrieval-Augmented Generation-Large Language Model-based systems in a smart factory environment. The proposed framework provides theoretical models and validation methodologies, laying the groundwork for future practical implementations. Existing Retrieval-Augmented Generation-Large Language Model systems rely on static knowledge bases that are not able to effectively reflect new information in a real-time, changing manufacturing environment. The proposed framework design uses a data stream processing layer, a data integration layer, and a continuous learning layer as core components; in particular, the knowledge integration layer provides a mechanism for the efficient processing of real-time data and continuous learning. This study is significant in that it presents a mathematical model and a systematic verification methodology that can quantitatively predict the performance and scalability of the proposed architecture, thus providing practical design guidance for the implementation of Retrieval-Augmented Generation-Large Language Model systems in smart factory environments. This paper is organized as follows: Materials and Methods provides a detailed description of the architecture and methodology. Theoretical Analysis and Discussion covers the theoretical analysis and discussion, including performance prediction models, validation methodologies, and their practical implications. Finally, Conclusions summarizes the research findings and outlines directions for future work.

Velocity as an overlooked driver in the echolocation behavior of aerial hawking vespertilionid bats.

Moving animals must gather information at sufficient rates, detail, and range relative to their velocity while filtering this information to that essential for a given task.1,2 Echolocators, because of their active sensory system, are exceptional models for investigating how animals filter and adjust information flow to motor patterns.3,4 During airborne prey capture, bats adjust echolocation and, by extension, how they probe for information in distance- and context-dependent ways.5,6,7 We investigated how sensory probing guides movement and how niche specializations shape strategies to integrate information acquisition and motion velocity. Specifically, we recorded three sympatric bats of the same foraging guild (edge-space hawkers), but different niches, as they intercepted airborne prey under identical conditions. When hawking, we find that the trawler, Myotis daubentonii, and the hawker, Pipistrellus pygmaeus, exhibit similar flight and echolocation behavior, whereas the gleaner, M. nattereri, flies slower and produces calls of lower duration and intensity, greater bandwidth and call interval, but similar beam breadth. Strikingly, these differences in echolocation behavior converge when accounting for flight speed. We show that these species move equivalent distances between call emissions and that all bats travel through their respective sonar ranges in the same time interval. Further, each echolocation call's duration is related to the two-way travel time of its sonar range, and thus velocity, the same way across species. The similarity in how these bats sample their environment relative to velocity suggests general mechanisms of information processing and conserved traits underlying auditory attention in vespertilionid bats and, perhaps, other echolocators.

Awakening the Disengaged: Can Driving-Related Prompts Engage Drivers in Partial Automation?

This study explores the effectiveness of conversational prompts on enhancing driver monitoring behavior and takeover performance in partially automated driving under two non-driving-related task (NDRT) scenarios with varying workloads. Driver disengagement in partially automated driving is a serious safety concern. Intermittent conversational prompts that require responses may be a solution. However, existing literature is limited with inconsistent findings. There is little consideration of NDRTs as an important context, despite their ubiquitous involvement. A method is also lacking to measure driver engagement at the cognitive level, beyond manual and visual engagements. Participants operated a partially automated vehicle in a simulator across six predefined drives. In each drive, participants either received driving-related prompts, daily-conversation prompts, or no prompts, with or without a takeover notification. The first experiment instructed participants to engage in NDRTs at their choice and the second experiment incentivized solving demanding anagrams with monetary rewards. When participants were voluntarily engaged in NDRTs, answering driving-related prompts and receiving takeover notifications improved their monitoring behavior and takeover performance. However, when participants were involved in the more demanding and incentivized NDRT, answering prompts had little effect. The study supports the importance of both maintaining appropriate workload and processing driving-related information during partially automated driving. Driving-related prompts improve driver engagement and takeover performance, but they are not robust enough to compete with NDRTs that have high motivational appeals and cognitive demands. The design of driver engagement tools should consider the workload and information processing mechanisms.

Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection

Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial–temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.

Parameterization of the Differences in Neural Oscillations Recorded by Wearable Magnetoencephalography for Chinese Semantic Cognition.

Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time-frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time-frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics. Subsequently, ODTFR was parameterized based on the definition of local events. Finally, this study calculated the specific time-frequency values at which oscillation differences occurred in multiple auditory-language-processing regions. It was found that these oscillatory differences appeared in most regions and were mainly concentrated in the beta band. The average peak frequency of these oscillatory differences was 15.7 Hz, and the average peak time was 457 ms. These findings offer a fresh perspective on the neural mechanisms underlying the processing of distinct Chinese semantics and provide references and insights for analyzing language-related brain activities recorded by OPM-MEG.

Incremental accumulation of linguistic context in artificial and biological neural networks

Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words. Next, we introduce an alternative LLM-based incremental-context model that combines incoming short-term context with an aggregated, dynamically updated summary of prior context. This model significantly enhances the prediction of neural activity in higher-order regions involved in long-timescale processing. Our findings reveal how the brain’s hierarchical temporal processing mechanisms enable the flexible integration of information over time, providing valuable insights for both cognitive neuroscience and AI development.

Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.

When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing. Critically, we show a stepwise hierarchical progression of progressively higher order features over time, reflected in both bottom-up (early) and top-down (late) processing stages. Linguistically driven top-down mechanisms take the form of late N400-like responses, suggesting a central role of predictive coding mechanisms at multiple levels. As expected, the neural processing of lower-level acoustic feature responses is bilateral or right lateralized, with left lateralization emerging only for lexical-semantic features. Finally, our results identify potential neural markers, linguistic level late responses, derived from TRF components modulated by linguistic content, suggesting that these markers are indicative of speech comprehension rather than mere speech perception.Significance Statement We investigate neural processing mechanisms as speech evolves from acoustic signals to meaningful language, using stimuli ranging from without any linguistic information to fully well-formed linguistic content. Computational models based on speech and linguistic hierarchy reveal that cortical responses time-lock to emergent features from acoustics to linguistic processes at the sentence level, with increasing the semantic information in the acoustic input. Temporal response functions (TRFs) uncovered millisecond-level processing dynamics as speech and language stages unfold. Each speech feature undergoes early and late processing stages, with the former driven by bottom-up activation and the latter influenced by top-down mechanisms. These insights enhance our understanding of the hierarchical nature of auditory language processing.

Robust assessment of the cortical encoding of word-level expectations using the temporal response function

Objective. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words. However, previous studies relied on evaluation metrics designed for continuous univariate sound features, overlooking the discrete and sparse nature of word-level features. This mismatch has limited effect sizes and hampered progress in understanding lexical prediction mechanisms in ecologically-valid experiments.Approach. We investigate these limitations by analyzing both simulated and actual electroencephalography (EEG) signals recorded during a speech comprehension task. We then introduce two novel assessment metrics tailored to capture the neural encoding of lexical surprise, improving upon traditional evaluation approaches.Main results. The proposed metrics demonstrated effect-sizes over 140% larger than those achieved with the conventional temporal response function (TRF) evaluation. These improvements were consistent across both simulated and real EEG datasets.Significance. Our findings substantially advance methods for evaluating lexical prediction in neural data, enabling more precise measurements and deeper insights into how the brain builds predictive representations during speech comprehension. These contributions open new avenues for research into predictive coding mechanisms in naturalistic language processing.

The relationship between working memory, production, and comprehension: evidence from children's errors in complex wh questions.

English-speaking children sometimes make errors in production and comprehension of biclausal questions, known as "Scope-Marking Errors". In production, these errors surface as medial wh questions (e.g., What do you think who the cat chased? (Thornton, 1990)). In comprehension, children respond to questions like How did the boy say what he caught? by answering what was caught (de Villiers & Roeper, 1995). These errors resemble wh-scope marking questions, attested in languages like German. Together, these errors suggest temporary adoption of multiple UG-licensed grammars (e.g., Yang, 2002). However, Lutken et al. (2020) found that children who make these errors in production do not necessarily make errors in comprehension and vice versa. They suggest these errors stem from children's immature processing mechanisms. This article examines children's production, comprehension, and processing capabilities, specifically working memory (WM). We find a correlation between WM and error rate and suggest separate causes for production and comprehension errors.

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments can lead to varied outcomes, and how we process those outcomes may depend on our emotional state. Understanding how individuals interpret the sources of uncertainty is crucial for understanding adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility describes how quickly conditions change. Stochasticity, on the other hand, refers to outcome randomness. We investigated how anxiety and apathy influenced people's perceptions of uncertainty, and how uncertainty perception shaped explore-exploit decisions. Participants (N = 1001, non-clinical sample) completed a restless three-armed bandit task that was analyzed using both latent state and process models. Anxious individuals perceived uncertainty as resulting more from volatility, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

A new vision of the role of the cerebellum in pain processing.

The cerebellum is a structure in the suprasegmental nervous system classically known for its involvement in motor functions such as motor planning, coordination, and motor learning. However, with scientific advances, other functions of the cerebellum, such as cognitive, emotional, and autonomic processing, have been discovered. Currently, there is a body of evidence demonstrating the involvement of the cerebellum in nociception and pain processing. The aim of this review is to present the current literature on the anatomical, physiological, and functional aspects of the cerebellum in pain processing and suggest functional mechanisms of pain processing based on the cerebellum and its connections with other brain structures. To achieve this, searches were conducted in databases to identify relevant studies on the topic. Studies with relevant data and information were collected and summarized. Current literature demonstrates that the cerebellum receives nociceptive afferents from different pathways and exhibits activity in different regions including the vermis, hemispheres, and deep cerebellar nuclei in pain processing. Through its connections with different brain regions, it is possible that the cerebellum participates in the multidimensional processing of pain, which may make it a potential therapeutic target for pain treatment.

Low-frequency neural activity tracks syntactic information through semantic mediation.

How our brain integrates single words into larger linguistic units is a central focus in neurolinguistic studies. Previous studies mainly explored this topic at the semantic or syntactic level, with few looking at how cortical activities track word sequences with different levels of semantic correlations. In addition, prior research did not tease apart the semantic factors from the syntactic ones in the word sequences. The current study addressed these issues by conducting a speech perception EEG experiment using the frequency-tagging paradigm. Participants (N=25, Meanage=23;4, 16 girls) were asked to listen to different types of sequences and their neural activity was recorded by EEG. We also constructed a model simulation based on surprisal values of GPT-2. Both the EEG results and the model prediction show that low-frequency neural activity tracks syntactic information through semantic mediation. Implications of the findings were discussed in relation to the language processing mechanism.

Effect of sign language learning on temporal resolution of visual attention.

The visual environment of sign language users is markedly distinct in its spatiotemporal parameters compared to that of non-signers. Although the importance of temporal and spectral resolution in the auditory modality for language development is well established, the spectrotemporal parameters of visual attention necessary for sign language comprehension remain less understood. This study investigates visual temporal resolution in learners of American Sign Language (ASL) at various stages of acquisition to determine how experience with sign language affects perceptual sampling. Using a flicker paradigm, we assessed the accuracy of identifying out-of-phase visual flicker objects at frequencies up to 60Hz. Our findings reveal that third-semester ASL learners show increased accuracy in detecting high-frequency flicker, indicating enhanced temporal resolution. Interestingly, as learners achieve higher proficiency in ASL, their perceptual sampling reverts to typical levels, likely because of a shift toward predictive processing mechanisms in sign language comprehension. These results suggest that the temporal resolution of visual attention is malleable and can be influenced by the process of learning a visual language.