Serial Processing Research Articles

Fast feature- and category-related parafoveal previewing support free visual exploration.

While humans typically saccade every ∼250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes ∼50 ms to initiate and execute a saccade, this leaves only ∼200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements. Using multivariate pattern analysis, we demonstrate that future parafoveal images could be decoded at the feature and category level with peak decoding at ∼110 ms and ∼165 ms respectively, while the decoding of fixated objects at the feature and category level peaked at ∼100 ms and ∼145 ms. The decoding of features and categories was contingent on the objects being saccade goals. In sum, we provide insight on the neuronal mechanism of pre-saccadic attention by demonstrating that feature and category specific information of foveal and parafoveal objects can be extracted in succession within a ∼200 ms intersaccadic interval. These findings rule out strict serial or parallel processing accounts but are consistent with a pipeline mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.Significance Statement We provide neural evidence that future parafoveal saccade goals are processed surprisingly quickly at the feature and the category level before we saccade to them. Specifically, using multivariate pattern analysis applied to magnetoencephalography and eye-tracking data, we found that information about the colour and the category of parafoveal objects emerged at ∼110 ms and ∼165 ms respectively, with the same information about foveal objects emerging ∼100 ms and ∼145 ms. Our findings provide novel insight into the neuronal dynamics of parafoveal previewing during free visual exploration. The dynamics rule out strict serial or parallel processing, but are consistent with a pipelining mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy.

Verbal and Spatial Working Memory Capacity in Blind Adults and the Possible Influence of Age at Blindness Onset: A Systematic Review and Meta-analysis.

The loss of a sense, such as vision, forces individuals to adapt to their environment and its demands in a variety of ways. In the case of blindness, significant neurofunctional and cognitive changes have been documented. However, there is no clear consensus on the differences in performance between adult blind participants and sighted controls in cognitive processes such as working memory (WM). Two variables are important, including the cognitive task used to measure working memory and the age at which vision loss occurs. This review is aimed at exploring potential disparities in verbal and spatial WM performance between blind and sighted adults, as well as understanding how these differences may be influenced by the age of vision loss. A systematic search across PsycArticles, PsycInfo, Medline, and Web of Science databases identified 21 pertinent studies. The studies were categorized, and effect sizes were calculated through meta-analysis, distinguishing between verbal (auditory simple forward and backward span, complex span, and n-back) and visuospatial WM tasks (adapted Corsi-block and simple storage tasks, imagery tasks, and complex storage tasks). Visual sensory loss induces adaptations affecting WM function in blind participants. In the verbal domain, improved phonological processing and/or serial item position encoding might facilitate WM retrieval. In contrast, in spatial WM, an over-reliance on serial processing may hinder strategic grouping in blind individuals. This review highlights the need to further explore the role of age at the time of vision loss. Although evidence suggests that adaptations to serial processing may be more pronounced in early development, particularly in comparison to those who become blind in adulthood, the available data are limited. The study calls for further research to deepen our understanding of cognitive adaptations and their temporal dynamics in response to vision loss.

Hypergraph Convolutional Networks with Multi-Ordering Relations for Cross-Document Event Coreference Resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of scientific and technical kinds of literature. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Deep learning and feature reconstruction assisted vis-NIR calibration method for on-line monitoring of key growth indicators during kombucha production

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar and bacterial concentration in real-time, ensuring kombucha quality during production. The feature fingerprints of residual sugar and bacterial concentration were extracted by four variable selection algorithms and then reconstructed using serial and parallel processing methods. Based on these reconstructed features, Partial Least Squares (PLS) and Convolutional Neural Networks (1DCNN and 2DCNN) models were developed and compared. The experimental results showed that the 2DCNN model based on reconstruction features achieved superior performance. The RPDs of the residual sugar and bacterial concentrations models were 4.49 and 6.88, while the MAEs were 0.42 mg/mL and 0.04 (Abs), respectively. These results suggest that the proposed modeling strategy effectively supports quality control during kombucha production and provides a new perspective for spectral analysis.

Hemp Seed Protein Hydrolysate Enriched with γ-Aminobutyric Acid and Peptides by Microbial Bioconversion

Hemp seed protein (HSP), a by-product of hemp oil processing, was converted into a functional protein ingredient enriched with γ-aminobutyric acid (GABA) and peptides through a two-step microbial fermentation process. To enhance peptide and free amino acid production from HSP, it was hydrolyzed using alkaline protease produced by Bacillus subtilis HA. The HSP was hydrolyzed at a degree of 40% at 55 °C for 24 h, yielding a pH of 6.55, an acidity of 1.22%, and 205.45 mg% tyrosine equivalents. This process resulted in the production of low molecular-weight peptides. (<5000 Da) The total amino acid content and branched-chain amino acids (leucine, isoleucine, and valine) were 6.78 mg/g and 1.47 mg/g. Subsequently, the production of γ-aminobutyric acid (GABA) in the HSP hydrolysate was optimized through co-fermentation with lactic acid bacteria in the presence of 5% MSG at 30 °C for 5 days. The serial co-fermented HSP hydrolysate exhibited a GABA content of 33.98 mg/g and a viable bacterial count of 9.51 log CFU/mL for Lb. plantarum KS2020. This serial co-fermentation process, combining proteolysis and lactic acid fermentation, not only increased the peptide content but also promoted GABA accumulation, positioning HSP hydrolysate as a promising candidate for functional foods with potential health benefits.

Challenges and possibilities of the manual metallographic serial sectioning process using the example of a quantitative microstructural analysis of graphite in cast iron

Abstract The 3D microstructure analysis presented in this study focuses on the nodular graphite of an EN-GJS grade cast iron. The shape is analyzed based on shape factor and aspect ratio as well as average particle size and distribution. The shape of graphites in cast iron materials is critical to the mechanical properties of these alloys and is essential for the characterization of the material. Metallographic serial sectioning creates a digital twin of the material, which is removed layer by layer and visualized layer by layer under an optical microscope. From this three-dimensional twin, any number of planes can be digitally projected and analyzed. Conventional quantitative 3D analysis examines the voxel count and composition of a body in 3D space. The analysis presented here applies stereological 2D analysis methods to three spatial planes of the material. Two of the planes are digital projections of the twin. The two reconstructed planes of the material are chosen so that the direction vectors of all three planes form an angle of 90° to each other. The methodology is described in detail and the challenges and opportunities of the serial section method presented here are discussed.

Rethinking real-time risk prediction from multi-step time series forecasting on highway car-following scenarios

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems.

Microbial Diversity Assessment in Soil from Dumping Sites: Isolation, DNA Extraction and Electrophoretic Analysis

This study aims to investigate the microbial diversity present in soil samples collected from various dumping sites, which are known for their complex and heterogeneous waste environments. The primary objective is to isolate and characterize the microbial communities inhabiting these soils, providing insights into their composition and potential ecological roles. Soil samples were collected by digging 5 cm deep at multiple dumping locations to ensure a representative sampling of the microbial populations. The collected soil samples underwent a serial dilution process, extending up to 10^-9 and 10^-10 dilutions, to facilitate the isolation of individual microbial colonies. These diluted samples were then inoculated onto Standard Potato Dextrose Agar (PDA) media, a nutrient-rich medium conducive to microbial growth. Following an incubation period, distinct bacterial colonies were observed and isolated for further analysis. DNA was extracted from the isolated bacterial colonies using a standard DNA extraction protocol. The quality and purity of the extracted DNA were assessed through gel electrophoresis, a technique that separates DNA fragments based on their size. Remarkably, the electrophoretic analysis revealed clear and distinct DNA bands along with RNA bands, indicating successful extraction of high-quality microbial DNA. Notably, this was achieved without the use of proteinase treatment, which is typically employed to remove protein contaminants from DNA samples. The results of this study underscore the robustness of the methodologies employed in isolating and characterizing microbial DNA from soil samples collected at dumping sites. The presence of distinct DNA and RNA bands highlights the effectiveness of the DNA extraction process, even in the absence of proteinase treatment. These findings provide valuable insights into the microbial diversity within dumping site soils, suggesting a rich and varied microbial community that may play significant roles in soil health and waste decomposition.

High-Quality Ge-Rich Nanosheets on Silicon on Insulator Substrates Based on Ultra-Low Temperature Epitaxy

Ge and Ge-rich SiGe channel materials are expected to enhance performance in next-generation electronic devices, such as e.g. reconfigurable field effect transistors [1]. However, an inherent lattice mismatch exists between Ge and Si of about 4%. Ge layers on Si grow under compressive in-plane strain, drastically limiting the thickness of high-quality Ge layers on Si to a few monolayers, i.e., far too thin for practical device applications.In principle, vertically grown nanowires grown by vapor-liquid-solid growth (VLS) can be used to overcome these limitations as their small footprint can mitigate the strain. That is why, to date, basically all Ge-rich nanoelectronics device concepts are based on VLS nanowires.However, for device fabrication, the nanowires grown on untypical (111) substrates need to be picked and placed onto Si(001) substrates, which is a serial process and, thus, barely scalable.Thus, whenever two-dimensional nanosheets of a material are available, they provide distinct advantages with respect to device integration. In this context, pure Ge and SiGe alloyed nanowires formed from through top-down lithography from nanosheets would enable extended device functionalities.The problem is that for high Ge contents, the achievable thickness of pseudomorphic, defect-free (Si)Ge epilayers is just a couple of monolayers if grown directly on Si(001) substrates [2] as the inherent strain between the layer and substrate leads to harmful plastic or elastic relaxation under common growth conditions. Thus, using conventional epitaxy, the attainable defect-free SiGe/Si layer thicknesses are typically far from usable in electronic transport devices.Therefore, we depart from established (Si)Ge epitaxy temperatures of ≥500°C and employ molecular beam epitaxy (MBE) growth at ultra-low temperatures (ULT), ranging from 100°C to 350°C.[3] We show that the lowered surface kinetics leads to a pronounced layer supersaturation, allowing us to access layer thicknesses that are more than an order of magnitude larger than previously reported values. We highlight that pristine growth pressures deep in the ultra-high vacuum range ( ≤10-10 mbar) are crucial to keep the density of unwanted impurities in the (Si)Ge layers to a minimum and, thus, enable excellent electrical and optical properties of the grown heterostructures. These low growth pressures are particularly important in ULT growth since the low thermal budget impedes the efficient desorption of residual gas molecules from the substrate.We show that such fully strained, defect-free (Si)Ge epilayers grown directly on SOI substrates form high-quality nanosheets [4] for which properties like thickness, SiGe(Sn) content, and barrier material, doping can be conveniently tuned during the epitaxy process beyond past limitations. These nanosheets are the highly scalable base for advanced transport devices, such as reconfigurable transistors with excellent performance characteristics [4,5,6].

Target-distractor similarity predicts visual search efficiency but only for highly similar features.

A major constraining factor for attentional selection is the similarity between targets and distractors. When similarity is low, target items can be identified quickly and efficiently, whereas high similarity can incur large costs on processing speed. Models of visual search contrast a fast, efficient parallel stage with a slow serial processing stage where search times are strongly modulated by the number of distractors in the display. In particular, recent work has argued that the magnitude of search slopes should be inversely proportional to target-distractor similarity. Here, we assessed the relationship between target-distractor similarity and search slopes. In our visual search tasks, participants detected an oddball color target among distractors (Experiments 1 & 2) or discriminated the direction of a triangle in the oddball color (Experiment 3). We systematically varied the similarity between target and distractor colors (along a circular CIELAB color wheel) and the number of distractors in the search array, finding logarithmic search slopes that were inversely proportional to the number of items in the array. Surprisingly, we also found that searches were highly efficient (i.e., near-zero slopes) for targets and distractors that were extremely similar (≤20° in color space). These findings indicate that visual search is systematically influenced by target-distractor similarity across different processing stages. Importantly, we found that search can be highly efficient and entirely unaffected by the number of distractors despite high perceptual similarity, in contrast to the general assumption that high similarity must lead to slow and serial search behavior.

Optimizing patient-controlled analgesia: a narrative review based on a single center audit process.

Intravenous patient-controlled analgesia (PCA) is valuable for delivering opioids in a flexible and timely manner. Although it is designed to offer personalized analgesia driven by the patients themselves, users often report insufficient pain relief, which can be addressed by optimizing its settings and multimodal analgesia. We adopted a systematic approach to modify PCA protocols by utilizing a serial audit process based on institutional PCA data. This review retrospectively examined the process, encompassing data from 13,230 patients who had used PCA devices. The two modifications to the fentanyl-based PCA protocols resulted in three distinct phases. In the first phase, high opioid consumption and unintended PCA withdrawal were the common issues. These were addressed in the second phase by omitting the routine use of basal infusion. However, this led to increased delivery-to-demand ratios, mitigated in the third phase by increasing the bolus dose from 15 μg to 20 μg. These serial protocol changes have produced varied outcomes across different surgical departments, underscoring the need for careful and gradual adjustments and thorough impact assessments. Drawing insights from this audit process, we incorporated findings from the literature on PCA settings and multimodal analgesic approaches. This review underscores the significance of iterative feedback and refinement of analgesic protocols to achieve optimal postoperative pain management. Additionally, it discusses critical considerations regarding the postoperative audit processes.

Serial and strategic memory processes in younger and older adults

ABSTRACT We investigated age-related differences in serial and strategic processing during the encoding and retrieval of high-value words. Younger and older adults were presented with word triads positioned left, center, and right, with one word being more valuable than the others. In Experiment 1, younger adults more effectively recalled the middle, high-value word, demonstrating enhanced strategic memory. Younger adults were more likely to initiate recall with a high-value word whereas older adults were equally likely to initiate recall with a left and high-value word. Additionally, older adults were more likely to recall words in their presented order while younger adults strategically recalled successive high-value words. However, both age groups demonstrated strategic processing in Experiments 2 and 3, even without prior knowledge of the high-value word’s location. Thus, serial and strategic processing may differ based on age and task demands, but strategic processing is preserved in older adults in certain contexts.

Importance of grain boundary processes for plasticity in the quartz-dominated crust: Implications for flow laws

When H2O is present along grain boundaries, the deformation processes responsible for plasticity in silicate mineral aggregates can deviate from what may be conventionally expected. Although a necessary component of understanding crustal deformation processes, there is no theoretical framework that incorporates grain boundary processes into polycrystalline quartz rheology. To address this issue, we carried out high-pressure and high-temperature deformation experiments on fine-grained quartz aggregates. Our study illustrates that grain boundary migration (GBM) through dissolution-precipitation (in the presence of an aqueous fluid) and grain boundary sliding (GBS) may act as accommodation mechanisms to prevent hardening from dislocation glide. GBM and GBS can relax incompatibilities resulting from an inadequate number of independent slip systems, plastic anisotropy between neighbouring grains, and non-planar grain boundaries together with grain boundary junctions. As demonstrated earlier in the literature, GBM may act as a recrystallization mechanism counteracting hardening, but also is a potential mechanism that allow H2O to enter in the quartz crystal (hydrolization) at the experimental time-scale. The above serial processes occur over a range of more than two orders of magnitude in grain size (∼3 to 200 μm) and explain a grain-size-insensitive stress exponent (n = 2) and low activation energy (Q = 110 kJ/mol). In the absence of a switch to grain size sensitive deformation mechanisms induced by grain size reduction, our results imply that only a modest weakening (∼5 times the strength of the protolith) is needed (or possible) to localize shear zones in the Earth's crust.

Getting Hold of the Tobamovirus Particle-Why and How? Purification Routes over Time and a New Customizable Approach.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages and presents a novel, efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses, from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. The best practices and rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls, and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwhile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of these multivalent soft-matter nanorods.

Social strife at work: unravelling the link between workplace relationship conflict and employee ostracism behavior

Purpose The importance of workplace inclusion continues to gain scholarly acclaim. However, in reality, many employees choose to ostracize their colleagues post workplace relationship strife. With this notion the present study aims to delve into the intricate linkages between workplace relationship conflict (WRC) and employee ostracism behavior (EOB), exploring the serial mediating roles of relational identification (RI) and emotional energy (EE). Additionally, the study examines the potential moderating effect of perceived forgiveness climate (PFC) to understand how forgiveness climate may serve as a boundary condition in shaping these crucial relationships. Design/methodology/approach The analysis utilized five-wave time-tagged data collected from 228 employees through scenario and survey methods. The Hayes PROCESS Macro was employed to examine the proposed hypotheses. Findings The results indicate a positive influence of WRC on EOB. Additionally, RI and EE sequentially mediate the relationship between WRC and EOB. Furthermore, PFC moderates the serial mediation process (RI and EE) between WRC and EOB as well as the adverse effects of WRC on RI. Originality/value Grounded in the theoretical framework of conservation of resource (COR) theory and cognitive-affective personality system (CAPS) theory, the present study offers new insights. By establishing the complicated interplay of RI and EE between WRC and EOB along with the moderating role of PFC, the study extends the understanding of the mechanisms involved, providing a more comprehensive perspective. By shedding light on these complicated interconnected links, the study paved the way for positive social dynamics at work.

Revisiting the Deviation Effects of Irrelevant Sound on Serial and Nonserial Tasks.

Two types of disruptive effects of irrelevant sound on visual tasks have been reported: the changing-state effect and the deviation effect. The idea that the deviation effect, which arises from attentional capture, is independent of task requirements, whereas the changing-state effect is specific to tasks that require serial processing, has been examined by comparing tasks that do or do not require serial-order processing. While many previous studies used the missing-item task as the nonserial task, it is unclear whether other cognitive tasks lead to similar results regarding the different task specificity of both effects. Kattner et al. (Memory and Cognition, 2023) used the mental-arithmetic task as the nonserial task, and failed to demonstrate the deviation effect. However, there were several procedural factors that could account for the lack of deviation effect, such as differences in design and procedures (e.g., conducted online, intermixed conditions). In the present study, we aimed to investigate whether the deviation effect could be observed in both the serial-recall and mental-arithmetic tasks when these procedural factors were modified. We found strong evidence of the deviation effect in both the serial-recall and the mental-arithmetic tasks when stimulus presentation and experimental design were aligned with previous studies that demonstrated the deviation effect (e.g., conducted in-person, blockwise presentation of sound, etc.). The results support the idea that the deviation effect is not task-specific.

Using machine learning to identify key subject categories predicting the pre-clerkship and clerkship performance: 8-year cohort study.

Medical students need to build a solid foundation of knowledge to become physicians. Clerkship is often considered the first transition point, and clerkship performance is essential for their development. We hope to identify subjects that could predict the clerkship performance, thus helping medical students learn more efficiently to achieve high clerkship performance. This cohort study collected background and academic data from medical students who graduated between 2011 and 2019. Prediction models were developed by machine learning techniques to identify the affecting features in predicting the pre-clerkship performance and clerkship performance. Following serial processes of data collection, data preprocessing before machine learning, and techniques and performance of machine learning, different machine learning models were trained and validated using the 10-fold cross-validation method. Thirteen subjects from the pre-med stage and 10 subjects from the basic medical science stage with an area under the ROC curve (AUC) >0.7 for either pre-clerkship performance or clerkship performance were found. In each subject category, medical humanities and sociology in social science, chemistry, and physician scientist-related training in basic science, and pharmacology, immunology-microbiology, and histology in basic medical science have predictive abilities for clerkship performance above the top tertile. Using a machine learning technique based on random forest, the prediction model predicted clerkship performance with 95% accuracy and 88% AUC. Clerkship performance was predicted by selected subjects or combination of different subject categories in the pre-med and basic medical science stages. The demonstrated predictive ability of subjects or categories in the medical program may facilitate students' understanding of how these subjects or categories of the medical program relate to their performance in the clerkship to enhance their preparedness for the clerkship.

Hybrid metaheuristic schemes with different configurations and feedback mechanisms for optimal clustering applications

This paper addresses the critical gap in the understanding of the effects of various configurations and feedback mechanisms on the performance of hybrid metaheuristics (HMs) in unsupervised clustering applications. Despite the widespread use of HMs due to their ability to leverage multiple optimization methods, the lack of comprehensive studies on their configuration and feedback mechanisms effects often results in sub-optimal clustering performances and premature convergence. To tackle these issues, we introduce two algorithms for implementing eight distinct HM schemes, focusing on the impacts of parallel and serial processing models along with different feedback mechanisms. Our approach involves selecting candidate metaheuristics based on a mix of evolutionary and swarm-based methods, including the k-means algorithm, to form various HM-based clustering schemes. These schemes were then rigorously evaluated across a range of datasets and feedback mechanisms, further assessing their efficiency in the deployment of smart grid base stations. Performance analysis was based on total fitness evaluations, timing capabilities, and clustering accuracy. The results revealed that parallel HMs with decoupled feedback mechanisms performed best in terms of accuracy but at the cost of slower convergence rates as compared to serial HMs. Our findings further suggest that serial HMs will be best suited for time-sensitive applications where a compromise between speed and accuracy is acceptable, while parallel HMs with decoupled feedback mechanisms are preferable for scenarios where precision is paramount. This research significantly contributes to the field by providing a detailed analysis of HM performance in varying conditions, thereby guiding the selection of appropriate HM schemes for specific clustering tasks.

Early cognitive predictors of spelling and reading in German-speaking children

Theoretical backgroundWhile reading and spelling skills often are interconnected in models of literacy development, recent research suggests that the two skills can dissociate and that reading and spelling are associated with at least partly different cognitive predictors. However, previous research on dissociations between reading and spelling skills focused on children who have already mastered the first phases of literacy development. These findings suggest that dissociations are due to distinct deficits in orthographic processing (i.e., unprecise orthographic representations vs. inefficient serial processing). It is therefore unclear whether dissociations already become apparent during the initial stages, or rather emerge later in development. This study aims to enhance the understanding of the predictors of early spelling and reading skills, investigating potential variations, by considering various cognitive factors beyond well-established ones.MethodsData were collected at two time points: cognitive predictors and early reading and spelling skills were assessed at the end of kindergarten (T1) before formal literacy instruction started, and reading and spelling skills were again assessed in Grade 1 (T2). The data analysis included 353 first-grade participants. Linear regression analyses assessed predictive patterns, while logistic regression analyses explained children's likelihood of belonging to different proficiency groups (at-risk or typical skills).ResultsResults revealed phonological processing, letter knowledge, and intelligence, as significant predictors for Spelling in grade 1 (T2), even after adding the autoregressor (Spelling in kindergarten at T1) and the respective other literacy skill (Reading T2). For Reading in grade 1 (T2), phonological processing, and rapid automatized naming (RAN) surfaced as significant predictors after adding the autoregressor (Reading T1). However, only RAN surfaced as a significant predictor for Reading T2 after adding the respective other literacy skill (Spelling T2). In line with these findings, logistic regression analyses revealed that phonological processing predicted group allocation for Spelling T2 and RAN predicted group allocation for Reading T2.ConclusionsOverall, the study underscores the importance of phonological processing and letter knowledge as early predictors of spelling and reading skills in Grade 1. Moreover, intelligence is identified as a predictor for early spelling, while rapid automatized naming (RAN) emerges as a predictor for early reading.