Large-scale Data Research Articles

Pure tone audiogram classification using deep learning techniques.

Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying audiograms across various commonly encountered tasks. This single-centre retrospective study was conducted in accordance with the STROBE guidelines. A total of 12 518 audiograms were collected from 6259 patients aged between 4 and 96 years, who underwent pure tone audiometry testing between February 2018 and April 2022 at Tongji Hospital, Tongji Medical College, Wuhan, China. Three experienced audiologists independently annotated the audiograms, labelling the hearing loss in degrees, types and configurations of each audiogram. A deep learning framework was developed and utilised to classify audiograms across three tasks: determining the degrees of hearing loss, identifying the types of hearing loss, and categorising the configurations of audiograms. The classification performance was evaluated using four commonly used metrics: accuracy, precision, recall and F1-score. The deep learning method consistently outperformed alternative methods, including K-Nearest Neighbors, ExtraTrees, Random Forest, XGBoost, LightGBM, CatBoost and FastAI Net, across all three tasks. It achieved the highest accuracy rates, ranging from 96.75% to 99.85%. Precision values fell within the range of 88.93% to 98.41%, while recall values spanned from 89.25% to 98.38%. The F1-score also exhibited strong performance, ranging from 88.99% to 98.39%. This study demonstrated that a deep learning approach could accurately classify audiograms into their respective categories and could contribute to assisting doctors, particularly those lacking audiology expertise or experience, in better interpreting pure tone audiograms, enhancing diagnostic accuracy in primary care settings, and reducing the misdiagnosis rate of hearing conditions. In scenarios involving large-scale audiological data, the automated classification system could be used as a research tool to efficiently provide a comprehensive overview and statistical analysis. In the era of mobile audiometry, our deep learning framework can also help patients quickly and reliably understand their self-tested audiograms, potentially encouraging timely consultations with audiologists for further evaluation and intervention.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

A bibliometric review of perceptions of the built environment supported by multi-source data

ABSTRACT Information and digital technologies have revolutionized research methodologies for measuring people’s perceptions of built environments. Vast amounts of urban data can now be collected, providing more accurate and intuitive presentation and analysis tools. This enhances the research on the built environment from a human-centered perspective. In this paper, with the help of the CiteSpace knowledge map analysis tool, manual deep intervention was combined with clustering and stratified sampling of literature information to identify hot topics and research trends. The results demonstrate that there are two new perspectives. The first is the up-bottom urban/group perspective, specifically a holistic study of group perceptions by large-scale data, represented by big data, with technologies such as artificial intelligence. The second is the bottom-up humanistic/individual perspective, which accurately examines individual perceptions through physiological data based on ergonomics techniques, psychological, and other small-scale data. Furthermore, these two perspectives are interconnected, indicating a cross-trend in both directions. By bridging the gap between subjective perception and the real-world environment, integrating multi-source data offers a more precise and efficient foundation for thoroughly evaluating the built environment, which would help us better understand the built environment and satisfy the users’ demands in the research and practice of urban design.

Measuring the reionization optical depth without large-scale CMB polarization

We study the possibility of measuring the optical depth at reionization τ without relying on large-scale cosmic microwave background (CMB) polarization. Our analysis is driven by the need to obtain competitive measurements that can validate the state-of-the-art constraints on this parameter, widely based on E-mode polarization measurements at ℓ≤30. This need is partially motivated by the typical concerns regarding anomalies observed in the Planck large-scale CMB data as well as by the remarkable fact that, excluding these latter, τ consistently exhibits correlations with anomalous parameters, such as Alens and Ωk, suggesting that slightly higher values of the optical depth at reionization could significantly alleviate or even eliminate anomalies. Within the Λ cold dark matter model, our most constraining result is τ=0.080±0.012, obtained by combining Planck temperature and polarization data at ℓ>30, the Atacama Cosmology Telescope (ACT) and Planck measurements of the lensing potential, baryon acoustic oscillations (BAOs), and type-Ia supernova data from the Pantheon+catalog. Notably, using only ACT temperature, polarization, and lensing data in combination with BAOs and supernovae, we obtain τ=0.076±0.015, which is entirely independent of Planck. The relative precision of these results is approaching the constraints based on large-scale CMB polarization (τ=0.054±0.008). Despite the overall agreement, we report a slight 1.8σ shift toward larger values of τ. We also test how these results change by extending the cosmological model. While in many extensions they remain robust, in general, obtaining precise measurements of τ may become significantly more challenging. Published by the American Physical Society 2024

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

Deep Time Series Forecasting Models: A Comprehensive Survey

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out.

Uterine leiomyoma causes an increase in systolic blood pressure: a two-sample Mendelian randomization study

ObjectivesHypertension and hypertensive disorders of pregnancy (HDP) are common diseases in women at different stages, which affect women’s physical and mental health, and the impact of the latter on the offspring cannot not be ignored. Observational studies have investigated the correlation between uterine leiomyoma (UL) and the above conditions, but the relationship remains unclear. In this study, we employed two-sample Mendelian randomization (MR) analysis to assess the association between UL and hypertension, HDP, as well as blood pressure.MethodsWe collected genetic association data of UL (35,474 cases), hypertension (129,909 cases), HDP (gestational hypertension with 8,502 cases, pre-eclampsia with 6,663 cases and eclampsia with 452cases), systolic blood pressure (SBP) and diastolic blood pressure (DBP) (both 757,601 participants) from published available genome-wide association studies (GWAS). The single nucleotide polymorphisms (SNPs) associated with UL phenotype were used as instrumental variables, and hypertension, three sub-types of HDP, SBP and DBP were used as outcomes. The inverse-variance weighted (IVW) method was employed as the primary method of causal inference. Heterogeneity was assessed using Cochran’s Q test, and sensitivity analyses were conducted using MR-Egger regression and MR pleiotropy residual sum and outlier (MR-PRESSO) tests to evaluate the pleiotropy of instrumental variables. PhenoScanner search was used to remove confounding SNP. Robustness and reliability of the results were assessed using methods such as the weighted median and weighted mode.ResultsThe IVW analysis revealed a positive correlation between genetically predicted UL and SBP [odds ratio (OR)= 1.67, 95% confidence interval (CI):1.24~2.25, P = 0.0007], and no statistical association was found between UL and hypertension, HDP, or DBP. The MR-Egger regression suggested that the above causal relationships were not affected by horizontal pleiotropy. The weighted median method and weighted model produced similar results to the IVW.ConclusionBased on large-scale population GWAS data, our MR analysis suggested a causal relationship between UL and SBP. Therefore, women with UL, especially pregnant women, should pay attention to monitoring their blood pressure levels. For patients with hypertension who already have UL, interventions for UL may serve as potential therapeutic methods for managing blood pressure.

Efficient Structure from Motion for Large-Size Videos from an Open Outdoor UAV Dataset

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos. Despite the wide range of applications for SfM, performing mainstream SfM methods on such videos poses challenges due to their high computational cost. Our method consists of three main steps. Firstly, we employ a visual SLAM (VSLAM) system to efficiently extract keyframes, keypoints, initial camera poses, and sparse structures from downsampled videos. Next, we propose a novel two-step keypoint adjustment method. Instead of matching new points in the original videos, our method effectively and efficiently adjusts the existing keypoints at the original scale. Finally, we refine the poses and structures using a rotation-averaging constrained global bundle adjustment (BA) technique, incorporating the adjusted keypoints. To enrich the resources available for SLAM or SfM studies, we provide a large-size (3840 × 2160) outdoor video dataset with millimeter-level-accuracy ground control points, which supplements the current relatively low-resolution video datasets. Experiments demonstrate that, compared with other SLAM or SfM methods, our method achieves an average efficiency improvement of 100% on our collected dataset and 45% on the EuRoc dataset. Our method also demonstrates superior localization accuracy when compared with state-of-the-art SLAM or SfM methods.

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Location retrieval using qualitative place signatures of visible landmarks

Location retrieval based on visual information is to retrieve the location of an agent (e.g. human, robot) or the area they see by comparing their observations with a certain representation of the environment. Existing methods generally treat the problem as a content-based image retrieval problem and have demonstrated promising results in terms of localization accuracy. However, these methods are challenging to scale up due to the volume of reference data involved; and the image descriptions might not be easily understandable/communicable for humans to describe surroundings. Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a coarse-to-fine qualitative location retrieval method is proposed in this work to quickly narrow down the initial location of an agent by exploiting the available information in large-scale open data. This approach describes and indexes a location/place using the perceived qualitative spatial relations between ordered pairs of co-visible landmarks from the perspective of viewers, termed as ‘qualitative place signatures’ (QPS). The usability and effectiveness of the proposed method were evaluated using openly available datasets, together with simulated observations by considering different types perception errors.

Estimating Forest Canopy Height based on GEDI Lidar Data and Multi-source Remote Sensing Images

Abstract. Estimating forest canopy height is crucial for assessing aboveground biomass and carbon sequestration. Light detection and ranging (lidar) is an important technology for its ability in capturing vertical structural information. However, due to instrument limitations and cost constraints, acquiring large-scale and continuous forest data solely through lidar is challenging. To compensate this, remote sensing images can be used to cover wide regions. Therefore, leveraging multi-source data for constructing canopy height models (CHMs) holds great promise in this field. The objective of this study is to evaluate and compare the contributions of multi-source remote sensing data and methods in estimating forest canopy height. In constructing the CHM, the commonly used random forest (RF) and fully convolutional network (FCN) are assessed. The canopy height obtained from GEDI was used as the reference data, and Landsat 8 and Sentinel-2 data were used for prediction. Multiple CHMs were constructed for the Dabie Mountains, Central China, in 2019 based on different data sources and methods, respectively, which are then comparatively analysed. The results showed that (1) the accuracy of the CHM using Sentinel-2 as input is marginally better than that using Landsat 8 based on RF, where the difference is insignificant; and (2) FCN is less accurate than RF despite domain-specific fine-tuning, although further improvement in accuracy is expected by weighing in more FCN models.

Increasing marine trophic web knowledge through DNA analyses of fish stomach content: a step towards an ecosystem-based approach to fisheries research.

Multispecies and ecosystem models, which are key for the implementation of ecosystem-based approaches to fisheries management, require extensive data on the trophic interactions between marine organisms, including changes over time. DNA metabarcoding, by allowing the simultaneous taxonomic identification of the community present in hundreds of samples, could be used for speeding up large-scale stomach content data collection. Yet, for DNA metabarcoding to be routinely implemented, technical challenges should be addressed, such as the potentially complicated sampling logistics, the detection of a high proportion of predator DNA, and the inability to provide reliable abundance estimations. Here, we present a DNA metabarcoding assay developed to examine the diet of five commercially important fish, which can be feasibly incorporated into routinary samplings. The method is devised to speed up the analysis process by avoiding the stomach dissection and content extraction steps, while preventing the amplification of predator DNA by using blocking primers. Tested in mock samples and in real stomach samples, the method has proven effective and shows great effectiveness discerning diet variations due to predator ecology or prey availability. Additionally, by applying our protocol to mackerel stomachs previously analyzed by visual inspection, we showcase how DNA metabarcoding could complement visually based data by detecting overlooked prey by the visual approach. We finally discuss how DNA metabarcoding-based data can contribute to trophic data collection. Our work reinforces the potential of DNA metabarcoding for the study and monitoring of fish trophic interactions and provides a basis for its incorporation into routine monitoring programs, which will be critical for the implementation of ecosystem-based approaches to fisheries management.

Trigger mechanism centralized but discursive frame fragmented: China as a case for studying populism at the grassroots level in the social media age

ABSTRACT While most research focuses on top-down populism practiced by political actors in democratic countries, we paid attention to populist phenomena at the grassroots level in an authoritarian context. Specifically, we proposed a perspective of investigating populist discourse embedded in public opinion and adopted a fine-grained discursive frame approach to study Chinese online populism. Using large-scale social media data around trending events and employing cutting-edge computational semantic analysis methods, we discovered that economic anxiety is central to the trigger mechanism of Chinese online populism, and the specific populist discourse is fragmented in terms of ideological resources, cultural toolboxes, and semantic structure. This study not only provides a more comprehensive understanding of contemporary Chinese populism phenomena, but it also contributes to global populism research by providing valuable empirical evidence in authoritarian contexts and broadening the theoretical perspective of studying populism at the grassroots level.

Mixed-batch scheduling to minimize total tardiness using deep reinforcement learning

This study addresses the issue of scheduling batch machine to minimize total tardiness. Vacuum heat treatment allows multiple jobs to be processed as a batch, as long as they do not exceed the machine's weight and size limits; the weight and size of the jobs both impact the batch processing time. By considering the differences in the release time and due date for each job, a mixed-integer linear programming model is developed and validated using small-scale instances. To tackle large-scale scheduling problems, an intelligent algorithm based on deep reinforcement learning is proposed. Double deep Q-learning networks are designed, and the environmental state feature matrix is extracted as input parameters for the networks. Four job-sorting rules and three scheduling time windows are defined, resulting in twelve action rules within the action space. The most suitable action rule is dynamically selected based on the current batch's environmental status during the scheduling process. Through extensive comparison using large-scale random data, the proposed algorithm demonstrates significantly improved scheduling performance compared to the benchmark algorithms.

Teacher autonomy support counters declining trend in intrinsic reading motivation across secondary school

AbstractStudents’ intrinsic motivation to read, which is relevant to all forms of learning, tends to decline throughout secondary school. Based on self-determination theory (SDT), this study examines whether this downward trend is slowed when students perceive greater autonomy support in the classroom. We used large-scale panel data from the NEPS comprising N = 8193 students in Germany who reported their intrinsic motivation to read and their perceived autonomy support from German teachers at annual intervals from fifth to eighth grade. Scalar longitudinal measurement invariance was found for intrinsic reading motivation (IRM) and teacher autonomy support (TAS). A dual change score model showed a decline in IRM and a negative, non-significant decrease in TAS over time. Confirming our hypothesis, the decline in IRM was slowed by earlier levels of TAS. We discuss methods to counteract the decline in intrinsic reading motivation.

Efficient Feature Matching for Large-scale Images based on Cascade Hash and Local Geometric Constraint

Abstract. Feature matching plays a crucial role in 3D reconstruction to provide correspondences between overlapped images. The accuracy and efficiency of feature matching significantly impact the performance of 3D reconstruction. The widely used framework with the exhaustive nearest neighbor searching (NNS) between descriptors and RANSAC-based geometric estimation is, however, low-efficient and unreliable for large-scale UAV images. Inspired by indexing-based NNS, this paper implements an efficient feature matching method for large-scale images based on Cascade Hashing and local geometric constraints. Our proposed method improves upon traditional feature matching approaches by introducing a combination of image retrieval, data scheduling, and GPU-accelerated Cascade Hashing. Besides, it utilizes a local geometric constraint to filter matching results within a matching framework. On the one hand, the GPU-accelerated Cascade Hashing technique generates compact and discriminative hash codes based on image features, facilitating the rapid completion of the initial matching process, and significantly reducing the search space and time complexity. On the other hand, after the initial matching is completed, the method employs a local geometric constraint to filter the initial matching results, enhancing the accuracy of the matching results. This forms a three-tier framework based on data scheduling, GPU-accelerated Cascade Hashing, and local geometric constraints. We conducted experiments using two sets of large-scale UAV image data, comparing our method with SIFTGPU to evaluate its performance in initial matching, outlier rejection, and 3D reconstruction. The results demonstrate that our method achieves a feature matching speed 2.0 times that of SIFTGPU while maintaining matching accuracy and producing comparable reconstruction results. This suggests that our method holds promise for efficiently addressing large-scale image matching.

Patents, Freedom to Operate, and Follow-on Innovation: Evidence from Post-Grant Opposition

We study the blocking effect of patents on follow-on innovation by others. We posit that follow-on innovation requires freedom to operate (FTO), which firms typically obtain through a license from the patentee holding the original innovation. Where licensing fails, follow-on innovation is blocked unless firms gain FTO through patent invalidation. Using large-scale data from post-grant oppositions at the European Patent Office, we find that patent invalidation increases follow-on innovation, measured in citations, by 16% on average. This effect exhibits a U-shape in the value of the original innovation. For patents on low-value original innovations, invalidation predominantly increases low-value follow-on innovation outside the patentee’s product market. Here, transaction costs likely exceed the joint surplus of licensing, causing licensing failure. In contrast, for patents on high-value original innovations, invalidation mainly increases high-value follow-on innovation in the patentee’s product market. We attribute this latter result to rent dissipation, which renders patentees unwilling to license out valuable technologies to (potential) competitors. This paper was accepted by Ashish Arora, entrepreneurship and innovation. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Collaborative Research Center TRR 190]. F. Gaessler acknowledges financial support from the Spanish Agencia Estatal de Investigación through the Severo Ochoa Programme for Centres of Excellence in R&D [Barcelona School of Economics CEX2019-000915-S]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2019.02294 .

Herpesviral Keratitis Following COVID-19 Vaccination: Analysis of NHIS Database in Korea.

The purpose of this study was to determine the risk of herpesviral keratitis associated with 4 coronavirus disease 2019 (COVID-19) vaccines approved in South Korea, using large-scale data from the National Health Insurance Service. The study included 8,528,254 individuals, with cohorts categorized based on COVID-19 vaccination status. Two investigations were conducted: The first aimed to assess the risk of new-onset herpesviral keratitis while the second study focused on the risk of relapse in individuals with a preexisting diagnosis. Propensity score matching was used for cohort balancing, and various covariates, including vaccine types and comorbidities, were considered. Statistical analyses, including Cox proportional hazard regression, were used to calculate adjusted hazard ratio (aHR) and assess the risk of herpesviral keratitis. Individuals receiving COVID-19 vaccination exhibited a higher risk of new-onset herpesviral keratitis compared with the unvaccinated control group (aHR 1.43, 95% confidence interval, 1.19-1.73). Both mRNA and non-mRNA vaccines demonstrated an increased risk. Individuals with preexisting herpetic keratitis who received COVID-19 vaccination showed a higher risk of relapse herpesviral keratitis compared with the unvaccinated control group (aHR 1.98, 95% CI, 1.29-3.03). Sensitivity analyses supported the robustness of the results. This analysis of a large national health insurance database suggests an increased risk of both new-onset and relapse of herpesviral keratitis associated with COVID-19 vaccination in South Korea. While COVID-19 vaccination is crucial for pandemic control, health care providers should be aware of potential herpesvirus reactivation and consider appropriate prophylaxis and treatment for at-risk individuals.

Parental warmth buffers the negative impact of weaker fronto-striatal connectivity on early adolescents' academic achievement.

In past decades, the positive role of self-control in students' academic success has attracted plenty of scholarly attention. However, fewer studies have examined the link between adolescents' neural development of the inhibitory control system and their academic achievement, especially using a longitudinal approach. Moreover, less is known about the role of parents in this link. Using large-scale longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (N = 9574; mean age = 9.94 years at baseline, SD = .63; 50% girls), the current study took an integrative biopsychosocial approach to explore the longitudinal link between early adolescents' fronto-striatal connectivity and their academic achievement, with attention to the moderating role of parental warmth. Results showed that weaker intrinsic connectivity between the frontoparietal network and the striatum was associated with early adolescents' worse academic achievement over 2 years during early adolescence. Notably, parental warmth moderated the association between fronto-striatal connectivity and academic achievement, such that weaker fronto-striatal connectivity was only predictive of worse academic achievement among early adolescents who experienced low levels of parental warmth. Taken together, the findings demonstrate weaker fronto-striatal connectivity as a risk factor for early adolescents' academic development and highlight parental warmth as a protective factor for academic development among those with weaker connectivity within the inhibitory control system.

Survey on MapReduce Scheduler Algorithms in Hadoop Framework

Hadoop serves as a robust framework tailored for the storage and processing of vast data volumes across clusters. Its foundation lies in the Hadoop Distributed File System (HDFS) for data storage, complemented by the MapReduce paradigm for data processing. MapReduce, functioning as a parallel computing framework, operates adeptly on distributed clusters to manage large-scale data processing tasks. Within this framework, scheduling emerges as a pivotal aspect, influencing the overall system's performance and efficiency. The essence of scheduling in MapReduce lies in enhancing performance, minimizing response times, and optimizing resource allocation. This paper undertakes a systematic exploration of existing scheduling algorithms, offering a fresh classification and detailed examination of each category. Furthermore, the analysis delves into the core principles, objectives, as well as the strengths and weaknesses of these scheduling algorithms.