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Socioeconomic status effects on children's vocabulary brain development
Abstract
Recent advancements in neuroimaging have made new ways of breaking down the dynamic interplay of genes and environmental influences, which affect the structure of accessible brain development. The impact on language abilities and verbal short-term memory of pre-school children from socioeconomic status (SES). Children from lower SES show less linguistic abilities and communication skills compared to higher SES children. The challenges of children's vocabulary brain development difference between lower and higher SES continues sometimes grows with age. Therefore this paper, machine learning assisted vocabulary development framework (MLVDF), has proposed a multimodal assessment of young children's brains' intellectual ability and capabilities. The proposed methodology is focused on the estimation and comparison with more developed strategies of nonverbal abilities using similar video interfaces. Machine learning technologies are used to determine a minimum number of variables capable of predicting particular children's cognitive skills. Thus, the findings highlight the complex effect of verbal and cognitive abilities on children's language and life experience. The results are obtained by the analysis of SES in Children's brain development as Cognitive skills ratio is 86.6%, Verbal abilities ratio is 87.12%, Children's brain capability ratio is 87.6%, Increasing the memory ratio is 83.5%, Learning attitude ratio is 93.8%, Reduce the effect of socioeconomic status ratio is 84.25%, Reading efficiency ratio is 87.6%.
... Cognitive errors are sometimes referred to as cognitive failures. To correct cognitive errors things that a person should be competent [19]. Cognitive failures such as poor memory, distractibility, and blunders result from cognitive failures. ...
Purpose: The COVID-19 pandemic has raised concerns of an imminent economic crisis, feared to be much worse than the Great Depression. Although the pandemic is global, its impact is deeply local. This paper aims to determine the socio-economic implications of the COVID-19 pandemic, particularly on the
various organisations and on the consumption patterns of individuals. On the social front, there will be a look into how social distancing, panic buying and other preventive measures necessitated by this pandemic have altered the behavioural pattern of individuals in their interactions with other economic
agents.
Design/Methodology/Approach: The methodology adopted is a qualitative one, where in-depth interviews were conducted across various sectors. Our focus is primarily on changes in consumption and occupational patterns. Accordingly, interviews of household members, labourers and employers have
been conducted. Such a study will have practical implications in terms of helping to build theory and
policy.
Findings: This study has analysed how global pandemics alter individual behaviour, from a socioeconomic standpoint.
Practical/Theoretical Implications: This research will prove useful in predicting human behaviour during any ‘flu-like’ epidemic in the future.
Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.
Objectives
Examine longitudinal associations between modes of travel to school and adiposity.
Setting
The UK.
Participants
8432 children surveyed at ages 7, 11 and 14 years from the UK Millennium Cohort Study.
Primary and secondary outcomes
Objective percentage body fat and body mass index (BMI). Transport mode was categorised as private motorised transport, public transport and active transport (walking or cycling). Socioeconomic position (SEP) was measured by household income group and occupational social class. We adjusted analyses for changes in the country of UK, frequency of eating breakfast, self-reported growth spurts, hours of screen time and days per week of moderate-to-vigorous physical activity. Longitudinal (panel) regression models adjusting for individual fixed effects examined associations in changes in mode of travel to school and adiposity, controlling for both time-varying and time-invariant potential confounders. Interaction tests and stratified analyses investigated differences by markers of SEP.
Results
At age 14 years, 26.1% of children (2198) reported using private motorised transport, 35.3% (2979) used public transport and 38.6% (3255) used active transport to get to school. 36.6% (3083) of children changed mode two times between the three waves and 50.7% (4279) changed once. Compared with continuing to use private transport, switching to active transport was associated with a lower BMI (−0.21 kg/m ² , 95% CI −0.31 to −0.10) and body fat (−0.55%, 95% CI −0.80% to −0.31%). Switching to public transport was associated with lower percentage body fat (−0.43%, 95% CI −0.75% to −0.12%), but associations with BMI did not reach statistical significance (−0.13 kg/m ² , 95% CI −0.26 to 0.01). Interaction tests showed a trend for these effects to be stronger in more deprived groups, but these interactions did not reach statistical significance.
Conclusion
This longitudinal study during a key life course period found switching to physically active forms of travel can have beneficial adiposity impacts; these associations may be more apparent for more disadvantaged children. Increasing active travel has potential to ameliorate inequalities.
Injuries are a leading cause of harm for children. This study explores the impact of determinants of health on children (0–19 years) injury-related mortality (namely remoteness and socio-economic disadvantage, calculated using the index of relative socio-economic advantage and disadvantage (IRSAD)). Cause of death data from the Australian Bureau of Statistics were sourced for children in Australia between 1 July 2007 to 30 June 2017. Fifteen injury categories (ICD-10-AM external cause codes) were used. Burden and trends by injury mechanism were explored. A total of 5153 children died; with road traffic incidents (3.39 per 100,000 population), intentional self-harm (2.46) and drowning (0.72) being the leading mechanisms. Female fatality rates in very remote areas (8.73) were nine times higher than in major cities (Relative Risk [RR] = 8.73; 95% Confidence Interval [95% CI]: 4.23–18.00). Fatality rates increased with remoteness; very remote areas recording an injury-related fatality rated six times (RR = 5.84; 95% CI: 3.76–9.12) that of major city residents. Accidental poisoning and intentional self-harm fatalities were more likely in high IRSAD areas, while road traffic fatalities were more likely in low and mid socio-economic areas (X2 = 69.1; p < 0.001). People residing in regional and remote areas and from low socio-economic backgrounds already face significant health and lifestyle challenges associated with disadvantage. It is time to invest in injury prevention interventions for these populations, as well as upstream policy strategies to minimize any further preventable loss of life
Background: Reward sensitivity (fun-seeking) is a risk factor for a wide range of high-risk behaviors. While high socioeconomic status (SES) is known to reduce reward sensitivity and associated high-risk behaviors, less is known about the differential effects of SES on reward sensitivity. It is plausible to expect weaker protective effects of family SES on reward sensitivity in racial minorities, a pattern called Minorities’ Diminished Returns (MDRs). Aim: We compared Caucasian and African American (AA) children for the effects of subjective family SES on children’s fun-seeking. Methods: This was a cross-sectional analysis of 7061 children from the Adolescent Brain Cognitive Development (ABCD) study. The independent variable was subjective family SES. The main outcome was children’s fun-seeking measured by the behavioral approach system (BAS) and behavioral avoidance system (BIS). Age, gender, marital status, and household size were the covariates. Results: In the overall sample, high subjective family SES was associated with lower levels of fun-seeking. We also found a statistically significant interaction between race and subjective family SES on children’s fun-seeking in the overall sample, suggesting that high subjective family SES is associated with a weaker effect on reducing fun-seeking among AA than Caucasian children. In race-stratified models, high subjective family SES was protective against fun-seeking of Caucasian but not AA children. Conclusion: Subjective family SES reduces the fun-seeking for Caucasian but not AA children.
Individuals with prenatal alcohol exposure (PAE) exhibit neurological deficits associated with brain injury including smaller brain volumes. Additional risk factors such as lower socioeconomic status (SES) may also have an impact on brain development for this population. This study examined how brain volumes are related to SES in both neurotypically developing children and adolescents, and those with PAE. 3D T1-weighted MPRAGE images were acquired from 69 participants with PAE (13.0 ± 3.2 years, range 7.1–18.8 years, 49% female) and 70 neurotypical controls (12.4 ± 2.9 years, range 7.0–18.5 years, 60% female) from four scanning sites in Canada. SES scores calculated using Hollingshead’s Four-Factor Index of Social Status from current caregiver placement were not significantly different between groups, though more children with PAE had lower SES scores compared to controls. Psychometric data comprised 14 cognitive measures, including executive functioning, attention and working memory, memory, math/numerical ability, and word reading. All cognitive scores were significantly worse in children with PAE compared to controls, though SES was not correlated with cognitive scores in either group after correction for multiple comparisons. All 13 brain volumes were smaller in children with PAE compared to children in the control group. Higher SES was associated with larger hippocampus and amygdala volumes in controls, but there were no such associations in children with PAE. Direct evaluation of the interaction between SES and diagnostic group did not show a significant differential impact of SES on these structures. These findings support previous links between SES and brain volumes in neurotypically developing children, but the lack of such a relationship with SES in children with PAE may be due to the markedly smaller brain volumes resulting from the initial brain injury and postpartum brain development, regardless of later SES.
Increased issues in the health of urban migrant children induced by mental stress have attracted worldwide attention. The stable growth of migrant children, which affects the sustainable stability of the community, is adversely impacted by sustained pressures without adequate assistance and advice. Therefore, psychological stress from each post on microblog has been established through research focused on detecting migrant children. Nevertheless, after stressful times, it is more important to consider stress from aspect to recognize stressful periods and stressful incidents that cause each stressful period. This paper identifies the problem of the recognition of migrant children in the open social network microblog stressful periods and stressor events. Besides, the improved Poisson-dependent probability model has been developed and focused on the study of the postings of migrant children during stressful activities in schools to compare stressors with a set of postings on Tencent Weibo (microblog). Through this model the average stressed times of migrant children which obtains more information regarding potential stressors affecting stressed periods.
Wearable sensors devices play an important role in healthcare applications. Further, The Body Sensor Network is a monitoring system extended to a health care context based on IoT technologies, composed of connected or implantable sensors. Medical therapy and general fitness are important elements of public health services. Therefore this paper suggested that Wearable electromagnetic measurement systems help the clinicians in physical therapy track and assess important recovery criteria and performance indicators. The swimming mechanism is easily captured by the movement of truck and limb action. Hence this approach concentrates on Cloud Assisted Recurrent Neural Network (CA-RNN) with multi-target optimization technique is mainly used to control the swimming movement and to balance the synapse weight between motor neuron. RNN is used for balancing the swimming motion and to control the weight transmission between the sensor and motor neurons. Cloud assisted frameworks can offer additional value to the safety, education, and quality of life due to the wide adoption of wearable sensor technology-based applications.
Humans with good health condition is some more difficult in today’s life, because of changing food habit and environment. So we need awareness about the health condition to the survival. The health-support systems faces significant challenges like lack of adequate medical information, preventable errors, data threat, misdiagnosis, and delayed transmission. To overcome this problem, here we proposed wearable sensor which is connected to Internet of things (IoT) based big data i.e. data mining analysis in healthcare. Moreover, here we design Generalize approximate Reasoning base Intelligence Control (GARIC) with regression rules to gather the information about the patient from the IoT. Finally, Train the data to the Artificial intelligence (AI) with the use of deep learning mechanism Boltzmann belief network. Subsequently Regularization _ Genome wide association study (GWAS) is used to predict the diseases. Thus, if the people has affected by some diseases they will get warning by SMS, emails. Etc., after that they got some treatments and advisory from the doctors.
Internet of Things (IoT) is changing the way many sectors operate and special attention is paid to promoting healthy living by employing IoT based technologies. In this paper, a novel approach is developed with IoT prototype of Wireless Sensor Network and Cloud based system to provide continuous monitoring of a patient’s health status, ensuring timely scheduled and unscheduled medicinal dosage based on real-time patient vitals measurement, life-saving emergency prediction and communication. The designed integrated prototype consists of a wearable expandable health monitoring system, Smart Medicine Dispensing System, Cloud-based Big Data analytical diagnostic and Artificial Intelligence (AI) based reporting tool. A working prototype was developed and tested on few persons to ensure that it is working according to expected standards. Based on the initial experiments, the system fulfilled intended objectives including continuous health monitoring, scheduled timely medication, unscheduled emergency medication, life-saving emergency reporting, life-saving emergency prediction and early stage diagnosis. In addition, based on the analysis reports, physicians can diagnose/decide, view medication side effects, medication errors and prescribe medication accordingly. The proposed system exhibited the ability to achieve objectives it was designed using IoT to alleviate the pressure on hospitals due to crowdedness in hospital care and to reduce the healthcare service delays.
Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.
Image segmentation is the significant tasks in maximum medical diagnosis tools. In recent years, perfect segmentation of medical images is of the great challenging issue. Maximum investigation in most of the areas exhibit that numerous individuals having brain tumors expired because of authentic circumstance for inaccurate recognition. The traditional K-Means algorithm suffers from numerous issue which results in less accurate segmentation, therefore an efficient approach need to be introduced. In this regard, the proposed methodology focused on the MRI medical images for segmentation. Dissimilar to the existing approaches, in this proposed approach a novel K-Means algorithm is introduced by means of incorporating the utmost dominant gray level of the image. This paper addressed the issue of arbitrarily selecting k no of pixels using dominant gray level of the image. The experimental consequences of the proposed approach showed that it has better performance in terms of the images when compared to the traditional K-Means approach.
Now-a-day’s brain abnormality is one among the dangerous neurological disorders that occurs because of the birth defects, brain stroke, brain injuries, genetic mutation and brain tumor. This brain disorder creates continue melancholia, bipolar disorder, stress disorder (PTSD) and so on. Due to this serious impact of the brain abnormalities, need to be identified within the beginning stage for eliminating the difficulties in humans day to day life. So, the automatic brain abnormality prediction process is created by utilizing electroencephalogram (EEG) for avoiding risk factor in future. As per the discussion, this paper introduces the evolutionary gravitational Neocognitron neural network(GNNN) for recognizing brain abnormalities with effective manner and it is especially suited for humans in war field. Initially, EEG signal is collected from patient; unwanted signal information is eliminated by using multi-linear principal component analysis from pre-processed signal, various features are extracted using affine invariant component analysis method and greedy global optimized features are chosen. The chosen features are analyzed using multi-layer virtual cortex model for predicting abnormal features. Finally the potency of the brain related abnormality prediction process developed using MATLAB tool and efficiency is examined using F-measure, Mathew correlation coefficient error rate, sensitivity, specificity, and accuracy. Along these lines the proposed framework effectively perceives the cerebrum variation from the norm with most astounding precision up to 99.48% with error rate.
Brain stroke is one of the three most deadly diseases in humans, especially since the time window from onset to treatment is very short, and this time determines whether the patient can get effective treatment. In addition, although individuals store a large amount of historical medical information in various medical organizations, the information is isolated and does not play its value. In response to the above problems, the business needs of Brain stroke prevention and emergency services have analyzed, and a networked collaborative service system for brain stroke prevention and first aid has designed and implemented. The services of the brain stroke prevention and first aid networked collaborative service system, discover and select candidate services, design service interfaces, and promote the collaborative work of medical organizations. Because the system involves the patient’s medical data, it has related to personal privacy. In the following work, this paper needs to further improve the security aspects of the system to ensure the safety and reliability of patient health information data.
The current study explores the influence of socioeconomic status (SES) and bilingualism on the linguistic skills and verbal short-term memory of preschool children. In previous studies comparing children of low and mid-high SES, the terms “a child with low-SES” and “a child speaking a minority language” are often interchangeable, not enabling differentiated evaluation of these two variables. The present study controls for this confluence by testing children born and residing in the same country and attending the same kindergartens, with all bilingual children speaking the same heritage language (HL-Russian). A total of 120 children (88 bilingual children: 44 with low SES; and 32 monolingual children: 16 with low SES) with typical language development, aged 5; 7–6; 7, were tested in the societal language (SL-Hebrew) on expressive vocabulary and three repetition tasks [forward digit span (FWD), nonword repetition (NWR), and sentence repetition (SRep)], which tap into verbal short-term memory. The results indicated that SES and bilingualism impact different child abilities. Bilingualism is associated with decreased vocabulary size and lower performance on verbal short-term memory tasks with higher linguistic load in the SL-Hebrew. The negative effect of bilingualism on verbal short-term memory disappears once vocabulary is accounted for. SES influences not only linguistic performance, but also verbal short-term memory with lowest linguistic load. The negative effect of SES cannot be solely attributed to lower vocabulary scores, suggesting that an unprivileged background has a negative impact on children’s cognitive development beyond a linguistic disadvantage. The results have important clinical implications and call for more research exploring the varied impact of language and life experience on children’s linguistic and cognitive skills.
Childhood poisoning is a major cause of morbidity in both developing and developed countries. In spite of the suc-cess of some interventions to prevent accidental poisoning in the pediatric population, toxic ingestions continue to be a common occurrence. This aim of this study was to identify the incidence rate and determinants of acute poison-ing among children (1-60 months old) who were admitted to the Poisoning Unit of a university hospital in Egypt. A study was conducted in the period from July 2011 until May 2012 at the poisoning unit of a university hospital. The studied children were from both rural and urban areas, were a mix of boys and girls, did not suffer from any mental disabilities, were aged between 1 month old to 60 months old, and were of Egyptian nationality. Data was collected by using a clinical examination form and a questionnaire. All parents/carers of the studied children were interviewed as well. Clinical assessment of the children included: general health conditions; AVPU (alert, respond to verbal stim-uli, respond to painful stimuli, unconsciousness); and clinical examinations. The findings of the study demonstrated that 18.5% of total admissions were children (1-60 months old), 62.5% were males, 83.3% did not attend nursery, 79.9% were from urban areas, 33% of mothers were illiterate, and 60.2% of poisonings were due to household prod-ucts. Kerosene alone was implicated in 24.3% of all cases; 37.4% of cases took place in the kitchen; 47.4% of cases were poisoned during the period between 8am and 4pm, and 65.4% reached the poisoning unit within 2 to 4 hours of accidental poisoning. Risk factors among the studied children were ordered by stepwise regression analysis as the following: non employed mothers; children who did not attend nursery; children of the male gender; and the educa-tion and literacy level of their mothers. Effective health promotion programs for parents and carers regarding poi-soning hazards are needed to increase awareness and reduce the incidence of poisoning among children. DOI: http://dx.doi.org/10.3329/seajph.v2i2.15943 South East Asia J Public Health | Jul-Dec 2012 | Vol 2 Issue 2 | 41-47
Children’s cognitive abilities and school achievements are deeply affected by parental socioeconomic status (SES). Numerous studies have reported lower cognitive performance in relation to unfavorable environments, but little is known about the effects of SES on the child’s neural structures. Here, we systematically explore the association between SES and brain anatomy through MRI in a group of 23 healthy 10-year-old children with a wide range of parental SES. We confirm behaviorally that language is one of the cognitive domains most affected by SES. Furthermore, we observe widespread modifications in children’s brain structure. A lower SES is associated with smaller volumes of gray matter in bilateral hippocampi, middle temporal gyri, left fusiform and right inferior occipito-temporal gyri, according to both volume- and surface-based morphometry. Moreover, we identify local gyrification effects in anterior frontal regions, supportive of a potential developmental lag in lower SES children. In contrast, we found no significant association between SES and white matter architecture. These findings point to the potential neural mediators of the link between unfavourable environmental conditions and cognitive skills.
Internet of Things (IoT) and Named Data Network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet Protocol (IP) based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this paper, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.
We propose a new bilateral generalization inhomogeneous short integer solution (Bi-GISIS)-based key exchange protocol with reusable key feature for post-quantum IoT security. It is aimed to reduce the time consumption in the key generation of key exchange protocols to be used in IoT devices. To obtain reusable key, we define modified bilateral pasteurization in the random oracle model. By ensuring reusable keys, the same key becomes available in several executions of the proposed protocol. This feature allows efficient usage of reusable keys in resource-constrained IoT architectures. The proposed scheme is suitable for quantum secure key exchange in D2D-aided fog computing environment. A key exchange protocol with improved key management process is constructed for D2D.
Work safety and health is an area that aims to protect the safety, health, and well-being of employed workers. In safety-critical industries, risk assessment has the primary role. This poses a variety of overall challenges, partially related to technological developments and rising necessities. In addition to contributing absences and the rate of disability at work, injured workers harm employee productivity and quality. Consequently, in this paper, Additive Heterogeneous Hybridized Computational Model (AHHCM) has been proposed for workplace safety and risk analysis. The study analyzed health and safety issues in the industries, along with their risk assessment and evaluated work-related diseases that impact work health and safety. The control hierarchy is a systematic way of reducing workers ' exposure and contributing to potential safety risks at the workplace. Appropriate measures can be taken immediately by recognizing uncertain behavior in real-time to prevent its reoccurrence. There is a positive relationship between undermining expectations and perceived job risk, which supports the assumption that employees' views of risk develop on the job.
Patient authentication acquires superior attention as a necessary security requirement in a remote health monitoring system. From a usability perspective, credential-based authentication techniques that use sensors, such as orientation, finger and camera, are not exceedingly well-convenient for medical applications. Moreover, patients could entrust credentials to someone else, thus violating the privacy policy. Therefore, further investigations are required to highlight the challenges and discuss the comprehensiveness of solutions. This study aims to discuss the open issues for mHealth authentication approaches derived from multilayer systematic reviews. Moreover, the study intends to discuss the design and develop and evaluate patient usability through a new sensor-based smartphone authentication technique. A new authentication methodology based on 3D touch and microphone sensors is proposed for real-time mHealth monitoring systems. The methodological aspects of the proposed approach are discussed, and three distinct and successive phases are presented. In the first phase, the adoption of two of the unexplored sensors as a new authentication technique is proposed. In the second phase, the development and data collection of the newly developed sensor are debated to put the methodology into perspective. Two techniques, namely ‘microphone sensor (inaudible acoustic signal recorder)’ and ‘Android versions’, are designed to represent the new mHealth authentication technique based on 3D touch and microphone sensors, respectively. In the third phase, the usability testing demonstration is discussed, and the main factor in the proposed usability (i.e. Remember) for test users is identified. The proposed methodology will be simulated and implemented in the future to serve as a guide for privacy preservation within authentication techniques, resulting in persistent and high-level usability within mHealth applications.
This study utilizes the 2012-2016 CFPS dataset and employs the structural equation modelling techniques to illustrate the mediating effects of parental practices and children’s learning attitudes between family social economic status (SES) and the child educational outcomes. Firstly, the results show that relative to family income and parental occupational levels, the key variable significantly links with children’s education as measured by children’s Chinese and math scores is parental educational levels. Parental education also bridges the gap in possible disparities in child outcomes arising from differences in region and Hukou. Secondly, the findings demonstrate the significant association between two mediators and child educational outcomes. It shows the importance of improving parenting practices and children’s learning attitudes for child development. Relevant policies or programs can be established to narrow the educational disparities among children between the lower and higher SES families. The findings are positive in that educational disparities can be narrowed and the vicious cycle of cultural reproduction can be broken.
Currently, deep-learning-based cognitive automation for decision-making in industrial informatics is a new hot topic in the field of cognitive computing, among which multiobjective architecture optimization is of great difficulty in the research area. When the existing algorithms face multiobjective cognitive model problems, it often takes a lot of time to continuously set different search preference parameters to generate a new search process. This article mainly aims to solve the problem in a multiobjective neural architecture search process, and the key issue is how to adapt user preferences during architectural search. We propose a new algorithm: linear-prefer coevolutionary algorithm. Compared to the original user-constrained method and the Pareto-dominant NSGA-II algorithm, we have faster adaptation time and better quality of adaptation. At the same time, it can respond to user’s needs at a relatively faster pace during the reasoning phase. Based on a large number of comparative test results, our algorithm is superior to the traditional cognitive automation algorithms for the multiobjective problem in search quality.
A large-scale cloud data center needs to provide high service reliability and availability with low failure occurrence probability. However, current large-scale cloud data centers still face high failure rates due to many reasons such as hardware and software failures, which often result in task and job failures. Such failures can severely reduce the reliability of cloud services and also occupy huge amount of resources to recover the service from failures. Therefore, it is important to predict task or job failures before occurrence with high accuracy to avoid unexpected wastage. Many machine learning and deep learning based methods have been proposed for the task or job failure prediction by analyzing past system message logs and identifying the relationship between the data and the failures. In order to further improve the failure prediction accuracy of the previous machine learning and deep learning based methods, in this paper, we propose a failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud. The trace-driven experiments show that our algorithm outperforms other state-of-art prediction methods with 93% accuracy and 87% for task failure and job failures respectively.
Aim
Very-low-birth-weight (VLBW) preterm infants have a higher rate of language impairments compared to children born full-term. Early identification of preterm infants at risk for language delay is essential to guide early intervention at a time of optimal neuroplasticity. This study examined near-term structural brain MRI and white matter (WM) microstructure assessed on diffusion tensor imaging (DTI) in relation to early language development in children born very preterm.
Methods
102 VLBW neonates (BW≤1500g, GA≤32wks) were recruited to participate from 2010-2011. Near-term structural MRI was evaluated for WM and cerebellar abnormalities. DTI fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were assessed. Language development was assessed with Bayley Scales of Infant-Toddler Development (BSID-III) at 18-22 months adjusted age. Multivariate models with leave-one-out cross-validation and exhaustive feature selection identified three brain regions most predictive of language function. Distinct logistic regression models predicted high-risk infants, defined by language scores >1 standard deviation below average.
Results
92/102 children returned for BSID-III evaluation. Composite language score mean±SD was 89.0±16.0; 31/92 children scored <85, including 15/92 <70, suggesting moderate-to-severe delay. Children with cerebellar asymmetry had lower receptive language sub-scores (p=0.016). Infants at high-risk for language impairments were predicted based on regional WM microstructure on DTI with high accuracy (sensitivity, specificity) for composite (89%, 86%); expressive (100%, 90%); and receptive language (100%, 90%).
Interpretation
Multivariate models of near-term structural MRI and WM microstructure on DTI may assist in identification of preterm infants at risk for language impairment, guiding early intervention.
Few studies have assessed the association of parental socioeconomic status (SES) with brain measures in neonates, at a time when exposure to the postnatal environment is minimal. Social support may buffer the adverse consequences of SES, and has been associated with better cognitive – emotional development in children. We studied the association of prenatal SES and social support with neonatal brain structure, and toddler cognition, and psychiatric symptoms. In a sample of 37 healthy neonates, we correlated a measure of SES and marital/partner status (an index of social support) with morphological features of the cerebral surface measured on high-resolution MRI scans between the 1st – 6th weeks of postnatal life. We then assessed how SES relates to cognitive and behavioral outcomes at age 24-months. We found that neonates born to mothers with lower SES had greater local volumes at the surface of the right occipital lobe, left temporal pole, and left inferior frontal and anterior cingulate regions. Partner status moderated the associations of SES on neonatal brain morphology. Lower SES was associated with poorer language scores and less severe ADHD and ODD symptoms. In summary, SES was associated with neonatal brain structure and language and behavioral outcomes at toddler age. Future studies with a greater sample size and longitudinal MRI scans will help to determine whether prenatal SES continues to relate to early brain development in the same or different brain regions.
Medical imaging has evolved as an essential component in many avenues of bio-medical research and clinical practices. It is a vast research area that is used to view the interior parts of the body for the diagnosis of diseases. Brain tumor extraction is a very crucial and highly challenging task in the medical field for that the MRI images are considered for the diagnosis of pathological structures present in the brain. Tumor segmentation using MRI image is done manually by the medical practitioners it causes inaccurate results due to the variability of size and shape of the brain tumors. In this paper, we have proposed the Multi-Objective Bat (MOB) Optimization followed by the Fuzzy-C-Means (FCM) Clustering to segment the tumor region efficiently with intelligent Wireless Sensor Network using NEMS for monitoring real world phenomenon. As a result, it provides the better sub-optimal solution compared with the existing approaches.
Objectives:
One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).
Methods:
We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.
Results:
Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole.
Conclusions:
The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.
Childhood socioeconomic status (SES) is associated with cognitive achievement throughout life. How does SES relate to brain development, and what are the mechanisms by which SES might exert its influence? We review studies in which behavioral, electrophysiological and neuroimaging methods have been used to characterize SES disparities in neurocognitive function. These studies indicate that SES is an important predictor of neurocognitive performance, particularly of language and executive function, and that SES differences are found in neural processing even when performance levels are equal. Implications for basic cognitive neuroscience and for understanding and ameliorating the problems related to childhood poverty are discussed.
Impacts of school closures on physical and mental health of children and young people: a systematic review
- Viner