Description:
This paper examines developmental outcomes for children prenatally exposed to methamphetamine through maternal use. PSYCHINFO, Scopus, PubMed and ERIC databases were systematically searched for studies up to December 2020. The search identified 38 articles examining cognitive, language, motor and neuroanatomical outcomes in children from birth to 16 years. Study quality was appraised using the Newcastle Ottawa Quality Assessment Scale. Findings from neuroanatomical studies suggested that prenatal methamphetamine exposure may alter whole brain microstructure and reduce subcortical volumes across multiple brain regions. Meta-analysis of 14 studies using a random-effects model revealed associations between exposure and poorer intellectual functioning (Cohen’s d = 0.89, 95 % CI: 0.47–1.30), problem solving skills (Cohen’s d = 0.82, 95 % CI: 0.07 −1.56), short-term memory (Cohen’s d = 0.91, 95 % CI: 0.38–1.43), and language development (Cohen’s d = 0.74, 95 % CI: 0.30–1.18). These results emphasise the significant impact of intrauterine methamphetamine exposure across multiple areas of child development, noting that limited total sample size, heterogeneity between studies and control for confounds suggested further studies are required. There is a need for further intervention studies to identify effective prevention and harm minimisation approaches. •Prenatal meth exposure relates to poorer intellectual functioning, memory, and language development.•Exposed children show persistent cognitive deficits across the lifespan.•In utero meth exposure is associated with whole brain microstructural alterations and reduced subcortical volumes.
Description:
Accurate estimation of the in situ strength of concrete at early age is very important as it provides the necessary information required to start subsequent construction operations. Overestimation of the strength may cause serious safety hazards and underestimation may lead to unnecessary costly delays. This study investigates the performance of the maturity method in estimating the strength of in situ concrete subjected to cold weather at early age. Instrumented concrete slabs were subjected to cold weather conditions at early ages and their strengths were measured using drilled core samples from the slab. Sensors embedded in the slabs measured the temperature in the concrete which was used to estimate the strength using the maturity method. The measured core strengths at 24 and 72 h after casting are then compared with the estimated strengths using the maturity method and its performance is evaluated. The core strengths are also compared with the strength of standard cylinders cured at the same condition as the slabs. More than 250 cylinders from two slab thicknesses and four batches of concrete were used in the experiments to obtain statistically significant experimental data. The results show that the maturity method performed much better than the standard cylinder strength. On average the standard cylinder strength underestimated the core strength by more than 40% while the maturity method overestimated the strength by less than 10% with a lower variation.
Description:
This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance.
Description:
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
Description:
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
Description:
Summary Wireless sensor networks (WSNs) consist of resource‐starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery‐powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy‐scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource‐constrained nature of sensor nodes, inaccessible human terrains, and error‐prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload‐based mutual authentication scheme for a cluster‐based hierarchical WSN. The proposed scheme, also known as payload‐based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.
Description:
Anxiety disorders are common, and effective treatments exist, however, many people with anxiety disorders do not access these treatments due to numerous barriers. The current study aimed to examine treatment barriers that are specific to anxiety disorders and to examine the utility of the Health Belief Model (HBM) variables in predicting intention to seek psychological help in relation to anxiety disorders. The study employed a cross-sectional design and participants were a convenience sample comprising first year psychology students and other individuals who were interested in participating. A total of 278 individuals voluntarily participated in the current study by completing a battery of online self-report measures. Of these participants, there was an 89% completion rate and 243 met inclusion criteria (81% female Mean age 25.58, SD = 10.69). The most commonly reported barriers in this population included "I would not be able to afford treatment" (52%), followed by "I think I can/should work out my own problems rather than talking to a psychologist" (49%). Regression analyses indicated that 51% of the variance in intention to seek psychological help can be accounted for by the HBM variables. Perceived treatment benefits were the strongest predictor of help-seeking intention. The study highlights that individuals must interpret psychological treatment as potentially helpful in order to seek help for anxiety disorders. In order to improve help-seeking for anxiety disorders it is essential that professional bodies use targeted marketing strategies to increase the perceived helpfulness of seeing a mental health professional.
Description:
As a popular social networking site, Facebook may assist in promoting health behaviours and delivering interventions. However, women rarely use social media for health-related purposes. This study aimed to identify factors influencing women's intentions to use Facebook to gain health-related support or information. Using an online survey, data were collected from 159 adult females (Mean age = 26.81-years). Two hierarchical regressions were performed (Outcomes: health-related emotional support-seeking intentions on Facebook health-relatedinformation-seeking intentions on Facebook). Predictors were age, past information/support-seeking on Facebook, time spent on Facebook per day (Step 1), health concern, appearance concern (Step 2), health locus of control (LOC Step 3) and Theory of Planned Behaviour (TPB) variables (Step 4). As hypothesised, the TPB constructs significantly predicted intention to use Facebook for health information and support. For information-seeking intention, TPB variables explained an additional 18.3% of the variance over and above that explained in previous steps of the model. Significant contributors were past information-seeking, attitude towards seeking health-related information on Facebook, and subjective norm. For support-seeking intention, TPB variables explained an additional 20.2% of variance. Significant contributors were attitude towards seeking health-related support on Facebook, past support-seeking, and powerful others LOC. Both final models accounted for 56.5% of the variance in information/support-seeking intentions. The TPB constructs effectively predict intentions to seek health information/support on Facebook in women. To increase women's engagement with Facebook for health information and support-seeking purposes, previous behaviours, attitudes, perceived social pressures, and locus of control warrant consideration.
Description:
•This systematic review indicates that the effects of different type of cerebellar noninvasive brain stimulation (NIBS) on the Corticospinal Excitability (CSE) is different.•This review showed cerebellar PAS 6, a-tDCS + PAS25, and cTBS decrease the CSE level.•This review showed there is an increase in the CSE after using cerebellar PAS 2, PAS 21.5 + a-tDCS, and 1 Hz rTMS.•This review gives a good understanding of the effects of different cerebellar NIBS techniques affect the CSE, and help us to select the best NIBS technique for researches in cerebellar stimulation field, based on the effects that we need. Numerous studies have indicated that non-invasive brain stimulation (NIBS) of the cerebellum could modulate corticospinal excitability (CSE) in young healthy individuals. However, there is no systematic review and meta-analysis that clarifies the effects of cerebellar NIBS on CSE. The aim of this study was to provide a meta-analytic summary of the effects of cerebellar NIBS on CSE. Seven search engines were used to identify any trial evaluating CSE before and after one session of cerebellar NIBS in healthy individuals up to June 2018. Twenty-six studies investigating the corticospinal responses following cerebellar NIBS were included. Meta-analysis was used to pool the findings from included studies. Effects were expressed as mean differences (MD) and the standard deviation (SD). Risk of bias was assessed with the Cochrane tool. Meta-analysis found that paired associative stimulation (PAS) with 2 ms interval, a combination of PAS with 21.5 ms interval and anodal transcranial direct current stimulation, and repetitive transcranial magnetic stimulation with a frequency of < 5 Hz increase CSE (P PAS2 < 0.00001, P PAS21.5 +a-tDCS = 0.02, P rTMS = 0.04). However, continuous theta burst stimulation, a combination of PAS with 25 ms interval and anodal transcranial direct current stimulation, and PAS with a 6 ms interval decreased CSE (P PAS6 < 0.00001, P cTBS < 0.00001, P PAS25 +a-tDCS = 0.003). The results of this review show that cerebellar NIBS techniques are a promising tool for increasing CSE.
Description:
High attrition rates in new graduate nurses maybe attributed to unrealistic perceptions of being a nurse. It is therefore important to identify nursing students' perceptions and the factors that influence them. The present study was conducted to identify and describe the literature relating to nursing students' perceptions of being a nurse. Research questions guiding the review were: (1) What are nursing students' perceptions of nursing? (2) What factors influence nursing students' perceptions of nursing? A scoping literature review was conducted between the years 2008 and March 2022 to capture nursing student perceptions from multiple countries, and year levels of study to note the changes in perceptions overtime. The data sources consisted of five electronic data bases CINAHL, Medline, PsychINFO, Scopus, and Web of Science and search engine Google Scholar revealed 39 relevant sources. A scoping review methodology informed by the Joanna Briggs Institute and a published Scoping Review Checklist guided this review. An inductive thematic analysis identified five key themes. Key themes: Attributes and characteristics of nurses Nursing as a profession Skills required for nursing Gender perceptions and Influence of time and previous health work experience. Findings provide insight for future research to ensure the adequacy of curricula experiences in preparing new graduates for having realistic perceptions for practice. For nursing students to be prepared for the role of a nurse they must have realistic perceptions of what it means to be a nurse. Whilst it is evident that perceptions can be influenced by a variety of sources, this review highlights a lack of research pertaining to perceptions related to the physical, emotional, and social effects on an individual from being a nurse. •The databases reviewed ensured the inclusion of multiple countries and year levels of study.•Nursing students' perceptions of nursing change over the time of the nursing program.•Further investigation is required to identify what experiences within the program influence changes in perceptions.•Nursing students lack realistic perceptions pertaining to the physical, emotional and social factors of being a nurse.
Description:
Although only representing 0.05% of global freshwater, or 0.001% of all global water, soil water supports all terrestrial biological life. Soil moisture behaviour in most models is constrained by hydraulic parameters that do not change. Here we argue that biological feedbacks from plants, macro-fauna and the microbiome influence soil structure, and thus the soil hydraulic parameters and the soil water content signals we observe. Incorporating biological feedbacks into soil hydrological models is therefore important for understanding environmental change and its impacts on ecosystems. We anticipate that environmental change will accelerate and modify soil hydraulic function. Increasingly, we understand the vital role that soil moisture exerts on the carbon cycle and other environmental threats such as heatwaves, droughts and floods, wildfires, regional precipitation patterns, disease regulation and infrastructure stability, in addition to agricultural production. Biological feedbacks may result in changes to soil hydraulic function that could be irreversible, resulting in alternative stable states (ASS) of soil moisture. To explore this, we need models that consider all the major feedbacks between soil properties and soil-plant-faunal-microbial-atmospheric processes, which is something we currently do not have. Therefore, a new direction is required to incorporate a dynamic description of soil structure and hydraulic property evolution into soil-plant-atmosphere, or land surface, models that consider feedbacks from land use and climate drivers of change, so as to better model ecosystem dynamics.
Description:
•A new reintegration-driven ICI framework, called R-ICI, is proposed.•The proposed R-ICI model improves frequency stability of islands using ESSs.•Transient voltage stability of islands is improved using ESSs and SVCs.•The interaction between frequency and voltage is considered by a linear IFR model.•An active/reactive charging and discharging scheme is introduced for ESSs. In this paper, a reintegration-based multi-objective intentional controlled islanding (ICI) model is proposed to enhance resiliency of electrical power systems under catastrophic events. This remedial measure plan relies on a mixed-integer linear programming model with two objective functions including reintegration risk and total load shedding value. While ensuring that each island includes only coherent generators, the proposed multi-objective model solves the controlled islanding problem using lexicographic optimization approach. To ease the islands’ reintegration, charging reactive power, reliability, capacity, and power flow disruption of transmission lines are considered in the model. After implementation of controlled islanding, each resulted island may face temporary active/reactive load-generation imbalance, which may put the islands at the risk of frequency instability, transient voltage instability or a combination of both. The proposed model reduces these risks by modeling energy storage systems (ESSs) and static VAR compensators (SVCs) as fast corrective control actions. In addition to modeling voltage dependent loads in the controlled islanding problem, a linear island frequency response (IFR) model is proposed for frequency stability assessment. The test results of the proposed ICI model on the IEEE 39-bus and IEEE 118-bus test systems demonstrate its performance.
Description:
Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Description:
Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Description:
Sports have a focus on increasing participation, which contributes to increasing population levels of physical activity, social cohesion and longevity of the sport. The primary aim of this study was to examine reasons for drop-out of a popular team sport in Australia, Field Hockey and identify opportunities to increase participation. This longitudinal study obtained routinely collected registered player data from Hockey New South Wales over two consecutive years, and survey data from registered players who dropped out. Logistic regression models identified demographic subgroups who were more likely to drop out of sport, and the reasons for dropping out. In 2018, 8463 (31%) of hockey players did not return to play hockey after the previous season and 805 (10%) of these completed a survey. Specific groups who were more likely to stop playing included 5-6 years (OR: 2.1, 95% CI 1.8-2.6 reference: 12-17 years), females (OR: 1.1, 95% CI 1.0-1.2 reference: males), Indigenous (OR: 1.2, 95% CI 1.1-1.4 reference: non-Indigenous), most disadvantaged (OR: 1.1, 95% CI 1.0-1.2 reference: least disadvantaged) or regional and remote (1.1, 95% CI 1.0-1.2 reference: major cities). Top reasons for drop out were medical/age (17%), change in circumstances (16%) and high cost (13%), lack of time (13%) and lack of enjoyment (7%). Although Hockey successfully reaches a large proportion of underrepresented groups in sport, these groups are more likely to drop out. Sports should consult these groups to develop enjoyable, flexible, and modifiable versions of the game that are appropriate to their needs.
Description:
Sports have a focus on increasing participation, which contributes to increasing population levels of physical activity, social cohesion and longevity of the sport. The primary aim of this study was to examine reasons for drop-out of a popular team sport in Australia, Field Hockey and identify opportunities to increase participation. This longitudinal study obtained routinely collected registered player data from Hockey New South Wales over two consecutive years, and survey data from registered players who dropped out. Logistic regression models identified demographic subgroups who were more likely to drop out of sport, and the reasons for dropping out. In 2018, 8463 (31%) of hockey players did not return to play hockey after the previous season and 805 (10%) of these completed a survey. Specific groups who were more likely to stop playing included 5-6 years (OR: 2.1, 95% CI 1.8-2.6 reference: 12-17 years), females (OR: 1.1, 95% CI 1.0-1.2 reference: males), Indigenous (OR: 1.2, 95% CI 1.1-1.4 reference: non-Indigenous), most disadvantaged (OR: 1.1, 95% CI 1.0-1.2 reference: least disadvantaged) or regional and remote (1.1, 95% CI 1.0-1.2 reference: major cities). Top reasons for drop out were medical/age (17%), change in circumstances (16%) and high cost (13%), lack of time (13%) and lack of enjoyment (7%). Although Hockey successfully reaches a large proportion of underrepresented groups in sport, these groups are more likely to drop out. Sports should consult these groups to develop enjoyable, flexible, and modifiable versions of the game that are appropriate to their needs.
Description:
•Improved sharing of CBPP risk information increases CBPP vaccine up-take.•Demand increases by 1.5 and 2.3 per cent for improved access and information.•WTP for a 1 h reduction in travel duration is KES 54 (USD 0.52) per vaccine.•WTP for receipt of CBPP risk information is KES 52 (USD 0.67) per vaccine.•Controlling for attribute non-attendance improves model fit and estimates. The study estimates cattle owners' willingness-to-pay (WTP) for Contagious Bovine Pleuropneumonia (CBPP) vaccine in Samburu county, Kenya. Of particular policy relevance, the study presents findings on WTP for i) improved access to vaccines and ii) timely access to disease-risk information. The mean price for a CBPP vaccine was estimated at KES 66 (USD 0.64). This price relates to a CBPP vaccine that requires a 1.8 h commute, cattle owners' receipt of timely information that the CBPP disease risk is low-moderate and the vaccine lowers the risk of either tail-drop or post-vaccine abortion. The conditional WTP for mean travel duration and high-risk information are similar at KES 53.9 and KES 51.5. The marginal effect on demand for a 1 h additional travel duration and provision of CBPP disease risk information was estimated as a 1.5 per cent reduction and 2.3 increase. The results of this study indicate that cattle owners value greater levels of knowledge concerning the changing risk profile of CBPP in their community and improved access to CBPP vaccination services. Enhanced engagement with cattle owners concerning CBPP would likely result in a greater utilisation of available CBPP vaccines, conditional on the perceived CBPP disease risk.
Description:
•Reliability of TMS-induced MEPs is greater at higher test intensities.•Variability of TMS-induced MEPs decreases as test intensity increases.•Greatest significant changes in MEP amplitude and variability occur between same test intensities. High degrees of variability reported in Transcranial magnetic stimulation (TMS) assessment of corticospinal excitability (CSE) highlight the need to investigate its reliability as an assessment tool. This study investigated the effect of TMS test intensity on the amplitude, variability and test-retest reliability of motor evoked potentials (MEP). Twenty-five MEPs were recorded at 105%, 120%, 135%, 150% and 165% of resting motor threshold across three sessions in twelve participants. Repeated measures analysis of variance (RM-ANOVA), recruitment curve gradients and standardised z-value SDs were utilized to investigate the effect of TMS test intensity on MEP amplitude and variability. Test-retest reliability of MEP amplitude was assessed using RM-ANOVA and intraclass correlations (ICC). RM-ANOVA reported MEP amplitude significantly increased between 105–120% and 120–135% with non-significant increases thereafter. Recruitment curve gradients reduced as TMS test intensity increased. Standardised z-value SDs reported MEP amplitude variability reduced as TMS test intensity increased with the only significant reduction occurring between 120% and 135%. RM-ANOVA reported no significant effect of time on MEP amplitude, indicating agreement between sessions. ICCs indicated significant intra- and inter-session reliability for all TMS test intensities except for 105%. The only significant reduction in variability and final significant increase in MEP amplitude occurred between the same TMS test intensities. Reduced variability and increased reliability at higher TMS test intensities validate the use of higher TMS test intensities to assess changes in CSE in future research. To increase accuracy in capturing true changes in CSE we recommend assessing changes in CSE across a range of TMS test intensities.
Description:
Government doctor absenteeism from their public posts is a sizable problem across developing economies. The consumer demand estimation for outpatient fever treatment presented in this paper investigates the interrelationship between government doctor absenteeism and the large informal healthcare sector. Using a counterfactual framework, this paper estimates treatment effect of eliminating government doctor absenteeism. The effects are measured by changes to the market share of government Bachelor of Medicine and Bachelor of Surgery (MBBS) providers and resulting own‐price elasticities of demand for government MBBS providers and unqualified providers. Modelling incorporates patients expected health outcomes by provider via the use of a qualitative measure of word‐of‐mouth recommendations. Results indicate that eliminating government MBBS provider absenteeism in North India would increase utilisation of government outpatient fever treatments from 18% to 50%.
Description:
•Maternal depression in pregnancy was not predictive of postpartum maternal emotional availability (EA).•Maternal depression in pregnancy was not associated with perceived social support.•Depressive symptoms negatively predict maternal EA when partner support is low.•Depressive symptoms negatively predict maternal EA when family support is low.•Support from friends is not a moderator between depressive symptoms and maternal EA. Social support theory suggests that parental social support may influence the nature of early parenting behaviours and specifically the mother-infant relationship. This study examines whether support from a partner, friends or family is associated with differences in quality of mother-infant interactions in the context of maternal depression. 210 women were followed from early pregnancy to six months postpartum within Australian pregnancy cohort, the Mercy Pregnancy and Emotional Wellbeing Study (MPEWS). Mother-infant interactions within a standardised observation at six months postpartum were measured by the Emotional Availability (EA) Scales using total scores of the parental scales. In early and late pregnancy and at six months postpartum, mothers rated perceived maternal social support from a partner, family and friends using subscales of the Multidimensional Scale of Perceived Social Support. Depression was measured in early pregnancy and at six months postpartum using the Structured Clinical Interview for the DSM-IV-TR, with repeated measurement of depressive symptoms by the Edinburgh Postnatal Depression Scale (EPDS). Data was analysed using structural equation models. There were significant interactions between depressive symptoms in early pregnancy and perceived maternal support from a partner (B = .18, 95% CI = 03, .31) and separately from family (B = .12, 95% CI = .03, .32) in predicting maternal emotional availability. No such interaction was found for support from friends. While partner and family support moderated the association between early depressive symptoms and emotional availability, there were no direct associations between maternal depressive disorder in early pregnancy and perceived support, and further, maternal depression was not a significant predictor of emotional availability. Future studies should consider extending measurement of the mother-infant relationship beyond the EA Scales, inclusion of a measure of maternal childhood trauma, and replicating our findings. Maternal perception of partner and family support in the postpartum is a predictor of the association between early pregnancy depressive symptoms and maternal emotional availability.
Description:
•A new dynamic generator resiliency model against earthquake is presented.•Nonlinear and time-variant behavior of earthquake disturbance is modeled.•Energy transfer of earthquake to mechanical input power of generator is modeled.•Synchronism status of generators impacted by an earthquake is determined.•The proposed model is tested using real-world data. Natural disasters, such as earthquakes, can cause significant disruptions in power systems, such as loss of generations and loads. To evaluate the behavior of a generator to remain in synchronism, subjected to the large disturbance of an earthquake, we propose a dynamic generator resiliency model. The proposed approach models the effects of earthquake energy transfer to a generator considering the time-variant behavior of earthquake disturbance. In addition, the proposed model analyzes the transient behavior of generators impacted by an earthquake considering earthquake ground acceleration and generators’ dynamic characteristics. Using this model, we can determine synchronism status of impacted generators and transient stability status of a power system in response to an earthquake. The proposed approach is tested using real-world data including the data of a real earthquake occurred on a real power plant. The obtained results illustrate the effectiveness of the proposed model to correctly predict the impact of an earthquake on a power plant, and to determine the effects of earthquake magnitude and generator robustness (in terms of generator inertia and damping torque) on the response of a generator to an earthquake.
Description:
Research of clinical patients with a pre-existing psychological disorder involved in a disaster is limited. This study investigated relationships between pre-and post-earthquake psychopathology (i.e., anxiety, depression, and posttraumatic stress), peritraumatic distress, work and social impairment, perceived support post-earthquake, and personality dimensions in an outpatient, anxiety disorder population. Thirty-seven patients with pre-existing anxiety disorders completed standard clinical assessments pre-earthquake. They then completed a second set of questionnaires 3 months after exposure to the 2010 Christchurch, Aotearoa New Zealand, earthquake. Bivariate correlations on the variables determined what relationships were present, and paired samples t-tests assessed differences in pre- and post-earthquake anxiety, depression, and alcohol and drug consumption as well as relationships with peritraumatic distress, posttraumatic stress, and personality variables. Significant relationships were found between pre-earthquake psychopathology, peritraumatic distress, post-earthquake psychopathology, and impairment. Paired samples t-tests demonstrated anxiety and depression scores were significantly lower post-earthquake. However, prior anxiety and depression, as well as peritraumatic distress, were significantly associated with post-earthquake psychopathology, including posttraumatic stress and impaired work and social functioning. There were no differences between pre- and post-event alcohol and drug consumption. The personality dimensions of harm avoidance, self-directedness, and persistence significantly associated with post-disaster anxiety and depression. Promisingly, post-earthquake perceived support was significantly negatively correlated with depression and posttraumatic stress. Keywords: Anxiety, depression, posttraumatic stress, peritraumatic distress, personality temperament, earthquake
Description:
The interrelationship between the public and private sectors, and formal and informal healthcare sectors effects market-level service quality, pricing behaviour and referral networks. However, health utilisation analysis of national survey data from many low and middle income countries is constrained by the lack of disaggregated health provider data. This study is concerned with the pattern of repeat outpatient consultations for a single episode of fever from public and private qualified providers and private unqualified providers. Cross-sectional survey data from 1173 adult respondents sampled from three districts within India's most populous state-Uttar Pradesh is analysed. Data was collected during the monsoon season-September to October-in 2012. Regression analysis focuses on the pattern of repeats visits for a single episode of mild-sever fever as the dependent variable. Results show that Women and Muslims in rural north India are more likely to not access healthcare, and if they do, consult with low quality unqualified outpatient healthcare providers. For fever durations of four or more days, men are more likely to access unqualified providers compared to women. Results of the current study supports the literature that women's utilisation of outpatient healthcare for communicable illnesses in LMICs is often less than men. A relative lack of access to household resources explains why fever duration parameter estimates for women and men differ.