Self-esteem and its associated factors among secondary school students in Klang District, Selangor.

Self-esteem is an important determinant of psychological well-being that is particularly problematic during adolescent life stage. There is a correlation between low self-esteem and other social problems among today's adolescents. This study was conducted to determine the mean self-esteem score, and to determine the association between self-esteem and age, sex, race, religion, number of siblings, ranking among siblings, family function, parental marital status and smoking among adolescents aged 12 to 20-years-old. A cross sectional study design using random cluster sampling method was done. Four out of a total of 35 secondary schools in Klang District, Selangor were selected. Respondents consisted of individual students in selected classes from the four selected schools. Data was collected using a self-administered, structured, pre-tested questionnaire and was analyzed using the SPSS version 12.0. Out of 1089 respondents, 793 completed the questionnaire (response rate 73.82%). The overall mean self-esteem score was 27.65. The mean self-esteem score for males (27.99) was slightly higher than females (27.31). The differences in the mean scores by race were statistically significant. There was a statistically significant relationship between mean self-esteem scores and sex, age, race, religion, number of siblings, smoking and family function. There was no statistically significant difference between mean self-esteem score with parental marital status and with ranking among siblings. The overall mean self-esteem score was 27.65. Self-esteem was associated with sex, age, race, religion, number of siblings, smoking and family function

Baseline adherence, socio-demographic, clinical, immunological, virological and anthropometric characteristics of 242 HIV positive patients on ART in Malaysia

Adherence to antiretroviral therapy (ART) prevents disease progression, and the emergence of resistant mutations. It also reduces morbidity, and the necessity for more frequent, complicated regimens which are also relatively more expensive. Minimum adherence levels of 95% are required for treatment success. Poor adherence to treatment remains a stumbling block to the success of treatment programs. This generates major concerns about possible resistance of the human immunodeficiency virus (HIV) to the currently available ARVs. This paper aims to describe baseline results from a cohort of 242 Malaysian patients receiving ART within the context of an intervention aimed to improve adherence and treatment outcomes among patients initiating ART. A single-blinded Randomized Controlled Clinical Trial was conducted between January and December, 2014 in Hospital Sungai Buloh. Data on socio-demographic factors, clinical symptoms and adherence behavior of respondents was collected using modified, pre-validated Adult AIDS Clinical Trials Group (AACTG) adherence questionnaires. Baseline CD4 count, viral load, weight, full blood count, blood pressure, Liver function and renal profile tests were also conducted and recorded. Data was analyzed using SPSS version 22 and R software. Patients consisted of 215 (89%) males and 27 (11%) females. 117 (48%) were Malays, 98 (40%) were Chinese, 22 (9%) were Indians while 5 (2%) were of other ethnic minorities. The mean age for the intervention group was 32.1 ± 8.7 years while the mean age for the control group was 34.7 ± 9.5 years. Mean baseline adherence was 80.1 ± 19.6 and 85.1 ± 15.8 for the intervention and control groups respectively. Overall mean baseline CD4 count of patients was 222.97 ± 143.7 cells/mm³ while overall mean viral load was 255237.85 ± 470618.9. Patients had a mean weight of 61.55 ± 11.0 kg and 61.47 ± 12.3 kg in the intervention and control groups, respectively. Males account for about 90% of those initiating ART in the HIV clinic, at a relatively low CD4 count, high viral load and sub-optimal medication adherence levels at baseline

neXtSIM: a new Lagrangian sea ice model

The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales

Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends

Sea ice volume has decreased in the last decades, evoked by changes in sea ice area and thickness. Estimates of sea ice area and thickness rely on a number of geophysical parameters which introduce large uncertainties. To quantify these uncertainties we use freeboard retrievals from ICESat and investigate different assumptions about snow depth, sea ice density and area. We find that uncertainties in ice area are of minor importance for the estimates of sea ice volume during the cold season in the Arctic basin. The choice of mean ice density used when converting sea ice freeboard into thickness mainly influences the resulting mean sea ice thickness, while snow depth on top of the ice is the main driver for the year-to-year variability, particularly in late winter. The absolute uncertainty in the mean sea ice thickness is 0.28 m in February/March and 0.21 m in October/November. The uncertainty in snow depth contributes up to 70% of the total uncertainty and the ice density 30–35%, with higher values in October/November. We find large uncertainties in the total sea ice volume and trend. The mean total sea ice volume is 10 120 ± 1280 km³ in October/November and 13 250 ± 1860 km³ in February/March for the time period 2005–2007. Based on these uncertainties we obtain trends in sea ice volume of −1450 ± 530 km³ a^−1 in October/November and −880 ± 260 km³ a^−1 in February/March over the ICESat period (2003–2008). Our results indicate that, taking into account the uncertainties, the decline in sea ice volume in the Arctic between the ICESat (2003–2008) and CryoSat-2 (2010–2012) periods may have been less dramatic than reported in previous studies. However, more work and validation is required to quantify these changes and analyse possible unresolved biases in the freeboard retrievals

Phylogeography of Bornean land snails suggests long-distance dispersal as a cause of endemism

Aim: Islands are often hotspots of endemism due to their isolation, making colonization a rare event, and hence facilitating allopatric speciation. Dispersal usually occurs between nearby locations according to a stepping-stone model. We aimed to reconstruct colonization and speciation processes in an endemic-rich system of land-based islands that does not seem to follow the obvious stepping-stone model of dispersal. Location: Five land-based habitat archipelagos of limestone outcrops in the floodplain of the Kinabatangan River in Sabah, Malaysian Borneo. Methods: We studied the phylogeography of three species complexes of endemic land snails, using multiple genetic markers. We calculated genetic distances between populations, applied BEAST2 to reconstruct phylogenies for each taxon, and subsequently reconstructed ancestral ranges using ‘BIOGEOBEARS’. Results: We found spatial genetic structure among nearby locations to be highly pronounced for each taxon. Genetic correlation was present at small spatial scales only, and disappeared at distances of five kilometres and above. Most archipelagos have been colonized from within the region multiple times over the past three million years, in 78% of cases as a result of long-distance dispersal or dispersal from non-adjacent limestone outcrops. The flow of the main geographical feature within the region, the Kinabatangan River, did not play a role. Main conclusions: Phylogeographic structure in these Bornean land snails has only partly been determined by small-scale dispersal, where it leads to isolation-by-distance, but mostly by long-distance dispersal. Our results demonstrate that island endemic taxa only very locally follow a simple stepping-stone model, whilst dispersal to non-adjacent islands, and especially long-distance dispersal, is most important. This leads to the formation of highly localized, isolated “endemic populations” forming the onset of a complex radiation of endemic species

Towards improving short-term sea ice predictability using deformation observations

Short-term sea ice predictability is challenging despite recent advancements in sea ice modelling and new observations of sea ice deformation that capture small-scale features (open leads and ridges) at the kilometre scale. A new method for assimilation of satellite-derived sea ice deformation into numerical sea ice models is presented. Ice deformation provided by the Copernicus Marine Service is computed from sea ice drift derived from synthetic aperture radar at a high spatio-temporal resolution. We show that high values of ice deformation can be interpreted as reduced ice concentration or increased ice damage – i.e. scalar variables responsible for ice strength in brittle or visco-plastic sea ice dynamical models. This method is tested as a proof of concept with the neXt-generation Sea Ice Model (neXtSIM), where the assimilation scheme uses a data insertion approach and forecasting with one member. We obtain statistics of assimilation impact over a long test period with many realisations starting from different initial times. Assimilation and forecasting experiments are run on synthetic and real observations in January 2021 and show increased accuracy of deformation prediction for the first 3–4 d. Similar conclusions are obtained using both brittle and visco-plastic rheologies implemented in neXtSIM. Thus, the forecasts improve due to the update of sea ice mechanical properties rather than the exact rheological formulation. It is demonstrated that the assimilated information can be extrapolated in space – gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed.</p

Data assimilation using adaptive, non-conservative, moving mesh models

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto-Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing

Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony

This article describes the observation techniques and suggests processing methods to estimate dynamical sea-ice parameters from data of the Earth Explorer 10 candidate Harmony. The two Harmony satellites will fly in a reconfigurable formation with Sentinel-1D. Both will be equipped with a multi-angle thermal infrared sensor and a passive radar receiver, which receives the reflected Sentinel-1D signals using two antennas. During the lifetime of the mission, two different formations will be flown. In the stereo formation, the Harmony satellites will fly approximately 300 km in front and behind Sentinel-1, which allows for the estimation of instantaneous sea-ice drift vectors. We demonstrate that the addition of instantaneous sea-ice drift estimates on top of the daily integrated values from feature tracking have benefits in terms of interpretation, sampling and resolution. The wide-swath instantaneous drift observations of Harmony also help to put high-temporal-resolution instantaneous buoy observations into a spatial context. Additionally, it allows for the extraction of deformation parameters, such as shear and divergence. As a result, Harmony's data will help to improve sea-ice statistics and parametrizations to constrain sea-ice models. In the cross-track interferometry (XTI) mode, Harmony's satellites will fly in close formation with an XTI baseline to be able to estimate surface elevations. This will allow for improved estimates of sea-ice volume and also enables the retrieval of full, two-dimensional swell-wave spectra in sea-ice-covered regions without any gaps. In stereo formation, the line-of-sight diversity allows the inference of swell properties in both directions using traditional velocity bunching approaches. In XTI mode, Harmony's phase differences are only sensitive to the ground-range direction swell. To fully recover two-dimensional swell-wave spectra, a synergy between XTI height spectra and intensity spectra is required. If selected, the Harmony mission will be launched in 2028

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.</p

Socio-demographic profile and predictors of outpatient clinic attendance among HIV-positive patients initiating antiretroviral therapy in Selangor, Malaysia

Background: Inconsistent literature evidence suggests that sociodemographic, economic, and system- and patient-related factors are associated with clinic attendance among the HIV-positive population receiving antiretroviral therapy (ART) around the world. We examined the factors that predict outpatient clinic attendance among a cohort of HIV-positive patients initiating ART in Selangor, Malaysia. Patients and Methods: This cross-sectional study analyzed secondary data on outpatient clinic attendance and sociodemographic, economic, psychosocial, and patient-related factors among 242 adult Malaysian patients initiating ART in Selangor, Malaysia. Study cohort was enrolled in a parent randomized controlled trial (RCT) in Hospital Sungai Buloh Malaysia between January and December 2014, during which peer counseling, medication, and clinic appointment reminders were provided to the intervention group through short message service (SMS) and telephone calls for 24 consecutive weeks. Data on outpatient clinic attendance were extracted from the hospital electronic medical records system, while other patient-level data were extracted from pre-validated Adult AIDS Clinical Trial Group (AACTG) adherence questionnaires in which primary data were collected. Outpatient clinic attendance was categorized into binary outcome - regular attendee and defaulter categories - based on the number of missed scheduled outpatient clinic appointments within a 6-month period. Multivariate regression models were fitted to examine predictors of outpatient clinic attendance using SPSS version 22 and R software. Results: A total of 224 (93%) patients who completed 6-month assessment were included in the model. Out of those, 42 (18.7%) defaulted scheduled clinic attendance at least once. Missed appointments were significantly more prevalent among females (n=10, 37.0%), rural residents (n=10, 38.5%), and bisexual respondents (n=8, 47.1%). Multivariate binary logistic regression analysis showed that Indian ethnicity (adjusted odds ratio [AOR] =0.235; 95% CI [0.063-0.869]; P=0.030) and heterosexual orientation (AOR =4.199; 95% CI [1.040-16.957]; P=0.044) were significant predictors of outpatient clinic attendance among HIV-positive patients receiving ART in Malaysia. Conclusion: Ethnicity and sexual orientation of Malaysian patients may play a significant role in their level of adherence to scheduled clinic appointments. These factors should be considered during collaborative adherence strategy planning at ART initiation