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This study uses data from the "Bangladesh Demographic and Health Survey (BDHS] 1999-2000" to investigate the predictors of child (age 1-4 years) mortality in a developing country like Bangladesh. The cross-tabulation and multiple logistic regression techniques have been used to estimate the predictors of child mortality. The cross-tabulation analysis shows that parents' education is the vital factor associated with child mortality risk, but in logistic regression analysis only the father's education has been found significant to reducing child mortality. Occupation of the father has been found to be a significant characteristic in both analyses; further, mother's standard of living index, breastfeeding status, and birth order has substantial impact on child mortality in Bangladesh. The findings also show that in both statistical analyses maternal health care variables such as timing of first antenatal check and tetanus toxoid (TT] during pregnancy has momentous effect on child mortality. Finally these findings specified that an increase in parents' education improve health care services, which should in turn raise child survival and should decrease child mortality in Bangladesh. (Contains 2 tables.)

Background: Antimalarial drug resistance poses a severe danger to global health. In Low- and Middle-Income Countries (LMICs), there is a lack of reliable information on antimalarial prescriptions for recent malarial fever in children under five. Our study aims to determine the prevalence of unqualified sources of antimalarial drug prescription for children under the age of five in 19 low- and middle-income countries. Methods: We performed a cross-sectional study of the Malaria Indicator Survey (MIS) datasets (n = 106265) across 19 LMICs. The recent MIS datasets were used, and the study only included children under five who had taken an antimalarial drug for a recent malarial fever. The outcome variable was classified into two distinct categories: those who had taken antimalarial drugs for malarial fever from qualified sources and those who did not. Findings: Among LMICs, we found that 87.1% of children under five received an antimalarial prescription from unqualified sources who had recently experienced malarial fever. In several LMICs (Tanzania, Nigeria, and Ghana), a substantial portion of recent antimalarial prescriptions for malaria was taken from unqualified sources (about 60%). Some LMICs (Guinea (31.8%), Mali (31.3%), Nigeria (20.4%), Kenya (2.6%), and Senegal (2.7%)) had low rates of antimalarial drug consumption even though children under five received a high percentage of antimalarial prescriptions from qualified sources for a recent malarial fever. Living in rural areas, having mothers with higher education, and having parents with more wealth were frequently taken antimalarial from qualified sources for recent malarial fever in children under five across the LMICs. Interpretation: The study draws attention to the importance of national and local level preventative strategies across the LMICs to restrict antimalarial drug consumption. This is because antimalarial prescriptions from unqualified sources for recent malarial fever in children under five were shockingly high in most LMICs and had high rates of unqualified prescriptions in certain other LMICs. [ABSTRACT FROM AUTHOR]

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted. [ABSTRACT FROM AUTHOR]

Magnesium ferrite (MF0.33) impregnated flower-shaped mesoporous ordered silica foam (MOSF) was successfully synthesized in present study. MOSF was added with precursor solution of MF0.33 during MF0.33 synthesis which soaked the materials and further chemical changes occurred inside the pore. Therefore, no additional synthesis process was required for magnesium ferrite impregnated mesoporous ordered silica foam (MF0.33-MOSF) synthesis. MF0.33-MOSF showed higher morphological properties compared to other magnesium ferrite modified nanomaterials and adsorbed arsenic III [As(III)] and arsenic V [As(V)] 42.80 and 39.73 mg/g respectively. These were higher than those of other Fe-modified adsorbents at pH 7. As MOSF has no adsorption capacity, MF0.33 played key role to adsorb arsenic by MF0.33-MOSF. Data showed that MF0.33-MOSF contain about 2.5 times lower Fe and Mg than pure MF0.33 which was affected the arsenic adsorption capacity by MF0.33-MOSF. Adsorption results best fitted with Freundlich isotherm model. The possible mechanism of arsenic adsorption might be chemisorption by electrostatic attraction and inner or outer-sphere surface complex formation. [ABSTRACT FROM AUTHOR]

Background: Attachment quality may affect psychological functioning. However, evidence on attachment representations and their correlates in children born to parents with schizophrenia and bipolar disorder is sparse. Methods: We compared attachment representations in a Danish sample of 482 children aged 7 years at familial high risk of schizophrenia, bipolar disorder, and population‐based controls and examined associations between attachment and mental disorders and daily functioning. Attachment representations were examined with the Story Stem Assessment Profile (SSAP). Mental disorders were ascertained in diagnostic interviews. Daily functioning was assessed with the Children's Global Assessment Scale. Results: We found no between‐group differences in attachment. Higher levels of secure attachment were associated with decreased risk of concurrent mental disorders in the schizophrenia high‐risk group. Higher levels of insecure and disorganized attachment were associated with increased risk of mental disorders across the cohort. Higher levels of secure and insecure attachment were associated with better and poorer daily functioning, respectively. In the current study, results regarding defensive avoidance could not be reported due to methodological limitations. Conclusion: Familial high risk of schizophrenia (FHR‐SZ) or bipolar disorder is not associated with less secure or more insecure attachment at age 7. Insecure and disorganized attachment representations index risk of mental disorders and poorer functioning. Secure attachment may be a protective factor against mental disorders in children at FHR‐SZ. Validation of the SSAP is needed. [ABSTRACT FROM AUTHOR]