Social Wellbeing Among Women Living with Cancer

Women living with cancer are gradually increases in number due to the increase prevalence of breast and cervical cancer worldwide. The social impact of cancer is underappreciated compared to physical and psychological impacts. This study aimed to: 1) compare and analyze the social wellbeing (SWB) between women living with breast and cervical cancer, and 2) determine the best predictor of SWB in both groups. This cross-sectional study involved 58 and 47 women living wih breast and cervical cancer (n=105). Questionnaire of QOL-CS part III was used in data collection. Various statistical tests were used in data analysis (α<0.05). Sufficient SWB was mostly found in both cases. Family stress, work life, home activities, worriness, social support, personal relation, sexuality, social isolation, and financial burden were significantly different between cases (p=0.021, p=0.027, p=0.004, p=0.022, p=0.000, p=0.000, p=0.000, p=0.000, and p=0.001 respectively), resulted in significant difference in overall SWB between cases (p=0.000). Home activities were the best predictor of SWB in both cases (R2=0.680 and R2=0.840 respectively) with more influences on cervical cancer (84% of influence). SWB was better in women living with breast cancer

Spiritual Wellbeing in Breast and Cervical Cancer Survivors: Differences in Each Stage of Survivorship

Spiritual needs in cancer survivors are underappreciated compared to physical and psychological needs. This study aimed to: 1) compare and analyze the differences in spiritual wellbeing (SWB) between breast and cervical cancer survivors (BCS and CCS) generally, and between stages of survivorship specifically, and 2) determine the best predictor of SWB in both cases. This cross-sectional study involved 58 BCS and 47 CCS (n=105). Questionnaire of QOL-CS part IV was used in data collection. Various statistical tests were used in data analysis (α<0.05). SWB was significantly different between BCS and CCS (p=0.002), which influenced by significant differences in religious activity, spiritual activity, uncertainty, positive life changes, life goals, and hope (all p<α). In BCS, overall SWB was not significantly different between survivorship stages (p=0.179); but religious activity, life goals, and hope were significantly different (p=0.043, p=0.022, and p=0.036 respectively) which indicate that these three aspects change overtime along with the survivorship stages. While in CCS, SWB and all of its aspects were not significantly different between survivorship stages (all p>α) which indicate that SWB is stable/stagnant across the survival life span in CCS. Spiritual life changes and religious activity are the best predictors of SWB in both cases and were accounted for 70.3% (R2=0.703) and 69.7% (R2=0.697) variance of SWB in BCS and CCS respectively

Scalable Neural Network Decoders for Higher Dimensional Quantum Codes

Machine learning has the potential to become an important tool in quantum error correction as it allows the decoder to adapt to the error distribution of a quantum chip. An additional motivation for using neural networks is the fact that they can be evaluated by dedicated hardware which is very fast and consumes little power. Machine learning has been previously applied to decode the surface code. However, these approaches are not scalable as the training has to be redone for every system size which becomes increasingly difficult. In this work the existence of local decoders for higher dimensional codes leads us to use a low-depth convolutional neural network to locally assign a likelihood of error on each qubit. For noiseless syndrome measurements, numerical simulations show that the decoder has a threshold of around $7.1\%$ when applied to the 4D toric code. When the syndrome measurements are noisy, the decoder performs better for larger code sizes when the error probability is low. We also give theoretical and numerical analysis to show how a convolutional neural network is different from the 1-nearest neighbor algorithm, which is a baseline machine learning method

FHCF: A simple and efficient scheduling scheme for IEEE 802.11e wireless networks

The IEEE 802.11e medium access control (MAC) layer protocol is an emerging standard to support quality of service (QoS) in 802.11 wireless networks. Some recent works show that the 802.11e hybrid coordination function (HCF) can improve signi¯cantly the QoS support in 802.11 networks. A simple HCF referenced scheduler has been proposed in the 802.11e which takes into account the QoS requirements of °ows and allocates time to stations on the basis of the mean sending rate. As we show in this paper, this HCF referenced scheduling algorithm is only e±cient and works well for °ows with strict constant bit rate (CBR) characteristics. However, a lot of real-time applications, such as videoconferencing, have some variations in their packet sizes, sending rates or even have variable bit rate (VBR) characteristics. In this paper we propose FHCF, a simple and e±cient scheduling algorithm for 802.11e that aims to be fair for both CBR and VBR °ows. FHCF uses queue length estimations to tune its time allocation to mobile stations. We present analytical model evaluations and a set of simulations results, and provide performance comparisons with the 802.11e HCF referenced scheduler. Our performance study indicates that FHCF provides good fairness while supporting bandwidth and delay requirements for a large range of network loads

The Best Predictor of Anxiety, Stress, and Depression Among Institutionalized Elderly

Anxiety, stress, and depression are the three most common negative emotional constructs found in the elderly. Evidences available worldwide about how psychological problem could be resulted in mental disorder, and there is significant difference in the context of living in the nursing home and in community setting. This study aimed to determine the best predictor of anxiety, stress, and depression in elderly living in the nursing home, useful for future modification and intervention development. This cross-sectional study involved 145 elderly in a private nursing home in Surabaya, Indonesia. HARS, SPST-20, and GDS were used in data collection. Linear regression and one way ANOVA tests were used in data analysis (α<0.05). Results showed that mostly in old individuals, mild anxiety and stress, and undepressed state were found. Sensory problems and concentration difficulties were the best predictor of anxiety and stress respectively, which were accounted for 61.2% and 65.6% variances of anxiety and stress in nursing home residents respectively. Spirits, life energy, happiness, and feeling wonderful to be alive could not predict depression significantly. Feeling inferior to others is the best predictor of depression, which was accounted for 25.9% variance of depression in this population. Low self-esteem leads to depression in nursing home residents

Predictors of Post Prandial Glucose Level in Diabetic Elderly

Post prandial glucose (PPG) level describes the speed of glucose absorption after 2 hours of macronutrient consumption. By knowing this, we could get the big picture of insulin regulation function and macronutrient metabolism in our body. In elderly, age-related slower glucose metabolism leads to diabetes mellitus (DM) in older age. This study aimed to analyze the predictors of PPG level in diabetics elderly which consist of functional status, self-care activity, sleep quality, and stress level. Cross-sectional study design was applied in this study. There were 45 diabetic elderly participated by filling in study instruments. Pearson and Spearman Rank correlation test were used in data analysis (α<.05). Results showed that most respondents were female elderly, 60-74 years old, had DM for 1-5 years with no family history, and only 33.33% respondents reported regular consumption of oral anti diabetes (OAD). Hypertension was found to be frequent comorbidity. Statistical analysis results showed that functional status, self-care activity, sleep quality, and stress level were not significantly correlated with PPG level in diabetic elderly (all p>α), therefore these variables could not be PPG level predictors. Other factors may play a more important role in predicting PPG level in diabetic elderly

Comparison of Blood Pressure and Blood Glucose Level Among Elderly with Non-communicable Disease

Due to increasing age, elderly are prone to non-communicable diseases (NCD), such as hypertension (HT) and diabetes mellitus (DM). Easy physical condition monitoring of people with HT and/or DM is by measuring their blood pressure (BP) and/or blood glucose level (BGL) periodically. This study aimed to compare and analyze the differences of BP and BGL among elderly with HT and/or DM in Bangkok and Surabaya. This cross-sectional study involved 100 and 96 elderly with HT and/or DM in communities of Bangkok and Surabaya respectively (n=196). There were three groups of samples which consisted of 60 DM, 68 HT, and 68 DM&HT cases. Instruments used were demography questionnaire, sphygmomanometer, and glucometer. Test of one-way ANOVA, Least Significant Difference (LSD), Kruskal-Wallis, and Mann-Whitney U were used for data analysis (α<.05). There was a significant difference of systolic and diastolic BP found between groups (p=.000 and p=.011 respectively), but no difference found between the groups of HT and DM&HT (p=.657 and p=.330 respectively). There was a significant difference of BGL found between groups (p=.002), but no difference found between the groups of HT and DM (p=.075) and between the groups of DM and DM&HT (p=.066). BP is significantly different between the group of HT and DM in term of systole and diastole, especially in elderly, but BGL is similar. The risk of being HT for elderly with DM is very high. Elderly with DM&HT have high BP and BGL similarly to those with single disease of HT or DM

Comparison of Career Decision Difficulties Between Nursing Freshmen and Interns

Career selection is one of the most important decisions an individual makes in his life. High career expectation could result in career decision difficulties. This study aimed to compare and analyze the career decision difficulties between nursing freshmen and interns. This cross-sectional study involved 110 and 66 nursing freshmen and interns respectively (n=176) in two private nursing colleges. Career decision difficulties questionnaire was used in data collection. Descriptive statistic and independent sample t test were used in data analysis (α<.05). Results showed that most respondents experienced moderate difficulties in both groups. Overall, there was no significant difference of career decision difficulty found between groups (p=.057), but indecisiveness, dysfunctional myths, lack of knowledge about the process of career decision making, lack of information, lack of information about occupations, lack of information about ways of obtaining additional information, and internal conflicts were significantly different between groups (all p<α). Career decision difficulties occur similarly both in nursing freshmen and interns

An optimal gap theorem

By solving the Cauchy problem for the Hodge-Laplace heat equation for $d$-closed, positive $(1, 1)$-forms, we prove an optimal gap theorem for K\"ahler manifolds with nonnegative bisectional curvature which asserts that the manifold is flat if the average of the scalar curvature over balls of radius $r$ centered at any fixed point $o$ is a function of $o(r^{-2})$. Furthermore via a relative monotonicity estimate we obtain a stronger statement, namely a `positive mass' type result, asserting that if $(M, g)$ is not flat, then $\liminf_{r\to \infty} \frac{r^2}{V_o(r)}\int_{B_o(r)}\mathcal{S}(y)\, d\mu(y)>0$ for any $o\in M$