Rebuilding after war: micro-level determinants of poverty reduction in Mozambique

"Rather than looking at the association between poverty and various household and individual characteristics on a one-to-one basis (bivariate analysis), which often oversimplifies complex relationships and can lead to erroneous conclusions, this report uses multiple regression to analyze poverty and living standards econometrically. As methodological choices can have a strong influence on the results,much of the report is given over to a detailed discussion of the methodology used to conduct the analysis and sensitivity analysis to assess the robustness of the findings to alternative methodological choices. These include the construction of region-specific poverty linesand the empirical model of poverty determinants used. Estimates of poverty levels and the results of the model are presented, followed by simulations that indicate the impact on poverty of specific policy interventions." from Text of AbstractConflict, Poverty alleviation, Living standards Mozambique,

Some Elementary Inequalities Between Mean and Standard Deviation

Some inequalities for the mean and standard deviation of continuous probability distributions are presented here in this paper and their geometrical significance has also been discussed. It has been shown that the inequalities obtained in this paper are better than the inequalities discussed by J. Muilwijk[6]

Hardware-assisted instruction profiling and latency detection

Debugging and profiling tools can alter the execution flow or timing, can induce heisenbugs and are thus marginally useful for debugging time critical systems. Software tracing, however advanced it may be, depends on consuming precious computing resources. In this study, the authors analyse state-of-the-art hardware-tracing support, as provided in modern Intel processors and propose a new technique which uses the processor hardware for tracing without any code instrumentation or tracepoints. They demonstrate the utility of their approach with contributions in three areas - syscall latency profiling, instruction profiling and software-tracer impact detection. They present improvements in performance and the granularity of data gathered with hardware-assisted approach, as compared with traditional software only tracing and profiling. The performance impact on the target system – measured as time overhead – is on average 2–3%, with the worst case being 22%. They also define a way to measure and quantify the time resolution provided by hardware tracers for trace events, and observe the effect of finetuning hardware tracing for optimum utilisation. As compared with other in-kernel tracers, they observed that hardware-based tracing has a much reduced overhead, while achieving greater precision. Moreover, the other tracing techniques are ineffective in certain tracing scenarios

Fine-grained nested virtual machine performance analysis through first level hypervisor tracing

Nowadays, nested VMs are often being used to address compatibility issues, security concerns, software scaling and continuous integration scenarios. With the increased adoption of nested VMs, there is a need for newer techniques to troubleshoot any unexpected behavior. Because of privacy and security issues, ease of deployment and execution overhead, these investigation techniques should preferably limit their data collection in most cases to the physical host level, without internal access to the VMs. This paper introduces the Nested Virtual Machine Detection Algorithm (NDA) - a host hypervisor based analysis method which can investigate the performance of nested VMs. NDA can uncover the CPU overhead entailed by the host hypervisor and guest hypervisors, and compare it to the CPU usage of Nested VMs. We further developed several graphical views, for the TraceCompass trace visualization tool, to display the virtual CPUs of VMs and their corresponding nested VMs, along with their states. These approaches are based on host hypervisor tracing, which brings a lower overhead (around 1%) as compared to other approaches. Based on our analysis and the implemented graphical views, our techniques can quickly detect different problems and their root causes, such as unexpected delays inside nested VMs

Sleep assessment by patients and nurses in the intensive care: An exploratory descriptive study

© 2016. Background: Sleep disruption is common in intensive care unit (ICU) patients, with reports indicating reduced quality and quantity of sleep in many patients. There is growing evidence that sleep in this setting may be improved. Aim: To describe ICU patients' self-report assessment of sleep, examine the relationship between patients' self-reported sleep and their reported sleep by the bedside nurse, and describe the strategies suggested by patients to promote sleep. Methods: An exploratory descriptive study was undertaken with communicative adult patients consecutively recruited in 2014-2015. Patients reported sleep using the Richards-Campbell Sleep Questionnaire (score range 0-100. mm; higher score indicates better sleep quality), with nursing assessment of sleep documented across a five level ordinal variable. Patients were asked daily to describe strategies that helped or hindered their sleep. Ethical approval for the study was gained. Descriptive statistical analysis was performed [median (interquartile range)]; relationships were tested using Spearman's rank correlation and differences assessed using the Kruskal-Wallis test; . p . <. 0.05 was considered significant. Results: Participants (n = 151) were recruited [age: 60 (46-71) years; ICU length of stay 4 (2-9) days] with 356 self-reports of sleep. Median perceived sleep quality was 46 (26-65) mm. A moderate relationship existed between patients' self-assessment and nurses' assessment of sleep (Spearman's rank correlation coefficient 0.39-0.50; . p . <. 0.001). Strategies identified by patients to improve sleep included adequate pain relief and sedative medication, a peaceful and comfortable environment and physical interventions, e.g. clustering care, ear plugs. Conclusion: Patients reported on their sleep a median of 2 (1-3) days during their ICU stay, suggesting that routine use of self-report was feasible. These reports revealed low sleep quality. Patients reported multiple facilitators and barriers for sleep, with environmental and patient comfort factors being most common. Interventions that target these factors to improve patient sleep should be implemented

Effectiveness of Solvent Vapor Annealing over Thermal Annealing on the Photovoltaic Performance of Non-Fullerene Acceptor Based BHJ Solar Cells

We explore two small molecules containing arms of dicyano-n-hexylrhodanine and diathiafulvalene wings terminated with benzothiadiazole linker, denoted as BAF-4CN and BAF-2HDT, respectively, as small molecule non-fullerene acceptors (SMNFAs) in organic solar cells. The proposed materials are mixed with a low band gap polymer donor PTB7-Th having broad absorption in the range of 400–750 nm to form solution-processed bulk heterojunctions (BHJs). The photoluminescence (PL) measurements show that both donor and acceptor can quench each other’s PL effectively, implying that not only electrons are transferred from PTB7-Th → SMNFAs but also holes are transferred from SMNFAs → PTB7-Th for efficient photocurrent generation. Furthermore, solvent vapor annealing (SVA) processing is shown to yield a more balanced hole and electron mobility and thus suppresses the trap-assisted recombination significantly. With this dual charge transfer enabled via fine-tuning of end-groups and SVA treatment, power conversion efficiency of approximately 10% is achieved, demonstrating the feasibility of the proposed approach

Selective activation of p38 mitogen-activated protein kinase in dopaminergic neurons of Substantia nigra leads to nuclear translocation of p53 in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated mice

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by the degeneration of the dopaminergic neurons in the substantia nigra pars compacta (SNpc). Activation of the mixed lineage kinase and c-Jun N-terminal kinase (JNK) has been reported in models of PD. Our focus was to discern whether distinct pathways were activated in cell-specific manner within the SNpc. We now demonstrate the selective phosphorylation of p38 MAP kinase within the dopaminergic neurons, whereas JNK activation occurs predominantly in the microglia. p38 activation results in downstream phosphorylation of p53 and increased p53 mediated transcription of Bax and Puma in the ventral midbrain. Treatment with p38 inhibitor, SB239063 protected primary dopaminergic neurons derived from human progenitor cells from MPP+ mediated cell death and prevented the downstream phosphorylation of p53 and its translocation to the nucleus in vivo, in the ventral midbrain. The increased staining of phosphorylated p38 in the surviving neurons of SNpc in human brain sections from patients with PD and in MPTP treated mice but not in the ventral tegmental area provides further evidence suggesting a role for p38 in the degeneration of dopaminergic neurons of SNpc. We thus demonstrate the cell specific activation of MAP kinase pathways within the SNpc after MPTP treatment emphasizing the role of multiple signaling cascades in the pathogenesis and progression of the disease. Selective inhibitors of p38 may therefore, help preserve the surviving neurons in PD and slow down the disease progression

Low overhead hardware-assisted virtual machine analysis and profiling

Cloud infrastructure providers need reliable performance analysis tools for their nodes. Moreover, the analysis of Virtual Machines (VMs) is a major requirement in quantifying cloud performance. However, root cause analysis, in case of unexpected crashes or anomalous behavior in VMs, remains a major challenge. Modern tracing tools such as LTTng allow fine grained analysis - albeit at a minimal execution overhead,and being OS dependent. In this paper, we propose HAVAna,a hardware-assisted VM analysis algorithm that gathers and analyzes pure hardware trace data, without any dependence on the underlying OS or performance analysis infrastructure. Our approach is totally non-intrusive and does not require any performance statistics, trace or log gathering from the VM. We used the recently introduced Intel PT ISA extensions on modern Intel Skylake processors to demonstrate its efficiency and observed that, in our experimental scenarios, it leads to a tiny overhead of up to 1%, as compared to 3.6-28.7% for similar VM trace analysis done with software-only schemes such as LTTng. Our proposed VM trace analysis algorithm has also been opensourced for further enhancements and to the benefit of other developers. Furthermore, we developed interactive Resource and Process Control Flow visualization tools to analyze the hardware trace data and present a real-life usecase in the paper that allowed us to see unexpected resource consumption by VMs