Impact of COVID-19 on routine malaria indicators in Uganda: An interrupted time series analysis

Background In March 2020, the government of Uganda implemented a strict lockdown policy in response to the COVID-19 pandemic. We performed an interrupted time series analysis (ITSA) to assess whether major changes in healthcare seeking behavior, malaria burden, and case management occurred after the onset of the COVID-19 epidemic. Methods Individual level data from all outpatient visits occurring from April 2017 through March 2021 at 17 facilities were analyzed. Outcomes included total outpatient visits, malaria cases, non-malarial visits, proportion of visits with suspected malaria, proportion of patients tested using rapid diagnostic tests (RDTs), and proportion of malaria cases prescribed artemether-lumefantrine (AL). Pre-COVID trends measured over a three-year period were extrapolated into the post-COVID period (April 2020- March 2021) using Poisson regression with generalized estimating equations or fractional regression. Effects of COVID-19 were estimated over the 12-month post-COVID period by dividing observed values by the predicted values and expressed as ratios. Results A total of 1,442,737 outpatient visits were recorded. Malaria was suspected in 55.3% of visits and 98.8% of these had a malaria diagnostic test performed. ITSA showed no differences in the observed versus predicted total outpatient visits, malaria cases, non-malarial visits, or proportion of visits with suspected malaria. However, in the second six months of the post-COVID period, there was a smaller mean proportion of patients tested with RDTs compared to predicted (Relative Prevalence Ratio (RPR) = 0.87, CI [0.78, 0.97]) and a smaller mean proportion of malaria cases prescribed AL (RPR = 0.94, CI [0.90, 0.99]. Conclusions There was evidence for a modest decrease in the proportion of RDTs used for malaria diagnosis and the proportion of patients prescribed AL in the second half of the post-COVID year, while other malaria indicators remained stable. Continued surveillance will be essential to monitor for changes in trends in malaria indicators so that Uganda can quickly and flexibly respond to challenges imposed by COVID-19.

Continuous Magnitude Production of Loudness

Continuous magnitude estimation and continuous cross-modality matching with line length can efficiently track the momentary loudness of time-varying sounds in behavioural experiments. These methods are known to be prone to systematic biases but may be checked for consistency using their counterpart, magnitude production. Thus, in Experiment 1, we performed such an evaluation for time-varying sounds. Twenty participants produced continuous cross-modality matches to assess the momentary loudness of fourteen songs by continuously adjusting the length of a line. In Experiment 2, the resulting temporal line length profile for each excerpt was played back like a video together with the given song and participants were asked to continuously adjust the volume to match the momentary line length. The recorded temporal line length profile, however, was manipulated for segments with durations between 7 to 12 s by eight factors between 0.5 and 2, corresponding to expected differences in adjusted level of −10, −6, −3, −1, 1, 3, 6, and 10 dB according to Stevens’s power law for loudness. The average adjustments 5 s after the onset of the change were −3.3, −2.4, −1.0, −0.2, 0.2, 1.4, 2.4, and 4.4 dB. Smaller adjustments than predicted by the power law are in line with magnitude-production results by Stevens and co-workers due to “regression effects.” Continuous cross-modality matches of line length turned out to be consistent with current loudness models, and by passing the consistency check with cross-modal productions, demonstrate that the method is suited to track the momentary loudness of time-varying sounds.

Relationship among anxiety, depression, social and self-efficacy in night-shift nurses

Night shift disturbs normal circadian rhythm, thus leads to several psychological problems. We aim to investigate the anxiety, depression, social support and self-efficacy of night-shift nurses compared with day-shift nurses and explore the association between emotional status and social support as well as the combined influence of social support and self-efficacy on emotional status. We conducted this quantitative comparative study in a hospital from January 1, 2019 to August 31, 2020, using the Hospital Anxiety and Depression Scale (HADS), Perceived Social Support Scale (PSSS), and General Self-Efficacy Scale (GSES). The HADS-A and HADS-D scores were higher for the night-shift nurses than for the day-shift nurses (7.38 ± 3.228 vs. 5.81 ± 3.180 and 6.79 ± 3.444 vs. 5.43 ± 3.155, respectively, P < 0.01). The family support, friend support, other support and total social support scores were lower for the night-shift nurses than for the day-shift nurses. In both groups, these scores were lower for nurses with suspected anxiety than for those without anxiety (61.16 ± 12.208 vs. 66.35 ± 9.976, P < 0.01) and were lower for nurses with suspected depression than for those without depression (59.91± 11.606 vs. 66.77 ± 10.320, P < 0.01). The item scores, total scores and total mean score for the night-shift nurses were significantly lower than those for the day-shift nurses (P < 0.01). Social support and self-efficacy had noticeable regression effects on nurses’ anxiety and depression, and both variables had significant negative effects on anxiety and depression. This study suggests that night-shift nurses may have higher anxiety and depression than day-shift nurses. Nurses with suspected anxiety and depression nurses may have lower social support than those without anxiety and depression.

A Path Analysis of Learning-Motivation-Behavior Affecting Factors Among Higher Vocational College Students

This paper made an investigation into the motivation-behaviors affecting factors of the carefully-selected 20 willing learners, 20 unwilling learners among 597 students from foreign languages department in two higher vocational colleges. An analysis has been made in the learning-motivation-behavior affecting factors route paths of students in higher vocational colleges. We found that there are universality and individuality in factors that predict the motivation behavior of three categories of students. Predictive factors of motivational behavior include motivation levels and learning strategies. The regression effects of learning strategies on motivational behavior are the greatest in the three categories of path analysis. It shows that learning strategies determine the performance of their motivational behavior, at the same time, it shows individual variance.

Research and Implementation of ε-SVR Training Method Based on FPGA

Online training of Support Vector Regression (SVR) in the field of machine learning is a computationally complex algorithm. Due to the need for multiple iterative processing in training, SVR training is usually implemented on computer, and the existing training methods cannot be directly implemented on Field-Programmable Gate Array (FPGA), which restricts the application range. This paper reconstructs the training framework and implementation without precision loss to reduce the total latency required for matrix update, reducing time consumption by 90%. A general ε-SVR training system with low latency is implemented on Zynq platform. Taking the regression of samples in two-dimensional as an example, the maximum acceleration ratio is 27.014× compared with microcontroller platform and the energy consumption is 12.449% of microcontroller. From the experiments for the University of California, Riverside (UCR) time series data set. The regression results obtain excellent regression effects. The minimum coefficient of determination is 0.996, and running time is less than 30 ms, which can meet the requirements of different applications for real-time regression.

The non-stationary bivariate INAR under an unconstrained inter correlation structure

It is commonly observed in medical and financial studies that large volume of time series of count data are collected for several variates. The modelling of such time series and the estimation of parameters under such processes are rather challenging since these high dimensional time series are influenced by time-varying covariates that eventually render the data non-stationary. This paper considers the modelling of a bivariate integer-valued autoregressive (BINAR(1)) process where the innovation terms are distributed under non- stationary Poisson moments. Since the full and conditional likelihood approaches are cumbersome in this situation, a Generalized Quasi-likelihood (GQL) approach is proposed to estimate the regression effects while the serial and time-dependent cross correlation effects are handled by method of moments. This new technique is assessed over several simulation experiments and the results demonstrate that GQL yields consistent estimates and is computationally stable since few non-convergent simulations are reported.

Effects of Organizational Justice on Job Outcomes: An Intervening Role of Affective Commitment

This research examines a model depicting the association between distributive justice, procedural justice, affective commitment and work outcomes in a special context of higher education institutes. While prior research has extensively used organisational justice and its consequences for work outcomes, this study in particular explores the above linkage in universities of KPK Pakistan. In addition, affective commitment has been employed as a mediator in between the relation of work outcomes and justice types (procedural and distributive justice). Turnover intention and Employee performance are undertaken as pertinent work outcomes. Data from a sample of 150 working staff from selected reputed universities of the country’s capital, i.e. Islamabad, were gathered and analysed for regression effects. The findings suggest that justice types (procedural and distributive) positively affect employee performance yet negatively influence turnover intention. Similarly, affective commitment positively mediates the relationship between organisational justice and employee performance but no mediation effect was found in case of turnover intention. Implications of the study are highlighted with a note on future research directions. The article ends with a short conclusion and limitations of the study.