Discrepancies between Objectively Assessed and Self-Reported Daily Social Media Time in the Age of Platform Swinging

Building on Junco’s (2013) study examining the accuracy of self-reported computer-specific time on Facebook, the current study investigates the accuracy of self-reported time on multiple social media (SM) platforms across multiple electronic devices and evaluates whether reporting accuracy is systematically associated with participant sex, individual SM platform in question, or total number of SM platforms used. Participants were 320 college students who downloaded software on their computers, tablets, and smartphones to track their active use of Facebook, Twitter, Instagram, and Snapchat over a 2-week surveillance period and then self-reported their daily average minutes on each platform immediately after. Larger proportions of students over- estimated than under-estimated their use, with the largest overestimations found on Snapchat and Instagram. Relative to males, females logged significantly more SM time and were less accurate in reporting. Overall, the likelihood of substantial inaccuracies in reporting total SM time and time on most individual platforms increased with each additional SM platform participants reported using. Findings from this study cast further doubt on the validity of self-report SM measures in the present SM landscape and underscore the need for either data analytic strategies to adjust for systematic reporting biases or a shift towards objective time-tracking methods.

Prediction of PM2.5 concentration based on BP neural network optimized by bee colony algorithm

According to the ambient air pollutants data and meteorological conditions data of Mianyang City in 2017, the BP neural network model based on MATLAB is established to predict the daily average PM2.5 concentration of Mianyang City in the next two days. However, the traditional BP network has the disadvantages of slow convergence speed and easy to fall into local optimum. In order to improve the prediction accuracy of the model, an optimization algorithm is added to the prediction model to avoid the model falling into local minimum. In this paper, the bee colony algorithm is added to the prediction model to improve the accuracy of BP neural network prediction model. The data from January to November are used for training, and the data from December are used as the verification results. The results show that the optimization model can accurately predict the daily average PM2.5 concentration of Mianyang City in the next two days, which provides a new idea for the prediction of PM2.5 concentration of the city, provides a theoretical basis for the early warning and decision-making of air pollution, and also provides more reliable prediction services for people’s daily travel.

Spatiotemporal characteristics of smallholder milk production under changing climate: A case of Nandi County, Kenya

Milk production in Kenya is predominantly smallholder and dependent on rainfall. The study assesses spatiotemporal characteristics of smallholder milk production in Nandi County under changing climate. Climate (Rainfall and temperature), fodder availability (Normalized Difference Vegetation Index (NDVI) and soil moisture content) and milk production data were used. Methods included trend analysis, spatial plots, correlation and multi-regression analysis. Monthly NDVI and soil moisture content were high between April and November with seasonal analysis indicating highest/lowest June-August (JJA)/December-February (DJF) values. Percentage change (%Δ) for NDVI was 6.0% (DJF), 1.96% (March-May, MAM), 2.13% (JJA), 4.16% (September-November, SON) and (2.53% (Annual). Seasonal and annual %Δ for soil moisture content ranged 7.2-17.1% at 0-10cm level and 8.1-23.7% at 10-40 level. Trend analysis of milk production showed positive change from 2007 to 2016 and highest/lowest in December/April with seasonal %Δ of up to 186% (MAM), 183% (JJA), 202% (SON), 214% (DJF) and 204% (Annual). Majority of household (HH) owned between 1 and 20 acres of land with only 0.5 to 2 acres allocated to dairy farming while those allocating less than 1 acre practiced zero grazing. On average, HH had 2 lactating cows throughout the year with majority of dairy farmers (98.6%) owning improved cow breeds. Amount of milk per HH supplied to the farmer organization varied between 2.3 litres and 3.8 litres with computed daily average milk produced per HH being 18.8 litres. Active milk suppliers were highest/lowest in December/April whereas daily average milk production per HH between 2010 and 2016 was highest/lowest in January (23.7 litres)/August (15.6 litres). Lowest/highest correlation coefficients were found in precipitation/minimum temperature. Multi-regression analysis indicated that precipitation had significant contribution to dairy productivity. Given the sensitivity of milk production to climate and fodder availability, adequate adaptation and mitigation measures are necessary in order to sustainably enhance milk production.

Germination of Balsa Seedlings (Ochroma lagopus Swartz) under Different Sowing Media in East New Britain, Papua New Guinea

A good sowing media ensures better anchorage of plants, provides a reservoir of nutrients and water, and enhance gaseous exchange with the atmosphere. Balsa (Ochroma lagopus Swartz); Vimmy variety, has proven its versatility in producing some of the best phenotypic characteristics such as higher jorquette height, less branching and high log volumes. This experiment was carried out using a combination of three different local materials; local garden soil, pumice soil and sawdust but in different combination ratios aimed to investigate the best combinations. Six treatments were tested: T1= Pure Garden soil, T2= Pumice, T3= Control (75% large coarse sawdust, 25% pure garden soil), T4= Pure Sawdust, T5= 50% medium coarse sawdust, 50% pure soil, and, T6= 33% medium coarse sawdust, 33% Pumice, 33% Pure garden Soil. The daily average germination count in Treatment 5 (50% medium coarse sawdust & 50% pure soil) produced constant germinations from day fifteen (15) to day twenty one (21). Treatments 1, 2, 3, 4 and 6 showed high variations in their daily average germination for the same period but did not produce a constant supply of germinations. Treatment 5 had the highest emergence rate index (ERI=71.76) followed by treatment 1 (ERI=66.59). Treatment 4 had the third highest seedling emergence (ERI=63.74) followed by treatment 3 (ERI=59.37), treatment 6 (ERI=57.22) and treatment 2 (ERI=53.81) at the lowest continuum. Substrates containing 50% soil and 50% medium coarse sawdust are regarded as better sowing media for O. lagopus seedlings.

Prolonged Exposure to Air Pollution Increases Periodontal Disease Risk: A Nationwide, Population-Based, Cohort Study

(1) Background: No association between air pollution and periodontitis has yet been shown. Thus, we merged two nationwide databases to evaluate the risk of periodontitis in Taiwanese residents with long-term exposure to air pollution. (2) Methods: We conducted a nationwide retrospective cohort study using the Longitudinal Generation Tracking Database and the Taiwan Air Quality-Monitoring Database. The daily average air pollutant concentrations were categorized into quartiles (Q1, Q2, Q3, and Q4). We carried out Cox proportional hazards models to compute the hazard ratios of periodontitis, with 95% confidence intervals, in Q2–Q4 of the daily average air pollutant concentrations, compared with Q1. (3) Results: the adjusted HR (95 CI%) for periodontitis in Q2–Q4 increased with increased exposure to SO2, CO, NO, NO2, NOX, PM2.5, and PM10 from 1.72 (1.70, 1.76) to 4.86 (4.78–4.94); from 1.89 (1.85–1.93) to 2.64 (2.59–2.70); from 1.04 (1.02–1.06) to 1.52 (1.49–1.55); from 1.61 (1.58–1.64) to 2.51 (2.47–2.56); from 1.48 (1.45–1.51) to 2.11 (2.07–2.15); from 2.02 (1.98–2.06) to 22.9 (22.4–23.4, and from 2.71 (2.66–2.77) to 17.2 (16.8–17.6), respectively, compared to Q1. (4) Conclusions: Residents in Taiwan with long-term exposure to higher levels of air pollutants had a greater risk of periodontitis.

Investigation on the statistical distribution of PM2.5 concentration in Chiang Mai, Thailand

Recently, it is found that Northern Thailand has very high levels of airborne particulates known as PM2.5. PM2.5 particulates can cause breathing problems and may raise the risks of heart disease and even some cancers. According to AirVisual, Chiang Mai, the capital of Northern Thailand which offers for tourists in both business and cultural center, had the highest levels of smog in the world in March 2018, reaching at least 183 on the PM2.5 Air Quality Index scale. The daily average PM2.5 concentration data are determined from July 2016 – June 2018 at two stations in Chiang Mai at Yupparaj Wittayalai school and City Hall. The Weibull, Gamma, Lognormal and Inverse Gaussian distributions are considered for finding the most appropriate probability functions of the daily average PM2.5 concentration. The results show that, as evaluated with the goodness- of-fit measures; Komolgorov-Smirnov and Anderson-Darling test statistics, the Inverse Gaussian distribution is the most suitable probability density functions of the daily average PM2.5 concentration for two stations. Furthermore, the return periods of the PM2.5 concentration are predicted by using the Largest Extreme Value distribution, which can be further applied in air quality management and related policy making.

Effects of Haze Experiments by Artificial Intervention on Atmospheric Mixing Layer Height (MLH) and Air Pollution in Pearl River Delta Region

In this paper, the effects of 16 times of aircraft artificial intervention operations on atmospheric MLH and air pollution in Pearl River Delta Region were investigated. By analyzing the surface observation meteorological data collected hourly each day from 2015 to 2019 using the Nozaki Method and Statistical Analysis Method, the differences of MLH’s daily variations on haze and non-haze days were studied. Then the variations of MLH, pollutant concentrations and visibility before and after artificial intervention were studied. And the variations in the concentration of fine particles were obtained by analyzing the depolarization ratio’s vertical distribution detected by Guangzhou Polarized Micropulse Lidar System. Finally, the analysis of daily average air pollutant concentrations and thickness of atmospheric mixing layer, together with the analysis of MLH, surface ventilation and the corresponding pollutant concentration sequence 18 hours post-experiment can lead to effects of MLH on air pollution. The results showed that (1) MLH varies daily significantly; (2) The atmospheric MLH, air pollutant concentration and visibility vary significantly after aircraft artificial precipitation intervention: (a) the MLH and surface ventilation increase during the first three hours of rainfall; (b) the visibility increases significantly; (c) the concentrations of PM2.5 and PM10 decrease while the concentrations of coarse and modal particles show a significant trend of decrease; (d) the subsequent dilution effect on PM2.5 and PM10 also show out in a clear way, especially on PM10. The daily average concentrations of PM2.5 and PM10 are positively correlated with the daily average MLH in the region and the correlation coefficients are -0.71 and -0.63 respectively. After haze experiments by artificial intervention, PM2.5, PM10, SO2, NO2, CO and AQI indexes were negatively correlated with MLH and surface ventilation while positively correlated with O3. The research results show its value in the aspects of the atmospheric environmental quality assessment and pollutant diffusion capacity improvement in the region. It also helps in future data demonstration tests for the effects of haze experiments by artificial intervention on atmospheric turbulence and air pollution elimination. And it provides scientific decision-making basis for future relevant measures for the quality of urban atmospheric environment improvement.

Nutrition in the Nursing Home: Expectation vs Reality

Background: Determine the nutritional value of the food provided to the average patient in the nursing home and compare to the guidelines for the age matched community dwelling individual. Methods: We obtained weekly meal plans. Nutritional value of each meal was calculated from the USDA food composition database with reference to the supply company. The 3 nursing homes ranged in ownership and in star rating and averaged 120 beds per facility. Food companies were comparable. Results: Patients received 1.58 cups of vegetables with a standard deviation (SD) of 0.31 daily. 1.26 cups of fruit (SD of 0.08) and 0.79 cups of dairy (SD 0.26) daily. Average of 5,308mg of sodium (SD 770.4) daily. 474.08 mg of added sugar (SD 137.88) daily. Saturated fats were 10.86% (SD 0.01) of the calories. Conclusions: Intake of fruits, dairy and vegetables was below recommended levels. Calories and sodium were above recommended values. Added sugar and saturated fats were within recommendations. Several changes can be made to improve nutrition in the nursing home to bring the nutrition closer to expectations in the average community dwelling adult values. We recommend further study concerning interventions.

Sodium content of menu and commissary provisions in rural jail exceeds heart-healthy dietary recommendations

Purpose This paper determined sodium provisions from a seven-day cycle menu and commissary at a rural Southwest County jail and compared it to Dietary Reference Intakes (DRI) and Dietary Approaches to Stop Hypertension (DASH) recommendations for sodium. Design/methodology/approach A seven-day cycle menu and commissary items were used to determine sodium content for each meal and commissary pack. Estimates for the menu and commissary packs paired with the menu (commissary scenarios) were converted to a daily average of sodium and compared to DRI and DASH recommendations. Findings Menu provisions provided 167% of daily DRI sodium recommendations and 256% of daily DASH sodium recommendations. The sodium content for individual commissary scenarios averaged 218% of DRI and 334% of DASH recommendations. Commissary items are notably high in sodium and if eaten can significantly exceed dietary recommendations. Originality/value Small changes to one meal within the cycle menu and the inclusion of fresh or frozen produce could reduce sodium content to align with DRI and DASH recommendations.

Self-Consumption and Self-Sufficiency Improvement for Photovoltaic System Integrated with Ultra-Supercapacitor

This research study uses a computer simulation based on real input data to examine the impact of a supercapacitor module working as a fast response energy storage unit in renewable energy systems to increase energy self-consumption and self-sufficiency. The evaluated system includes a photovoltaic system with a capacity of 3.0 kWp and between 0 and 5 supercapacitor units with a capacity of 500 F per module. The study was carried out using experimental data for electrical load, solar irradiance, and ambient temperature for the year 2020, with a 1 min temporal resolution. The daily average ambient temperature was 10.7 °C, and the daily average solar irradiance was 3.1 kWh/m2/day. It is assumed that the supercapacitor could only be charged from a photovoltaic system using renewable energy and not from the grid. The simulation results showed that using the supercapacitors to feed the short and large peaks of the electrical load significantly increases energy self-consumption and self-sufficiency. With only five supercapacitor modules, yearly energy self-sufficiency increases from 28.09% to 40.77%.