Neural Network Model of Estimation of Body Mass Index Based on Indirect Input Factors

A well-prepared One of the main concerns of people in developing and developed societies is increasing the Body Mass Index (BMI) level. BMI, in fact can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighborhoods in Shiraz, Iran. The Pearson correlation coefficient, independent sample T-test and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN) was applied to predict the level of personal BMI. Artificial Neural Network-based methodology results showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern

Electron inertia effects in 3D hybrid-kinetic collisionless plasma turbulence

The effects of the electron inertia on the current sheets that are formed out of kinetic turbulence are relevant to understand the importance of coherent structures in turbulence and the nature of turbulence at the dissipation scales. We investigate this problem by carrying out 3D hybrid-kinetic Particle-in-Cell (PIC) simulations of decaying kinetic turbulence with our CHIEF code. The main distinguishing feature of this code is an implementation of the electron inertia without approximations. Our simulation results show that the electron inertia plays an important role in regulating and limiting the largest values of current density in both real and wavenumber Fourier space, in particular near and, unexpectedly, even above electron scales. In addition, the electric field associated to the electron inertia dominates most of the strongest current sheets. The electron inertia is thus important to accurately describe the properties of current sheets formed in turbulence at electron scales.Comment: 34 pages, 10 figures. Revised version. Published in Physics of Plasma

SWCNT-Based Biosensor Modelling for pH Detection

Different forms of CNT delivery have been discovered with several biomedical functions during past decades. The mechanisms of the cellular uptake of CNTs are mainly maintained due to the chemical nature, the cell type, and the features of the molecules, which are used to functionalize the nanotube exterior. Since single-wall carbon Nanotube (SWCNT) has unique chemical and physical properties, it is a great applicant for pH sensing. In addition, ion sensitive FET (ISFET) base on nanostructured SWCNT have covered a new method to help genetic investigators restructure metabolic pathways in cells, recognize the progression of disease, and expand diagnostics and therapeutics. Particularly, because PH sensing is very crucial for the constancy of enzymes, it is essential to extend the cost efficient types of this sensing. In this research, the conductance changes of the CNT-based ISFET device with different pH values can be modelled by ion concentration of the solution. In addition, the electrical current of channel is imagined as a function of pH levels, which can be controlled by a control factor (α). Thus, ISFET based nanostructured SWCNT is proposed focusing on the area of electrical detection of hydrogen ions of the electrolyte membrane. Besides, electrical detection of hydrogen ion applications is suggested to be used by modelling the delivery of SWCNT sheets. In the end, after comparing the proposed model and experimental data, it has been reported that there is a good compatibility between them

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI):21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.</p

Impacts of Logging-Associated Compaction on Forest Soils: A Meta-Analysis

Soil compaction associated with mechanized wood harvesting can long-lastingly disturb forest soils, ecosystem function, and productivity. Sustainable forest management requires precise and deep knowledge of logging operation impacts on forest soils, which can be attained by meta-analysis studies covering representative forest datasets. We performed a meta-analysis on the impact of logging-associated compaction on forest soils microbial biomass carbon (MBC), bulk density, total porosity, and saturated hydraulic conductivity (Ksat) affected by two management factors (machine weight and passage frequency), two soil factors (texture and depth), and the time passed since the compaction event. Compaction significantly decreased soil MBC by −29.5% only in subsoils (>30 cm). Overall, compaction increased soil bulk density by 8.9% and reduced total porosity and Ksat by −10.1 and −40.2%, respectively. The most striking finding of this meta-analysis is that the greatest disturbance to soil bulk density, total porosity, and Ksat occurs after very frequent (>20) machine passages. This contradicts the existing claims that most damage to forest soils happens after a few machine passages. Furthermore, the analyzed physical variables did not recover to the normal level within a period of 3–6 years. Thus, altering these physical properties can disturb forest ecosystem function and productivity, because they play important roles in water and air supply as well as in biogeochemical cycling in forest ecosystems. To minimize the impact, we recommend the selection of suitable logging machines and decreasing the frequency of machine passages as well as logging out of rainy seasons especially in clayey soils. It is also very important to minimize total skid trail coverage for sustainable forest management

Keeping thinning-derived deadwood logs on forest floor improves soil organic carbon, microbial biomass, and enzyme activity in a temperate spruce forest

Deadwood is a key component of forest ecosystems, but there is limited information on how it influences forest soils. Moreover, studies on the effect of thinning-derived deadwood logs on forest soil properties are lacking. This study aimed to investigate the impact of thinning-derived deadwood logs on the soil chemical and microbial properties of a managed spruce forest on a loamy sand Podzol in Bavaria, Germany, after about 15 years. Deadwood increased the soil organic carbon contents by 59% and 56% at 0–4 cm and 8–12 cm depths, respectively. Under deadwood, the soil dissolved organic carbon and carbon to nitrogen ratio increased by 66% and 15% at 0–4 cm depth and by 55% and 28% at 8–12 cm depth, respectively. Deadwood also induced 71% and 92% higher microbial biomass carbon, 106% and 125% higher microbial biomass nitrogen, and 136% and 44% higher β-glucosidase activity in the soil at 0–4 cm and 8–12 cm depths, respectively. Many of the measured variables significantly correlated with soil organic carbon suggesting that deadwood modified the soil biochemical processes by altering soil carbon storage. Our results indicate the potential of thinned spruce deadwood logs to sequester carbon and improve the fertility of Podzol soils. This could be associated with the slow decay rate of spruce deadwood logs and low biological activity of Podzols that promote the accumulation of soil carbon. We propose that leaving thinning-derived deadwood on the forest floor can support soil and forest sustainability as well as carbon sequestration

Particulate number emissions during cold-start with diesel and biofuels: A special focus on particle size distribution

The share of biofuels in the transportation sector is increasing. Previous studies revealed that the use of biofuels decreases the size of particles (which is linked to an increase in particulate toxicity). Current emission regulations do not consider small particles (sub-23 nm); however, there is a focus in future emissions regulations on small particles. These and the fact that within cold-start emissions are higher than during the warmed-up operation highlight the importance of a research that studies particulate matter emissions during cold-start. This research investigates the influence of biofuel on PN and PM concentration, size distribution, median diameter and cumulative share at different size ranges (including sub-23 nm and nucleation mode) during cold-start and warm-up operations using diesel and 10, 15 and 20% mixture (coconut biofuel blended with diesel). During cold-start, between 19 and 29% of total PN and less than 0.8% of total PM were related to the nucleation mode (sub-50 nm). Out of that, the share of sub-23 nm was up to 9% for PN while less than 0.02% for PM. By using biofuel, PN increased between 27 and 57% at cold-start; while, the increase was between 4 and 19% during hot-operation. The median diameter also decreased at cold-start and the nucleation mode particles (including sub-23 nm particles) significantly increased. This is an important observation because using biofuel can have a more adverse impact within cold-start period which is inevitable in most vehicles’ daily driving schedules.<br/

Trapped air metamaterial concept for ultrasonic sub-wavelength imaging in water

Funding for this work was provided through the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Numbers EP/N034163/1, EP/N034201/1 and EP/N034813/1.Acoustic metamaterials constructed from conventional base materials can exhibit exotic phenomena not commonly found in nature, achieved by combining geometrical and resonance effects. However, the use of polymer-based metamaterials that could operate in water is difficult, due to the low acoustic impedance mismatch between water and polymers. Here we introduce the concept of “trapped air” metamaterial, fabricated via vat photopolymerization, which makes ultrasonic sub-wavelength imaging in water using polymeric metamaterials highly effective. This concept is demonstrated for a holey-structured acoustic metamaterial in water at 200–300 kHz, via both finite element modelling and experimental measurements, but it can be extended to other types of metamaterials. The new approach, which outperforms the usual designs of these structures, indicates a way forward for exploiting additive-manufacturing for realising polymer-based acoustic metamaterials in water at ultrasonic frequencies.Publisher PDFPeer reviewe

OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy

Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus, ensuring privacy-preserving data sharing is crucial. Differential privacy (DP) is regarded as the gold standard in data privacy. Despite this, current DP methods tend to generate privacy-preserving tabular datasets that often suffer from limited practical utility due to heavy perturbation and disregard for the tables' utility dynamics. Besides, there has not been much research on selective attribute release, particularly in the context of controlled partially perturbed data sharing. This has significant implications for scenarios such as cross-agency data sharing in real-world situations. We introduce OptimShare: a utility-focused, multi-criteria solution designed to perturb input datasets selectively optimized for specific real-world applications. OptimShare combines the principles of differential privacy, fuzzy logic, and probability theory to establish an integrated tool for privacy-preserving data sharing. Empirical assessments confirm that OptimShare successfully strikes a balance between better data utility and robust privacy, effectively serving various real-world problem scenarios

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown

Background: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. / Discussion: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. / Conclusions: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive