Footprint Variables Research Articles

Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

Real-world waste dispersion modelling for benthic integrated multi-trophic aquaculture.

In real-world situations, marine fish farms accommodate multiple fish species and cohorts within the farm, leading to diverse farm layouts influenced by cage dimensions, configurations, and intricate arrangements. These cage management practices are essential to meet production demands, however, farm-level complexities can impact model predictions of waste deposition and benthic impact near fish cages. This is of particular importance when the cages are used for integrated multi-trophic aquaculture (IMTA) with benthic feeders, where this waste not only affects environmental conditions but also provides a potential food source. The Cage Aquaculture Particulate Output and Transport (CAPOT) model incorporated multiple species, cohorts, and cage arrangements to estimate waste distribution from a commercial fish farm in the Mediterranean between October 2018 and July 2019. This spreadsheet model estimated dispersion for individual fish cages using a grid resolution of 5 m x 5 m. The study categorized discrete production periods for each fish cage every month, aligning with intermittent changes in biomass and food inputs due to different cage management practices throughout production. This approach facilitated the use of detailed input data and enhanced model representativeness by considering variations in cage biomass, food types, settling velocities, and configurations. Model outputs, represented in contour plots, indicated higher deposition directly below fish cages that varied monthly throughout fish production cycles. Deposition footprints reflected changes in cage biomass, food inputs, and farm-level practices reflecting this real-world scenario where aquaculture does not follow a production continuum. Moreover, cohort dynamics and cage movements associated with the cage management practices of the fish farm influenced the quantity and fate of wastes distributed around fish cages, revealing variability in deposition footprints. Clearly, these findings have important implications for the design of benthic IMTA systems, with species such as sea cucumber and polychaetes. Variability in waste deposition creates challenges in identifying where the benthic organisms should be placed to allow optimal uptake of waste to meet their food requirements and increase survivability. Evidently, models have an important role to play and this study emphasizes the need for representative input data to describe actual food inputs, cage biomass changes, and management practices for more representative farm-scale modelling and essentially to improve particulate waste management. To effectively mitigate benthic impacts through IMTA, models must quantify and resolve particulate waste distribution and impact around fish farms to maintain a balanced system with net removal of wastes. Resolving farm-level complexities provides vital information about the variability of food availability and quality for extractive organisms that helps improve recycling of organic wastes in integrated systems, demanding a more representative modelling approach.

Environmental Kuznets Curve Hypothesis: Before and After Sustainable Development Goals

The Environmental Kuznets Curve (EKC) hypothesis is an idea that examines the inverse “U” curve relationship between environmental degradation and economic development. This study contributes to analyzing the application of EKC hypothesis in G20 participating countries, as well as the relationship between the variables of gross domestic product (GDP), foreign direct investment (FDI), population, and renewable energy on environmental degradation represented by ecological footprint variables in the same area by comparing the period before and after the implementation of the SDGs. This study uses the panel data regression method for the period 2011-2018, where data is obtained from the World Bank and the Global Footprint Network. The results show that from 2011-2018 the EKC hypothesis formed an inverted U-curve. This study also shows that before the SDGs (2015) the variables of GDP, FDI, population, and renewable energy had an effect on increasing the value of the ecological footprint. Meanwhile, after the implementation of the SDGs program, the variables of GDP, FDI, population, and renewable energy contributed to reducing the value of the ecological footprint, which indicated a decrease in people’s dependence on natural resources. Where the GDP variable has a positive and significant effect and the renewable energy variable has a negative and significant effect. While FDI and population variables have a positive but insignificant effect.
 Keywords: EF, EKC, FDI, GDP, GDP2, POP, RE, SDGs

Effects of Myofascial Induction Therapy on Ankle Range of Motion and Pressure Pain Threshold in Trigger Points of the Gastrocnemius-A Clinical Trial.

The myofascial induction technique (MIT) has been shown to increase shoulder range of motion (ROM) in breast cancer survivors and decrease pain pressure threshold over the radial nerve in patients with epicondylalgia. To the authors' best knowledge, no study on trigger points and MIT has been published to date. The effect on ROM of latent trigger points is also unknown. A total of 20 twins with one latent trigger point of the gastrocnemius muscle were evaluated pre- and post-MIT in the calf. We measured static footprint variables in a pre-post study. We found differences in PPT (p = 0.001) and no differences in ROM with knee flexed (p = 0.420) or stretched (p = 0.069). After Calf MIT, latent myofascial trigger points improve PPT but no change in ankle dorsiflexion with knee bent or knee flexed were found in non-restriction healthy subjects.

Relation Between Financial Literacy and Carbon Footprint: Review on Implications for Sustainable Development

Introduction. Financial literacy has been recognized worldwide as a significant element of stability and economic and financial growth. With the evolution of financial instruments, the growing importance of financial inclusion, its correlation with financial literacy, and the effects they have on sustainability, the concept of financial literacy is dramatically changing and getting more inclusive, spreading the focus on sustainability, sustainable consumption, and environmental preservation.
 Aim and tasks. The aim of the study is to examine the connection between the population's financial literacy level and greenhouse gas emissions. The working hypothesis claims that there is a relationship between financial literacy and the carbon footprint.
 Results. The correlation and regression analyses were the main tools in the study, while the dataset for 2014 covered 137 countries, with the main dependent variables being carbon emissions per capita, per unit of gross domestic product, and per unit of energy. The partial correlation coefficients between financial literacy rating and carbon footprint variables were insignificant when controlled for economic development, represented by per capita gross domestic product. Estimated econometric models with financial literacy in quadratic form were adequate and showed a significant connection between financial literacy and carbon emissions per capita and per gross domestic product at the 5% level. The relationship with carbon emissions per unit of energy was significant at the 10% level. In all three models, the relationships followed an inverse U-shape, with low financial literacy increasing the carbon footprint and higher financial literacy decreasing it. The turning numbers for financial literacy were 35.8% for carbon emissions per capita, 41.4% for emissions per unit of gross domestic product, and 32.4% for emissions per unit of energy.
 Conclusions. Financial literacy was indeed associated with carbon emissions in a complex, non-linear way. The effect of energy consumption on carbon emissions was stronger than financial literacy and appeared to be the driving force for the increase in carbon emissions. With low financial literacy observed in underdeveloped countries, the situation was not favorable for the environment. As financial literacy increased, welfare, income, and consumption increased too, leading to an increase in greenhouse gas emissions, i.e., a bigger CO2 footprint. Once a certain stage of economic development was reached, the relationship was reversed, i.e., in developed countries, financial literacy worked towards reducing the carbon footprint and protecting the environment.

Variability in greenhouse gas footprints of the global wind farm fleet

AbstractWhile technological characteristics largely determine the greenhouse gas (GHG) emissions during the construction of a wind farm and meteorological circumstances the actual electricity production, a thorough analysis to quantify the GHG footprint variability (in g CO2eq/kWh electricity produced) between wind farms is still lacking at the global scale. Here, we quantified the GHG footprint of 26,821 wind farms located across the globe, combining turbine‐specific technological parameters, life‐cycle inventory data, and location‐ and temporal‐specific meteorological information. These wind farms represent 79% of the 651 global wind (GW) capacity installed in 2019. Our results indicate a median GHG footprint for global wind electricity of 10 g CO2eq/kWh, ranging from 4 to 56 g CO2eq/kWh (2.5th and 97.5th percentiles). Differences in the GHG footprint of wind farms are mainly explained by spatial variability in wind speed, followed by whether the wind farm is located onshore or offshore, the turbine diameter, and the number of turbines in a wind farm. We also provided a metamodel based on these four predictors for users to be able to easily obtain a first indication of GHG footprints of new wind farms considered. Our results can be used to compare the GHG footprint of wind farms to one another and to other sources of electricity in a location‐specific manner.

On the source of metals and the environmental sustainability of battery electric vehicles versus internal combustion engine vehicles: The lithium production case study

Several studies support that the battery electric vehicles (BEVs) are more environmentally friendly than internal combustion engine vehicles (ICEVs). However, these studies assume that one unit of metal used in the manufacturing process is a commodity in terms of its environmental footprint. In this study, estimations for water and energy consumption and carbon dioxide emissions are used as proxies of the environmental footprint variability within the production of various metals used in the automotive industry. Through these estimations, probability distribution functions are fit to assess the sustainability of BEVs and ICEVs when considering the commodities' environmental heterogeneity.Two scenarios are assessed, which considers: (1) the manufacture of vehicles, and (2) their useful life. The results show the existence of a range of potential environmental footprints for BEVs and ICEVs, depending on the footprints of the specific metals being used. Including this input's variability, when only the manufacturing process is considered, ICEVs outperform BEVs in most of the realization cases and for all the analyzed indicators. However, if their useful life is incorporated, the BEV consistently produces significantly less carbon dioxide emissions, as well as consume the same energy and more water than ICEV.A special case study for lithium is presented as it has shown to be a critical resource and a major environmental concern for BEV production. The results show that by switching lithium from pegmatite to brine, the water consumption required for manufacturing the vehicles is reduced by 0.17%, which is contrary to the common view regarding water use in brine extraction.

Evaluation of Maize yield and Water Footprint Variability by AquaCrop-GIS Model

برآورد پتانسیل تولید هر گیاه تابعی از شرایط اقلیمی، پتانسیل‌های ژنتیکی گیاه و دیگر عوامل گوناگون محیطی و مدیریتی است. ارزیابی توانمندی مناطق، در به‌فعلیت‌رساندن پتانسیل‌های ژنتیکی گیاهان، از نکات مهم برنامه‌ریزی‌های کلان در کشاورزی به‌حساب می‌آید. با توجه به جایگاه استان قزوین در تولید محصول ذرت و اهمیت کشت این محصول در استان، برآورد هرچه دقیق‌تر میزان عملکرد این محصول استراتژیک بسیار ضرورت دارد. در همین زمینه، با مطالعة یک دوره داده‌برداری یازده‌ساله، عملکرد محصول ذرت با مدل گیاهی جدید AquaCrop-GIS برآورد شد. پهنه‌بندی شاخص‌های کلیدی محصول از طریق مدل در سطح استان شبیه‌سازی شد. با بررسی نتایج این پارامترهای کلیدی، مشخص شد ایستگاه‌های مطالعاتی قزوین و معلم‌کلایه با میزان تبخیر‌و‌تعرق مرجع کمتر دارای بهره‌وری آب بیشتری است. در ادامه با استفاده از عملکرد محاسباتی، اجزای ردپای آب و ردپای آب کل محصول در محدودة ایستگاه‌های مطالعاتی تخمین زده شد. با بررسی معادلات رگرسیونی میان اجزای ردپای آب و ردپای آب کل محصول با عملکرد محصول در هر ایستگاه، مشخص شد روابط ردپای آب آبی با عملکرد به‌نسبت دیگر اجزای ردپای آب درمورد تمامی ایستگاه‌ها، از میزان ضریب تعیین (43/0= R2، 51/0= R2، 43/0= R2، 77/0= R2 و 79/0 = R2 به‌ترتیب درمورد ایستگاه‌های قزوین، آوج، معلم‌کلایه، تاکستان و بویین‌زهرا) و سطح معنی‌داری برخوردار است. به‌طورکلی ضریب تعیین این روابط در ایستگاه بویین‌زهرا با 88/0= R2، 79/0= R2، 56/0= R2 و 53/0= R2 به‌ترتیب برای ردپای آب سبز، آبی، خاکستری و ردپای آب نسبت به سایر ایستگاه‌ها، بیشتر برآورد شد؛ به این معنی که کاهش عملکرد در این ایستگاه تأثیر بسزایی بر افزایش ردپای آب کل محصول داشت.

Determinants of Food Consumption Water Footprint in the MENA Region: The Case of Tunisia

Tunisia, like most countries in the Middle East and North Africa (MENA) region, has limited renewable water resources and is classified as a water stress country. The effects of climate change are exacerbating the situation. The agricultural sector is the main consumer (80%) of blue water reserves. In this study, to better understand the factors that influence the food water footprint of Tunisian consumers, we used a multiple linear regression model (MLR) to analyze data from 4853 households. The innovation in this paper consists of integrating effects of socio-economic, demographic, and geographic trends on the food consumption water footprint into the assessment of water and food security. The model results showed that regional variations in food choices meant large differences in water footprints, as hypothesized. Residents of big cities are more likely to have a large water footprint. Significant variability in water footprints, due to different food consumption patterns and socio-demographic characteristics, was also noted. Food waste is also one of the determining factors of households with a high water footprint. This study provides a new perspective on the water footprint of food consumption using “household” level data. These dietary water footprint estimates can be used to assess potential water demand scenarios as food consumption patterns change. Analysis at the geographic and socio-demographic levels helps to inform policy makers by identifying realistic dietary changes.

Predicting Potential Habitat of a Plant Species with Small Populations under Climate Change: Ostryarehderiana

Ostrya rehderiana is a famous plant species with extremely small populations. With ongoing global climate change, the extremely small populations would face more uncertainties and risks, including the loss of genetic diversity and extirpation. Thus, assessing the impact of climate change on suitable habitat of O. rehderiana is particularly important for its conservation and restoration. Here, we built niche models with climate variables and soil and human footprint variables. Furthermore, new methods were applied to avoid confounding effects between climate and soil and human footprint variables to simulate the potential habitats of O. rehderiana in current and future climates. We found that the Hargreaves climatic moisture deficit, degree-days below 0 °C, chilling degree-days, and the temperature difference between mean warmest month temperature and mean coldest month temperature, or continentality, were the most important climate factors. The topsoil USDA texture classification, topsoil cation exchange capacity of (clay), and topsoil sodicity (ESP) were the key soil factors determining the suitable distribution of O. rehderiana. Compared with soil factors, human footprint has less influence on the suitable distribution of O. rehderiana. The niche range of this species was projected to expand and shift to north in the Representative Concentration Pathway (RCP) 4.5 scenario for the 2050s. Our study results could be referenced in further extremely small populations ecological restoration studies and provide the scientific strategies for the conservation and restoration of O. rehderiana.

Drivers of variability in greenhouse gas footprints of crop production

Identifying drivers of farm-level greenhouse gas (GHG) footprints of crop production can reveal opportunities to improve farming practices and enable more targeted GHG mitigation strategies. Although many studies evaluated the GHG footprints of crop production, differences between and within crops have not been systematically evaluated for a large number of farms so far. Here, we evaluated possible sources of variability in GHG footprints (in terms of kg CO2-eq/kg crop produced) of 26 crops, grown in compliance with Unilever's Sustainable Agriculture Code, using data from 4565 farms in 36 countries from 2013 through 2016. We quantified crop-farm-specific GHG footprints based on four components: (i) emissions from electricity use, (ii) emissions from fossil fuel (petrol and diesel) use, (iii) emissions from crop and pruning residue application, and (iv) emissions from fertilizer use. On average, fertilizer use contributed most to the GHG footprint for 23 out of the 26 crops in our dataset. We further found that variability in GHG footprints was smaller between crops (45%) than within crops (55%). Regression modelling revealed that on average 44% of the GHG footprint variability within crops could be attributed to (a selection of) three explanatory variables, i.e., yield, area of production, and year of production. Of these, yield was the most important explanatory variable. Lower GHG footprints were associated with higher yields for 24 out of the 26 crops. Relationships with area and year of production were less clear, and directions of the relationships were more variable between crops. Strategies to improve fertilizer use efficiencies while maintaining or increasing yields are preferable in a GHG reduction programme.

Evaluation of Feed Strategies and Changes of Stocking Rate to Decrease the Carbon Footprint in a Traditional Cow-Calf System: A Simulation Model.

One of the main production challenges associated with climate change is the reduction of carbon emissions. Increasing the efficiency of resource utilization is one way to achieve this purpose. The modification of production systems through improved reproductive, genetic, feed, and grazing management practices has been proposed to increase technical–economic efficiency, even though the “environmental viability” of these modifications has not always been evaluated. The objective of this study was to evaluate the use of feeding and management strategies on the carbon footprint (CF) and economic variables in the traditional cow–calf system in southern Chile using a simulation model. The modifications evaluated corresponded to combinations of stocking rate, use of creep feeding practices with different supplementation levels, and the incorporation of feed additives to the supplement, using factorial experiments. Additionally, the scenarios were evaluated with and without carbon sequestration. The CF for the baseline scenarios was 12.5 ± 0.3 kg of CO2−eq/kg of live weight (LW) when carbon sequestration was considered and 13.0 ± 0.4 kg of CO2−eq/kg of LW in the opposite case. Changes in stocking rate, supplementation level, and consideration of carbon sequestration in pasture and soil had a significant effect on the CF in all simulated scenarios. The inclusion of additives in the supplement did not have a significant effect on production costs. With regard to reducing greenhouse gas (GHG) emissions, incorporating canola oil presented the best average results. The model developed made the selection of environmentally viable feed strategies or management adaptations possible.

Drivers of distributions and niches of North American cold‐adapted amphibians: evaluating both climate and land use

Species distribution estimates are often used to understand the niche of a species; however, these are often based solely on climatic predictors. When the influences of biotic factors are ignored, erroneous inferences about range and niche may be made. We aimed to integrate climate data with a unique set of available land cover and land use data for the six cold-adapted amphibians of North America (Ambystoma macrodactylum, Anaxyrus hemiophrys, Anaxyrus boreas, Pseudacris maculata, Rana sylvatica, Rana luteiventris) to determine the relative importance of climate and non-climate drivers through the use of ecological niche models for present-day range estimates. We compared climate-only, land use-only, and combination models of climate and land use, derived from two different model selection techniques, to determine which was most likely to drive current distributions of cold-adapted amphibian species. Land use layers included land cover type, human population, vegetation type, ecoregion, and the overall human footprint. The most supported models included both climate and land use, with climate and human footprint variables having the highest permutation importance and percent contribution. Models that incorporated climate and land use data performed best as measured with AIC and AUC, although qualitatively most underestimated the northern range edge, implying potential sampling bias or locations of reduced habitat quality for these species in the northern area of the ranges. There were small differences in overall combination models dependent on the method of model selection. The overall effect sizes of landscape factors within the combination models were small except for one landscape feature: human footprint, which incorporated multiple aspects of anthropogenic change on the landscape, including human population density, travel access, and agricultural impact. This aspect of the landscape was just as important as climate, and counter to what we expected, the association was mostly positive, with a negative response only occurring at very high levels. This highlights the importance of moving beyond climate only species range estimates as land cover, specifically human impact, may be driving the patterns of species' ranges.

Flexor Digitorum Brevis Muscle Dry Needling Changes Surface and Plantar Pressures: A Pre-Post Study.

Background: The effects of the dry needling technique and pain reduction have been demonstrated in numerous quality studies. However, the mechanical effects of dry needling are largely unknown. Methods: A total of 18 subjects with flexor digitorum brevis muscle myofascial trigger point were evaluated pre- and post-deep dry needling. We measured static footprint variables in a pre–post study. Main findings: We found differences in rearfoot maximum pressure (119.22–111.63 KPa; p = 0.025), midfoot maximum pressure (13.68–17.26 KPa; p = 0.077), midfoot medium pressure (4.75–6.24 KPa; p = 0.035) and forefoot surface (86.58–81.75 cm2; p = 0.020). All variables with significant differences decrease, with the exception of forefoot surface which showed an increase. Conclusions: After flexor digitorum brevis muscle dry needling, midfoot plantar pressures (maximum and medium) and forefoot surface were increased, and rearfoot maximum pressure was decreased.

Variability of Io’s poynting flux: A parameter study using MHD simulations

Io’s plasma interaction creates an electromagnetic coupling between Io and Jupiter through Alfvén waves triggering the generation of auroral footprints in Jupiter’s southern and northern hemispheres. The brightness of Io’s footprints undergoes periodic variations that are primarily modulated by Io’s local plasma interaction through the Poynting flux radiated away from the moon. The periodic pattern with two maxima near 110∘ and 290∘ Jovian longitude where Io crosses the dense plasma sheet is generally understood. However, some characteristics, like the 2–4 times stronger brightening of the southern footprint near Jovian longitude 110∘ or the lack of response to Io’s eclipse passage, are not fully understood. We systematically study variations in Io’s plasma interaction and the Poynting flux using a 3D magnetohydrodynamic model, performing a series of simulations with different upstream plasma conditions and models of Io’s atmosphere. Our results indicate that the strong Jovian magnetic field near 110∘ plays a more important role than previously estimated for the strong brightening there. We find that the Poynting flux is not fully saturated for a wide range of possible atmospheric densities (6×1018–6×1021 m−2) and that density changes in the atmosphere by a factor of > 3, as possibly happening during Io’s eclipse passage, lead to a change of the Poynting flux by > 20%. Assuming that these expected changes in Poynting flux also apply to the footprints, the non-detection of a dimming in the footprint during the eclipse by Juno-UVS suggests that Io’s global atmospheric density decreases by a factor of < 2.5. We show that for smaller atmospheric scale heights (i.e. a more confined atmosphere), changes in the atmospheric density have less effect on the Poynting flux. The missing response of the footprint to the eclipse hence might also be consistent with a density decrease by a factor of > 3, if the effective atmospheric scale height is small (<120 km). Finally, we provide new analytical approximations that can be used for analyzing the effect of the local interaction responsible for the footprint variability in future studies.

Comparison of eddy covariance CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; fluxes from mined and recently rewetted sections in a northwestern German cutover bog

Abstract. With respect to their role in the global carbon cycle, natural peatlands are characterized by their ability to sequester atmospheric carbon. This trait is strongly connected to the water regime of these ecosystems. Large parts of the soil profile in natural peatlands are water saturated, leading to anoxic conditions and to a diminished decomposition of plant litter. In functioning peatlands, the rate of carbon fixation by plant photosynthesis is larger than the decomposition rate of dead organic material. Over time, the amount of carbon that remains in the soil and is not converted back to carbon dioxide grows. Land use of peatlands often goes along with water level manipulations and thereby with alterations of carbon flux dynamics. In this study, carbon dioxide (CO2) and methane (CH4) flux measurements from a bog site in northwestern Germany that has been heavily degraded by peat mining are presented. Two contrasting types of management have been implemented at the site: (1) drainage during ongoing peat harvesting on one half of the central bog area and (2) rewetting on the other half that had been taken out of use shortly before measurements commenced. The presented 2-year data set was collected with an eddy covariance (EC) system set up on a central railroad dam that divides the two halves of the (former) peat harvesting area. We used footprint analysis to split the obtained CO2 and CH4 flux time series into data characterizing the gas exchange dynamics of both contrasting land use types individually. The time series gaps resulting from data division were filled using the response of artificial neural networks (ANNs) to environmental variables, footprint variability, and fuzzy transformations of seasonal and diurnal cyclicity. We used the gap-filled gas flux time series from 2 consecutive years to evaluate the impact of rewetting on the annual vertical carbon balances of the cutover bog. Rewetting had a considerable effect on the annual carbon fluxes and led to increased CH4 and decreased CO2 release. The larger relative difference between cumulative CO2 fluxes from the rewetted (13±6 mol m−2 a−1) and drained (22±7 mol m−2 a−1) section occurred in the second observed year when rewetting apparently reduced CO2 emissions by 40 %. The absolute difference in annual CH4 flux sums was more similar between both years, while the relative difference of CH4 release between the rewetted (0.83±0.15 mol m−2 a−1) and drained (0.45±0.11 mol m−2 a−1) section was larger in the first observed year, indicating a maximum increase in annual CH4 release of 84 % caused by rewetting at this particular site during the study period.

Pressure and Traction Technique Improves Postural Control More Than Tactile Stimulation in Foot Plantar Fascia: A Randomized Single-Blind Trial

ObjectiveTo check the acute effects of manual pressure and traction technique on balance and plantar footprint variables. DesignA single-blind clinical study with 2 groups. SettingPrivate practice. ParticipantsHealthy participants (N=40; 28 female and 12 male) were recruited to carry out a single-blind study. InterventionsExperimental group performed a bilateral plantar fascia manual pressure and traction technique. Control group performed a tactile stimulation. The position of the participant, the therapist, and the time of application of the techniques (5min) were the same for both interventions. Main Outcome MeasuresWe measured stabilometry variables and static footprint. The footprint variables were divided in rear, middle, and front foot areas. ResultsSignificant differences were found in stabilometry variables. There was an improvement in experimental group at X displacement with eyes open (P=.014) and surface eyes closed (P=.046) variables. ConclusionsAfter technique the experimental group improved the stabilometry variables, specifically surface with eyes closed and X displacement with eyes open. The static footprint variables have not shown differences after the technique compared with the control group.

Acute effects of myofascial induction technique in plantar fascia complex in patients with myofascial pain syndrome on postural sway and plantar pressures: A quasi-experimental study

ObjectivesThe prevalence of myofascial pain syndrome varies from 21% 93%. Several studies have shown that myofascial induction is effective in treating myofascial pain syndrome. Although these techniques have shown some effectiveness in clinical practice, there have been little study into their effects, and have deep effects. The purpose of this study was to investigate if the application of a single myofascial induction technique for each foot, targeted to the plantar fascia resulted in changes in balance and footprint variables. DesignA quasi-experimental study. SettingAn outpatient clinic. Subjects20 healthy participants (12 females and 8 males) were evaluated pre and post Myofascial induction technique for each foot in plantar fascia during 5 min. MethodsWe measured static footprint and stabilometry variables in asymptomatic subjects. The footprint surface area was divided: bilateral rear foot, bilateral midfoot, bilateral fore foot. ResultsWe found differences in the footprint variables: maximun pressure in forefoot (p = 0.025), surface in forefoot (p = 0.03). The myofascial induction has no effects on stabilometry variables. ConclusionsThe immediate effects of the longitudinal technique of myofascial induction of the plantar fascia are the increase of surface and maximum pressure in fore foot.

Comparative analysis of static and dynamic bare footprint dimensions in a north Indian population

Footprints recovered from the scene of a crime may be made while the perpetrator is standing, termed static, or walking, termed dynamic. Numerous studies on the medical and forensic aspects of static and dynamic footprints have been done and determining whether a footprint found at a crime scene is static or dynamic may have important forensic implications. Yet, little research has focused on the similarities or differences between static and dynamic footprints in the forensic context. The present study compared static and dynamic footprint two-dimensional variables to determine if statistically significant differences existed between them and if one can be estimated from the other. Footprints were taken from a sample of randomly selected 461 Jatt Sikh adults; major north Indian population. A total of 230 males and 231 females aged between 19 and 32 years were included in the study. Static and dynamic footprints were obtained from the participants using standard methodology. Seven linear footprint measurements and three footprint indices were calculated from each footprint. The dynamic footprint variables showed higher magnitudes than the static variables, and the differences were statistically significant for the length and width measurements. Furthermore, all measurements on the static and dynamic parameters exhibited statistically significant sexual dimorphism and bilateral differences. An attempt was made to estimate the static footprint dimensions from the dynamic footprint dimensions using the regression models to check the extent of differences between the two to help the investigators in estimating dimensions of one from another.

Understanding spatial variability of methane fluxes in Arctic wetlands through footprint modelling

The Arctic is warming at twice the rate of the global mean. This warming could further stimulate methane (CH4) emissions from northern wetlands and enhance the greenhouse impact of this region. Arctic wetlands are extremely heterogeneous in terms of geochemistry, vegetation, microtopography, and hydrology, and therefore CH4 fluxes can differ dramatically within the metre scale. Eddy covariance (EC) is one of the most useful methods for estimating CH4 fluxes in remote areas over long periods of time. However, when the areas sampled by these EC towers (i.e. tower footprints) are by definition very heterogeneous, due to encompassing a variety of environmental conditions and vegetation types, modelling environmental controls of CH4 emissions becomes even more challenging, confounding efforts to reduce uncertainty in baseline CH4 emissions from these landscapes. In this study, we evaluated the effect of footprint variability on CH4 fluxes from two EC towers located in wetlands on the North Slope of Alaska. The local domain of each of these sites contains well developed polygonal tundra as well as a drained thermokarst lake basin. We found that the spatiotemporal variability of the footprint, has a significant influence on the observed CH4 fluxes, contributing between 3% and 33% of the variance, depending on site, time period, and modelling method. Multiple indices were used to define spatial heterogeneity, and their explanatory power varied depending on site and season. Overall, the normalised difference water index had the most consistent explanatory power on CH4 fluxes, though generally only when used in concert with at least one other spatial index. The spatial bias (defined here as the difference between the mean for the 0.36 km2 domain around the tower and the footprint-weighted mean) was between ∣51∣% and ∣18∣% depending on the index. This study highlights the need for footprint modelling to infer the representativeness of the carbon fluxes measured by EC towers in these highly heterogeneous tundra ecosystems, and the need to evaluate spatial variability when upscaling EC site-level data to a larger domain.