Search

Background and aims: The current systematic review aimed to elucidate the effects of lipid variability on microvascular complication risk in diabetic patients. The lipid components studied were as follows: High-density lipoprotein (HDL), High-density lipoprotein (LDL), Triglyceride (TG), Total Cholesterol (TC), and Remnant Cholesterol (RC). Method: We carried out a systematic search in multiple databases, including PubMed, Web of Science, and SCOPUS, up to October 2nd, 2023. After omitting the duplicates, we screened the title and abstract of the studies. Next, we retrieved and reviewed the full text of the remaining articles and included the ones that met our inclusion criteria in the study. Result: In this research, we examined seven studies, comprising six cohort studies and one cross-sectional study. This research was conducted in Hong Kong, China, Japan, Taiwan, Finland, and Italy. The publication years of these articles ranged from 2012 to 2022, and the duration of each study ranged from 5 to 14.3 years. The study group consisted of patients with type 2 diabetes aged between 45 and 84 years, with a diabetes history of 7 to 12 years. These studies have demonstrated that higher levels of LDL, HDL, and TG variability can have adverse effects on microvascular complications, especially nephropathy and neuropathic complications. TG and LDL variability were associated with the development of albuminuria and GFR decline. Additionally, reducing HDL levels showed a protective effect against microalbuminuria. However, other studies did not reveal an apparent relationship between lipid variations and microvascular complications, such as retinopathy. Current research lacks geographic and demographic diversity. Increased HDL, TG, and RC variability have been associated with several microvascular difficulties. Still, the pathogenic mechanism is not entirely known, and understanding how lipid variability affects microvascular disorders may lead to novel treatments. Furthermore, the current body of this research is restricted in its coverage. This field's lack of thorough investigations required a more extensive study and comprehensive effort. Conclusion: The relationship between lipid variation (LDL, HDL, and TG) (adverse effects) on microvascular complications, especially nephropathy and neuropathic (and maybe not retinopathy), is proven. Physicians and health policymakers should be highly vigilant to lipid variation in a general population. [ABSTRACT FROM AUTHOR]

Tessier clefts are skeletal and soft tissue abnormalities of a neonate's facial structures. They could be classified as syndromic and non-syndromic clefts, which can be attributed to disruptions in fetal development and genetic mutations, respectively. Reported cases of these clefts typically document the presence of additional abnormalities associated with these clefts. In this systematic review, we analyzed reports of Tessier clefts accompanied by cardiovascular anomalies, as one of the commonly encountered anomalies. We systematically searched PubMed (MEDLINE), Scopus, Web of Science, Science Direct, and Google Scholar. We selected and included case reports, case series, and case reviews on patients with Tessier cleft and cardiovascular anomalies. The critical appraisal of the included studies was performed by two independent investigators using the Consensus-based Clinical Case Reporting Guideline Development (CARE) checklist. Overall, 20 reports (18 case reports and 2 case series) were eligible for inclusion in this review. Tessier clefts 3 and 30 were the most commonly observed. In addition, the most prevalent cardiovascular anomalies consisted of the ventricular septal defect (VSD), double-outlet right ventricle, and atrial septal defect (ASD). Most of the patients received cosmetic and cardiovascular surgeries. However, some were not proper candidates for cardiovascular surgery because of their unstable condition and therefore did not survive. Conclusion: Regardless of the focus placed on the cleft and subsequent plastic surgery procedures in these cases, it is important to prioritize other abnormalities that may be associated with mortality. A complete cardiovascular system and associated disorders assessment should be performed before facial cosmetic surgeries. What is Known: • Tessier clefts are congenital defects in the soft tissues and bones of the face and like many other congenital defects, they are accompanied by defects in other parts of the body. • In the current literature, the emphasis is on clefts and the cosmetic issues rather than the coinciding defects, particularly cardiovascular anomalies. What is New: • Review the cardiovascular anomalies that are commonly encountered in patients with Tessier clefts. [ABSTRACT FROM AUTHOR]

The changing and intensifying landscape of global, national, and local disaster risks, driven by socio-political, environmental, and technological shifts, underscores the critical need for risk assessment by international agencies and governments. The Risk Matrix, introduced in 1995, has been widely used for risk assessment in different contexts, lauded for its simplicity and effectiveness. This model relies on the core risk components of consequence and likelihood, making it a favored tool for risk managers. To enhance the precision of risk assessment, various adaptations and extensions of the risk matrix have emerged; while some indirectly address resilience aspects, none explicitly integrate resilience into the matrix. This paper explores the risk matrix and its extensions, advocating for the inclusion of resilience in risk assessment. It introduces an empirical approach to quantify resilience, through a survey targeting small and medium-sized businesses in Southern Ontario, Canada. By developing two types of risk matrices--one with resilience considerations and one without--our work demonstrates how resilience alters risk prioritization, highlighting the importance of preparedness. This research underscores the pivotal role of resilience in risk assessment and urges its explicit integration into risk matrices to enhance accuracy and efficacy. Through practical examples and empirical data, the paper builds a compelling case for the central role of resilience in modern risk assessment practices. [ABSTRACT FROM AUTHOR]

Ensuring sustainable supplier selection (SSS) is crucial for companies that seek to procure goods and services responsibly. On the other hand, emergence of new technologies, such as the blockchain technology, has made a significant impact in the way businesses interact with each other. The blockchain technology offers several advantages, including enhanced information security, transparency, traceability, decentralization, and enabling the use of smart and trustless contracts, among others. While previous research has studied blockchain technology in supply chain management, its application towards advancing SSS remains unexplored. This study fills this gap by specifically addressing the blockchain technology adoption into the sustainable supplier selection and order allocation process. In doing so, a novel two-phase framework is proposed to deal with the problem under consideration. In phase I, first, the Best Worst Method (BWM) is used for calculating the importance weights of criteria for supplier selection in four categories of economic, circular, social, and blockchain compatibility to ensure a comprehensive evaluation of suppliers. Next, the MULTIMOORA method is applied to derive the scores of suppliers, where a new scoring method, addressing the shortcomings of the dominance theory approach, is proposed in the MULTIMOORA method in this paper. Then, the phase II of the framework determines the final selected suppliers and the optimal allocation of orders to each selected supplier through a bi-objective Mixed Integer Linear Programming (MILP) model. The developed model incorporates a novel cost objective, which includes information coordination and oracle-related costs corresponding to blockchain infrastructure deployment, and also a blockchain-related threshold constraint to ensure that the buyer does not exceed its predefined limits towards blockchain attunement. The proposed approach in this paper represents a significant contribution to the field of supplier selection by integrating sustainability and blockchain-related attributes into the decision-making process. The numerical results demonstrate the practicality and efficacy of the proposed approach in identifying blockchain-enabled and sustainable suppliers, making it a valuable decision-making tool for businesses seeking to enhance their procurement processes. [Display omitted] • Spotting blockchain technology in sustainable supplier selection and order allocation. • Developing an integrated BWM, MULTIMOORA, and bi-objective programming methodology. • Using WEE nexus status criterion which considers the equilibrium of three criteria. • Proposing a modified MUTIMOORA method for evaluating suppliers. • Considering the usage of trustless contracts with blockchain-enabled companies. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is believed to be an impressive factor in hydrological process, agricultural productivity, and monitoring dangerous outcomes of climate changes. The present study aimed to monitor and recognize the pattern of spatial and temporal variation of SMAP soil moisture in five subcatchments of Simineh-Zarrineh catchment in northwest of Iran from 2015 to 2017. Precipitation data of 35 meteorological stations and 287 soil moisture points were derived from the SMAP and used to monitor SM variations in the time scale. The results indicated that the SM variations are subject to precipitation variations throughout the monthly scale in the catchment. In all the seasons of this period, SM was found to have a decreasing trend from north to south of the catchment. On the contrary, no such constant pattern was observed from east to west. Oscillation SM patterns in this period were completely coordinated with the precipitation pattern. The determination coefficient between monthly SMAP soil moisture and precipitation for each subcatchment was 0.9, 0.83, 0.7, 0.84, and 0.71 for Bokan, Saqqez, Takab, Saeinqaleh, and Miandoab subcatchments, respectively. Spatial variability of standard deviation for SM values was used to find the amount of deviation from the average value during dry and wet seasons. The results revealed that in the seasonal scale, northwestern (0.067 to 0.069 cm³.cm³) and eastern parts (0.057 to 0.061 cm³.cm³) of the study area had higher values of the SM standard deviations in autumn. Additionally, according to the findings, a high value of standard deviation was observed in autumn because of irregular precipitation events and fluctuation of the temperature. [ABSTRACT FROM AUTHOR]

Soil moisture is an influential parameter in land surface hydrology and precise soil moisture data that can help researcher to realize the climate changes and land-atmosphere interactions. A initial struggle for the utilize of soil moisture data from satellite sensors is their reliability. It is important to appraise the dependability of those data before they can be regularly used at a global or local scale. In this study, the satellite soil moisture data was evaluated from the Soil Moisture Active/Passive (SMAP) over Simineh-Zarrineh Catchment in Bokan region, NW of Iran. A total of 287 soil samples as ground-based observations in the time period of 03 April to 03 December 2017 were taken for SMAP data validation. Results showed that the satellite data and in situ observation has a good correlation, with a mean correlation (r) value of 0.63 in total. This correlation level showed that, commonly, the SMAP soil moisture products over Simineh-Zarrineh Catchment (Bokan) have great quality, and it would be valuable for versatile utilization, including monitoring of land surface, weather prediction, modeling of hydrological process, soil loess monitoring, and climate studies. The results reveal that the remotely sensed data demonstrate the good correlation with in situ observation across the dry land with mean correlation (r) values of 0.67 throughout the time period. Particularly, SMAP soil moisture reveal a constant structure for obtain the spatial distribution of surface soil moisture. Additional researches are necessary for well realizing the SMAP data. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a compact microstrip lowpass filter (LPF) is designed, analyzed, simulated, and tested. The proposed design that consists of a basic part in the central section of the structure and two parts of suppression of harmonics that are inserted in the first and last sections of the basic part, have wide-stop band, high suppression factor (−38 dB attenuation level from 2.4 GHz up to 27.4 GHz), low normalized size and sharp roll-off. The proposed LPF has a −3 dB cut-off frequency of 2.4 GHz, a wide stopband bandwidth around 11th harmonic suppression and an insertion loss less than 0.1 dB at passband. This filter has been fabricated and tested and good agreement is observed between the simulated and measured results. [ABSTRACT FROM AUTHOR]

Interrill erodibility can be affected by soil aggregates, especially by those aggregate size classes that are dominant in the soil. In the Water erosion Prediction Project (WEPP) model, interrill soil erodibility (K i ) is estimated using very fine sand content. Despite that some studies have indicated an effect of aggregate stability on the K i , information on the relationship between the aggregate size class and K i and factors controlling it, particularly in semi-arid region is limited. This study was conducted to determine the variation of K i for different aggregate size classes under various rainfall intensities and evaluation of the WEPP model in estimating the K i for different aggregate fractions. Five aggregate size classes (0.25–2, 2–4.75, 4.75–5.6, 5.6–9.75 mm, and 9.75–12.7 mm) were separated from a sandy clay loam soil sampled in an agricultural land and put in laboratory flumes of 100 cm × 50 cm. The flumes were placed on a 9% slope and exposed to ten sequential rainfall simulations varying from 10 to 60 mm h −1 for 30 min. The K i of each aggregate size classes was determined using the interrill sediment delivery rate and compared this with the values estimated using WEPP. All physicochemical properties were also determined in the aggregate size classes. Organic matter content in the aggregate size classes was very low (0.65–0.73%) and didn't show strong relationships with the aggregate stability and hydraulic conductivity, whereas clay was major factor controlling determining these properties for the different aggregate fractions. Significant differences were found among the aggregate size classes in clay content ( P  < 0.05), aggregate stability measured using both wet-sieving method ( P  < 0.05) and water drop test method P  < 0.05, saturated hydraulic conductivity (K s ), and measured K i ( P  < 0.05). The measured K i was about 34 and 90 times bigger than the estimated K i for the fine aggregates and coarse aggregates, respectively. The fine aggregates showed higher susceptibility to interrill detachment with increasing rainfall intensity as compared with the coarse aggregates. Significant decrease was observed in the measured K i with increasing the aggregate size which was associated with increases in clay content, aggregate stability and K s . The stability of aggregates against raindrop impact (CND) was an important indicator describing the effect of aggregate size on the interrill erodibility in semi-arid soils. Therefore, this indicator can be taken into account as a soil structure measure to develop a proper equation for estimating interrill erodibility (K i ) for agricultural lands. The minimum use of tillage practices is essential to prevent aggregate breakdown and control interrill erosion in semi-arid regions. [ABSTRACT FROM AUTHOR]

Rail crude oil shipments have witnessed a steady increase over the past decade, which underscore the long-term viability of this transport mode. Although incidents involving these shipments could be catastrophic, having link-level information could be useful for designing appropriate emergency response network and responding to such episodes. We present a data-driven methodology that makes use of analytics to estimate the amount of crude oil on different rail-links in Canada until 2030. The resulting analyses facilitated identifying high-risk links around Canada based on the current practice of the railroad industry, and to suggest that incurring marginally higher transportation costs could reduce network risk. In addition, the availability of the proposed pipeline infrastructure would change the supply and demand location configurations over the forecast horizon, with the maximum changes to the current crude oil traffic flow pattern stemming from the completion of the Energy East pipeline project. [ABSTRACT FROM AUTHOR]

Check dams can be used as a source of information for studies on sediment characteristics and soil particle erodibility. In this study, sediment yield and grain size distribution (GSD) were measured in twenty small catchments draining into a rock check dam in NW Iran for different runoffs during 2010–2011. Significant correlations were found between sediment yield and slope steepness, vegetation cover and soil erodibility factor (K) of the catchments. The erodibility of soil particles was determined using the comparison of GSD between sediment and original soil. Clay was the most erodible soil particle which showed 2.05 times more percentage in sediment than the original soil. The erodibility of soil particles were strongly affected by the rainfall erosivity (EI 30 ). Check dams showed more effectiveness in trapping coarse particles (sand and gravel). The effectiveness of check dams in trapping coarse particles enhanced with increase in the remaining capacity of check dams. [ABSTRACT FROM AUTHOR]

The effects of land use, tillage methods and slope gradient on soil erosion have been widely investigated. However, there is limited information about their effects on very weakly aggregated soils that can be found in semi-arid, nutrient limited regions of low productivity. This study was carried out to investigate the interaction of land use, tillage method and slope gradient on runoff and soil loss in a weakly aggregated soil in the north-west of Iran. Plots (3 m × 10 m) with three treatments and two replicates were installed in each of four slopes gradient categories (12.6, 15.3, 17.0, 19.4%). The three treatments were pasture with poor vegetation cover (27%) and rainfed wheat either cultivated up to down slope or along the contour. Wheat was sown in early autumn and runoff and soil loss was measured under natural rainfall during the eight months long growing period. Out of 53 precipitation events, 8 storms were classified erosive. The susceptibility of all plots to produce runoff and soil loss increased pronouncedly and highly significantly with subsequent rainfalls. The potential of a rainfall to produce runoff and soil loss was thus highly dependent on preceding rainfalls. Wheat cultivation increased runoff 13 times and soil loss 60 times compared to pasture. Runoff and soil loss increased about 5.5 and 35 times in plots cultivated up to down slope as compared with contour cultivated plots. The effects of land use increased pronouncedly with increasing slope gradient. These results showed that erosivities of rainfalls separated by several months were not independent from each other and that the effects of land use and slope gradient interacted. Also the effect of slope gradient on soil loss was much larger than estimated by current equations. All effects could be explained by the extremely weak aggregates. Soil erosion was thus not detachment limited but transport limited. Transport limitation decreased in the sequence of rainfalls due to loss of surface roughness and with increasing slope gradient due to increasing failure of roughness elements. Under conditions leading to extremely weak soil aggregates, land use and slope gradient thus exert a much larger impact on runoff and soil loss than what is commonly assumed. This should be better considered in soil conservation efforts but also in runoff and erosion modelling. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases are the world’s leading cause of mortality. These diseases are rooted in an unhealthy lifestyle. In order to confront this subject, it is essential to identify several risk factors that contribute to heart disease (HD) in people with different attitudes, values, beliefs, expectations and motivations. This study was therefore an attempt to explain the adaptive experiences of children whose parents were involved in myocardial infarction since they were more likely subjected to get the so-called disease. Objective: To identify the risk factors and to clear ambiguity using a qualitative research method from the experiences of people at risk of the above mentioned disease. Methods: This qualitative study was a directed content analysis. Eighteen children (above 18 years old) of parents with a history of myocardial infarction participated, and were chosen with purposive sampling and the highest diversity. Data were collected through deep and semi structure interviews based on Protection Motivation Theory (PMT) from March to November 2015, and were analyzed along with their data collection and with usage of Lundman and Graneheim method. Interviews were conducted in non-stressful conditions with a place and time agreement. Results: During content analysis process, 220 codes were extracted. After reviewing several times and summarizing, the codes were categorized based on similarity and proportion, and finally 12 subcategories and three categories were elicited including efforts to perform self-care in order to prevent HD, poor life style as a factor not to do preventive HD and health continuation with positive changes in life style. Conclusions: Most participants, despite intending to do self-care behaviors to prevent HD, due to factors such as time constraint, high costs, laziness, impatience and prioritizing other life affairs, did not pay attention to their health. Therefore, providing the training programs with an emphasis on life skills can play an important role in reducing perceived response cost and promoting health. [ABSTRACT FROM AUTHOR]

Soil erosion by water is a three-phase process that consists of detachment of soil particles from the soil mass, transportation of detached particles either by raindrop impact or surface water flow, and sedimentation. Detachment by raindrops is a key component of the soil erosion process. However, little information is available on the role of raindrop impact on soil losses in the semi-arid regions where vegetation cover is often poor and does not protect the soil from rainfall. The objective of this study is to determine the contribution of raindrop impact to changes in soil physical properties and soil losses in a semiarid weakly-aggregated agricultural soil. Soil losses were measured under simulated rainfalls of 10, 20, 30, 40, 50, 60 and 70 mm h − 1 , and under two conditions: i) with raindrop impact; and, ii) without raindrop impact. Three replications at each rainfall intensity and condition resulted in a total of 42 microplots of 1 m × 1.4 m installed on a 10% slope according to a randomized complete block design. The contribution of raindrop impact to soil loss was computed using the difference between soil loss with raindrop impact and without raindrop impact at each rainfall intensity. Soil physical properties (aggregate size, bulk density and infiltration rate) were strongly damaged by raindrop impact as rainfall intensity increased. Soil loss was significantly affected by rainfall intensity under both soil surface conditions. The contribution of raindrop impact to soil loss decreased steadily with increasing rainfall intensity. At the lower rainfall intensities (20–30 mm h − 1 ), raindrop impact was the dominant factor controlling soil loss from the plots (68%) while at the higher rainfall intensities (40–70 mm h − 1 ) soil loss was mostly affected by increasing runoff discharge. At higher rainfall intensities the sheet flow protected the soil from raindrop impact. [ABSTRACT FROM AUTHOR]

There is a strong need to develop a simple method for rapid estimation of erodibility using readily available data. In this study, soil erodibility was measured using eleven soil textures at the plot scale (60 cm × 80 cm) on a slope of 9% in a semi-arid region. A total of 110 soil erosion experiments were conducted using ten simulated rainfalls (50 mm h − 1 for 30 min). A regression model was developed based on silt and clay content (R 2 = 0.82, p < 0.001) and was applied to estimate erodibility for 231 soils in the textural triangle. Kriging was used to spatial interpolate erodibility using these data to unknown soils on the textural triangle. A soil erodibility triangle was developed using kriging technique and its accuracy was evaluated using seven other soils. The technique showed a 5.4% error and allowed the prediction of soil erodibility in semi-arid areas by using the soil erodibility triangle. [ABSTRACT FROM AUTHOR]

• We study emergency response network design for rail hazmat shipments. • A two-stage stochastic programming model is proposed. • Problem instances based on the infrastructure of a railroad operator is studied. • Design should incorporate information from historical and projected accident data. • Redistribution of assets and emphasizing disutility improves coverage. Railroad is one of the primary transportation modes for hazardous materials (hazmat) shipments in North America. Designing an emergency response network capable of providing commensurate response is one of the primary levers to contain (or mitigate) the adverse consequences from rail hazmat incidents. To this end, we propose a two-stage stochastic programming model to determine the location of response facilities and equipment packages to be stockpiled therein. Several publicly available reports were consulted to estimate the different model parameters, which in turn facilitated the analyses of the case study on a realistic railroad network in Ontario (Canada). Our study suggests that the strategic emergency response network design decision made only on the basis of empirical data would undermine the effectiveness of the resulting network. We also explore several underlying trade-offs in our model, evaluate the incremental benefit of establishing (and stockpiling) additional response facilities. Furthermore, our analyses highlight that the mean coverage can be improved by redistributing the equipment in the network, purchasing equipment with higher containment capacity, and making use of a disutility multiplier factor. [ABSTRACT FROM AUTHOR]

• Surface characteristics captured soil moisture variations more accurately than regional scales. • Surface heterogeneities have important & complex effect on soil moisture spatial distribution. • Land surface temperature has a crucial role in soil moisture modeling. • Combined Bootstrap & Triangle method showed promise in reducing estimation uncertainty. Modeling soil moisture (SM) spatial distribution is important for hydrological forecasting, runoff estimation and catchment management and other applications. The objective of this study was to quantify the effect of surface heterogeneity including vegetation, soil, topography etc. on SM at the regional and surface characteristics classes (SCC) scales. Landsat 8 images, ASTER digital elevation model, land cover map, climatic data, and SM measured at 148 locations in the Balikhli-Chay catchment of Iran were used. Greenness, brightness, wetness, and land surface temperature are the surface biophysical characteristics and the topographical characteristics including elevation, slope, aspect, Topographic Wetness Index (TWI) and solar local incident angle calculated using Tasseled Cap Transformation, Single Channel algorithm used in this study. The Triangular method and the bootstrapping model were used to model SM and mitigate prediction uncertainty. Correlation coefficient (r) and Root Mean Square Error (RMSE) between the modeled and measured SM values were used to evaluate the modeled SM. The mean r and RMSE between the modeled and measured SM for different months were 0.83 and 1.84 (volumetric percentage), respectively. The degree of heterogeneity of the spatial distribution of SM in different classes of surface biophysical and topographical characteristics was different. The impact of surface heterogeneity on SM varied across regional and SCC scales. The mean r values between SM and surface biophysical and topographical characteristics in the regional and SCC strategies for different months were 0.41 and 0.54, respectively. Using the SCC scale instead of the regional scale heterogeneity information can increase the accuracy of SM modeling. [ABSTRACT FROM AUTHOR]

• Multitemporal RS images & digital soil mapping were used to predict soil moisture. • Concept of dynamic and static environmental covariates (ECs) were introduced & used. • Dynamic EC helped capture spatiotemporal variation in soil moisture with confidence. • A general model in predicting soil moisture was proposed using multitemporal images. Soil moisture (SM), a critical component of the global hydrological cycle, is affected by individual or combinations of multiple factors including soil properties, climate, and topography. Despite its importance to many disciplines, predicting SM continuously, accurately, and inexpensively over a large area is a great challenge due to its dynamic nature controlled mostly by the spatial and temporal variability of these factors. Static environmental covariates, such as those derived from a digital elevation model, are commonly used in digital soil mapping (DSM); these are typically less suitable for predicting dynamic properties. Easily available multi-temporal satellite images show strong promise to capture this variability. The objective of this study was to predict SM from multi-temporal satellite images using a DSM approach. Specifically, we examined the feasibility of using dynamic, static, and combinations of environmental covariates (ECs) to predict SM in the Balikhli_Chay watershed in Iran on four separate dates in June, July, August, and September 2018 coincident with satellite overpass. Cubist and random forest (RF) machine learning algorithms (MLAs) were trained for making SM predictions for individual dates, and the data was then compiled without considering the date to create generalized models. The baseline for comparisons were the models developed using only static ECs. For June, July, August, and September, Cubist R2 improvements were 96%, 78%, 185% and 120%, respectively. Using the generalized models, R2 improved by as much as 223% and RMSE decreased by as much as 47% when comparing the best SM prediction model in each month to models developed using only static ECs for that same month using the Cubist model. Similar model improvements were seen for the RF model. The generalized Cubist and RF MLAs performed equally well with concordance of 0.91 and 0.90 for Cubist and RF respectively, and low RMSE of 3.04 and 2.98. The best Cubist and RF MLAs by month were always those developed with dynamic, or satellite-derived, ECs. Based on the variable importance statistics, land surface temperature (LST) was the most important EC. This study showed the strong predictions, and the practical feasibility of using multi-temporal satellite data as a dynamic EC that could help to capture the spatial and temporal variations of soil moisture. This approach could likely be extended to other dynamic soil property (e.g., soil temperature). [ABSTRACT FROM AUTHOR]

• Temporal decrease in the contribution of raindrop impact (RI) in rill detachment. • More contribution of RI in rill detachment in soils with higher stable aggregates. • The use of sediment's PSD for detemining the sediment size selectivity in rills. • More dependency of sediment enrichment ratio on soil texture than slope gradient. • Quantifying the sediment homogeneity using d g and δ g measures. Limited information is available on the role of raindrop impact (RI) in the particle size distribution of sediment (PSD s) and the homogeneous of sediment particles eroded in rills under slope gradients particularly in semi-arid soils. Toward this, a laboratory experiment was designed with three dominant semi-arid soils consist of loamy sand, clay and sandy clay, at four slope gradients (5, 10, 15, and 20%) under two soil surface conditions (SSC) i.e, with raindrop impact (RI) and without RI using a rainfall simulator with 90 mm h−1 in intensity for 30 min. Forty eight rainfall events were setted up to soil samples packed into the flumes with 0.4 m width by 4 m length. Runoff and sediment ratio for the two SSC was computed using the ratio of values with RI to their values without RI in each time interval from starting runoff. Based on the results, the soils appeared different behavior in runoff and sediment ratio during runoff generation time. In clay and sandy clay, contribution of RI in runoff and sediment increased during runoff generation in the rills which was associated to higher percentage of water-stable aggregates. The two soils produced higher runoff and sediment under RI with increasing slope gradient. The sediment selectivity by concentrated flow was determined using the comparison between PSD s in steady-state runoff and the PSD of original soil (PSD o). The PSD s curves of loamy sand were similar for the two SSC at slope gradients and overlapped almost with the PSD o , indicating lower importance of RI in the sediment transport selectivity in the soil with lower water-stable aggregates. Enrichment ratio of particles in sediment (ER s) varied with both soil texture and slope gradient. The contribution of RI in variation of ER s was more obvious in sand, indicating lower selectivity of sand to detachment by concentrated flow. The geometric mean diameter (d g) and standard deviation (δ g) of the sediment particles were defined as a measure to determination of the homogeneity of sediment. Values of these indices increased under RI, indicating the contribution of RI in the transportability of all soil particles especially coarse particles. This study revealed that the RI is the major factor controlling runoff, sediment, the selectivity of soil particle by concentrated flow and sediment homogeneity in semi-arid soils. These variables can be quantified by PSD s , ER s , d g and δ g for the two soil surface conditions and compared with the original soil. [ABSTRACT FROM AUTHOR]

Accurate information on soil moisture (SM) is critical in various applications including agriculture, climate, hydrology, soil and drought. In this paper, various predictive relationships including regression (Multiple Linear Regression, MLR), machine learning (Random Forest, RF; Triangular regression, Tr) and spatial modeling (Inverse Distance Weighing, IDW and Ordinary kriging, OK) approaches were compared to estimate SM in a semi-arid mountainous watershed. In developing predictive relationship, Remote Sensing datasets including Landsat 8 satellite imagery derived surface biophysical characteristic, ASTER digital elevation model (DEM) derived surface topographical characteristic, climatic data recorded at the synoptic station and in situ SM data measured at Landsat 8 overpass time were utilized, while in spatial modeling, point-based SM measurements were interpolated. While 70%(calibration set) of the measured SM data were used for modeling, 30%(validation set) were used to evaluate modeling accuracy. Finally, the SM uncertainty maps were created for different models based on a bootstrapping approach. Among the environmental parameter sets, land surface temperature (LST) showed the highest impact on the spatial distribution of SM in the region at all dates. Mean R2(RMSE) between measured and modeled SM on three dates obtained from the MLR, RF, IDW, OK, and Tr models were 0.70(1.97%), 0.72(1.92%), 0.59(2.38%), 0.59(2.27%) and 0.71(1.99%), respectively. The results showed that RF and IDW produced the highest and lowest performance in SM modeling, respectively. Generally, the performance of RS-based models was higher than interpolation models for estimating SM due to the influence from combination of topographic parameters and surface biophysical characteristics. Modeled SM uncertainty with different models varies in the study area. The highest uncertainty in SM modeling was observed at the north part of the study area where the surface heterogeneity is high. Using RS data increased the accuracy of SM modeling because they can capture the surface biophysical characteristics and topographical properties heterogeneity. Unlabelled Image • Spectral and spatial based approaches were used to map soil moisture. • Spectral models were superior than interpolation models to estimate soil moisture. • Combined topographic & surface biophysical characters supported higher performance. • Higher surface heterogeneity made soil moisture estimation more uncertain. • Land surface temperature showed the highest impact on soil moisture distribution. [ABSTRACT FROM AUTHOR]

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R2 values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications. Unlabelled Image • We introduced dynamic & static environmental covariates (ECs) for digital soil mapping. • Dynamic EC improved soil prediction over static ECs including terrain indices. • Multi-date satellite images captured the variations from change in soil properties. • Multi-date satellite images also reduced the uncertainty in prediction and mapping. • Combination of dynamic and static ECs had a larger influence on soil prediction. [ABSTRACT FROM AUTHOR]

In this letter, a compact microstrip lowpass filter (LPF) is designed using serial connecting of two modified asymmetric hairpin resonators. The proposed design is consists of four open‐circuited stubs that are used to suppress spurious harmonics in the stopband. The proposed filter has wide‐stop band with attenuation level better than −28 dB from 2.16 up to 26.1 GHz and sharp roll‐off. The proposed LPF has −3 dB cutoff frequency equal to 2.11 GHz, wide stopband width around 12th harmonic suppression and insertion loss less than 0.1 dB at about 85% of the passband. The excellent agreement is observed between the simulated results and measured values of the fabricated LPF. [ABSTRACT FROM AUTHOR]

This study was conducted in a company that produces palm oil-based products such as cooking oil and margarine. The study aimed to encounter defects in packaging pouches. This study integrated the overall equipment effectiveness (OEE) with the six sigma DMAIC method. The OEE was performed to measure the efficiency of the machine. Three factors were measured in OEE: availability, performance, and quality. These factors were calculated and compared to the OEE world-class value. Then, the Multiple Linear Regression was performed using SPSS to determine the correlation between measurement variables toward the OEE value. Lastly, the six sigma method was implemented through the DMAIC approach to find the solution and improve the packaging quality. Supposing the recommendations are implemented, the OEE is expected to increase from 82% to 85%, with availability ratio, performance ratio, and quality ratio at, 99%, 86%, and 99.8%, respectively. [ABSTRACT FROM AUTHOR]

Background and Objective: Metabolic syndrome, especially prediabetes, is one of the most common health problems due to incomplete glucose metabolism that has a direct relationship with lifestyle. This study was conducted to determine the factors that enable and nurture physical activity in women with prediabetes based on the PEN-3 model. Material and Method: This descriptive-analytical study was conducted on 41 prediabetic women aged 30–65 years and 9 service providers in health centers. Data were collected from a semi-structured individual interview based on the PEN-3 model. The Graneheim and Lundman method was used to analyze the data. Results: During data analysis, two main themes that include enabling factors and nurturing factors in the domain of physical activity and 6 classes including enablers (positive, negative, and existential) and nurturers (positive, negative, and existential) were extracted from the data content. Conclusion: By determining enablers and nurturers, the providers of services can facilitate the participation of prediabetic women in physical activity by applying positive social and structural effects and also to eliminate negative environmental conditions. [ABSTRACT FROM AUTHOR]

Land abandonment is widespread in the Mediterranean mountains. The impact of agricultural abandonment results in a shift in ecosystem evolution due to changes in soil erosion, but little is known about long-term soil and water losses. This paper uses 11 years of measurements in two paired plots (abandoned vs control) with four subplots to determine how soil and water losses evolved after abandonment within an agricultural parcel. For two years (2004–2005) both plots were under tillage, and after 2006 one plot was abandoned. The monitored plots measured runoff and sediment concentration after each rainfall event. The results show that during the two years after abandonment there was an increase in sediment yield followed by a decrease. Once the field was abandoned, a sudden increase in runoff (× 2.1 times) and sediment concentration (× 1.2 times) was found due to the lack of vegetation and tillage. After one year, the sediment concentration and, after two years, the runoff rates were lower in the abandoned than in the tilled plots. This short transition period ended in contrasting responses between the control and abandoned plot as the impact of abandonment resulted in 21 times less sediment yield after nine years of abandonment. This occurred despite the fact that the year after the abandonment the abandoned plot had 2.9 times more erosion due to low vegetation recovery and the development of a soil crust. Agriculture land abandonment resulted in lower erosion rates over the long term, but showed an increase in soil and water losses over the short term (two years). Therefore, in the first two years after abandonment there is a particular need to apply nature-based soil and water conservation strategies to prevent soil erosion. [ABSTRACT FROM AUTHOR]

Objective: Problem-solving ability is one of the most important means of family stability that enables the families to understand their roles, functions, and performances. Self-efficacy deficiency in problem-solving runs through many families. This qualitative study was conducted to investigate and describe how couples solve problems in their families. Method: This study was conducted to detect couples' self-efficacy deficiency in problem-solving using purposive sampling method. Several deep semi-structured interviews based on McMaster model and observations were conducted by nine family therapists and psychiatrists on four couples (eight persons) living in Yazd (Iran). The interviews were performed, audio-recorded, and transcribed verbatim. The analysis was interpreted through directed content analysis methods. Results: Families in Yazd (Iran) made some attempts to solve their problems, but their efforts were not enough, and thus they suffered from self-efficacy deficiency, which included 8 categories. The main theme distilled from the data of 17 participants was self-efficacy deficiency, which included the following categories: avoidance, insolvency, interference from others, ineffective self-treatment, behavioral problems, stubbornness, superficiality, and denial . Conclusion: It is of paramount importance to identify self-efficacy deficiency in families and promote problem- solving programs to increase family stability. In the present study, the main deficiencies in problem-solving were detected. [ABSTRACT FROM AUTHOR]

The aim of the present study was to experimentally investigate the interaction between a low replacement ratio of different nano-particles (SiO2, Al2O3, clay, and CaCO3) and aminosilane in the matrices of cement paste and mortar. Results showed that the optimum content of aminosilane for improving the 28-day compressive strength of cement mortar was 0.75% (by weight of the total binder). The utilization of nano-SiO2 and nano-clay particles improved the strengths of the cement mortar containing hybrid systems of nano-particles/aminosilane at early (7days) and middle curing ages (28 and 91days). The 28-day compressive strength enhancement of cement mortar with hybrid systems of nano-SiO2/aminosilane and nano-clay/aminosilane was about 19% and 20%, respectively. Additionally, the application of aminosilane with nano-CaCO3 and nano-Al2O3 particles showed lower efficacy on mechanical performance of the cement-based composites in comparison with nano-clay and nano-SiO2. Despite the fact that the utilization of aminosilane with and without different nano-particles enhanced the flowability of the cement paste and mortar, it reduced the electrical resistivity of the cement mortar. In this regard, the minimum electrical resistivity was achieved for specimens with only aminosilane. This reduction was about 19.5% for samples containing 0.75% aminosilane for a curing time of 28days. [ABSTRACT FROM AUTHOR]

The modeling of Near-Surface Temperature Lapse Rate (NSTLR) is of great importance in various environmental applications. This study proposed a new approach for modeling the NSTLR based on the Normalized Land Surface Temperature (NLST). A set of remote sensing imagery including Landsat images, MODIS products, and ASTER Digital Elevation Model (DEM), land cover maps, and climatic data recorded in meteorological stations and self-deployed devices located in the three study area were used for modeling and evaluation of NSTLR. First, the Split Window (SW) and Single Channel (SC) algorithms were used to estimate LST, and the spectral indices were used to model surface biophysical characteristics. The solar local incident angle was obtained based on topographic and time conditions for different dates. In the second step, the NSTLR value was calculated based on the LST-DEM feature space at the regional scale. The LST was normalized relative to the surface characteristics based on Random Forest (RF) regression and the NSTLR was calculated based on the NLST-DEM feature space. Finally, the coefficient of determination (R2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between DEM and LST for the study area A, B and C, respectively. The R2 and RMSE between the observed NSTLR and the Landsat derived NSTLR based on NLST for the study area A (B, C) were improved 0.30 (0.26, 0.35) and 0.81 (0.80, 0.94) °C Km−1, respectively, rather than the Landsat derived NSTLR based on LST. Also, for the study area A, the R2 and RMSE between the observed NSTLR and the MODIS derived NSTLR based on NLST in spring, summer, autumn and winter were improved 0.17, 0.12, 0.10, and 0.22; and 0.51, 0.44, 0.27, and 0.51 °C Km−1, respectively, rather than the MODIS derived NSTLR based on LST. Model assessment results (R2 and RMSE) and comparing modeled NSTLR (all strategies) with observed NSTLR, for both Landsat and MODIS, showed that the use of NLST instead of LST, significantly improved the accuracy of the obtained NSTLR in the mountainous regions. • A new approach to model surface temperature lapse rate (STLR) is introduced. • Remote sensing images, biophysical, terrain, meteorological and climate data were used. • STLR was calculated at regional scale using LST-DEM feature space. • Local STLR was calculated using LST-DEM normalized to surface characteristics. • Environmental lapse rate prediction significantly increased using NLST over LST. [ABSTRACT FROM AUTHOR]