Explore the vast range of titles available.

Microbial/Enzyme-induced carbonate precipitation uses bacteria/urease to drive the biogeochemical reactions to generate CaCO3 precipitation. The goal of this study was to assess the impact of initial concentrations of urease and bacteria on precipitation kinetics and crystal morphology (crystal shape and chemical composition) of calcium carbonate precipitation. Experimental results showed that the CaCO3 precipitation kinetics were well-fitted by a modified exponential logistic model with a confidence value of 95%, and higher concentrations of bacteria and urease could increase the precipitation rate of CaCO3. The results of XRD, FTIR and SEM indicated that vaterite phase was the dominant form of CaCO3 crystals in bacteria-induced system, and calcite phase was the primary form of the CaCO3 crystals in urease-induced system. The results also showed that the effect of initial concentrations of bacteria and urease on the morphology of CaCO3 crystals was insignificant.

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

Heatwaves pose a serious threat and are projected to amplify with changing climate and social demographics. A comprehensive understanding of heatwave exposure to the communities is imperative for the development of effective strategies and mitigation plans. This study explores spatiotemporal characterization of heatwaves across the historically vulnerable communities in Mississippi, United States. We derive multiple heatwave metrics including frequency, duration, and magnitude based on temperature data for urban-specific daytime, nighttime, and day–night combined conditions. Our analysis depicts a rising heatwave trend across all counties, with the most extreme shifts observed in prolonged day–night events lacking overnight relief. We integrate physical heatwave hazards with a socioeconomic vulnerability index to develop an integrated urban heatwave risk index. Integrated metric identifies the counties in northwest Mississippi as heat-prone areas, exhibiting an urgent need to prioritize heat resilience and adaptive strategies in these regions. The compounding urban heatwave and vulnerability risks in these communities highlights an environmental justice imperative to implement equitable policies that protect disadvantaged populations. Although this study is focused on Mississippi, our framework is scalable and can be employed to urban regions globally. This study provides a solid foundation for developing timely heatwave preparedness and mitigation to avert preventable heat-related tragedies as extremes intensify with climate change.

PurposeThis paper aims to understand the challenges of managing projects in hybrid organizations. The authors explore how organizations with persistent competing institutional logics strive to balance competing priorities, and the authors craft a research agenda to examine the capabilities to manage projects in hybrid organizations.Design/methodology/approachThe authors focus on the social enterprise hybrid organizational form to study how such organizations manage persistent competing social and economic logics. The authors review the project management and social enterprise literature to generate new insights and suggest future research directions for theory development for project management.FindingsThe understanding of the influences of the institutional context on the management of projects is still quite limited. The authors propose that project managers need adaptive capabilities to address how the dual logics, and their corresponding different expectations, can be flexibly combined. The objective is not to reduce the complexity due to the different logics, which is the focus of much of the literature on institutional complexity. Instead, the focus is on how to incorporate dual logics into a successfully blended hybrid organization.Originality/valueThere is a dearth of literature about how projects are successfully managed in hybrid organizations with persistent competing institutional logics, like social enterprises, and important questions remain to be answered. This paper offers new insights on the capabilities required to flexibly combine dual logics that would generally compete and create conflict on projects in hybrid organizations.

Disasters have different shapes, and one of them is sudden landslides, which can put the safety of highway users at risk and result in crucial economic damage. Along with the risk of human losses, each day a highway malfunctions causes high expenses to citizens, and repairing a failed highway is a time- and cost-consuming process. Therefore, correct highway functioning can be categorized as a high-priority reliability factor for cities. By detecting the failure factors of highway embankment slopes, monitoring them in real-time, and predicting them, managers can make preventive, preservative, and corrective operations that would lead to continuing the function of intracity and intercity highways. Expansive clay soil causes many infrastructure problems throughout the United States, and much of Mississippi’s highway embankments and fill slopes are constructed of this clay soil, also known as High-Volume Change Clay Soil (HVCCS). Landslides on highway embankments are caused by recurrent volume changes due to seasonal moisture variations (wet-dry cycles), and the moisture content of the HVCCS impacts soil shear strength in a vadose zone. Soil Matric Suction (SMS) is another indication of soil shear strength, an essential element to consider. Machine learning develops high-accuracy models for predicting the SMS. The current work aims to develop hybrid intelligent models for predicting the SMS of HVCCS (known as Yazoo clay) based on field instrumentation data. To achieve this goal, six Highway Slopes (HWS) in Jackson Metroplex, Mississippi, were extensively instrumented to track changes over time, and the field data was analyzed and generated to be used in the proposed models. The Artificial Neural Network (ANN) with a Bayesian Regularization Backpropagation (BR-BP) training algorithm was used, and two intelligent systems, Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA) were developed to optimize the ANN-BR algorithm for predicting the HWS’ SMS by utilizing 13,690 data points for each variable. Several performance indices, such as coefficient of determination (R2), Mean Square Error (MSE), Variance Account For (VAF), and Regression Error Characteristic (REC), were also computed to analyze the models’ accuracy in prediction outcomes. Based on the analysis results, the PSO-ANN outperformed the BOA-ANN, and both had far better performance than ANN-BR. Moreover, the rainfall had the highest impact on SMS among all other variables and it should be carefully monitored for landslide prediction HWS. The proposed hybrid models can be used for SMS prediction for similar slopes.

Introduction: chronic vertigo is a disabling disease that influences the quality of life. there are simple and low-cost treatments in iranian traditional medicine (itm) with minimal side effects for some subtypes of this disease based on the ancient classification. one of them is gastric-related vertigo (grv) that is diagnosed by a rational relationship between digestive symptoms and vertigo. case presentation: two adult patients with chronic vertigo were visited in the behesht - Iranian traditional- medicine outpatient clinic of iran university of medical sciences in tehran, Iran, in the year 2017. they were evaluated and treated according to gastricrelated vertigo management. the patients received 5 ml of pomegranate concentrated juice after each meal for four weeks. their symptoms were checked after four weeks and the validated persian version of dizziness handicap inventory (dhi) was completed for them as a pre-treatment and post-treatment monitoring tool. conclusions: pomegranate concentrated juice as a stomach tonic led to the improvement of the digestive symptoms and vertigo. the six-month follow-ups after treatment were normal.

The unloading creep behavior of soft soil under lateral unloading stress path and excess pore water pressure is the core problem of time-dependent analysis of surrounding rock deformation under excavation of soft soil. The soft soil in Shenzhen, China, was selected in this study. The triaxial unloading creep tests of soft soil under different initial excess pore water pressures (0, 20, 40, and 60 kPa) were conducted with the K0 consolidation and lateral unloading stress paths. The results show that the unloading creep of soft soil was divided into three stages: attenuation creep, constant velocity creep, and accelerated creep. The duration of creep failure is approximately 5 to 30 mins. The unloading creep behavior of soft soil is significantly affected by the deviatoric stress and time. The nonlinearity of unloading creep of soft soil is gradually enhanced with the increase of the deviatoric stress and time. The initial excess pore water pressure has an obvious weakening effect on the unloading creep of soft soil. Under the same deviatoric stress, the unloading creep of soft soil is more significant with the increase of initial excess pore water pressure. Under undrained conditions, the excess pore water pressure generally decreases during the lateral unloading process and drops sharply at the moment of unloading creep damage. The pore water pressure coefficients during the unloading process were 0.73–1.16, 0.26–1.08, and 0.35–0.96, respectively, corresponding to the initial excess pore water pressures of 20, 40, and 60 kPa.

Background: Insomnia is an important problem in medical sciences students and has implications for their educational progress. The current study aimed to estimate the prevalence of sleep disorders and investigating the impact of sweet almond on quality of sleep in students of the Tehran University of Medical Sciences (TUMS), Tehran, Iran who live in dormitories. Methods: This is a before-after study conducted in 2017. At first, using the ISI questionnaire prevalence of sleep disorders was determined. Sweet almond was the study intervention. Each day, 10 almonds were given to 446 students for 14 d. At the end of the second week, again ISI questionnaire was filled. SPSS was used to analyze data. The McNemar, Wilcoxson Signed Ranks, and Repeated Measures tests were used. Results: Out of 442 participants, 217 (49.1%) were female. Before intervention, 343 (77.6%) had insomnia and 99 (22.4%) had normal sleep. After intervention, 306 (69.2%) had insomnia and 136 (30.8%) had normal sleep. Having sweet almond for two weeks is associated with reducing insomnia (P<0.05). Investigating the almond impact in different categories also showed that it has a reducing impact on severe, mild, weak and normal sleep categories (P<0.05). Conclusion: Sweet almond has impacts on quality of sleep of those students of the TUMS that are living in dormitories. Intervention programs to improve quality of sleep are necessary and with regard to the high prevalence of insomnia, students must be protected, guided and consulted.

The article's abstract is no available.

According to the experience of these physicians, most of the diseases which become chronic and difficult to cure happen during old age (7, 8). With regard to the concentration of traditional physicians on the natural heat and natural moisture as the sources of regeneration especially in seminal organs, we suggest the development of culture environments using the medicines utilized by traditional physicians, in order to reduce the long-term effects of chronic and refractory diseases such as type 1 diabetes, blood cell malignancies, and diseases dealing with the regeneration of nerves, muscles, vessels and bones and will be very beneficial in future researches (Table 1). Organ Regeneration and Their Reconstruction Power From the Viewpoint of Iranian-Islamic Physicians Organ Sub Group Seminal Organs Bones bones, cartilages and joints Nerves nerves, ligaments, tendons and membranes Vessels arteries, veins and some nameless and thick (lymph) vessels Sanguine Organs Flesh flesh and muscles, glandular tissues Adipose fat tissues Footnotes Authors’ Contribution: Design and conduct of the study collection: