Randomized study of singledose, three-day, and seven-day treatment of cystitis in

We evaluated the following five treatment regimens for acute cystitis in nonpregnant women: cefadroxil, 1,000 mg single-dose; cefadroxil, 500 mg twice a day for three days; cefadroxil, 500 mg twice a day for seven days; trimethoprim-sulfamethoxazole (TMP-SMZ), 320-1,600 mg single-dose, and TMP-SMZ, 160-800 mg twice a day for three days. At four week

Study on Spatial and Temporal Characteristics of Surface Albedo at the Northern Edge of the Badain Jaran Desert Based on C + STNLFFM Model

Obtaining surface albedo data with high spatial and temporal resolution is essential for measuring the factors, effects, and change mechanisms of regional land-atmosphere interactions in deserts. In order to obtain surface albedo data with higher accuracy and better applicability in deserts, we used MODIS and OLI as data sources, and calculated the daily surface albedo data, with a spatial resolution of 30 m, of Guaizi Lake at the northern edge of the Badain Jaran Desert in 2016, using the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM) and topographical correction model (C model). We then compared the results of STNLFFM and C + STNLFFM for fusion accuracy, and for spatial and temporal distribution differences in surface albedo over different underlying surfaces. The results indicated that, compared with STNLFFM surface albedo and MODIS surface albedo, the relative error of C + STNLFFM surface albedo decreased by 2.34% and 3.57%, respectively. C + STNLFFM can improve poor applicability of MODIS in winter, and better responds to the changes in the measured value over a short time range. After the correction of the C model, the spatial difference in surface albedo over different underlying surfaces was enhanced, and the spatial differences in surface albedo between shifting dunes and semi-shifting dunes, fixed dunes and saline-alkali land, and the Gobi and saline-alkali land were significant. C + STNLFFM maintained the spatial and temporal distribution characteristics of STNLFFM surface albedo, but the increase in regional aerosol concentration and thickness caused by frequent dust storms weakened the spatial difference in surface albedo over different underlying surfaces in March, which led to the overcorrection of the C model

Optimizing Internet of Things Fog Computing: Through Lyapunov-Based Long Short-Term Memory Particle Swarm Optimization Algorithm for Energy Consumption Optimization

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption

Impacts of coal fly ash on plant growth and accumulation of essential nutrients and trace elements by alfalfa (Medicago sativa) grown in a loessial soil

Coal fly ash (CFA) is a problematic solid waste all over the world. One distinct beneficial reuse of CFA is its utilization in land application as a soil amendment. A pot experiment was carried out to assess the feasibility of using CFA to improve plant growth and increase the supply of plant-essential elements and selenium (Se) of a loessial soil for agricultural purpose. Plants of alfalfa (Medicago sativa) were grown in a loessial soil amended with different rates (5%, 10%, 20% and 40%) of CFA for two years and subjected to four successive cuttings. Dry mass of shoots and roots, concentrations of plant-essential elements and Se in plants were measured. Shoot dry mass and root dry mass were always significantly increased by 5%, 10% and 20% CFA treatments, and by 40% CFA treatment in all harvests except the first one. The CFA had a higher supply of exchangeable phosphorus (P), magnesium (Mg), copper (Cu), zinc (Zn), molybdenum (Mo), and Se than the loessial soil. Shoot P, calcium (Ca), Mg, Mo, boron (B), and Se concentrations were generally markedly increased, but shoot potassium (K), Cu, and Zn concentrations were generally reduced. The CFA can be a promising source of some essential elements and Se for plants grown in the loessial soil, and an application rate of not higher than 5% should be safe for agricultural purpose without causing plant toxicity symptoms in the studied loessial soil and similar soils. Field trials will be carried out to confirm the results of the pot experiment. (C) 2017 Elsevier Ltd. All rights reserved

Growth, morphological and physiological responses of alfalfa (Medicago sativa) to phosphorus supply in two alkaline soils

Phosphorus (P) deficiency is a major problem for alfalfa (Medicago sativa) productivity on alkaline soils on the Loess Plateau, China. Our aim was to investigate growth, morphological and physiological responses of alfalfa to P supply in two alkaline soils when water supply is limited. A pot experiment was carried out to grow alfalfa in two alkaline soils supplied with different rates of P. Parameters of plant growth and root morphology, rhizosphere pH and carboxylates, and plant concentrations of mineral nutrients were measured. Plant growth and nutrient uptake were enhanced by supplying P, but shoot growth was not further increased when P supply was > 20 mu g P g(-1) soil. Specific root length was only responsive to changes in soil P when P supply was low in the loessial soil. The rhizosphere carboxylate amount was significantly greater when no P was supplied than when P was supplied to the loessial soil. The rhizosphere pH was lower than the bulk soil pH, but did not vary with soil P. A P supply of 20 mu g P g(-1) soil was optimal for alfalfa growth. The responses of specific root length and rhizosphere carboxylates depended on soil type