Schlaganfall bei normotensiven und hypertensiven Ratten: Effekte von Lithium und Hemmstoffen des Renin-Angiotensin-Systems

In the present study, the effect of lithium alone or in combination with the ACE inhibitor, captopril or the AT1 receptor antagonist, telmisartan, were investigated in two animal models of stroke: First, the stroke-prone spontaneously hypertensive rat (SHRSP), an animal model of severe hypertension associated with the development of cerebral, renal and cardiac dysfunction. In these animals, the onset of stroke is accelerated by salt-loading.. Second, a model of focal cerebral ischemia with 90 min occlusion of the middle cerebral artery (MCAO) followed by reperfusion. In the first set of experiment, we demonstrate that renin-angiotensin system (RAS) inhibitors markedly prolong survival time in salt-loaded SHRSP, and majority of animals not longer die from stroke after treatment with RAS inhibitors. RAS inhibitors reduce the number of apoptotic/necrotic cells in the brain tissue after 5 weeks of treatment. Lithium does not improve the effect of sufficiently dosed RAS inhibitors. In the secondary set of experiment, chronic treatment with lithium decreases the infarct volume and improves the neurological outcome in the normotensive Wistar rats with transient MCAO. The neuroprotective effects of lithium are associated with a reduction of activated caspase-3 expression and inhibition of DNA fragmentation in the ischemic penumbra of the brain cortex. RAS inhibitors do not influence the neurological deficits and infarct volume in these animals. RAS inhibitors reduce apoptosis and the degree of microglial activation in the ischemic penumbra of the brain cortex. However, there are no synergistic effects between RAS inhibitors and lithium in the rats with focal cerebral ischemia

Modification of wool fiber using steam explosion

Wool fiber was modified by steam explosion in this study. SEM results show that some scales on the fiber surface were cleaved and tiny grooves generated during the explosion. FTIR results suggest no evident changes in the chemical composition of the fiber after the explosion treatment. However, the crystallinity of the fiber decreased slightly as the steam pressure increased based on the X-ray results. In the thermal analysis, DSC results show that the temperature corresponding to vaporization of absorbed water and cleavage of disulfide bonds respectively decreased as the steam pressure increased. The reduction in thermal decomposition energy of the treated fiber indicates that steam explosion might have destroyed some crystals and crosslinks of macromolecular chains in the fiber. The treatment also led to some alterations of the fiber properties, including reduction in strength, moisture regain and solubility in caustic solution.<br /

Feasibility study of a scroll expander for recycling low-pressure exhaust gas energy from a vehicle gasoline engine system

The growing number of vehicles on the road has led to a rapid increase in fuel consumption and toxic gas emissions, so the challenges in fuel efficiency improvement and reduction of CO2 and NOx emissions have always been on the top agenda of the automotive industry. The paper presents a feasibility study of recovering the low-pressure exhaust gas energy via by-pass connection of a scroll expander to the engine system exhaust. The paper starts with the description of the proposed new exhaust energy recycling scheme and the mathematical modelling of the system. A feasibility study is carried out to investigate whether this new scheme can work with the engine operation conditions specified by the engine test data. The initial study indicated that the scroll expander structure needs to be modified; otherwise, it cannot be used for exhaust energy recovery. The experimental test and simulation results presented in this paper indicate that it is feasible to recover the low-pressure exhaust gas energy using a scroll expander with a modified structure. The proposed energy recovery system has the potential to produce over 400 W power output with over 90% of engine exhaust flow recycling

Ecological network construction based on minimum cumulative resistance for the City of Nanjing, China

With economic growth and the improvement of the urbanization level, human activities have constantly interfered with landscape patterns, resulting in serious threats to regional ecological security. Therefore, it is of great significance to study the evolution and optimization of the landscape patterns. Based on three TM images from 1990, 2000, and 2010, and selected landscape pattern indexes, the changes in the landscape pattern of Nanjing in the past twenty years were studied based on landscape ecology theory using Remote Sensing (RS) and a Geographical Information System (GIS). The ecological network was built on the basis of extracted ecological nodes and the minimum cumulative resistance. The results show that changes in the landscape pattern of the city of Nanjing were notable. Class-level indexes indicate that the farmland landscape area decreased and the degree of patch fragmentation increased. The construction land area increased, and it tended to show dispersed distribution. The proportion of forest land increased and the shape of patches became more complex. The proportion of water firstly showed a decrease, followed by an increase, and the shape of the water became more regular. Landscape-level indexes indicate that biological diversity and the degree of fragmentation increased. Spatial heterogeneity of the natural landscape increased, and the patch shape of each landscape type developed similarly. The results also call for stepping-stones to enhance the connectivity and optimization of the ecological network, which will help improve ecological services and improve the landscape pattern of the city

Observations of Magnetic Helicity Proxies in Solar Photosphere: Helicity with Solar Cycles

Observations of magnetic helicity transportation through the solar photosphere reflect the interaction of turbulent plasma movements and magnetic fields in the solar dynamo process. In this chapter, we have reviewed the research process of magnetic helicity inferred from the observed solar magnetic fields in the photosphere and also the solar morphological configurations with solar cycles. After introducing some achievements in the study of magnetic helicity, some key points would like to be summarized. The magnetic (current) helicity in the solar surface layer presents a statistical distribution similar to that of the sunspot butterfly diagram, but its maximum value is delayed from the extreme value of the sunspot butterfly diagram and corresponds in the phase with the statistical eruption of solar flares. During the spatial transport of magnetic (current) helicity from the interior of the sun into the interplanetary space at the time-space scale of the solar cycle, it shows the statistical distribution and the fluctuation with the hemispheric sign rule. These show that the current helicity and magnetic helicity transport calculation methods are complementary to each other. We also notice that the study of the inherent relationship between magnetic helicity and the solar cycle still depends on the observed accuracy of the solar magnetic field.Comment: 48 page,17 figure

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability

Model-based multiobjective evolutionary algorithm optimization for HCCI engines

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual