Optimizing message-passing performance within symmetric multiprocessor systems

The Message Passing Interface (MPI) has been widely used in the area of parallel computing due to its portability, scalability, and ease of use. Message passing within Symmetric Multiprocessor (SMP) systems is an import part of any MPI library since it enables parallel programs to run efficiently on SMP systems, or clusters of SMP systems when combined with other ways of communication such as TCP/IP. Most message-passing implementations use a shared memory pool as an intermediate buffer to hold messages, some lock mechanisms to protect the pool, and some synchronization mechanism for coordinating the processes. However, the performance varies significantly depending on how these are implemented. The work here implements two SMP message-passing modules using lock-based and lock-free approaches for MPLi̲te, a compact library that implements a subset of the most commonly used MPI functions. Various optimization techniques have been used to optimize the performance. These two modules are evaluated using a communication performance analysis tool called NetPIPE, and compared with the implementations of other MPI libraries such as MPICH, MPICH2, LAM/MPI and MPI/PRO. Performance tools such as PAPI and VTune are used to gather some runtime information at the hardware level. This information together with some cache theory and the hardware configuration is used to explain various performance phenomena. Tests using a real application have shown the performance of the different implementations in real practice. These results all show that the improvements of the new techniques over existing implementations

Effect of preparation method of palygorskite-supported Fe and Ni catalysts on catalytic cracking of biomass tar

In this study, the effect of catalyst preparation and additive precursors on the catalytic decomposition of biomass using palygorskite-supported Fe and Ni catalysts was investigated. The catalysts were characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM). It is concluded that the most active additive precursor was Fe(NO3)3·9H2O. As for the catalyst preparation method, co-precipitation had superiority over incipient wetness impregnation at low Fe loadings

Kinetic study of goethite dehydration and the effect of aluminium substitution on the dehydrate

Goethite and Al-substituted goethite were synthesized and were characterized using XRD and XRF. The kinetic study of goethite dehydrate was investigated by TG and DTG at different heating rates (2, 5, 10, 15, 20 ◦C/min) and the effect of Al substitution for Fe on dehydrate was studied. The results showed that two types of absorbed water with the same Ed values of 3.4, 6.2 kJ/mol were confirmed on goethite and Alsubstituted goethite. Three types of hydroxyl units were proved, one being on the surface and the other two being in the structure of goethite. The substitution of Al for Fe in the structure of goethite decreases the desorption rate of hydroxyl, increases the dehydroxylation temperature, broadens the desorption peaks in DTG curves, and improves the Ed values from 19.4, 20.4, 26.1 kJ/mol to 21.6, 30, 33.6 kJ/mol when Al substitution comes to 9.1%

SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks

Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and real-world signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures

Occurrence and Aquatic Ecological Risk Assessment of Typical Organic Pollutants in Water of Yangtze River Estuary

AbstractThe occurrence and distribution of organic pollutants were investigated and their initial aquatic ecological risks were assessed in water of the Yangtze River Estuary (YRE). A total of 18 samples were collected from South Branch of YRE during the flood season in August 2012. Out of 956 organic compounds, 23 organic pollutants were detected by GC-MS and NAGINATATM software which were dominated by phthalate esters (PAEs), petroleum hydrocarbons (PHCs) and substituted benzenes. The total concentration of detected 23 organic pollutants varied from 0.585 to 53.7μg/L in the studied sites. Moreover, the total amounts of PAEs (∑PAEs), PHCs (∑PHCs) and substituted benzenes (∑substituted benzenes) were in the range of 0.184-53.344μg/L, 0-0.164μg/L, and 0.196-1.559μg/L, respectively. The study revealed that PEC/PNEC ratios of 8 organic pollutants were higher than 1 (PEC: Predicted environmental concentration; PNEC: Predicted no effect concentration), while 3 of them Bis(2-ethylhexyl)phthalate, octadecane and nonadecane were found to be >100 and the remaining organic pollutants including diisobutyl phthalate, tridecane, dihexyl phthalate, methyl palmitate and methyl stearate ranged from 1 to 100. These results indicated significant ecological risks of the specific organic pollutants to the aquatic environment of YRE

The investigation into the adsorption removal of ammonium by natural and modified zeolites: Kinetics, isotherms, and thermodynamics

The objectives of this study were to modify Chinese natural zeolite by NaCl and to investigate its suitability as a low-cost clay adsorbent to remove ammonium from aqueous solution. The effect of pH on ammonium removal was investigated by batch experiments. The findings indicated that pH has a significant effect on the removal of ammonium by M-Zeo and maximum adsorption occured at pH 8. Ion exchange dominated the ammonium adsorption process at neutral pH, with the order of exchange selectivity being Na+ > Ca2+ > K+ > Mg2+. The Freundlich model provided a better description of the adsorption process than the Langmuir model. The maximum ammonium adsorption capacity was 17.83 mg/g for M-Zeo at 293K. Considering the adsorption isotherms and thermodynamic studies, the adsorption of ammonium by M-Zeo was endothermic and spontaneous chemisorption. Kinetic studies indicated that the adsorption of ammonium onto M-Zeo is well fitted by the pseudo-second-order kinetic model. Ea in the Arrhenius equation suggested the adsorption of ammonium on M-Zeo was a fast and diffusion-controlled process. The regeneration rate was 90.61% after 5 cycles. The removal of ammonium from real wastewater was carried out, and the removal efficiency was up to 99.13%. Thus, due to its cost-effectiveness and high adsorption capacity, M-Zeo has potential for use in ammonium removal from aqueous solutions.Keywords: zeolite, sodium chloride modified, adsorbent, regeneration, wastewate

Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

PURPOSERadiomics analysis is a promising image analysis technique. This study aims to extract a radiomics signature from baseline computed tomography (CT) to predict malignant cerebral edema (MCE) in patients with acute anterior circulation infarction after endovascular treatment (EVT).METHODSIn this retrospective study, 111 patients underwent EVT for acute ischemic stroke caused by middle cerebral artery (MCA) and/or internal carotid artery occlusion. The participants were randomly divided into two datasets: the training set (n = 77) and the test set (n = 34). The clinico-radiological profiles of all patients were collected, including cranial non-contrast-enhanced CT, CT angiography, and CT perfusion. The MCA territory on non-contrast-enhanced CT images was segmented, and the radiomics features associated with MCE were analyzed. The clinico-radiological parameters related to MCE were also identified. In addition, a routine visual radiological model based on radiological factors and a combined model comprising radiomics features and clinico-radiological factors were constructed to predict MCE.RESULTSThe areas under the curve (AUCs) of the radiomics signature for predicting MCE were 0.870 (P < 0.001) and 0.837 (P = 0.002) in the training and test sets, respectively. The AUCs of the routine visual radiological model were 0.808 (P < 0.001) and 0.813 (P = 0.005) in the training and test sets, respectively. The AUCs of the model combining the radiomics signature and clinico-radiological factors were 0.924 (P < 0.001) and 0.879 (P = 0.001) in the training and test sets, respectively.CONCLUSIONA CT image-based radiomics signature is a promising tool for predicting MCE in patients with acute anterior circulation infarction after EVT. For clinicians, it may assist in diagnostic decision-making

Self-propelling Microdroplets Generated and Sustained by Liquid-liquid Phase Separation in Confined Spaces

Flow transport in confined spaces is ubiquitous in technological processes, ranging from separation and purification of pharmaceutical ingredients by microporous membranes and drug delivery in biomedical treatment to chemical and biomass conversion in catalyst-packed reactors and carbon dioxide sequestration. In this work, we suggest a distinct pathway for enhanced liquid transport in a confined space via self-propelling microdroplets. These microdroplets can form spontaneously from localized liquid-liquid phase separation as a ternary mixture is diluted by a diffusing poor solvent. High speed images reveal how the microdroplets grow, break up and propel rapidly along the solid surface, with a maximal velocity up to ~160 um/s, in response to a sharp concentration gradient resulting from phase separation. The microdroplet self-propulsion induces a replenishing flow between the walls of the confined space towards the location of phase separation, which in turn drives the mixture out of equilibrium and leads to a repeating cascade of events. Our findings on the complex and rich phenomena of self-propelling droplets suggest an effective approach to enhanced flow motion of multicomponent liquid mixtures within confined spaces for time effective separation and smart transport processes.Comment: This is the authors' submitted version of the manuscrip