Increase In Number Of Clusters Research Articles

Preconditioning optimization of hydraulic fracturing in block caving of hard rock mass

Introduction: Cavability issues often arise in hard rock mines employing the block caving mining method. Hydraulic fracturing stands out as a promising pre-conditioning technology for hard rock masses, with its pre-conditioning effects dependent on variable parameters.Methods: The impact of cluster number, injection rate, and liquid volume on fracture propagation was investigated through on-site hydraulic fracturing practices. Fracturability was initially verified via true triaxial fracturing tests, and preconditioning parameters were subsequently optimized using numerical simulations. The optimized on-site construction was monitored using electromagnetic methods.Results: The study revealed a negative correlation between fracture radius and the number of clusters, gradually decreasing with an increase in cluster number. However, the fracture weakening area increased with the rise in the number of clusters. The fracture weakening area also increased with the injection rate. Additionally, the fracture radius initially increased and then decreased. Both fracture weakening area and radius increased with an increase in liquid volume. The optimized parameters were identified as four clusters, an injection rate of 4 m3/min, and a liquid volume of 500 m3.Discussion: Building upon these findings, on-site experiments were conducted and monitored using electromagnetic methods. The monitored results demonstrated that the optimized parameters effectively achieved hydraulic fracturing expansion, accomplishing the goal of weakening hard rock.

Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration

Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. In this paper, we propose a clustering-based segmented regression method for particulate matter (PM) sensor in-field calibration. Interference from relative humidity and temperature are taken into consideration in the particulate matter concentration calibration model. Samples for modeling are divided into clusters and each cluster has an individual multiple linear regression equation. The final calibrated result of one sample is calculated from the regression model of the cluster the sample belongs to. The proposed method is evaluated under in-field deployment and performs better than a global multiple regression method both on PM2.5 and PM10 pollutants with, respectively, at least 16% and 9% improvement ratio on RMSE error. In addition, the proposed method is insensitive to reduction of training data and increase in cluster number. Moreover, it may bear lighter calculation cost, less overfitting problems and better interpretability. It can improve the efficiency and performance of post-deployment sensor calibration.

Cluster Head Selection in a Homogeneous Wireless Sensor Network Ensuring Full Connectivity with Minimum Isolated Nodes

The research work proposes a cluster head selection algorithm for a wireless sensor network. A node can be a cluster head if it is connected to at least one unique neighbor node where the unique neighbor is the one that is not connected to any other node. If there is no connected unique node then the CH is selected on the basis of residual energy and the number of neighbor nodes. With the increase in number of clusters, the processing energy of the network increases; hence, this algorithm proposes minimum number of clusters which further leads to increased network lifetime. The major novel contribution of the proposed work is an algorithm that ensures a completely connected network with minimum number of isolated nodes. An isolated node will remain only if it is not within the transmission range of any other node. With the maximum connectivity, the coverage of the network is automatically maximized. The superiority of the proposed design is verified by simulation results done in MATLAB, where it clearly depicts that the total numbers of rounds before the network dies out are maximum compared to other existing protocols.

An Energy-Efficient Hierarchical Routing for Wireless Sensor Networks

Abstract: The popularity of Wireless Sensor Networks (WSN) has increased tremendously in recent time due to growth in MicroElectro-Mechanical Systems (MEMS) & wireless communication technology. The main purpose of such networks is to collect information from the environments and deliver the same to the applications to determine characteristics of the environment or detect an event. As sensor nodes depend on batteries, they have limited amount of energy. One of the major issues in wireless sensor networks is to develop an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. Many energy efficient routing protocols have been proposed to solve this problem and increase the lifetime of the network. This paper proposes a clustering technique which is based on LEACH protocol. In this, clusters are formed based on their geographical location & cluster heads are selected on the basis of highest residual energy within the cluster as well as minimum distance to the base station from the cluster heads. The network lifetime for various levels of hierarchical routing techniques is compared. There are two outcomes from the implemented protocol. These are prolonged network lifetime & increased mean residual energy with the increase in number of clusters in the wireless sensor network.

α‐Synuclein involvement in hippocampal synaptic plasticity: role of NO, cGMP, cGK and CaMKII

Synaptic plasticity involves a series of coordinate changes occurring both pre- and postsynaptically, of which alpha-synuclein is an integral part. We have investigated on mouse primary hippocampal neurons in culture whether redistribution of alpha-synuclein during plasticity involves retrograde signaling activation through nitric oxide (NO), cGMP, cGMP-dependent protein kinase (cGK) and calmodulin-dependent protein kinase II. We have found that deletion of the alpha-synuclein gene blocks both the long-lasting enhancement of evoked and miniature transmitter release and the increase in the number of functional presynaptic boutons evoked through the NO donor, DEA/NO, and the cGMP analog, 8-Br-cGMP. In agreement with these findings both DEA/NO and 8-Br-cGMP were capable of producing a long-lasting increase in number of clusters for alpha-synuclein through activation of soluble guanylyl cyclase, cGK and calcium/calmodulin-dependent protein kinase IIalpha. Thus, our results suggest that NO, cGMP, GMP-dependent protein kinase and calmodulin-dependent protein kinase II play a key role in the redistribution of alpha-synuclein during plasticity.

Analysis of word clustering in verbal fluency of school-aged children

The use of clustering strategies in semantic and phonological fluency was tested in children in third (age 8–9) and fifth (age 10–11) grades. To analyze the development of clustering strategies in the fluency task, the latency to the first word and intervals between clustered and non-clustered words were recorded. Semantic fluency was greater than phonological fluency in both age groups. Children in fifth grade had greater semantic and phonological fluency than children in the third grade, concomitant with an increase in number of clusters, but not in cluster size. The greater facility of the children with semantic fluency was confirmed by a shorter latency to the first word, and significantly shorter intervals between clustered words compared to non-clustered words. Girls had shorter latencies to the first word and made more phonological clusters in the semantic task than boys. The increase in cluster number and concomitant increase in fluency in older children might be related to the development of cognitive flexibility. These data suggest that measurement of clustering strategies in the verbal fluency task can be used to assess executive function deficits in children with acquired or developmental neurological impairment or attention deficits.

Characterization of cell proliferation in the adult dentate under normal conditions and after kainate induced seizures using ribonucleotide reductase and BrdU

Ribonucleotide reductase (RNR), an enzyme for DNA synthesis, was recently used as a marker of proliferating cells in the dentate gyrus and subventricular zone in normal adult mammalian brains. However, the duration of RNR expression in normal adult brain and the expression pattern of RNR in the adult dentate gyrus following brain injury have not been explored. In this study, we examined the duration of the RNR expression in newborn cells in the normal adult rat brain by analysis of RNR and BrdU double-labeled specimens at different time intervals after BrdU application. Secondly, we induced, in adult rats, seizures by kainic acid and investigated the changes in expression of RNR following seizures, and characterized the phenotype of RNR-positive cells using a variety of other markers. Our results revealed that RNR was detectable in proliferating cells from 2 h to at least 1 day. At 7 and 28 days after seizures, there was a fivefold increase in number of clusters of RNR-positive cells in the dentate gyrus, and a doubling of the number of BrdU-labeled cells in each cluster. Proliferating astrocytes and neuronal precursors were recognized in each RNR-positive cell cluster, and both types increased in number after seizures. Colocalization of RNR and activated caspase-3 was observed at 7 days, indicating that proliferating cells were susceptible to status epilepticus induced damage. RNR immunohistochemistry provides a useful approach in experiments investigating a change in cell proliferation, revealing the location, number, morphology and fate of newly formed cells after, e.g., brain injury.

Denervation alters the size, number, and distribution of clusters of acetylcholine receptorlike molecules on frog cardiac ganglion neurons

Acetylcholine receptor (AChR)-like molecules are found in clusters on the surface of parasympathetic neurons in the frog cardiac ganglion. Electron microscopy of immunoperoxidase-stained tissue reveals that in normally innervated ganglia most of these clusters are located at synaptic sites. Denervation for 2–3 weeks results in a 64% reduction in the total surface area occupied by AChR-like clusters; this change is brought about by the combined effects of a 4-fold decrease in cluster size and a 30% increase in cluster number. Denervation also changes the distribution of AChR-like clusters: clusters, normally restricted to portions of the cell surface, are more widely distributed following denervation. Denervation of amphibian skeletal muscle for a comparable period of time has no effect on the size or the number of synaptic clusters of AChRs. These results suggest that AChRs in nerve and in muscle are regulated differently by innervation.

Clustering effects on the recall of unrelated words

The purpose of this experiment was to examine directly how various numbers of clusters affect recall performance. Thirty subjects were presented 36 unrelated nouns for six study-recall trials. Each of six subjects was told to organize the words into 3, 7, or 11 clusters. Two control groups were also used: free recall (FR; no clustering instructions) and free cluster (FC; clustering instructions, but no specific number of clusters were assigned). The similarity between total recall and all other measures for the three-cluster, FC, and FR groups suggests that organizing the words into approximately three clusters optimizes recall performance. This study also supports a previous finding that recall increases over trials mainly due to increases in cluster size rather than increases in number of clusters.

Reproductive cycles in oregon populations of the echinoid, Strongylocentrotus purpvratus (Stimpson). II. Seasonal changes in oocyte growth and in abundance of gametogenic stages in the ovary

During the annual reproductive cycle in the echinoid, Strongylocentrotus purpuratus (Stimpson) two major seasonal events in oogenesis have been demonstrated by several different quantitative methods. Both seasonal changes in size and frequency distributions of stages of the oocytes indicate that in the intertidal populations studied, there is an abrupt increase in the growth rate of oocytes during October, accompanied by an increase in the rate at which oogonia become primary oocytes. Between March, just after spawning is completed, and October, oocytes increase in volume at a rate of ≈ 2 % per day. In October and November, the growth rate of oocytes approaches 6 % per day, and thereafter declines for oocytes reaching maturity in December. The absolute numbers of oocytes and oogonial clusters/mm 2 of ovary wall were estimated and adjusted for changes produced by volume changes in the gonad. The lowest numbers of oogonial clusters occur from December to March. Renewed proliferation begins soon after spawning. There is a significant increase in number of clusters in March, and the absolute cluster number increases to a maximum in June, reaching approximately four times the initial number. The proliferation rate during this period is ≈ 180 clusters/mm 2/month; some proliferation continues into September. The number of small oocytes present remains the same from March to August, when it begins to increase. The total number of oocytes produced during the fall period is approximately three times the number initially present at the start of the annual cycle in March. Two populations studied differ in the absolute numbers of oogonial clusters and oocytes produced in the annual cycle; environmental factors must influence the processes involved. In S. purpuratus the large majority of oocytes differentiate from oogonia during the same reproductive cycle in which they reach maturity as ova.