Conventional Clusters Research Articles

Arabic Clustering Through Advanced Stemming and WordNet-Based Extraction for Water Cycle Cluster

Natural language processing represents human language in computational technique, which is to achieve the extraction of important words. The verbs and nouns found in the Arabic language are significantly pertinent in the process of differentiating each class label available for the purpose of machine learning, specifically in 'Arabic Clustering'. This paper implemented the extraction of verbs and nouns sourced from the Qur'an and text clustering for further evaluation by using two datasets. The limitations of conventional clusters were identified, such as k-means clustering on the initial centroids. Therefore, the current work incorporated a novel clustering optimisation technique known as the water cycle algorithm; when combined with k-means, the algorithm would select the optimal initial centroids. Consequently, the experiments revealed the proposed extraction technique to outperform other extraction methods when using an actual Qur'an dataset.

Variations in Canine Behavioural Characteristics across Conventional Breed Clusters and Most Common Breed-Based Public Stereotypes.

Dog breeds are grouped based on scientific agreement, whether for traditional reasons or specific tasks during their domestication. Discrepancies may occur between public views of breed behaviour and actual evidence. This research aims to investigate differences in five behavioural traits (aggression towards people, aggression towards animals, fearfulness, responsiveness to training, and activity/excitability) by using the Dog Personality Questionnaire (DPQ) across six conventional groups/clusters of dog breeds (herding, hunting, guarding, companion dogs, potentially aggressive breeds, and mixed-breed dogs) and to assess hypotheses derived from common public presumptions. A cohort of 1309 dog owners sourced through diverse online platforms took part in the study. Contrary to stereotypes, the findings indicate that breeds labelled as "potentially aggressive" display lower levels of aggression compared to guarding breeds (χ2 (5) = 3.657, p = 0.041) and mixed-breeds (χ2 (5) = 3.870, p = 0.002). Additionally, mixed-breed dogs exhibited the highest levels of fearfulness among the six conventional clusters. In terms of aggression and gender, males demonstrated higher aggression levels towards both humans and animals compared to females (p = 0.001). These results challenge established assumptions and emphasise the necessity of evidence-based methodologies in the assessment of canine behaviour.

Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis

F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoencoder algorithm that constructs a compressed representation of input data. We then evaluated the prognostic value of the image-feature clusters thus extracted. Conventional image parameters including metabolic tumor volume (MTV) were measured on volumes-of-interests (VOIs) covering only the bones. Features were extracted with the autoencoder algorithm on bone-covering VOIs. Supervised and unsupervised clustering were performed on image features. Survival analyses for progression-free survival (PFS) were performed for conventional parameters and clusters. In result, supervised and unsupervised clustering of the image features grouped the subjects into three clusters (A, B, and C). In multivariable Cox regression analysis, unsupervised cluster C, supervised cluster C, and high MTV were significant independent predictors of worse PFS. Supervised and unsupervised cluster analyses of image features extracted from FDG PET/CT scans of MM patients by an autoencoder allowed significant and independent prediction of worse PFS. Therefore, artificial intelligence algorithm–based cluster analyses of FDG PET/CT images could be useful for MM risk stratification.

Effect of pseudo-Tsai cluster incorporation on the magnetic structures of R−Au−Si (R=Tb,Ho) quasicrystal approximants

In cluster-based quasicrystals, tetrahedra located in conventional Tsai clusters may be replaced by single rare-earth $(R)$ ions at the cluster centers (pseudo-Tsai clusters). In this study, we investigate the effect of the pseudo-Tsai cluster incorporation on the magnetic structures of two approximants, the Tsai-type Tb-Au-Si [denoted TAS(0)] and Ho-Au-Si [denoted HAS(52)] with partial replacement of conventional Tsai clusters by pseudo-Tsai clusters, up to 52%. The mixture of Tsai and pseudo-Tsai clusters can be considered a different source of randomness/disorder other than the conventional chemical mix sites (Au/Si). The effect of the latter has been previously discussed regarding the origin/cause of spin-glass-like ordering and Anderson localization of electronic states in quasicrystals and approximant crystals. Single crystal neutron diffraction experiments at 2 K were performed and bulk physical properties (magnetization and specific heat) were investigated. In addition, earlier collected powder neutron diffraction data of TAS(14) with 14% replacement was reanalyzed in light of the results on TAS(0) and HAS(52). We find that the arrangement of ordered magnetic spins in the icosahedral shells of these phases is similar, while the cluster-center $R$ magnetic states are different. In the case of TAS(14), the cluster-center Tb magnetic moments seem to affect the arrangement of surrounding icosahedral magnetic moments, and the magnetic structure of the icosahedral shell deviates from that of TAS(0). In the case of HAS(52), however, the icosahedral $R$ magnetic moments are less affected by the cluster-center $R$, while the averaged cluster-center $R$ magnetic moments are significantly diminished. We discuss these results considering the magnetic ordering effect on the bulk physical properties.

Single-metal catalytic sites via high-throughput mechanochemistry enable selective and efficient CO2 photoreduction

In this work, we first report synthesizing a series of single-atom metals (covering main-group, transition, precious, and rare earth metals) photocatalysts MSA/TiO2 using a simple, efficient, and high-yield mechanochemistry (high-energy ball milling) and evaluate their efficiency towards CO2 photoreduction. In the synthesized single-atom catalyst (SAC), the CH4 yield from CO2 photoreduction using PdSA/TiO2 reaches as high as 271.6 μmol·g−1·h−1 with the selectivity of ~98.0%, far surpassing those of conventional Pd clusters and nanoparticles. The experimental results and density functional theory (DFT) calculations reveal that the strong adsorption at single-atom catalytic sites (Pd) leads to significant bending of OCO bond angle from 180.0 to 151.0 ° and length from 1.16 to 1.20 Å. The induced deformation greatly ‘energizes’ the CO2, thus reducing the kinetic energy barrier significantly and offering high catalytic activity. Meanwhile, combined with in-situ Fourier-transform infrared (FT-IR), a rational reaction pathway of CO2 photoreduction over efficient SACs is proposed.

Reconfigurable switches for high performance and flexible MPI collectives

AbstractThere has been much effort in offloading MPI collective operations into hardware. But while NIC‐based collective acceleration is well‐studied, offloading their processing into the switching fabric, despite numerous advantages, has been much more limited. A major problem with fixed logic implementations is that either only a fraction of the possible collective communication is accelerated or that logic is wasted in the applications that do not need a particular capability. Using reconfigurable logic has numerous advantages: exactly the required operations can be implemented; the level of desired performance can be specified; and new, possibly complex, operations can be defined and implemented. We have designed an in‐switch collective accelerator, MPI‐FPGA, and demonstrated its use with seven MPI collectives and over a set of benchmarks and proxy applications (MiniApps). The accelerator uses a novel two‐level switch design containing fully pipelined vectorized aggregation logic units. Essential to this work is providing support for sub‐communicator collectives that enables communicators of arbitrary shape, and that is scalable to large systems. A streaming interface improves the performance for long messages. While this reconfigurable design is generally applicable, we prototype it with an FPGA‐centric cluster. A sample MPI‐FPGA design in a direct network achieves considerable speedups over conventional clusters in the most likely scenarios. We also present results for indirect networks with reconfigurable high‐radix switches and show that this approach is competitive with SHArP technology for the subset of operations that SHArP supports. MPI‐FPGA is fully integrated into MPICH and is transparent to MPI applications.

Efficient and Stable Co/β-Mo2C Catalyst for Hydroformylation

The hydroformylation of olefins is one of the most important industrial processes used for aldehyde synthesis, which commonly involves the use of scarce and expensive rhodium-based catalysts. It is therefore important to develop inexpensive catalysts, e.g., cobalt-based catalysts with sufficiently high activity and stability for hydroformylation. However, conventional metallic Co clusters, acting as active centers, suffer from low activity and severe metal leaching. Here, we show that Mo6C2-bonded Co can serve as an efficient active center for propene hydroformylation, with a total turnover number of 3158 after five in situ cycles, which is approximately 7.5 times higher than that of conventional metallic Co clusters. Furthermore, the developed Co6Mo6C2 catalyst effectively suppresses cobalt leaching, demonstrating its stability in a long-lasting test. The results of theoretical calculations and characterization analyses demonstrate that the enhanced activity and stability of Mo6C2-bonded Co result from a reduction in the overall energy barrier for hydroformylation and the stability of the Co6Mo6C2 crystal structure, which originates from the strong interaction between Co and the support.

Effectiveness of Distance Technology in Promoting Physical Activity in Cardiovascular Disease Rehabilitation: Cluster Randomized Controlled Trial, A Pilot Study.

BackgroundPhysical activity is beneficial for cardiovascular rehabilitation. Digitalization suggests using technology in the promotion of physical activity and lifestyle changes. The effectiveness of distance technology interventions has previously been found to be similar to that of conventional treatment, but the added value of the technology has not been frequently studied.ObjectiveThe aim of this pilot study was to investigate whether additional distance technology intervention is more effective in promoting physical activity than non-technology–based treatment in 12 months of cardiac rehabilitation.MethodsThe cardiovascular disease rehabilitation intervention consisted of three 5-day inpatient periods in a rehabilitation center and two 6-month self-exercise periods at home in between. Participants were recruited from among cardiac patients who attended the rehabilitation program and were cluster-randomized into unblinded groups: conventional rehabilitation control clusters (n=3) and similar rehabilitation with additional distance technology experimental group clusters (n=3). Experimental groups used Fitbit Charge HR for self-monitoring, and they set goals and reported their activity using Movendos mCoach, through which they received monthly automated and in-person feedback. Physical activity outcomes for all participants were measured using the Fitbit Zip accelerometer and the International Physical Activity Questionnaire.ResultsDuring the first 6 months, the experimental group (n=29) engaged in light physical activity more often than the control group (n=30; mean difference [MD] 324.2 minutes per week, 95% CI 77.4 to 571.0; P=.01). There were no group differences in the duration of moderate to vigorous physical activity (MD 12.6 minutes per week, 95% CI –90.5 to 115.7; P=.82) or steps per day (MD 1084.0, 95% CI –585.0 to 2752.9; P=.20). During the following 6 months, no differences between the groups were observed in light physical activity (MD –87.9 minutes per week, 95% CI –379.2 to 203.3; P=.54), moderate to vigorous physical activity (MD 70.9 minutes per week, 95% CI –75.7 to 217.6; P=.33), or steps per day (MD 867.1, 95% CI –2099.6 to 3833.9; P=.55).ConclusionsThe use of additional distance technology increased the duration of light physical activity at the beginning of cardiac rehabilitation (for the first 6 months), but statistically significant differences were not observed between the two groups for moderate or vigorous physical activity or steps per day for both 6-month self-exercise periods.Trial RegistrationISRCTN Registry ISRCTN61225589; https://doi.org/10.1186/ISRCTN61225589

Operating an HPC/HTC Cluster with Fully Containerized Jobs Using HTCondor, Singularity, CephFS and CVMFS

High performance and high throughput computing (HPC/HTC) is challenged by ever increasing demands on the software stacks and more and more diverging requirements by different research communities. This led to a reassessment of the operational concept of HPC/HTC clusters at the Physikalisches Institut at the University of Bonn. As a result, the present HPC/HTC cluster (named BAF2) introduced various conceptual changes compared to conventional clusters. All jobs are now run in containers and a container-aware resource management system is used which allowed us to switch to a model without login/head nodes. Furthermore, a modern, feature-rich storage system with powerful interfaces has been deployed. We describe the design considerations, the implemented functionality and the operational experience gained with this new-generation setup which turned out to be very successful and well-accepted by its users.

10.4314/sajas.v40i5.65326

Udder health considerably affects the economics of dairy farming. There are different reasons for poor udder health; one of them is the milking technique. In earlier studies it was shown that automatic mil king systems (AMS) have advantages over conventional milking systems (CMS). Quarter individual milking and automation are therefore possibilities to improve t he milking process. Single tube guiding allows cont rolling each quarter individually to measure cell count or end the process according to the cows needs. This s tudy evaluates the effect of a single tube milking syste m used in a conventional milking parlour. Forces on the teats regarding different udder formations and vacu um behaviour were recorded. The investigated single tube milking system MultiLactor ® is produced by the company Siliconform GmbH, Turkheim, Germany. Balanced allocation of vertical force for all teats is important for proper and gentle milk withdrawal an d to maintain udder health. Measurements of force distributi on in AMS and MultiLactor ® systems proved their superiority to conventional milking clusters. Furthermore , all the other force directions were reduced to ne arly zero in AMS and MultiLactor ® . Higher adaptability to irregular udder formations i s also expected. In conclusion wrong positioning of the teat cups is expected to be solved by implementing clawless milking cluster s.

An Event-Driven Approach to Serverless Seismic Imaging in the Cloud

Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional HPC clusters is therefore in many cases an infeasible solution for migrating HPC applications to the cloud. As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud. Instead of permanently running compute instances, our workflow is based on a serverless architecture with high throughput batch computing and event-driven computations, in which computational resources are only running as long as they are utilized. We demonstrate that this approach is very flexible and allows for resilient and nested levels of parallelization, including domain decomposition for solving the underlying partial differential equations. While the event-driven approach introduces some overhead as computational resources are repeatedly restarted, it inherently provides resilience to instance shut-downs and allows a significant reduction of cost by avoiding idle instances, thus making the cloud a viable alternative to on-premise clusters for large-scale seismic imaging.

Rules governing genetic exchanges among viral types from different Enterovirus A clusters

The species Enterovirus A (EV-A) consists of two conventional clusters and one unconventional cluster. At present, sequence analysis shows no evidence of recombination between conventional and unconventional EV-A types. However, the factors underlying this genetic barrier are unclear. Here, we systematically dissected the genome components linked to these peculiar phenomena, using the viral reverse genetic tools. We reported that viral capsids of the unconventional EV-A types expressed poorly in human cells. The trans-encapsidation outputs across conventional and unconventional EV-A types were also with low efficiency. However, replicons of conventional types bearing exchanged 5′-untranslated region (UTR) or non-structural regions from the unconventional types were replication-competent. Furthermore, we created a viable recombinant EVA71 (conventional type) with its P3 region replaced by that from EVA89 (unconventional type). Thus, our data for the first time reveal the potential for fertile genetic exchanges between conventional and unconventional EV-A types. It also discloses that the mysterious recombination barriers may lie in uncoordinated capsid expression and particle assembly by different EV-A clusters.

Integrated Woodencraft Cluster : Program Akselerasi Usaha Kecil dan Menengah (UKM) Go International sebagai Upaya Pemberdayaan UKM Potensial Yogyakarta

Small and Medium Enterprises (SMEs) have a very important role in the development of the Indonesian economy. Special Region of Yogyakarta (DIY) is one of the provinces that has many SMEs. The SMEs has the potential to penetrate the international market, one of which is a wooden handicraft SMEs. Woodencraft can be used as a cluster because it fulfills three factors that enable it to compete in the international market, namely the factor of global market needs, the factor of superior products, and potential SME product factors. However, woodencraft SMEs still face obstacles in product penetration abroad, namely obstacles from internal SMEs, the government, and international markets. The solution offered to help woodencraft SMEs to go international a program called Integrated Woodencraft Cluster, which is a cluster of woodencraft SMEs that integrates supply chain management (SCM) based distribution systems with four main lines (suppliers, production, sales, and information), internet-based information systems, and Just In Time (JIT) principle. The program involves local government, civil service, wood handicraft SMEs, suppliers of raw materials, and academics. The advantages of Integrated Woodencraft Clusters compared to conventional clusters include having a clear vision for exporting production, a structured managerial system, having an export agency, supplying raw materials, clear lines of information, and being able to facilitate all SME woodencraft, both on a small scale and intermediate.

Energy Sensitive Cluster Level Security Selection Scheme for MANET

Most of the current Mobile Ad hoc Network (MANET) nodes are battery powered devices with different processing and data handling capacities. The ratio of sensitive data is increasing rapidly day-by-day. Providing Security with moderate power utilization is one of the vital tasks in MANET architecture. In this paper an energy sensitive security selection schema that operates in the cluster level is introduced. A new clustering procedure that classifies MANET nodes into several virtual-energy-clusters and they bonded with the conventional routing clusters. Several multi-security and multi-weight cryptography procedures are integrated in the virtual-energy-clusters. Routing Cluster Table takes care of the fundamental communication where as the additional Virtual-energy-cluster table constantly monitoring nodes’ energy levels to maintain the mobility and life time of the entire network. For stable network communication, a correlation between Routing Clusters and Virtual-energy-clusters is maintained in this proposed method. By selecting correlated cryptography procedure in swift and dynamic mode, this dual cluster based network model takes care of providing higher security communication with moderate power consumption.

Large-scale parallel execution of urban-scale traffic simulation and its performance on K computer

We attempt to perform many-case urban-scale traffic simulations by performing massive parallel computing using K computer, and CARAVAN job manager. We obtain 1025 variations of simulation results with the same condition and different random seeds within 13 h. Each of simulation runs took about 6 h, which is twice longer than the case of using conventional workstations or clusters, and our approach allows further massive parallel computation. The performance and limitations when using K computer are discussed.

Grid production with the ATLAS Event Service

ATLAS has developed and previously presented a new computing architecture, the Event Service, that allows real time delivery of fine grained workloads which process dispatched events (or event ranges) and immediately streams outputs. The principal aim was to profit from opportunistic resources such as commercial cloud, supercomputing, and volunteer computing, and otherwise unused cycles on clusters and grids. During the development and deployment phase, its utility also on the grid and conventional clusters for the exploitation of otherwise unused cycles became apparent. Here we describe our experience commissioning the Event Service on the grid in the ATLAS production system. We study the performance compared with standard simulation production. We describe the integration with the ATLAS data management system to ensure scalability and compatibility with object stores. Finally, we outline the remaining steps towards a fully commissioned system.

Energy-efficient multi-mode clusters maintenance (M2CM) for hierarchical wireless sensor networks

How to energy-efficiently maintain the topology of wireless sensor networks (WSNs) is still a difficult problem because of their numerous nodes, highly dynamic nature, varied application scenarios and limited resources. An energy-efficient multi-mode clusters maintenance (M2CM) method is proposed based on localized and event-driven mechanism in this work, which is different from the conventional clusters maintenance model with always periodically re-clustered among the whole network style based on time-trigger for hierarchical WSNs. M2CM can meet such demands of clusters maintenance as adaptive local maintenance for the damaged clusters according to its changes in time and space field., the triggers of M2CM include such events as nodes' residual energy being under the threshold, the load imbalance of cluster head, joining in or exiting from any cluster for new node or disable one, etc. Based on neighboring relationship of the damaged clusters, one can start a single cluster (inner-cluster) maintenance or clusters (inter-cluster) maintenance program to meet diverse demands in the topology management of hierarchical WSNs. The experiment results based on NS2 simulation show that the proposed method can significantly save energy used in maintaining a damaged network, effectively narrow down the influenced area of clusters maintenance, and increase transmitted data and prolong lifetime of network compared to the traditional schemes.

Optimization of Truss Structure Using Genetic Algorithm Performed on GPU

Modern Graphics Processing Units (GPU), offer a tremendous computing power, that is frequently an order of magnitude larger than even the most modern multi-core CPUs, making them an attractive platform for high performance computing due to their relative cheapness compared with conventional PC clusters. General purpose computing on GPUs (GPGPU) is becoming popular in High Performance Computing (HPC) because of its high peak performance. In this paper, a typical two-dimensional truss structure optimization problem is solved using Binary Genetic Algorithm (BGA) on both CPU and GPU. The kernel inside the GPU code computes the nodal displacements and elemental stresses by Finite Element Analysis (FEA) to evaluate the objective function and the constraints while making use of the Single Instruction Multiple Data (SIMD) structure of GPU to attain parallelization. The results are assessed for different values of parameters, such as complexity of the problem, number of elements, population size, maximum allowable generations and number of threads etc. to demonstrate how the value of speedup varies with these parameters and to provide a basic guideline for choosing the parameters for a different problem. The results clearly establish that calculations are performed considerably faster through the GPU than through the CPU in general.

PSIN: A scalable, Parallel algorithm for Seismic INterferometry of large-N ambient-noise data

Seismic interferometry is a technique for extracting deterministic signals (i.e., ambient-noise Green's functions) from recordings of ambient-noise wavefields through cross-correlation and other related signal processing techniques. The extracted ambient-noise Green's functions can be used in ambient-noise tomography for constructing seismic structure models of the Earth's interior. The amount of calculations involved in the seismic interferometry procedure can be significant, especially for ambient-noise datasets collected by large seismic sensor arrays (i.e., “large-N” data). We present an efficient parallel algorithm, named pSIN (Parallel Seismic INterferometry), for solving seismic interferometry problems on conventional distributed-memory computer clusters. The design of the algorithm is based on a two-dimensional partition of the ambient-noise data recorded by a seismic sensor array. We pay special attention to the balance of the computational load, inter-process communication overhead and memory usage across all MPI processes and we minimize the total number of I/O operations. We have tested the algorithm using a real ambient-noise dataset and obtained a significant amount of savings in processing time. Scaling tests have shown excellent strong scalability from 80 cores to over 2000 cores.

An experimental evaluation of datacenter workloads on low-power embedded micro servers

This paper presents a comprehensive evaluation of an ultra-low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy efficiency of micro servers have driven both academia and industry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro servers. Existing attempts mostly focus on mobile-class micro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technologies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clusters involve careful workload choosing and laborious parameter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison cluster achieves up to 3.5x improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales linearly on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads despite coordination overhead.