Parallel Distributed System Research Articles

Estimating the Effectiveness of Public Versus Private Distribution Systems: Insights from a Fertilizer Policy Reform in Malawi

Throughout much of the world, critical goods and services are provided by parallel public and private distribution systems. However, it is often difficult to causally differentiate the performance of these two channels. In this article, we utilize a policy change in Malawi to estimate the relative efficiency of public and private channels in distributing inputs to smallholder farmers. In the 2015/16 agricultural season, the government allowed a group of private-sector fertilizer dealers to distribute subsidized fertilizer in nine of the country’s 28 districts. We use a panel dataset and a difference-in-differences (DID) strategy to estimate the impact of the pilot reform on a set of variables encompassing the entire process of subsidized fertilizer acquisition and appropriate utilization. We find that households in districts where the private sector distributed subsidized fertilizer were 11 percentage points more likely to acquire it earlier in the planting season (p < 0.01). We also find that households in pilot districts were six percentage points more likely to apply inorganic fertilizer (p < 0.10), but this effect was no longer statistically significant after using household fixed-effects and correcting for multiple hypothesis testing. Additionally, the reform did not affect other outcomes such as timely fertilizer application or maize yields.

RELATIONAL DIMENSION VERSUS ARTIFICIAL INTELLIGENCE.

Thirty years ago, we proposed the similarity between the functioning of artificial intelligence and the human psyche, suggesting multiple parallels between the Freudian model proposed in the "Project for Psychology for Neurologists" and the connectionist theories applied in the generation of parallel distributed processing systems (PDP), also known as connectionist models. These models have been and continue to be the foundation of general artificial intelligences like ChatGPT, evolving and gaining prominence in everyday life. From the earliest applications in psychiatry, recreating computationally simulated modes of illnesses, to the use of deep learning models, especially in the field of computer vision for tasks such as image recognition, segmentation, and classification. Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) are employed for tasks involving sequences of data, such as natural language processing, or models based on the Transformer architecture, like BERT and GPT (Generative Pre-trained Transformer), which have revolutionized natural language processing. In this present work, we analyze the significance of the emergence and exponential growth of these types of tools in the field of healthcare, from medical diagnosis and patient care to psychological attention and psychotherapeutic treatment, exploring the changes and transformations in the forms of subjective expression that are arising. We also examine and argue for the importance and validity of the relational dimension proposed by our psychoanalytic approach in contrast to the potential use of these tools as treatment models.

Study on an autonomous distribution system for smart parks based on parallel system theory against the background of Industry 5.0

The autonomous distribution systems used in smart parks against the background of Industry 5.0 require not only the consideration of the single goal of the economic benefits of enterprises, but also the fulfillment of their social responsibilities. Consequently, the scheduling of autonomous distribution systems and the trajectory planning of intelligent logistics vehicles have become increasingly more complex. Although technologies such as swarm intelligence have gradually been applied to the solution of independent distribution systems, there remain challenges in how to ensure that the production enterprises bear their responsibility to the public and consumers. Parallel system theory provides theoretical support for the concrete embodiment of people-oriented values in the smart park environment. In this work, based on parallel system theory, a parallel autonomous driving system is established. The system is mainly used for the autonomous transportation of finished products and materials in smart parks. The goal is to enhance the flexibility and efficiency of the distribution system in the park, and to highlight the people-oriented goal. Based on swarm intelligence theory and the A* algorithm, an improved swarm search optimization algorithm called IGSO-A* is developed to support the scheduling of parallel distribution systems and the trajectory planning of intelligent logistics vehicles. In two types of simulation experiments, compared with three other cutting-edge algorithms, the performance of the designed IGSO algorithm is improved by 4.6% on average. Moreover, compared with the A* algorithm, the performance of the proposed IGSO-A* algorithm is improved by 11.49%. The results prove the effectiveness of the proposed parallel autonomous distribution system in the distribution of finished products and materials in smart parks.

Fault tolerance analysis for hamming graphs with large-scale faulty links based on k-component edge-connectivity

The L-ary n-dimensional hamming graph KLn is one of the most attractive interconnection networks for parallel processing and computing systems. Analysis of the link fault tolerance of topology structure can provide the theoretical basis for the design and optimization of the interconnection networks. The k-component edge-connectivity cλk(G) of an interconnection network G, is the minimum number of set of faulty links, such that these malfunctions disconnect the network G with at least k connected subnetworks. It gives a more precise quantitative analysis of indicators of the robustness of a parallel distributed system in the event of faulty links. Let t=⌊n2⌋ if L is even, and t=⌈n2⌉ if L is odd. In this paper, we prove that the (k+1)-component edge-connectivity of L-ary n-dimensional hamming graphs is cλk+1(KLn)=(L−1)nk−exk(KLn)2 for k≤Lt, n≥7, where exk(KLn) represents the maximum degree sum of the subgraph induced by all k processors of KLn. As the exact values of (k+1)-component edge-connectivity of KLn oscillate greatly, an efficient O(log⁡N) algorithm is designed to determine the exact values of cλk+1(KLn) for each k≤Lt and N=Ln. Our result improves those of Xu et al. [33] and Liu et al. [27].

Construction Means of Soil Microbial Synusiologic Network Based on ANN.

With the construction of synusiologic civilization and synusiologic environmental protection entering a new era driven by data, the breadth and depth of application of the DM technique in the domain of synusiologic environmental protection are constantly strengthened. If reasonable planning is not carried out in the process of social construction, it will cause unpredictable damage to the synusiologic environment. However, traditional synusiologic planning means too much human interference, and there are still some shortcomings in accuracy and operability, which means they cannot guide synusiologic construction well. In order to analyze the contribution of the soil nutrient data to soil fertility and dig out the knowledge describing soil fertility, this paper studies the construction means of soil microbial synusiologic network by ANN. By simulating the learning, memorizing, and processing problems of human brain neurons, the artificial network establishes a parallel distributed processing system computing DMG model with a large number of connections, which can quickly acquire knowledge from the outside world and store and process it and respond to the changes in the external environment in time. According to the research in this paper, the network performance of this algorithm is 18% better than that of the traditional algorithm, and it is suitable to be widely put into practice.

A Survey: Improvement of QoS Via Load Balancing Approach in Parallel Distributed System

A Survey: Improvement of QoS Via Load Balancing Approach in Parallel Distributed System

II. Your Present Moment in Spacetime

This essay extends the argument begun in "Why Quantum Mechanics Makes Sense," exploring the conditions under which a physical world can define and communicate information. I argue that like the structure of quantum physics, the principles of Special and General Relativity can be understood as reflecting the requirements of a universe in which things are observable and measurable. I interpret the peculiar hyperbolic structure of spacetime not as the static, four-dimensional geometry of an unobservable "block universe", but as the background metric of an evolving web of communicated information that we, along with all our measuring instruments and recording devices, actually experience in our local "here and now." Our relativistic universe is conceived as a parallel distributed processing system, in which a common objective reality is constantly being woven out of many kinds of facts determined separately in countless local measurement-contexts.

Selective Shut-off Strategy in Distributed Battery Systems

Rapid development in electric mobility raise distance anxiety. Thus, high ca-pacity systems with high efficiency are offered as a solution. However, the ca-pacity of the system is limited by the available space in the vehicle. To outcome this problem, the parallel distributed battery system is presented especially for heavy-duty vehicles like buses and trucks. Having a parallel distributed structure brings the necessity of providing a safety strategy not only for the single pack but also for the whole system. Every single pack has al-ready its fault protection system. Whenever an electrical fault occurs in the pack, it is cleared by either a contactor or a fuse. Thus, the pack becomes out of order, therefore, the vehicle cannot continue its operation and must be trailered. By utilizing a parallel dis-tributed battery system, vehicle continuity can be provided even after an electri-cal fault with a selective shut-off strategy. The presented strategy in this paper, not only provides vehicle continuity but also ensures additional safety on the system.

Developing a parallel distributed memory system of stories: A preliminary report

The author’s basic design concept of an artificial cognitive system is that story generation is an essential dimension of the mind. In this paper, a brief preliminary report on the development of a memory system is presented. The memory system is positioned as a partial system of a cognitive system. Its fundamental roles are to organize a world as a collection of stories and form the forces of generating stories. The memory system is designed as a parallel distributed system. In particular, each memory item—stories, events, concepts, and schemas—corresponds to an internal agent that operates automatically. These memory agents are dynamically organized in a network form through incorporating external simple narrative texts. This organizational process is performed via parallel distributed operations of connection formation and spreading activation among memory agents. The system is implemented in a proof-of-concept fashion. Several preliminary analyses of the system behavior are also presented.

Changes in biofilm composition and microbial water quality in drinking water distribution systems by temperature increase induced through thermal energy recovery

Drinking water distribution systems (DWDSs) have been thoroughly studied, but the concept of thermal energy recovery from DWDSs is very new and has been conceptualized in the past few years. Cold recovery results in a temperature increase of the drinking water. Its effects on drinking water quality and biofilm development are unclear. Hence, we studied both bulk water and biofilm phases for 232 days in two parallel pilot scale distribution systems with two temperature settings after cold recovery, 25 °C and 30 °C, and compared these with a reference pilot system without cold recovery. In all three pilot distributions systems (DSs) our results showed an initial increase in biomass (ATP) in the biofilm phase, along with occurrence of primary colonizers (Betaproteobacteriales) and subsequently a decrease in biomass and an increasing relative abundance of other microbial groups (amoeba resisting groups; Xanthobacteraceae, Legionellales), including those responsible for EPS formation in biofilms (Sphingomonadaceae). The timeline for biofilm microbial development was different for the three pilot DSs: the higher the temperature, the faster the development took place. With respect to the water phase within the three pilot DSs, major microbial contributions came from the feed water (17–100%) and unkown sources (2–80%). Random contributions of biofilm (0–70%) were seen between day 7–77. During this time period six-fold higher ATP concentration (7–11 ng/l) and two-fold higher numbers of high nucleic acid cells (5.20–5.80 × 104 cells/ml) were also observed in the effluent water from all three pilot DSs, compared to the feed water. At the end of the experimental period the microbial composition of effluent water from three pilot DSs revealed no differences, except the presence of a biofilm related microbial group (Sphingomonadaceae), within all three DSs compared to the feed water. In the biofilm phase higher temperatures initiated the growth of primary colonizing bacteria but this did not lead to differences in microbial diversity and composition at the end of the experimental period. Hence, we propose that the microbiological water quality of DWDSs with cold recovery should be monitored more frequently during the first 2–3 months of operation.

K-EDES: 국내 변전소 지진 피해예측 웹시스템

Recently, various research and developments related to the large-scale earthquake source generation model and facility damage estimation model have been conducted. However, the earthquake damage estimation model and visualization research for electrical substations in South Korea are insufficient. Therefore, we propose K-EDES which includes an earthquake source generation model considering topography characteristics and damage estimation for electrical substations in South Korea. K-EDES consists of large-scale seismic source synthesis, damage estimation model for electrical substations, model simulator, visualization of damage estimation results, and provides an environment for integrated management. In addition, K-EDES can be performed in real-time because it includes distributed parallel systems and resource management functions for resource status and work schedule management. The results of this study helps real-time response of electrical substations management in the event of an actual earthquake. Therefore, it is possible to shorten the period of earthquake damage recovery and reduce the repair and maintenance costs.

Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning.

Lifelong learning has deeply underpinned the resilience of biological organisms respect to a constantly changing environment. This flexibility has allowed the evolution of parallel-distributed systems able to merge past information with new stimulus for accurate and efficient brain-computation. Nowadays, there is a strong attempt to reproduce such intelligent systems in standard artificial neural networks (ANNs). However, despite some great results in specific tasks, ANNs still appear too rigid and static in real life respect to the biological systems. Thus, it is necessary to define a new neural paradigm capable of merging the lifelong resilience of biological organisms with the great accuracy of ANNs. Here, we present a digital implementation of a novel mixed supervised-unsupervised neural network capable of performing lifelong learning. The network uses a set of convolutional filters to extract features from the input images of the MNIST and the Fashion-MNIST training datasets. This information defines an original combination of responses of both trained classes and non-trained classes by transfer learning. The responses are then used in the subsequent unsupervised learning based on spike-timing dependent plasticity (STDP). This procedure allows the clustering of non-trained information thanks to bio-inspired algorithms such as neuronal redundancy and spike-frequency adaptation. We demonstrate the implementation of the neural network in a fully digital environment, such as the Xilinx Zynq-7000 System on Chip (SoC). We illustrate a user-friendly interface to test the network by choosing the number and the type of the non-trained classes, or drawing a custom pattern on a tablet. Finally, we propose a comparison of this work with networks based on memristive synaptic devices capable of continual learning, highlighting the main differences and capabilities respect to a fully digital approach.

Physics Data Production on HPC: Experience to be efficiently running at scale

The Solenoidal Tracker at RHIC (STAR) is a multi-national supported experiment located at the Brookhaven National Lab and is currently the only remaining running experiment at RHIC. The raw physics data captured from the detector is on the order of tens of PBytes per data acquisition campaign, making STAR fit well within the definition of a big data science experiment. The production of the data has typically run using a High Throughput Computing (HTC) approach either done on a local farm or via Grid computing resources. Especially, all embedding simulations (complex workflow mixing real and simulated events) have been run on standard Linux resources at NERSC’s Parallel Distributed Systems Facility (PDSF). However, as per April 2019 PDSF has been retired and High Performance Computing (HPC) resources such as the Cray XC-40 Supercomputer known as “Cori” have become available for STAR’s data production as well as embedding. STAR has been the very first experiment to show feasibility of running a sustainable data production campaign on this computing resource. In this contribution, we hope to share with the community the best practices for using such resource efficiently. The use of Docker containers with Shifter is the standard approach to run on HPC at NERSC – this approach encapsulates the environment in which a standard STAR workflow runs. From the deployment of a tailored Scientific Linux environment (with the set of libraries and special configurations required for STAR to run) to the deployment of third-party software and the STAR specific software stack, we’ve learned it has become impractical to rely on a set of containers comprising each specific software release. To this extent, a solution based on the CernVM File System (CVMFS) for the deployment of software and services has been deployed but it doesn’t stop there. One needs to make careful scalability considerations when using a resource like Cori, such as avoiding metadata lookups, scalability of distributed filesystems, and real limitations of containerized environments on HPC. Additionally, CVMFS clients are not compatible on Cori nodes and one needs to rely on an indirect NFS mount scheme using custom services known as DVS servers designed to forward data to worker nodes. In our contribution, we will discuss our strategies from the past and our current solution based on CVMFS. The second focus of our presentation will be to discuss strategies to find the most efficient use of database Shifter containers serving our data production (a near “database as a service” approach) and the best methods to test and scale your workflow efficiently.

On Performance of Peer Review for Academic Journals: Analysis Based on Distributed Parallel System

A simulation model based on parallel systems is established, aiming to explore the relation between the number of submissions and the overall standard of academic journals within a similar discipline under peer review. The model can effectively simulate the submission, review, and acceptance behaviors of academic journals in a distributed manner. According to the simulation experiments, it could possibly happen that the overall standard of academic journals deteriorates due to excessive submissions.

A bio-inspired self-responding emotional behavior system for virtual creatures

Abstract Communication is a fundamental aspect of the interaction among human beings. In particular, our physical behaviors provide a large part of this communication, because it expresses our emotional internal states. Most of these behaviors are autonomous and reactive, detonated by the assessment of a stimuli perceived in the environment. Within this type of self-responding behaviors are the behaviors of basic emotions. In this paper we propose a conceptual model for the generation of self-responding behaviors for virtual creatures inspired by neuroscientific evidence, focusing on those centered on basic emotions. The conceptual model is implemented as a concurrent and parallel distributed system, which allows virtual creatures to adapt to their environment and generate more credible behavior. The results of this implementation are shown in this article through a case study, in which the execution of the process is observed when the creature interacts with the environment.

Survey of Storage Systems for High-Performance Computing

In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing.

An Average Polynomial Algorithm for Solving Antagonistic Games on Graphs

An algorithm that determines the winner in a cyclic game in polynomial time is proposed. The two-person game occurs continuously on the edges of a directed graph until a vertex visited earlier is reached. If the weight of the resulting cycle is nonnegative, then the maximizing player wins; if this cycle has a negative weight, then the minimizing player wins. A polynomial estimate of the expected algorithm execution time is obtained under the condition that the weights of the game’s graph edges are distributed uniformly. A brief justification of the time estimate of the algorithm is given. Such games have applications in the validating the correctness of parallel-distributed computer systems, including problems of making up a feasible schedule with logical precedence conditions and preprocessing possibilities.

Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data

This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually; they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.

Study on Noise Extraction and Correction of Time Series Satellite Profile Data Using Parallel Distributed System

Study on Noise Extraction and Correction of Time Series Satellite Profile Data Using Parallel Distributed System

Bacteriological water quality changes in parallel pilot distribution systems

In a component of an extensive pilot distribution system (PDS) study, the effects of four different water qualities on biological stability in distributed water were investigated through identical (parallel) single-pass pipe arrangements. Through 24 months of monitoring, a number of key observations were made. Incorporation of a biological treatment step reduced the overall dissolved organic carbon (DOC) loss through the PDS by reducing biodegradable DOC (BDOC) within the water prior to distribution. In the absence of chlorine residuals, the proliferation of culturable organisms was favoured with considerably higher heterotrophic plate counts in samples at the outlet of the PDS. Despite different bacterial cell counts (measured by flow cytometry) entering each PDS from the four treatment streams, equivalent outlet cell numbers were achieved in all systems after 8 months' operation; however multi-step treatment streams took longer to reach equilibrium.