Multi-process Approach Research Articles

Encrypted Traffic Decryption Tools: Comparative Performance Analysis and Improvement Guidelines

With the exponential growth of encrypted communication over the internet, research into systems capable of analyzing large volumes of encrypted traffic is essential. This study focuses on evaluating the performance of two prominent tools, ssldump and tshark, in decrypting and inspecting encrypted network traffic, assuming an environment where decryption keys are available. The performance of ssldump and tshark was assessed using various metrics, including execution time, and the ability to handle different file sizes and session counts. The results showed that tshark exhibited faster processing speeds for smaller file sizes and a higher number of sessions, while ssldump demonstrated better performance for larger file sizes and fewer sessions. However, notable performance differences were not observed based solely on the type of cipher suite or encryption method used. To enhance performance, the study proposes the session-based split and conquer (SSC) technique for automating parallelization using a multi-process approach. SSC shows up to a 39× improvement in performance, depending on system capabilities and workload.

The Effects of Positive Versus Negative Self-Talk on Vertical Jump in Soccer Players: The Moderating Role of Need for Cognition.

The current study analyzed the effects of positive versus negative self-talk on physical performance in soccer players from a multiprocess approach. We operationalized the process distinction using the need-for-cognition (NC) construct. Thus, NC was measured and self-talk (i.e.,positive vs. negative) was manipulated between participants (i.e.,126 soccer players, age 18years or older, who were competing in national, regional, or local competitions). Physical performance was assessed by a vertical-jump test. According to hypothesis, regression analyses indicated that positive versus negative self-talk influenced physical performance to a greater extent for high-NC individuals than for low-NC individuals. Specifically, among high-NC soccer players, relative to baseline, positive self-talk produced greater physical performance in the vertical-jump test than negative self-talk. In contrast, among low-NC soccer players, no difference was found between positive and negative self-talk on physical performance. These results supported the moderating role of NC.

ANALYSIS OF LABORATORY STUDIES OF HYDROPHOBIC EMULSIONS TO ENSURE WATER INSULATION WORK IN OIL FIELDS

This article is devoted to the research and implementation of methods for ensuring water insulation works (WIW) in the oil sector using hydrophobic emulsions. In the context of modern requirements for environmental safety and efficiency of production processes in the oil industry, where water can become a source of problems, the introduction of innovative materials and technologies plays an important role. Conducting laboratory studies of hydrophobic emulsions for WIW in the oil sector is a multiprocess approach, starting from the initial assessment of the chemical component and ending with thorough testing for compliance with the requirements of specific operating conditions. The analysis evaluates the physico-chemical properties of the emulsion, such as density, viscosity, pH level, degree of dispersion, as well as its adhesive and cohesive characteristics. Special attention is paid to the study of the surface properties of hydrophobic emulsions, since their ability to form stable water-repellent coatings directly affects the effectiveness of protection against water exposure. To do this, tests are carried out on wettability, contact angle, adhesion to various types of surfaces, which allows us to determine the optimal conditions for the use of emulsions in specific situations. The article describes the principles of action of hydrophobic emulsions in the context of waterproofing works, emphasizing their ability to create a protective barrier, preventing moisture penetration into oil facilities. The paper presents the results of laboratory studies demonstrating the effectiveness of the use of hydrophobic emulsions in various operating conditions. The article discusses the technical aspects of the use of hydrophobic emulsions in the WIW process, analyzes their resistance to various environmental factors, and also considers the prospects for the development of this technology in the oil industry. The results obtained can be useful for specialists dealing with water insulation in oil companies, as well as for the scientific and engineering community interested in the development of innovative technologies in the oil sector. The purpose of this article is to analyze laboratory research and provide recommendations on the use of polymer composition.

Hybrid Printing of Silver-Based Inks for Application in Flexible Printed Sensors

This study explores the potential benefits of combining different printing techniques to improve the production of flexible printed sensors, which is a relevant application for modern coating and surface design. The demand for cheap, flexible, precise, and scalable sensors for wearable electronics is increasing, and printed electronics techniques have shown great potential in meeting these requirements. To achieve higher performance and synergy, the paper introduces the concept of hybrid printing of electronics by combining aerosol jet printing and screen printing. This multi-process approach allows for large-scale production with high printing precision. The study prepares hybrid connections on a flexible substrate foil for use in flexible printed sensor manufacturing. The research team tests different combinations of printed layers and annealing processes and finds that all prepared samples exhibit high durability during mechanical fatigue tests. Surface morphology, SEM images, and cross-section profiles demonstrate the high quality of printed layers. The lowest resistance among the tested hybrid connections obtained was 1.47 Ω. The study’s findings show that the hybrid printing approach offers a novel and promising solution for the future production of flexible sensors. Overall, this research represents an interdisciplinary approach to modern coating and surface design that addresses the need for improved production of wearable electronics. By combining different printing techniques, the study demonstrates the potential for achieving high-volume production, miniaturization, and high precision, which are essential for the ever-growing market of wearable sensors.

PSciLab: An Unified Distributed and Parallel Software Framework for Data Analysis, Simulation and Machine Learning—Design Practice, Software Architecture, and User Experience

In this paper, a hybrid distributed-parallel cluster software framework for heterogeneous computer networks is introduced that supports simulation, data analysis, and machine learning (ML), using widely available JavaScript virtual machines (VM) and web browsers to accommodate the working load. This work addresses parallelism, primarily on a control-path level and partially on a data-path level, targeting different classes of numerical problems that can be either data-partitioned or replicated. These are composed of a set of interacting worker processes that can be easily parallelized or distributed, e.g., for large-scale multi-element simulation or ML. Their suitability and scalability for static and dynamic problems are experimentally investigated regarding the proposed multi-process and communication architecture, as well as data management using customized SQL databases with network access. The framework consists of a set of tools and libraries, mainly the WorkBook (processed by a web browser) and the WorkShell (processed by node.js). It can be seen that the proposed distributed-parallel multi-process approach, with a dedicated set of inter-process communication methods (message- and shared-memory-based), scales up efficiently according to problem size and the number of processes. Finally, it is demonstrated that this JavaScript-based approach for exploiting parallelism can be used easily by any typical numerical programmer or data analyst and does not require any special knowledge about parallel and distributed systems and their interaction. The study is also focused on VM processing.

Development of a robust, accurate and efficient coupling between PLEIADES/ALCYONE [formula omitted] fuel performance code and the OpenCalphad thermo-chemical solver

In this paper, a robust, accurate and efficient coupling between the PLEIADES/ALCYONE 2.1 fuel performance code and the OpenCalphad thermo-chemical solver is presented. A challenging 3.5D simulation of a power ramp, consisting of the 3D simulations of all the fuel pellets within a complete rodlet, serves as a base case to illustrate the difficulty inherent to extensive thermochemical equilibrium calculations (more than 1.5 million in this example) within fuel performance simulations, related both to the wide temperature and pressure range encountered in irradiated nuclear fuels. In the first part of this paper, a detailed analysis of the main quantities of interest (i.e. temperature, hydrostatic pressure, fission products concentration) is provided to illustrate the couplings at hand during a power ramp and, above all, to demonstrate the good agreement of the results with those already published in the literature and with available measurements of xenon, cæsium and iodine releases. In the core of this paper, several numerical strategies are proposed to reduce the overall time spent in the thermochemical solver by defining at each node and for each time step a good initial estimate of the set of stable phases that are likely to form. The first one, referred to as the spatial strategy, relies on an intelligent reorganization of the mesh nodes as a function of temperature or hydrostatic pressure with an appropriate initialization of the thermochemical equilibrium calculation at a node by the phase composition calculated at the node with the closest thermo-mechanical conditions. The second strategy, referred to as the hybrid strategy, combines the spatial strategy during the power transient (where large power variations take place at each time step) with an initialization of the nodal equilibria from the solution obtained at the previous time step in case of small power variations. The 3.5D simulation has been run several times in order to assess and compare the spatial and hybrid strategies. The simulations show that the spatial strategy is the most efficient. It leads to an overall increase of the calculation time related to the incorporation of thermochemistry in the 3.5D fuel performance simulation of less than 2.25%, showing the feasibility of large thermal-chemical–mechanical simulations within the PLEIADES/ALCYONE 2.1 fuel performance code. Finally, as a first step towards more complicated simulations (i.e. including a greater number of phases and/or performed with a finer spatial discretization), a primitive parallelization algorithm based on a multiprocess approach is proposed. Combined with the most efficient spatial strategy, it leads to a substantial reduction of the extra (real) calculation time related to thermochemistry which become less than 0.65% of the total computation time when the thermochemical equilibrium calculations are distributed over 4 cores.

Urine collection in a multi-story building and opportunities for onsite recovery of nutrients and non-potable water

Abstract The purpose of this research was to demonstrate urine collection in a multi-story building and evaluate the quantity of phosphorus (P), nitrogen (N), potassium (K), and water recovered by struvite precipitation, ammonia stripping–acid absorption, and distillation, respectively, and the quality of fertilizers (struvite, ammonium sulfate, potash) and non-potable water generated. Urine was collected from a 5-story building in which floors 2–5 had one flush urinal, and the urinals were piped separately from building wastewater to a storage tank on the 1st floor. The extent of urea hydrolysis, defined as total ammonia nitrogen (TAN) divided by total nitrogen (TN), was measured in urine entering the storage tank (in the pipe) and after urine had been collected for 8 h (in the storage tank). In the pipe, complete hydrolysis of urea, i.e., greater than 90 %, occurred at TN concentrations below 600 mg N/L. In the storage tank, complete hydrolysis of urea was realized after each 8 h urine collection. The pH, conductivity, inorganic anions and cations of urine collected for 8 h on 17 different days is also reported. Three batches of urine were put through a multi-process approach of struvite precipitation, ammonia stripping–acid absorption, and distillation. The mass of N, P, K, and water recovered was over 90 % and the synthesis of value-added products compared well with non-potable water and fertilizers on the market such as struvite, ammonium sulfate, and potash.

Integrating LHCb workflows on HPC resources: status and strategies

High Performance Computing (HPC) supercomputers are expected to play an increasingly important role in HEP computing in the coming years. While HPC resources are not necessarily the optimal fit for HEP workflows, computing time at HPC centers on an opportunistic basis has already been available to the LHC experiments for some time, and it is also possible that part of the pledged computing resources will be offered as CPU time allocations at HPC centers in the future. The integration of the experiment workflows to make the most efficient use of HPC resources is therefore essential. This paper describes the work that has been necessary to integrate LHCb workflows at a specific HPC site, the Marconi-A2 system at CINECA in Italy, where LHCb benefited from a joint PRACE (Partnership for Advanced Computing in Europe) allocation with the other Large Hadron Collider (LHC) experiments. This has required addressing two types of challenges: on the software application workloads, for optimising their performance on a many-core hardware architecture that differs significantly from those traditionally used in WLCG (Worldwide LHC Computing Grid), by reducing memory footprint using a multi-process approach; and in the distributed computing area, for submitting these workloads using more than one logical processor per job, which had never been done yet in LHCb.

Building up and wearing down episodic memory: Mnemonic discrimination and relational binding.

Our capacity to form and retrieve episodic memories improves over childhood but declines in old age. Understanding these changes requires decomposing episodic memory into its components. Two such components are (a) mnemonic discrimination of similar people, objects, and contexts, and (b) relational binding of these elements. We designed novel memory tasks to assess these component processes using animations that are appropriate across the life span (ages 4-80 in our sample). In Experiment 1, we assessed mnemonic discrimination of objects as well as relational binding, in a common task format. Both components follow an inverted U-shaped curve across age but were positively correlated only in the aging group. In Experiment 2, we examined mnemonic discrimination of context and its effect on relational binding. Relational memory in low-similarity contexts showed robust gains between the ages of 4 and 6, whereas 6-year-olds performed similarly to adults. In contrast, relational memory in high-similarity contexts showed more protracted development, with 4- and 6-year-olds both performing worse than young adults and not differing from each other. Relational memory in both context conditions declined in aging. This multiprocess approach provides important theoretical insights into life span changes in episodic memory. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Integrated, multi-process approach to total nutrient recovery from stored urine

This research investigated an integrated, multi-process approach of struvite precipitation, ammonia stripping–acid absorption, and evaporation to recover NPK from stored urine.

A balanced calibration of water quantity and quality by multi-objective optimization for integrated water system model

Summary Due to the high interactions among multiple processes in integrated water system models, it is extremely difficult, if not impossible, to achieve reasonable solutions for all objectives by using the traditional step-by-step calibration. In many cases, water quantity and quality are equally important but their objectives in model calibration usually conflict with each other, so it is not a good practice to calibrate one after another. In this study, a combined auto-calibration multi-process approach was proposed for the integrated water system model (HEQM) using a multi-objective evolutionary algorithm. This ensures that the model performance among inseparable or interactive processes could be balanced by users based on the Pareto front. The Huai River Basin, a highly regulated and heavily polluted region of China, was selected as a case study. The hydrological and water quality parameters of HEQM were calibrated simultaneously based on the observed series of runoff and ammonia-nitrogen (NH 4 -N) concentrations. The results were compared with those of the step-by-step calibration to demonstrate the rationality and feasibility of the multi-objective approach. The results showed that a Pareto optimal front was formed and could be divided into three clear sections based on the elastic coefficient of model performance between NH 4 -N and runoff, i.e., the dominated section for NH 4 -N improvement, the trade-off section between NH 4 -N and runoff, and the dominated section for runoff improvement. The trade-off of model performance between runoff and NH 4 -N concentration was clear. The results of the step-by-step calibration fell in the dominated section for NH 4 -N improvement, where just the optimum of the runoff simulation was achieved with a large potential to improve NH 4 -N simulation without a significant degradation of the runoff simulation. The overall optimal solutions for all the simulations appeared in the trade-off section. Therefore, the Pareto front provided different satisfactory solutions for users to choose according to their specific objectives. This study is expected to promote the application of multi-objective calibration in water system modeling, and provide scientific and technological supports for the implementation of integrated river basin management.

Binding dynamics of a monomeric SSB protein to DNA: a single-molecule multi-process approach.

Single-stranded DNA binding proteins (SSBs) are ubiquitous across all organisms and are characterized by the presence of an OB (oligonucleotide/oligosaccharide/oligopeptide) binding motif to recognize single-stranded DNA (ssDNA). Despite their critical role in genome maintenance, our knowledge about SSB function is limited to proteins containing multiple OB-domains and little is known about single OB-folds interacting with ssDNA. Sulfolobus solfataricus SSB (SsoSSB) contains a single OB-fold and being the simplest representative of the SSB-family may serve as a model to understand fundamental aspects of SSB:DNA interactions. Here, we introduce a novel approach based on the competition between Förster resonance energy transfer (FRET), protein-induced fluorescence enhancement (PIFE) and quenching to dissect SsoSSB binding dynamics at single-monomer resolution. We demonstrate that SsoSSB follows a monomer-by-monomer binding mechanism that involves a positive-cooperativity component between adjacent monomers. We found that SsoSSB dynamic behaviour is closer to that of Replication Protein A than to Escherichia coli SSB; a feature that might be inherited from the structural analogies of their DNA-binding domains. We hypothesize that SsoSSB has developed a balance between high-density binding and a highly dynamic interaction with ssDNA to ensure efficient protection of the genome but still allow access to ssDNA during vital cellular processes.

Individual latent error detection: is there a time and a place for the recall of past errors?

A task carried out in error creates a latent condition that can result in a future undesirable outcome if the error is not detected later. The paper presents a study of the relatively under-researched phenomenon of post-task latent error detection. Focusing on UK naval aircraft maintenance, it was hypothesised that time, location and systems cues influence individual latent error detection amongst naval air engineers who experience the phenomenon. The systems view of human error is combined with a multi-process approach to post-task latent error research, for which findings suggest that distributed cognition across the entire socio-technical system may be influential in post-task latent error detection. Directions for future research will be of benefit to those interested in furthering safety resilience using a systems approach to minimise the consequences arising from latent error. Relevance to human factors/ergonomics theoryThe nature and extent of post-task latent error detection is explored using a systems approach, for which distributed cognition across the entire socio-technical system appears influential. The aim of current research is to develop interventions to further mitigate for latent errors and thus advance the systems application of ergonomics theory.

Individual latent error detection and recovery in naval aircraft maintenance: introducing a proposal linking schema theory with a multi-process approach to human error research

Error recovery has been observed amongst UK naval air engineers, which appears to be a result of some seemingly spontaneous recollection of past activity. Despite an extensive literature review, the nature and extent of this phenomenon are not understood fully and appears to be an under-researched area; causes of error and proximal error detection having been researched widely. To explore this phenomenon, a new theoretical framework is introduced based on a multi-process approach that combines theories on prospective memory, attentional monitoring and schemas. Several examples from a UK military safety database are then analysed for existence of the phenomenon and evidence of the applicability of the multi-process approach. Thus the intent of current research is not to explain why error occurred; it is to understand the nature and extent of situational error from a systems stance, for which individual latent error detection is the effect to be observed.

Opening the Black Box: Understanding the Microdynamics of Board Involvement in Product Innovation

Research has long assumed that boards of directors use their human capital to monitor strategy or advise on it, but less is known about how directors share and extend their varying expertise in order to become involved. In this study, we address this issue by studying board involvement in product innovation - a major, long-term strategic challenge. Using a multiple-case study of three listed pharmaceutical firms, we find that a multi-channel board design facilitates two board involvement processes. First, the specialized director and the board subgroup channels monitor scientific issues and advise on them through dynamic knowledge sharing exchanges with employees at multiple levels. Second, the full board channel reacts to innovation proposals in formal board meetings and focuses on the strategic aspects of innovation when monitoring and advising. Outside directors’ specialized knowledge and the full board’s diverse human capital are jointly leveraged for innovation through these processes. Our findings contribute to corporate governance research by providing a systematic and fine-grained framework that conceptualizes governance as a multi-channel and multi-process approach and depicts the microdynamics of board involvement.

A STEP-NC approach for multi-process manufacturing, simulation and optimisation

With STEP-NC object oriented programming standard, the next generation of CNC machine tools promises to be more open, intelligent and interoperable. As this ISO standard totally integrates CNC controllers into the CAD/CAM/CNC numerical chain, it provides an opportunity to stand as a common data model for various manufacturing processes. This paper first proposes a multi-process manufacturing approach by using STEP-NC as the optimal data model for process planning, process specificities and inter-processes simulations. Then, experimental validations are ensured by the advanced CNC platform for multi-process manufacturing developed in the laboratory. Finally, the realisation of a part on the platform by using the multi-process manufacturing concept shows the efficiency of the proposed approach.

Multi-process Bioremediation as an Improved Approach for Petroleum-contaminated Soil Restoration

A multiprocess bioremediation approach was applied to treat petroleum-contaminated soil from Dagang Oilfield, China. The bioremediation processes involved the use of four exogenous microbial strains and six herbage plants screened from a large number of species to remove low levels of total petroleum hydrocarbons (TPH) in contaminated soil. The experimental results indicated that the reduction of TPH increased with the improved community structure from the exogenous petroleum-degrading bacteria by over 35% as compared with that using the indigenous bacterial community. The refreshed microbial consortium was also able to accelerate the reduction of TPH via plant roots (phytoremediation) by over 47%. The TPH reduction rate diminished over time. Molecular biomarker ratios such as Ph/nC17, Pr/nC18 increased during the experiment but the ratio of Pr/Ph decreased. The results suggested that the multi-process bioremediation may significantly shorten the bioremediation duration and can be quite effective for treatment of soils contaminated by lower levels of petroleum.

Multicore in production: advantages and limits of the multiprocess approach in the ATLAS experiment

The shared memory architecture of multicore CPUs provides HEP developers with the opportunity to reduce the memory footprint of their applications by sharing memory pages between the cores in a processor. ATLAS pioneered the multi-process approach to parallelize HEP applications. Using Linux fork() and the Copy On Write mechanism we implemented a simple event task farm, which allowed us to achieve sharing of almost 80% of memory pages among event worker processes for certain types of reconstruction jobs with negligible CPU overhead. By leaving the task of managing shared memory pages to the operating system, we have been able to parallelize large reconstruction and simulation applications originally written to be run in a single thread of execution with little to no change to the application code. The process of validating AthenaMP for production took ten months of concentrated effort and is expected to continue for several more months. Besides validating the software itself, an important and time-consuming aspect of running multicore applications in production was to configure the ATLAS distributed production system to handle multicore jobs. This entailed defining multicore batch queues, where the unit resource is not a core, but a whole computing node; monitoring the output of many event workers; and adapting the job definition layer to handle computing resources with different event throughputs. We will present scalability and memory usage studies, based on data gathered both on dedicated hardware and at the CERN Computer Center.

Studying ROOT I/O performance with PROOF-Lite

Parallelism aims to improve computing performance by executing a set of computations concurrently. Since the advent of today's many-core machines the full exploitation of the available CPU power has been one of the main challenges. In High Energy Physics (HEP) final data analysis the bottleneck is not only the available CPU but also the available I/O bandwidth. Most of today's HEP analysis frameworks depend on ROOT I/O. In this paper we will discuss the results obtained studying the ROOT I/O performance using PROOF-Lite, a parallel multi-process approach whose results can be directly applied to the generic case of many jobs running concurrently on the same machine. We will also discuss the impact of running the applications in virtual machines.

Simple Reconstruction of Tree Branches from a Single Range Image

3D modeling of trees in real environments is a challenge in computer graphics and computer vision, since the geometric shape and topological structure of trees are more complex than conventional artificial objects. In this paper, we present a multi-process approach that is mainly performed in 2D space to faithfully construct a 3D model of the trunk and main branches of a real tree from a single range image. The range image is first segmented into patches by jump edge detection based on depth discontinuity. Coarse skeleton points and initial radii are then computed from the contour of each patch. Axis directions are estimated using cylinder fitting in the neighborhood of each coarse skeleton point. With the help of axis directions, skeleton nodes and corresponding radii are computed. Finally, these skeleton nodes are hierarchically connected, and improper radii are modified based on plant knowledge. 3D models generated from single range images of real trees demonstrate the effectiveness of our method. The main contributions of this paper are simple reconstruction by virtue of image storage order of single scan and skeleton computation based on axis directions.