Distributed File Research Articles

Capital Commodification in the Field of Tarling Music Digitalization

Tarling’s method for creating Tarling music is evolving over time. The digitalization field observes the agents’ ways in production, distribution, and consumption of digital music files. Agents build their own habitus in employing their numerous capitals in the digitalization space. By combining the correlation of habitus and field theories of Bourdieu and Mosco’s perspective on commodification, this research is presented in a qualitative descriptive way with the research locus in the Cirebon region, West Java. The purpose of this research is to find out the commodification practice of Tarling’s music in the digitalization field, including the production, distribution and consumption sub-fields. As a result, commodification is carried out by producer agents, managers, and singers, who tend to be submissive and adaptive to the production structure built through the role of agents in their respective habitus. The production, distribution, and consumption of Tarling music tends to ignore the value of authenticity that the practice of commodification in the digitalization fields produces a pseudo-culture due to standardization and uniformity of form, in which Tarling music becomes a profit commodity.
 Keywords: commodification, Tarling music, digitalization, Mosco, Bourdieu

Leveraging Blockchain-based Smart Contracts to Develope Trustworthy Customer Agreements

The With the advancement of computer, electronic, and telecommunications technology, there has been a significant change in the world of applications, which makes it easier for humans to carry out various activities, both at work and in interacting on social media.CV. Albahritech faces the challenge of not having a quick and easy office equipment maintenance application. This limitation can have a negative impact on company operations. Currently, the maintenance process still relies on communication via WhatsApp or phone, which causes customer appointments to be missed or forgotten due to the large number of calls for customer maintenance services that cannot be fulfilled. In addition, transactions and visit reports are not properly recorded, which can cause customers to experience delays in getting treatment services or even cause customers to look for other treatment services.The currently designed application uses a blockchain-like decentralized network model, known as a DApp. This DApp has several important features that utilize blockchain technology[1], including the ability to make payments using cryptocurrencies such as Ether and the feature to record service lists for customers. In this application, smart contracts are used to create a mechanism of trust and automation in agreements or deals between the parties involved. This ensures that agreements and transactions made within the app are transparent, secure, and without the need for a third party. In addition, the app also uses IPFS for customer data storage. IPFS is a peer-to-peer file distribution system that serves to store and distribute data on the interne[2]t. The use of IPFS in this application enables distributed and decentralized data storage, thereby improving data security and reliability.
 
 
 Keywords: Helpdesk, Ticketing, Blockchain, Smart Contract,IPFS.

An Optimal Algorithm for Partial Order Multiway Search

Partial order multiway search (POMS) is an important problem that finds use in crowdsourcing, distributed file systems, software testing, etc. In this problem, a game is played between an algorithm A and an oracle, based on a directed acyclic graph G known to both parties. First, the oracle picks a vertex t in G called the target; then, A aims to figure out which vertex is t by probing reachability. In each probe, A selects a set Q of vertices in G whose size is bounded by a pre-agreed value k, and the oracle then reveals, for each vertex q 2 Q, whether q can reach the target in G. The objective of A is to minimize the number of probes. This article presents an algorithm to solve POMS in O(log1+k n + d k log1+d n) probes, where n is the number of vertices in G, and d is the largest out-degree of the vertices in G. The probing complexity is asymptotically optimal.

Faktor yang Berhubungan dengan Penyediaan Dokumen Rekam Medis Rawat Jalan di RSUP H. Adam Malik Medan

<em><span>According to Permenkes 269/2008 concerning medical records in Article paragraph 1, medical records are files containing notes and documents about patient identity, examination, treatment, action, and other services that have been provided to patients. Several things that can support the distribution of outpatient medical record files can be carried out properly are the ability of medical record officers, medical record management systems and adequate storage locations. The purpose of this study was to determine the factors related to the time of providing outpatient medical record files at RSUP H. Adam Malik Medan.</span><span>The study used an analytic survey method with a cross sectional approach. The population of this study was outpatient medical record officers in as many as 75 officers with a sample using a total sample technique of 75 officers. Data analysis used univariate analysis and bivariate analysis with chi-square test statistics.</span><span>The results of the chi-square test analysis showed that the p-value of each variable, including knowledge, obtained p-value = 0.026, human resources obtained p-value = 0.013 and infrastructure obtained p-value = 0.005.</span><span>The conclusion of this study is that there is an influence of knowledge, human resources and infrastructure with the time of providing outpatient medical record files. It is hoped that it can increase the knowledge of medical record officers in hospitals through the provision of medical record training as well as improve the function of the quality committee in hospitals related to the implementation and provision of medical record documents, increase the number of human resources in the field of medical records in each unit and improve monitoring and evaluation functions for staff officers who do not work according to the standard of providing medical records, increasing the number of file storage racks, and providing the use of different lifts so that the distribution of RM documents is carried out faster</span></em>

Video File Allocation for Wear-Leveling in Distributed Storage Systems With Heterogeneous Solid-State-Disks (SSDs)

With the advent of new large-capacity solid-state disks (SSDs) such as quad-level-cells (QLC), SSD arrays can be effectively used in video storage systems that require large-capacity storage space. Typically, SSD manufacturers specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes written per day to the total capacity in bytes, to ensure an SSD’s specified lifetime; it is important to limit the number of write operations by considering the DWPD for each SSD. We propose a new video file allocation technique to effectively manage the heterogeneous DWPD characteristics of SSDs in distributed storage systems. To express the degree of wear-leveling for heterogeneous SSDs, we first introduce the concept of ADWD, which is the actual number of bytes written per day compared to DWPD. We then propose two algorithms for file placement and migration. The file placement algorithm places files greedily based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the bandwidth usage and storage of the SSD. The file migration algorithm moves files from overloaded to underloaded SSDs to meet bandwidth limit requirements while minimizing the overall ADWD as a result of migration, and then migrates additional popular files to improve SSD bandwidth utilization. To use these algorithms in actual distributed file systems, we implemented a suite of tools for file placement and migration in the Hadoop distributed file system (HDFS). Experimental results show that the proposed algorithm reduces the mean of ADWD by 35.44% and its standard deviation by 69.78% compared to the benchmark methods on average.

PbDinEHR: A Novel Privacy by Design Developed Framework Using Distributed Data Storage and Sharing for Secure and Scalable Electronic Health Records Management

Privacy in Electronic Health Records (EHR) has become a significant concern in today’s rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR’, we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients’ medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain.

Design and Analysis of Planar Waveguide-Based SPR Sensor for Formalin Detection Using Ag-Chloride-BP Structure.

Nowadays, Food additives and preservatives have become a hot topic especially formalin, which is a chemical substance used to preserve food. Chronic cancer is caused by swallowing the formalin-contaminated food on a regular basis. As a result, detection of the formalin in food ingredients is a critical need, and this requirement is becoming increasingly important in new terrains. Therefore, a new approach, planar waveguide-based surface plasmon resonance (SPR) sensor is presented in this paper employing silver-chloride materials-black phosphorus structure to detect the formalin. In this study, the optimization of metal thickness and optimal performance of chloride material are analyzed by observing the transmittance power and resonance shift for conventional and chloride-based sensors, respectively. Moreover, the performance parameters, such as sensitivity of 344°/RIU, quality-factor of 166.99RIU-1, detection-accuracy of 3.34 and figure-of-merit of 164.74, are observed for the proposed sensor. Furthermore, the performance comparative study is conducted and found that the proposed sensor highlighted the enhanced the SPR sensor and highlighted the performance of FOM. Finally, the electromagnetic filed distributions of the proposed WG-based SPR sensor are shown using the Opti-FDTD software.

A Literature Review of Big Data and Metadata Management Systems

Abstract: Metadata is essential for the successful operation of data files, as it acts as the underlying data that helps maintain and manage the namespace, permissions, and address of the file data blocks. The performance of metadata services has a direct impact on the overall performance of the file system, as these operations can account for a significant portion, up to 80%, of file system activity. Large-scale distributed file systems (DFS) have emerged to meet the rising demand for data storing. These systems provide users with access to numerous storage units spread across various locations. The widespread use of large-scale DFSs as the foundation for a range of computing systems and applications, particularly in the era of Big Data, highlights the importance of efficient metadata management. This paper seeks to offer an enhanced knowledge of the current status of metadata systems in large-scale DFSs, this paper presents a comprehensive overview of the latest research in the field. DFSs are defined by three aspects: scalability, performance, and availability. Moreover, the study evaluates various challenges and limitations in the current research, providing valuable insight for future studies. Overall, the study attempts to give a complete knowledge of the present scenario of metadata services across large-scale DFSs and to recommend areas for future research and development.

Amazon Biobank: a collaborative genetic database for bioeconomy development

Biodiversity is proposed as a sustainable alternative for the economic development of high-biodiversity regions. Especially in the field of biodiversity genomics, the development of low-cost DNA sequencing opens an opportunity for new actors beyond academia to engage in genomic sequencing. However, it is challenging to adequately compensate non-academic actors such as local populations for their contribution to the innovation process, preventing better bioeconomy development. Although many repositories register genomic data to support biodiversity research, they do not facilitate the fair sharing of economic benefits. In this work, we propose the creation of the Amazon Biobank, a community-based genetic database. We employed blockchain to build a transparent and verifiable log of transactions involving genomic data, and we used smart contracts to implement an internal monetary system for all participants who collect, insert, process, store, and validate genomic data. We also used peer-to-peer solutions to allow users with commodity computers to collaborate with the storage and distribution of DNA files. By combining emerging technologies, Amazon Biobank provides adequate benefit-sharing among all participants that collaborate with data, knowledge, and computational resources. It also provides traceability and auditability, allowing easy association between biotechnological research and DNA data. In addition, the solution is highly scalable and less dependent on the trust deposited in any system player. Therefore, Amazon Biobank can become an important steppingstone to unlock the potential of bioeconomy in rich ecosystems such as the Amazon Rainforest.

Robust Dual Digital Watermark Applied to Antique Digitized Cinema Images: Resistant to Print-Scan Attack

Nowadays, advances in information and communication technologies along with easy access to electronic devices such as smartphones have achieved an agile and efficient storing, edition, and distribution of digital multimedia files. However, lack of regulation has led to several problems associated with intellectual property authentication and copyright protection. Furthermore, the problem becomes complex in a scenario of illegal printed exploitation, which involves printing and scanning processes. To solve these problems, several digital watermarking in combination with cryptographic algorithms has been proposed. In this paper, a strategy of robust watermarking is defined consisting of the administration and detection of unauthorized use of digitized cinematographic images from Mexican cultural heritage. The proposed strategy is based on the combination of two types of digital watermarking, one of visible-camouflaged type based on spatial domain and another of invisible type based on frequency domain, together with a particle swarm optimization. The experimental results show the high performance of the proposed algorithm faced to printing-scanning processes or digital-analogue attack, and common image geometric and image processing attacks such as JPEG compression. Additionally, the imperceptibility of the watermark is evaluated by PSNR and compared with other previously proposed algorithms.

Blockchain-Based Decentralized Architecture for Software Version Control

Version control is an important component of configuration management, and most enterprise-level software uses different tools and technologies to manage the software version control such as CVS, Subversion, or Perforce. Following the success of bitcoin, the first practical application of blockchain, it is being implemented in other fields such as healthcare, supply chains, financial management, real estate, electoral systems, and so on. Blockchain’s core features include decentralization, immutability, and interminability. Most version control repositories are centralized and can be modified by external sources, implying that they are in danger of being corrupted or controlled. In this study, we present the BDA-SCV architecture for implementing a version control system in blockchain technology. Our proposed approach would replace the necessity for a centralized system, with a decentralized approach implemented in the blockchain using distributed file storage, for which we will use the InterPlanetary File System (IPFS), which is a distributed file system. The proof of authority (PoA) consensus algorithm will be used to approve the developer communicating modifications to the private blockchain network; the authority will only provide permission and will not be able to add, edit, or delete code files. For each change, a ledger block will be created with a reference to the file stored in the distributed repository. A block cannot be manipulated once it has been created. Smart contracts will be used to register developers, create blocks, and manage the repository. The suggested model is implemented using the Hyperledger Fabric network, and the developer and authorizer ends are built into the dotnet web application.

Distributed file systems performance tests on Kubernetes/Docker clusters

Modern data centers need distributed file systems to provide user applications with access to data stored on a large number of nodes. The ability to mount a distributed file system and leverage its native application programming interfaces in a Docker container, combined with the advanced orchestration features provided by Kubernetes, can improve flexibility in installing, monitoring and recovering data management and transfer services. At INFN-CNAF some distributed file systems (i.e. IBM Spectrum Scale, CephFS and Lustre-ZFS) deployment tests with Kubernetes and Docker have been conducted recently with positive results. The purpose of this paper is to show the throughput scores of the previously mentioned file systems when their servers are containerized and run on bare metal machines using a container orchestration framework. This is a preliminary study: for the time being, only sequential read/write tests have been considered.

Algorithms for Pre-Compiling Programs by Parallel Compilers

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest- Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s.

Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.

Hydra: A Decentralized File System for Persistent Memory and RDMA Networks

Emerging byte-addressable persistent memory (PM) has the potential to disrupt the boundary between memory and storage. Combined with high-speed RDMA networks, distributed PM-based storage systems offer the opportunity to provide huge increases in storage performance by closely coupling PM and RDMA features. However, existing distributed file systems adopt the conventional centralized client-server architecture designed for traditional disks, leading to excessive access latency, limited scalability, and high recovery overhead. In this paper, we propose a fully decentralized PM-based file system, Hydra. By exploiting the performance advantages of local PM, Hydra leverages data access locality to achieve high performance. To accelerate file transmission among Hydra nodes, file metadata and data are decoupled and updated differentially through one-sided RDMA reads. Hydra also batches RDMA requests and classifies RPCs into synchronous and asynchronous types to minimize network overhead. Decentralization enables Hydra to tolerate node failures and achieve load balancing. Experimental results show that Hydra outperforms existing distributed file systems by a large margin, and shows good scalability on multi-threaded and parallel workloads.

The State of the Art of Metadata Managements in Large-Scale Distributed File Systems — Scalability, Performance and Availability

File system metadata is the data in charge of maintaining namespace, permission semantics and location of file data blocks. Operations on the metadata can account for up to 80% of total file system operations. As such, the performance of metadata services significantly impacts the overall performance of file systems. A large-scale distributed file system (DFS) is a storage system that is composed of multiple storage devices spreading across different sites to accommodate data files, and in most cases, to provide users with location independent access interfaces. Large-scale DFSs have been widely deployed as a substrate to a plethora of computing systems, and thus their metadata management efficiency is crucial to a massive number of applications, especially with the advent of the Big Data age, which poses tremendous pressure on underlying storage systems. This paper reports the state-of-the-art research on metadata services in large-scale distributed file systems, which is conducted from three indicative perspectives that are always used to characterize DFSs: high-scalability, high-performance, and high-availability, with special focus on their respective major challenges as well as their developed mainstream technologies. Additionally, the paper also identifies and analyzes several existing problems in the research, which could be used as a reference for related studies.

Nanocrystalline copper iodide enabling high-efficiency organic LEDs

High-efficiency organic light-emitting diodes (LEDs) can be achieved by employing efficiency-effective materials coupling with suitable device architectures. Among them, there have been numerous hole-injection and -transport materials reported, but having issues of high-cost, solution-process feasibility, and toxic solvent involvement. Here, we demonstrated a low-cost solution-processable nanocrystalline copper iodide (CuI) as a hole-injection and -transport material for the fabrication of high-efficiency organic LEDs. The CuI dispersion can be prepared by using a non-toxic ammonia-water solution with processing being carried out at a relatively low temperature of 110 o C. The annealed CuI film exhibited more superior optical properties, a better hole injection capability, and a better crystalline nature as comparing with the unannealed one or the typical hole-injection counterpart. The annealing resulted nanocrystalline CuI had enabled a green device with a maximum external quantum efficiency (EQE max ) of 17%, a maximum power efficacy (PE max ) of 64 lm W −1 and a maximum current efficacy (CE max ) of 62 cd A −1 , an increment of 54, 33 and 15%, respectively, as comparing with the unannealed counterpart. The respective increments are 35, 41 and 38% as comparing with the typical hole-injection material. Additionally, a blue hazard-free candlelight organic LED has also been fabricated with an EQE max of 15%, PE max of 43 lm W −1 and CE max of 41 cd A −1 . Electrical simulation was also performed to get recombination rate and electric field distribution in active layer. CuI based OLED exhibited higher recombination rate and lower electric filed distribution as comparing with PEDOT:PSS based OLED. The superior performance may be attributed to the enhanced recombination due to efficient injection, transport of holes, and a potential inner light scattering medium aiding light extraction due to its nanocrystalline nature. Our results show that the nanocrystalline CuI can be an inexpensive substitute for the conventionally used hole-injection materials to achieve high-efficiency lighting sources. • Nanocrystalline CuI as an effective Hole-injection/transport layer for organic LEDs. • CuI dispersion is prepared in a non-toxic ammonia-water solution. • Electro-simulation study comparison of CuI and PEDOT:PSS. • Superior device performance in comparison with PEDOT: PSS and amorphous counterpart.

Biological heat and mass transport mechanisms behind nanoparticles migration revealed under microCT image guidance

Background and objectivesBlood perfusion, infusion rate, distribution and heating induced redistribution of magnetic nanoparticles inside tumour interstitium governs the heating protocol designs. This study aims to answer three important questions: Why do magnetic nanoparticles redistributes? What causes their migration? Why it is important to be included in priori-treatment planning simulations? Our aim is to test how the regulation of magnetic nanoparticles affects the temperature distribution and how this temperature change modifies tumour microenvironment. We have tested our hypothesis for two cases: (1) when the nanoparticles are lying in close proximity to the flank position of tumour i.e. near the interface of tumour and healthy tissue and (2) when the nanoparticles are located near the skin surface of tumour. Materials and methodsSAS® and MATLAB programs are used to average micro-CT cross-sectional slices containing three-dimensional clusters of pixels corresponds to nanoparticles as a SAR file and Concentration distribution file import to the COMSOL-Multiphysics® 5.2 FEA software for evaluation of temperature distribution and its resulted collateral thermal damage. The volumetric heat generation rate (qMNH‴) is kept ∼ 0.37 W for infusion rate of 4 μL/min. Pennes bioheat equation is three-way coupled with traditional Arrhenius thermal damage model, modified concentration equation in which heat source term (SAR) varies in a directly proportional relationship with Concentration distribution. All variables i.e. Diffusion coefficient, porosity, blood perfusion, metabolic heat generation are varying as a function of thermal damage for a coupled biophysics problem. ResultsThermal signatures of cell-death were evaluated for two case scenarios. The initial distribution volume is 188.46 mm3. On compare and contrast, 6.24% larger thermal diffusion is seen at outer extremities case than at the interface. However, the treatment times are 12.90% (1.13 fold more) to achieve a Ω ≥ 4.6 with ΔT=10°C for latter case. On evaluation of “Source maps” (before heating) with “Destination maps” (after heating) suggests migration of nanoparticles from the regions of higher concentration to the regions of lower concentration due to cell-necrosis induced enhancement in tumour interstitial space. Thermal-diffusion of nanoparticles suggests an increase in redistribution volumes by 23.44% and 26.30% respectively during 99.6% transformation of cancerous cells. This study precisely elucidate qualitative and quantitative aspects of this issue. One of the possible reasons of nanoparticle migration is that the dead cells release intracellular solution after the rupturing of cell membrane. We speculate that five-fold increase in diffusion coefficient of nanoparticles might have played a significant role in particle migration. ConclusionsCNIDC (Cell necrosis induced diffusivity change) and CNIPE (Cell necrosis induced porosity enhancement) may be the two intricate mechanisms that promotes nanoparticles migration.

Automatic mapping of configuration options in software using static analysis

Configuration errors are some of the main reasons for software failures. Some configuration options may even negatively impact the software’s security, so that if a user sets the options inappropriately, there may be a huge security risk for the software. Recent studies have proposed mapping option read points to configuration options as the first step in alleviating the occurrence of configuration errors. Sadly, most available techniques use humans, and the rest require additional input, like an operation manual. Unfortunately, not all software is standardized and friendly. We propose a technique based on program and static analysis that can automatically map all the configuration options of a program just by reading the source code. Our evaluation shows that this technique achieves 88.6%, 97.7%, 94.6%, 94.8%, and 92.6% success rates with the Hadoop modules Common, Hadoop distributed file system, MapReduce, and YARN, and also PX4, when extracting configuration options. We found 53 configuration options in PX4 that were not documented and submitted these to the developers. Compared with published work, our technique is more effective in mapping options, and it may lay the foundation for subsequent research on software configuration security.