Portable Userspace Virtual Filesystem Switch

Modern file systems associate the deletion of a file with the immediate release of storage, and file writes with the irrevocable change of file contents. We argue that this behavior is a relic of the past, when disk storage was a scarce resource. Today, large cheap disks make it possible for the file system to protect valuable data from accidental delete or overwrite.This paper describes the design, implementation, and performance of the Elephant file system, which automatically retains all important versions of user files. Users name previous file versions by combining a traditional pathname with a time when the desired version of a file or directory existed. Storage in Elephant is managed by the system using file-grain user-specified retention policies. This approach contrasts with checkpointing file systems such as Plan-9, AFS, and WAFL that periodically generate efficient checkpoints of entire file systems and thus restrict retention to be guided by a single policy for all files within that file system.Elephant is implemented as a new Virtual File System in the FreeBSD kernel.

Centralized feedback stabilization of multiple nonholonomic agents under input constraints

Managing many computers is difficult. Recent virtualization trends exacerbate this problem by making it easy to create and deploy multiple virtual appliances per physical machine, each of which can be configured with different applications and utilities. This results in a huge scaling problem for large organizations as management overhead grows linearly with the number of appliances. To address this problem, we introduce Strata, a system that combines unioning file system and package management semantics to enable more efficient creation, provisioning and management of virtual appliances. Unlike traditional systems that depend on monolithic file systems, Strata uses a collection of individual sotware layers that are composed together into the Virtual Layered File System (VLFS) to provide the traditional file system view. Individual layers are maintained in a central repository and shared across all file systems that use them. Layer changes and upgrades only need to be done once in the repository and are then automatically propagated to all virtual appliances, resulting in management overhead independent of the number of appliances. Our Strata Linux prototype requires only a single loadable kernel module providing the VLFS support and doesn't require any application or source code level kernel modifications. Using this prototype, we demonstrate how Strata enables fast system provisioning, simplifies system maintenance and upgrades, speeds system recovery from security exploits, and incurs only modest performance overhead.

Linux run-time errors are represented by integer values referred to as error codes. These values propagate across long function-call chains before being handled. As these error codes propagate, they are often temporarily or permanently encoded into pointer values. Error-valued pointers are not valid memory addresses, and therefore require special care by programmers. Misuse of pointer variables that store error codes can lead to serious problems such as system crashes, data corruption, unexpected results, etc. We use static program analysis to find three classes of bugs relating to error-valued pointers: bad dereferences, bad pointer arithmetic, and bad overwrites. Our tool finds 56 true bugs among 52 different Linux file system implementations, the virtual file system (VFS), the memory management module (mm), and 4 drivers.

Objective: In recent years, the neutrophil-to-lymphocyte ratio (NLR) has become a parameter sought in the prediction of aggressive prognosis or mortality in many studies including sepsis, infectious diseases, and malignancies. There are publications studying the correlation between the high values of this parameter, which can be interpreted as an indirect indicator of the host’s immune response, in the COVID-19 infection with mortality. The primary objective of our study is to evaluate the correlation between the height of NLR values in the patients followed up in our intensive care units (ICU) with the final diagnosis of COVID-19 infection (PCR(+)), the continuation of such high values throughout the treatment and mortality, and finally obtain a cut-off value in predicting the prognosis. Method: The case files with final diagnosis of COVID-19 infection followed up and treated in our 3rd Level Intensive Care Unit from 21 March 2020 to 1 July 2020 were screened retrospectively and included in the study. These cases were examined in two groups: exitus (n=113) and survivors (n=70). The demographics of the cases such as age, gender, comorbid diseases as well as their vital signs and clinical and laboratory features were screened from the Hospital Operating System (HOS) and archive files, and recorded. The correlation among the data obtained as well as the correlation between such data and mortality were analyzed. Findings: In the study conducted on 183 cases, the ages, APACHE II scores, admission NLR values, study endpoint NLR values, neutrophil, procalcitonin, D-dimer, PT and INR levels, and MV (Mechanical Ventilation) durations of the exitus cases were found statistically significantly higher than the survivor cases. In the exitus cases, the cut-off point found for the admission NLR was >7.82. During the course of treatment, the patients with high NLR level had poor prognosis. The patients’ NLR values at the study endpoint were analyzed. The conclusion is that >6 as the cut-off point value is significant from the viewpoint of mortality. In addition, it was observed that a NLR level over 6 despite the treatment increased the mortality rate by 15.6 times. Conclusion: We have found out that old age, long MV duration, prolonged high levels of D-dimer, neutrophil, PT, INR and NLR levels increase the mortality rate. Being able to predict the course of prognosis and using early warning systems will increase the survival rate. In this regards, we consider closer monitoring of the patients with high NLR level, particularly in the intensive care units even if they do not have respiratory distress, will decrease mortality significantly. NLR can help identifying the COVID-19 patients with high risk level. High NLR level during the intensive care monitoring process may be regarded as insufficient immune response, and thus correlated with mortality and aggressive prognosis.

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.

Despite the many advantages and increasing adoption of Electron Beam Powder Bed Fusion (PBF-EB) additive manufacturing by industry, current PBF-EB systems remain largely unstable and prone to unpredictable anomalous behaviours. Additionally, although featuring in-situ process monitoring, PBF-EB systems show limited capabilities in terms of timely identification of process failures, which may result into considerable wastage of production time and materials. These aspects are commonly recognized as barriers for the industrial breakthrough of PBF-EB technologies. On top of these considerations, in our research we aim at introducing real-time anomaly detection capabilities into the PBF-EB process. To do so, we build our case-study on top of a Arcam EBM A2X system, one of the most diffused PBF-EB machines in industry, and make access to the most relevant variables made available by this machine during the layering process. Thus, seeking a proficient interpretation of such data, we introduce a deep learning autoencoder-based anomaly detection framework. We demonstrate that this framework is able not only to early identify anomalous patterns from such data in real-time during the process with a F1 score around 90%, but also to anticipate the failure of the current job by 6 h, on average, and in one case by almost 20 h. This avoids waste of production time and opens the way to a more controllable PBF-EB process.