A Self Healing Microservices Architecture: A Case Study in Docker Swarm Cluster

One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behaviour as well as implement a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. Often the behaviour of a microservices architecture continuously changes over time and the identification of both normal and abnormal behaviours of running services becomes a challenging task. This paper proposes a self-healing Microservice architecture that continuously monitors the operational environment, detects and observes anomalous behaviours, and provides a reasonable adaptation policy using a multi-dimensional utility-based model. This model preserves the cluster state and prevents multiple actions to taking place at the same time. It also guarantees that the executed adaptation action fits the current execution context and achieves the adaptation goals. The results show the ability of this model to dynamically scale the architecture horizontally or vertically in response to the context changes

Diagnosis of enteric fever in the emergency department: a retrospective study from Pakistan

Background:Enteric fever is one of the top differential diagnoses of fever in many parts of the world. Generally, the diagnosis is suspected and treatment is initiated based on clinical and basic laboratory parameters.Aims: The present study identifies the clinical and laboratory parameters predicting enteric fever in Patients visiting the emergency department of a tertiary care hospital in Pakistan.Methods:This is a retrospective chart review of all adult Patients with clinically suspected enteric fever admitted to the hospital through the emergency department during a 5-year period (2000-2005).Results:A total of 421 emergency department Patients were admitted to the hospital with suspected enteric fever. There were 53 cases of blood culture-positive enteric fever and 296 disease-negative cases on culture. The mean age in the blood culture-positive group was 27 years (SD: 10) and in the group with negative blood culture for enteric fever, 35 years (SD: 15) with a male to female ratio of 1:0.6 in both groups. Less than half (48%) of all Patients admitted with suspected enteric fever had the discharge diagnosis of enteric fever, of which only 13% of the Patients had blood culture/serologically confirmed enteric fever. None of the common clinical and laboratory parameters differed between enteric fever-positive Patients and those without it.Conclusion:Commonly cited clinical and laboratory parameters were not able to predict enteric fever

On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Study

Simulation of a dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) multiple sclerosis brain dataset is described. The simulated images in the implemented version have 1×1×1mm3 voxel resolution and arbitrary temporal resolution. Addition of noise and simulation of thick-slice imaging is also possible. Contrast agent (Gd-DTPA) passage through tissues is modelled using the extended Tofts-Kety model. Image intensities are calculated using signal equations of the spoiled gradient echo sequence that is typically used for DCE imaging. We then use the simulated DCE images to study the impact of slice thickness and noise on the estimation of both semi- and fully-quantitative pharmacokinetic features. We show that high spatial resolution images allow significantly more accurate modelling than interpolated low resolution DCE images.acceptedVersio

Insights into deposition of Lower Cretaceous black shales from meager accumulation of organic matter in Albian sediments from ODP site 763, Exmouth Plateau, Northwest Australia

The amount and type of organic matter present in an exceptionally complete upper Aptian to lower Cenomanian sequence of sediments from ODP site 763 on the Exmouth Plateau has been determined. Organic carbon concentrations average 0.2%. Organic matter is marine in origin, and its production and preservation was low over the ca. 20-million-year interval recorded by this sequence. Because this section was tectonically isolated from mainland Australia in the early Aptian, it better represents global oceanic conditions than the many basin-edge locations in which Albian-age black shales have been found. Formation of the basin-edge black shales evidently resulted from rapid, turbiditic burial of organic matter rather than from enhanced oceanic production or from basin-wide anoxia during the Albian.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47134/1/367_2005_Article_BF02202605.pd

Perspectives on the use of transcriptomics to advance biofuels

As a field within the energy research sector, bioenergy is continuously expanding. Although much has been achieved and the yields of both ethanol and butanol have been improved, many avenues of research to further increase these yields still remain. This review covers current research related with transcriptomics and the application of this high-throughput analytical tool to engineer both microbes and plants with the penultimate goal being better biofuel production and yields. The initial focus is given to the responses of fermentative microbes during the fermentative production of acids, such as butyric acid, and solvents, including ethanol and butanol. As plants offer the greatest natural renewable source of fermentable sugars within the form of lignocellulose, the second focus area is the transcriptional responses of microbes when exposed to plant hydrolysates and lignin-related compounds. This is of particular importance as the acid/base hydrolysis methods commonly employed to make the plant-based cellulose available for enzymatic hydrolysis to sugars also generates significant amounts of lignin-derivatives that are inhibitory to fermentative bacteria and microbes. The article then transitions to transcriptional analyses of lignin-degrading organisms, such as Phanerochaete chrysosporium, as an alternative to acid/base hydrolysis. The final portion of this article will discuss recent transcriptome analyses of plants and, in particular, the genes involved in lignin production. The rationale behind these studies is to eventually reduce the lignin content present within these plants and, consequently, the amount of inhibitors generated during the acid/base hydrolysis of the lignocelluloses. All four of these topics represent key areas where transcriptomic research is currently being conducted to identify microbial genes and their responses to products and inhibitors as well as those related with lignin degradation/formation.clos

The life and scientific work of William R. Evitt (1923-2009)

Occasionally (and fortunately), circumstances and timing combine to allow an individual, almost singlehandedly, to generate a paradigm shift in his or her chosen field of inquiry. William R. (‘Bill’) Evitt (1923-2009) was such a person. During his career as a palaeontologist, Bill Evitt made lasting and profound contributions to the study of both dinoflagellates and trilobites. He had a distinguished, long and varied career, researching first trilobites and techniques in palaeontology before moving on to marine palynomorphs. Bill is undoubtedly best known for his work on dinoflagellates, especially their resting cysts. He worked at three major US universities and spent a highly significant period in the oil industry. Bill's early profound interest in the natural sciences was actively encouraged both by his parents and at school. His alma mater was Johns Hopkins University where, commencing in 1940, he studied chemistry and geology as an undergraduate. He quickly developed a strong vocation in the earth sciences, and became fascinated by the fossiliferous Lower Palaeozoic strata of the northwestern United States. Bill commenced a PhD project on silicified Middle Ordovician trilobites from Virginia in 1943. His doctoral research was interrupted by military service during World War II; Bill served as an aerial photograph interpreter in China in 1944 and 1945, and received the Bronze Star for his excellent work. Upon demobilisation from the US Army Air Force, he resumed work on his PhD and was given significant teaching duties at Johns Hopkins, which he thoroughly enjoyed. He accepted his first professional position, as an instructor in sedimentary geology, at the University of Rochester in late 1948. Here Bill supervised his first two graduate students, and shared a great cameraderie with a highly motivated student body which largely comprised World War II veterans. At Rochester, Bill continued his trilobite research, and was the editor of the Journal of Paleontology between 1953 and 1956. Seeking a new challenge, he joined the Carter Oil Company in Tulsa, Oklahoma, during 1956. This brought about an irrevocable realignment of his research interests from trilobites to marine palynology. He undertook basic research on aquatic palynomorphs in a very well-resourced laboratory under the direction of one of his most influential mentors, William S. ‘Bill’ Hoffmeister. Bill Evitt visited the influential European palynologists Georges Deflandre and Alfred Eisenack during late 1959 and, while in Tulsa, first developed several groundbreaking hypotheses. He soon realised that the distinctive morphology of certain fossil dinoflagellates, notably the archaeopyle, meant that they represent the resting cyst stage of the life cycle. The archaeopyle clearly allows the excystment of the cell contents, and comprises one or more plate areas. Bill also concluded that spine-bearing palynomorphs, then called hystrichospheres, could be divided into two groups. The largely Palaeozoic spine-bearing palynomorphs are of uncertain biological affinity, and these were termed acritarchs. Moreover, he determined that unequivocal dinoflagellate cysts are all Mesozoic or younger, and that the fossil record of dinoflagellates is highly selective. Bill was always an academic at heart and he joined Stanford University in 1962, where he remained until retiring in 1988. Bill enjoyed getting back into teaching after his six years in industry. During his 26-year tenure at Stanford, Bill continued to revolutionise our understanding of dinoflagellate cysts. He produced many highly influential papers and two major textbooks. The highlights include defining the acritarchs and comprehensively documenting the archaeopyle, together with highly detailed work on the morphology of Nannoceratopsis and Palaeoperidinium pyrophorum using the scanning electron microscope. Bill supervised 11 graduate students while at Stanford University. He organised the Penrose Conference on Modern and Fossil Dinoflagellates in 1978, which was so successful that similar meetings have been held about every four years since that inaugural symposium. Bill also taught many short courses on dinoflagellate cysts aimed at the professional community. Unlike many eminent geologists, Bill actually retired from actively working in the earth sciences. His full retirement was in 1988; after this he worked on only a small number of dinoflagellate cyst projects, including an extensive paper on the genus Palaeoperidinium

A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning

Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-physical system over the last decade. Defending against DDoS attack is not only challenging but also strategic. Tons of new strategies and approaches have been proposed to defend against different types of DDoS attacks. The ongoing battle between the attackers and defenders is full-fledged due to its newest strategies and techniques. Machine learning (ML) has promising outcomes in different research fields including cybersecurity. In this paper, ensemble unsupervised ML approach is used to implement an intrusion detection system which has the noteworthy accuracy to detect DDoS attacks. The goal of this research is to increase the DDoS attack detection accuracy while decreasing the false positive rate. The NSL-KDD dataset and twelve feature sets from existing research are used for experimentation to compare our ensemble results with those of our individual and other existing models