Software tool for evaluation of reliability and survivability of complex technical system based on logical-probabilistic methodology

The paper presents solutions for estimation and analysis of complex system (CS) reliability and survivability indicators based on the logical-probabilistic approach. Modified logical-probabilistic method and software tool for evaluating the reliability and survivability of onboard equipment (OE) of small satellites were developed (SS). The correctness of the suggested method and software tool was shown by computational experiments on some systems of CS SS similar to Belarusian SS, and later compared with the “Arbitr” software complex results.The paper presents solutions for estimation and analysis of complex system (CS) reliability and survivability indicators based on the logical-probabilistic approach. Modified logical-probabilistic method and software tool for evaluating the reliability and survivability of onboard equipment (OE) of small satellites were developed (SS). The correctness of the suggested method and software tool was shown by computational experiments on some systems of CS SS similar to Belarusian SS, and later compared with the “Arbitr” software complex results

LVAD Therapy Versus Medical Management in Heart Failure: An Integrative Review

Background: Advancements in technology have increased management options for heart failure (HF) patients. Options include guideline-directed medical therapy (GDMT), left ventricular assist device (LVAD) therapy, and/or heart transplant. Due to resource allocations, the most accessible options for many HF patients include GDMT and LVAD therapy. Authors of this integrative review (IR) sought to examine quality of life (QOL) and hospitalization rate outcomes among patients receiving GDMT versus LVAD therapy. Methods: 417 articles were screened across multiple databases (CINAHL, Medline, ProQuest, Ovid, PubMed) for inclusion into the integrative review based on inclusion criteria: published within five years, peer-reviewed, written in English, considered adults ages ≥ 18, and considered patients with NYHA HF classification stages III-IV. In total, 13 articles were appraised and thematically analyzed. Results: IR findings were presented according to identified themes. Results showed that LVAD therapy poses unique risks: social limitations, higher risk for adverse events, and higher hospitalization rates. Results demonstrated that both GDMT and LVAD therapy improve the following outcome measures in HF patients: survivability, QOL, and functional capacity. It was noted among articles discussing GDMT that combination GDMT has superior outcomes when compared to solo GDMT. Limited research was available that directly compared GDMT and LVAD outcomes. Limited research was available surrounding GDMT outcomes. Conclusions: While effective, LVAD therapy for HF patients incurs greater complication risk when compared to GDMT. Both GDMT and LVAD therapy improve QOL, functional capacity, and survivability among HF patients. More research is warranted regarding direct comparisons between LVAD and GDMT outcomes

City of dred – a tabletop RPG learning experience

Learning experiences are not typically used to describe formal learning activities, such as in classroom, transmissive methods. Centred in the student, this term describe that the learner is experiencing something that, hopefully, contributes to a change in thinking, understanding, or behaviour afterwards. For this to happen, learning experiences should be active, meaningful, with social meaning, integrative, and diversified. We consider active learning experiences when the student has the main learning role. They should provide knowledge and skills that directly contribute to the learner’s ability to perform more effectively in the context of workplace learning. Sharing and cooperation is fundamental, allowing the learner to interact with other active learners. The inherent increase in complexity demands the integration of different dimensions of knowledge, better achieved through diversified strategies. In this context, teaching and learning is more than the mere acquisition of content. It represents a process of learning by thinking-do-thinking.info:eu-repo/semantics/publishedVersio

Quantitative dependability and interdependency models for large-scale cyber-physical systems

Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

Survivability: The Human Element

Modern warship design is facing a number of drivers in terms of design, procurement and operation and these have both direct and indirect impacts on issues such as survivability and the human element. Guidance has been developed regarding Human Factors Integration (HFI), but this has generally focussed on detail design and fatigue. The UK MOD HFI Initiative describes HFI with 7 more holistic domains which are seen to have wider ship design impacts. This paper considers three current drivers on warship design for their impacts on survivability in the context of the human element. There were seen to be some interactions between different aspects of modern warship design and operation that again require a more holistic assessment of HF issues. The paper concludes that, although a more holistic approach is required, the increasing computerisation of the preliminary ship design process should allow tools to be developed to support this

Restructuring and Business Reengineering in Integrative Processes

Globalisation has intensified competition to such an extent that the corporations, merely with one’s own resources, cannot achieve acceptable success any longer. Objectives, which had been set-up prior to establishing the alliance in order to justify the investment, frequently will not be possible to achieve if during the integrative period revolutionary methods of change are not applied, to which one can classify restructuring and reengineering. Therefore, it is essential to be successful, not only in rules and principles of strategic alliances but in the methods of radical changes.strategic alliance, integration, restructuring, reengineering, crisis

Machine Learning Approaches for Cancer Analysis

In addition, we propose many machine learning models that serve as contributions to solve a biological problem. First, we present Zseq, a linear time method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors, such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Studying the abundance of select mRNA species throughout prostate cancer progression may provide some insight into the molecular mechanisms that advance the disease. In the second contribution of this dissertation, we reveal that the combination of proper clustering, distance function and Index validation for clusters are suitable in identifying outlier transcripts, which show different trending than the majority of the transcripts, the trending of the transcript is the abundance throughout different stages of prostate cancer. We compare this model with standard hierarchical time-series clustering method based on Euclidean distance. Using time-series profile hierarchical clustering methods, we identified stage-specific mRNA species termed outlier transcripts that exhibit unique trending patterns as compared to most other transcripts during disease progression. This method is able to identify those outliers rather than finding patterns among the trending transcripts compared to the hierarchical clustering method based on Euclidean distance. A wet-lab experiment on a biomarker (CAM2G gene) confirmed the result of the computational model. Genes related to these outlier transcripts were found to be strongly associated with cancer, and in particular, prostate cancer. Further investigation of these outlier transcripts in prostate cancer may identify them as potential stage-specific biomarkers that can predict the progression of the disease. Breast cancer, on the other hand, is a widespread type of cancer in females and accounts for a lot of cancer cases and deaths in the world. Identifying the subtype of breast cancer plays a crucial role in selecting the best treatment. In the third contribution, we propose an optimized hierarchical classification model that is used to predict the breast cancer subtype. Suitable filter feature selection methods and new hybrid feature selection methods are utilized to find discriminative genes. Our proposed model achieves 100% accuracy for predicting the breast cancer subtypes using the same or even fewer genes. Studying breast cancer survivability among different patients who received various treatments may help understand the relationship between the survivability and treatment therapy based on gene expression. In the fourth contribution, we have built a classifier system that predicts whether a given breast cancer patient who underwent some form of treatment, which is either hormone therapy, radiotherapy, or surgery will survive beyond five years after the treatment therapy. Our classifier is a tree-based hierarchical approach that partitions breast cancer patients based on survivability classes; each node in the tree is associated with a treatment therapy and finds a predictive subset of genes that can best predict whether a given patient will survive after that particular treatment. We applied our tree-based method to a gene expression dataset that consists of 347 treated breast cancer patients and identified potential biomarker subsets with prediction accuracies ranging from 80.9% to 100%. We have further investigated the roles of many biomarkers through the literature. Studying gene expression through various time intervals of breast cancer survival may provide insights into the recovery of the patients. Discovery of gene indicators can be a crucial step in predicting survivability and handling of breast cancer patients. In the fifth contribution, we propose a hierarchical clustering method to separate dissimilar groups of genes in time-series data as outliers. These isolated outliers, genes that trend differently from other genes, can serve as potential biomarkers of breast cancer survivability. In the last contribution, we introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have significant value in guiding treatment decisions. Our study also supports PTGFR, NREP, scaRNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease

Software tool for evaluation of reliability and survivability of complex technical system based on logical-probabilistic methodology

The paper presents solutions for estimation and analysis of complex system (CS) reliability and survivability indicators based on the logical-probabilistic approach. Modified logical-probabilistic method and software tool for evaluating the reliability and survivability of onboard equipment (OE) of small satellites were developed (SS). The correctness of the suggested method and software tool was shown by computational experiments on some systems of CS SS similar to Belarusian SS, and later compared with the “Arbitr” software complex results

Chilled mist as a viable alternative method for transporting commercially caught crustacean and mollusc species

Shellfish are often transported live to markets to ensure freshness upon arrival. Traditional transport methods involve large volumes of water (1:1 animal:water, vivier) hence reducing the mass of animals that can be transported with one journey, or they are transported dry, with both methods often inducing high stress levels. To assess the viability of an alternative method, the physiological stress of three commercially important species (Buccinum undatum, Nephrops norvegicus, and Homarus gammarus) was measured over time (24h – N. norvegicus, 72h – H. gammarus, and 96h – B. undatum) within an experimental re-circulating intermittent (IM) and continuous (CM) mist environment. Haemolymph stress parameters such as L-lactate, ammonia, D- glucose, total protein, pH, and behaviour were measured every 24h to determine the condition of the animals. The responses of animals in the misted environments were compared to the traditional method of transport for each species: vivier or dry. The mist was effective at reducing levels of haemolymph ammonia in the animals compared to simulated dry transport (3 and 2.4 fold lower ammonia concentration in B. undatum and H. gammarus haemolymph; at 96 hours and 72 hours; respectively). The IM group had 8.8 times lower ammonia concentration in the reservoir water compared to the CM group at 96 hours for the B. undatum trials, suggesting that IM may be a more efficient use of water for longer journeys. In its current form, misting is not suitable for the transport of N. norvegicus, as high mortalities were recorded in both IM – 30%, CM – 10%, compared to traditional vivier – 0%, however IM reduced mortality rates compared to traditional dry transport of B. undatum (IM - 5.25%, CM – 28.07%, dry - 22.8%). The efficacy of Accutrend handheld meter for L-lactate determination in decapod crustaceans is discussed in detail within this study. This study offers a novel, easily implemented method of transport with potential for replacing traditional methods, whilst maintaining animal health. This study can be used by fishers as a base for developing more efficient, cost-effective methods of live shellfish transport