UACI: Uncertain Associative Classifier for Object Class Identification in Images

Abstract Uncertainty is inherently present in many real-worl

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency

The role of weak interactions in the phase transition and distinct mechanical behavior of two structurally similar caffeine co-crystal polymorphs studied by nanoindentation

Although weak interactions, such as C-H⋯O and π-stacking, are generally considered to be insignificant, it is their reorganization that holds the key for many a solid-state phenomenon, such as phase transitions, plastic deformation, elastic flexibilit

Magnetic and mechanical anisotropy in a manganese 2-methylsuccinate framework structure

Hybrid inorganic-organic framework materials exhibit unique properties that can be advantageously tuned through choice of the inorganic and organic components and by control of the crystal structure. We present a new hydrothermally prepared 3D hybrid framework, [Mn(2-methylsuccinate)]n (1), comprising alternating 2D manganese oxide sheets and isolated MnO6 octahedra, pillared via syn, anti-syn carboxylates. Powder magnetic characterization shows that the compound is a homospin MnII ferrimagnet below 2.4 K. The easy-axis is revealed by single-crystal magnetic susceptibility studies and a magnetic structure is proposed. Anisotropic elastic moduli and hardness, observed through nanoindentation on differing crystal facets, were correlated with specific structural features. Such measurements of anisotropy are not commonly undertaken, yet allow for a more comprehensive understanding of structure-property relationships. Magnetic and mechanical anisotropy: A 3D manganese 2-methylsuccinate framework was constructed by alternating manganese oxide layers and isolated MnO6 octahedra, pillared via syn, anti-syn carboxylates (see figure). Single-crystal studies reveal that it exhibits highly anisotropic homospin ferrimagnetism, elastic moduli, and hardness along different single-crystal directions

Mechanical properties of a metal-organic framework containing hydrogen-bonded bifluoride linkers

We report the mechanical properties of a framework structure, [Cu 2F(HF)(HF2)(pyz)4][(SbF6) 2]n (pyz = pyrazine), in which [Cu(pyz)2] 2+ layers are pillared by HF2 - anions containing the exceptionally strong F-H⋯F hydrogen bonds. Nanoindentation studi

Interaction anisotropy and shear instability of aspirin polymorphs established by nanoindentation

Nanoindentation is applied to the two polymorphs of aspirin to examine and differentiate their interaction anisotropy and shear instability. Aspirin provides an excellent test system for the technique because: (i) polymorphs I and II exhibit structural similarity in two dimensions, thereby facilitating clear examination of the differences in mechanical response in relation to well-defined differences between the two crystal structures; (ii) single crystals of the metastable polymorph II have only recently become accessible; (iii) shear instability has been proposed for II. Different elastic moduli and hardness values determined for the two polymorphs are correlated with their crystal structures, and the interpretation is supported by measured thermal expansion coefficients. The stress-induced transformation of the metastable polymorph II to the stable polymorph I can be brought about rapidly by mechanical milling, and proceeds via a slip mechanism. This work establishes that nanoindentation provides "signature" responses for the two aspirin polymorphs, despite their very similar crystal structures. It also demonstrates the value of the technique to quantify stability relationships and phase transformations in molecular crystals, enabling a deeper understanding of polymorphism in the context of crystal engineering

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Abstract Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters