Teaching with GSS: Techniques for Enabling Student Participation

Learning requires cognitive effort and two way communication. In the classroom setting, it is difficult to give every student a significant amount of time to participate. Group support systems (GSS) have been shown to make meetings more effective (Nunamaker, Dennis, Valacich, Vogel and George 1991). If the classroom is viewed as a meeting where the students are called upon to contribute, GSS can bring the same benefits to the classroom. This paper first describes our goals for improving classroom learning and then describes our experiences and techniques to help others apply them to their classroom situation. The techniques described are domain independent. They apply to any subject area and almost every level of education

If Not Here, There. Explaining Machine Learning Models for Fault Localization in Optical Networks

Machine Learning (ML) is being widely investigated to automate safety-critical tasks in optical-network management. However, in some cases, decisions taken by ML models are hard to interpret, motivate and trust, and this lack of explainability complicates ML adoption in network management. The rising field of Explainable Artificial Intelligence (XAI) tries to uncover the reasoning behind the decision-making of complex ML models, offering end-users a stronger sense of trust towards ML-Automated decisions. In this paper we showcase an application of XAI, focusing on fault localization, and analyze the reasoning of the ML model, trained on real Optical Signal-To-Noise Ratio measurements, in two scenarios. In the first scenario we use measurements from a single monitor at the receiver, while in the second we also use measurements from multiple monitors along the path. With XAI, we show that additional monitors allow network operators to better understand model's behavior, making ML model more trustable and, hence, more practically adoptable

Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

Machine Learning (ML) adoption for automated failure management is becoming pervasive in today's communication networks. However, ML-based failure management typically requires that monitoring data is exchanged between network devices, where data is collected, and centralized locations, e.g., servers in data centers, where data is processed. ML algorithms in this centralized location are then trained to learn mappings between collected data and desired outputs, e.g., whether a failure exists, its cause, location, etc. This paradigm poses several challenges to network operators in terms of privacy as well as in terms of computational and communication resource usage, as a massive amount of sensible failure data is transmitted over the network. To overcome such limitations, Federated Learning (FL) can be adopted, which consists of training multiple distributed ML models at multiple decentralized locations (called 'clients') using a limited amount of locally-collected data, and of sharing these trained models to a centralized location (called 'server'), where these models are aggregated and shared again with clients. FL reduces data exchange between clients and a server and improves algorithms' performance thanks to sharing knowledge among different domains (i.e., clients), leveraging different sources of local information in a collaborative environment. In this paper, we focus on applying FL to perform failure-cause identification in microwave networks. The problem is modeled as a multi-class ML classification problem with six pre-defined failure causes. Specifically, using real failure data from an operational microwave network composed of more than 10000 microwave links, we emulate a multi-operator scenario in which one operator has partial knowledge of failure causes during the training phase. Thanks to knowledge sharing, numerical results show that FL achieves up to 72% precision in identifying an unknown particular class concerning traditional ML (non- FL) approaches where training is performed without knowledge sharing

Design, development and numerical analysis of honeycomb core with variable crushing strength

A honeycomb core with half-circular cut-away sections at the spine (the adjoining cell walls) is designed and developed and numerically tested under axial dynamic load condition. The parametric study is invoked to identify the effect of various circular cut-away dimensions. In one embodiment a half-circular shaped cuts are removed from the top of the cell where the cell is impacted and its radius decreases toward the trailing edge of the cell. Numerical (FE) analysis was performed using explicit ANSYS/LS-DYNA and LS-DYNA codes to investigate the crushing performance, where impact angles 30° and 90° was combined with velocity of 5:3 m/sec. The crushing strength and internal energy absorption of the modified honeycomb cores with cut-away sections are then monitored to define the design parameters. The representative Y-section (axisymmetric model) is used for numerical analysis which simulates the honeycomb crushing performance. The numerical results of these innovative models show cyclic buckling effect in which crushing strength increases linearly as the rigid wall passes through. The FE results are validated with corresponding published experiments of the original unmodified honeycomb core (without cut-away)

An in vitro assessment of growth promoting activity of a synthetic basic fibroblast growth factor (b-FGF) using Rama-27 cell line

AbstractGrowth factors (GFs) are naturally occurring proteins or steroid hormones which act as signaling molecules between cells that play a key role in the processes of proliferation, cell differentiation and maturation of a wide variety of cells and tissues. A recently purified synthetic basic b-FGF was assessed using a routine tissue culture assay via application of a wide range of doses ranged between 0.1 and 300ng/mL of the basic fibroblast growth factor (b-FGF) in phosphate buffer saline (PBS) and 10% fetal calf serum (FCS) on the growth rate of Rama-27 mammary cell line. Applying SPSS “Student T-test” biostatistics the result showed significant increase (p≤0.05), almost 7 folds in tissue proliferation at a low dose of 0.3ng/mL FGF in comparison with control tissue (PBS) only. It is concluded that 0.3ng/mL dose represents the lower optimal dose suggesting its possibility of an in vivo technique to test its potency in curing skin wounds in rats

Finite-Element Model for Pretensioned Prestressed Concrete Girders

This paper presents a nonlinear model for pretensioned prestressed concrete girders. The model consists of three main components: a beam-column element that describes the behavior of concrete, a truss element that describes the behavior of prestressing tendons, and a bond element that describes the transfer of stresses between the prestressing tendons and the concrete. The model is based on a two-field mixed formulation, where forces and deformations are both approximated within the element. The nonlinear response of the concrete and tendon components is based on the section discretization into fibers with uniaxial hysteretic material models. The stress transfer mechanism is modeled with a distributed interface element with special bond stress-slip relation. A method for accurately simulating the prestressing operation is presented. Accordingly, a complete nonlinear analysis is performed at the different stages of prestressing. Correlation studies of the proposed model with experimental results of pretensioned specimens are conducted. These studies confirmed the accuracy and efficiency of the model

Pharmacological targeting of the GABAʙ receptor alters Drosophila's behavioural responses to alcohol

When exposed to ethanol, Drosophila melanogaster display a variety of addiction‐like behaviours similar to those observed in mammals. Sensitivity to ethanol can be quantified by measuring the time at which 50% of the flies are sedated by ethanol exposure (ST50); an increase of ST50 following multiple ethanol exposures is widely interpreted as development of tolerance to ethanol. Sensitivity and tolerance to ethanol were measured after administration of the gamma‐aminobutyric acid receptor B (GABAʙ) agonist (SKF 97541) and antagonist (CGP 54626), when compared with flies treated with ethanol alone. Dose‐dependent increases and decreases in sensitivity to ethanol were observed for both the agonist and antagonist respectively. Tolerance was recorded in the presence of GABAʙ drugs, but the rate of tolerance development was increased by SKF 97451 and unaltered in presence of CGP 54626. This indicates that the GABAʙ receptor contributes to both the sensitivity to ethanol and mechanisms by which tolerance develops. The data also reinforce the usefulness of Drosophila as a model for identifying the molecular components of addictive behaviours and for testing drugs that could potentially be used for the treatment of alcohol use disorder (AUD)

Availability Evaluation of Service Function Chains Under Different Protection Schemes

Network Function Virtualization (NFV) calls for a new resource management approach where virtualized network functions (VNFs) replace traditional network hardware appliances. Thanks to NFV, operators are given a much greater flexibility, as these VNFs can be deployed as virtual nodes and chained together to form Service Function Chains (SFCs). An SFC represents a set of dedicated virtualized resources deployed to provide a certain service to the consumer. One of its most important performance requirements is availability. In this paper, the availability achieved by SFCs is evaluated analytically, by modelling several protection schemes and given different availability values for the network components. The cost of each protection scheme, based on its network resource consumption, is also taken into account. Extensive numerical results are reported, considering various SFC characteristics, such as availability requirements, number of NFV nodes and availability values of network components. The lowest-cost protection strategy, in terms of number of occupied network components, which meets availability requirement, is identified. Our analysis demonstrates that, in most cases, resource-greedy protection schemes, such as end-to-end protection, can be replaced by less aggressive schemes, even when availability requirements are in the order of five or six nines, depending on the number of elements in the service function chain

Minimizing equipment and energy cost in mixed 10G and 100G/200G filterless horseshoe networks with hierarchical OTN boards

Emerging 5G services are changing the way operators manage and optimize their optical metro networks, and the transmission technology and network design process must be tailored to the specific conditions in this segment of the network. Ensuring cost-efficient and energy-efficient network design requires novel approaches that optimize across all network layers. Therefore, to moderate the growth of operators’ expenses, in this paper, we investigate low-cost and energy-efficient cross-layer deployment of hierarchical optical transport network (OTN) boards minimizing equipment and energy consumption cost in mixed 10G and 100G/200G filterless metro networks. We propose an integer linear programming (ILP) model and a genetic algorithm (GA) approach that decide: (i) the node structure by deploying various stacked OTN boards (performing traffic-grooming at the electrical layer) and (ii) lightpath establishment considering coherent and non-coherent transmission technologies. Simulative results on real filterless horseshoe networks with real traffic matrices show that our proposed approaches achieve up to 50% cost savings compared to real-world benchmark deployments