Altered Neurocircuitry in the Dopamine Transporter Knockout Mouse Brain

The plasma membrane transporters for the monoamine neurotransmitters dopamine, serotonin, and norepinephrine modulate the dynamics of these monoamine neurotransmitters. Thus, activity of these transporters has significant consequences for monoamine activity throughout the brain and for a number of neurological and psychiatric disorders. Gene knockout (KO) mice that reduce or eliminate expression of each of these monoamine transporters have provided a wealth of new information about the function of these proteins at molecular, physiological and behavioral levels. In the present work we use the unique properties of magnetic resonance imaging (MRI) to probe the effects of altered dopaminergic dynamics on meso-scale neuronal circuitry and overall brain morphology, since changes at these levels of organization might help to account for some of the extensive pharmacological and behavioral differences observed in dopamine transporter (DAT) KO mice. Despite the smaller size of these animals, voxel-wise statistical comparison of high resolution structural MR images indicated little morphological change as a consequence of DAT KO. Likewise, proton magnetic resonance spectra recorded in the striatum indicated no significant changes in detectable metabolite concentrations between DAT KO and wild-type (WT) mice. In contrast, alterations in the circuitry from the prefrontal cortex to the mesocortical limbic system, an important brain component intimately tied to function of mesolimbic/mesocortical dopamine reward pathways, were revealed by manganese-enhanced MRI (MEMRI). Analysis of co-registered MEMRI images taken over the 26 hours after introduction of Mn^(2+) into the prefrontal cortex indicated that DAT KO mice have a truncated Mn^(2+) distribution within this circuitry with little accumulation beyond the thalamus or contralateral to the injection site. By contrast, WT littermates exhibit Mn^(2+) transport into more posterior midbrain nuclei and contralateral mesolimbic structures at 26 hr post-injection. Thus, DAT KO mice appear, at this level of anatomic resolution, to have preserved cortico-striatal-thalamic connectivity but diminished robustness of reward-modulating circuitry distal to the thalamus. This is in contradistinction to the state of this circuitry in serotonin transporter KO mice where we observed more robust connectivity in more posterior brain regions using methods identical to those employed here

PLL-less three-phase droop-controlled inverter with inherent current-limiting property

In this paper, a novel droop control method for three-phase grid-connected inverters is proposed to guarantee closed-loop system stability and an inherent current-limiting property without the need of a PLL. The inverter is connected to the grid via a filter and a line. Based on the synchronously rotating dq frame modelling and nonlinear ultimate boundedness theory, it is analytically proven that the proposed control scheme maintains the inverter current below a certain upper bound. This current limitation is guaranteed independently of the grid, line and filter parameters; thus increasing the controller robustness. In addition, asymptotic stability of the desired equilibrium point of the closed-loop system is guaranteed under different values of the proposed controller gain. To verify the effectiveness of the proposed nonlinear control strategy, extensive simulations are realized using Matlab/Simulink, where both the stability and the current-limiting property of the controller are validated

Three-phase grid-connected inverters equipped with nonlinear current-limiting control

Voltage source inverters are essential devices to integrate renewable energy sources to the main grid and control the injection of real and reactive power. Due to their inherent nonlinear dynamics, the stability and particularly the current limitation of power controlled inverters represent challenging tasks under grid variations or unrealistic power demands. In this paper, using the synchronously rotating dq transformation, a nonlinear current limiting controller is proposed for threephase inverters connected to the grid through an LCL filter. The proposed controller introduces a cascaded control structure with inner current and voltage control loops and an outer power controller that includes a droop function to support the grid and rigorously guarantee a limit for the grid currents. Using nonlinear closed-loop system analysis and based on input-to-state stability theory, the limits for the d- and q-axis grid currents are proven independently from each other without adding any saturation units into the system that can lead to instability. Extensive simulation results of the proposed nonlinear currentlimiting controller are provided to demonstrate its effectiveness and current-limiting property

Poster Abstract: Towards Scalable and Trustworthy Decentralized Collaborative Intrusion Detection System for IoT

An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically work collaboratively to build intelligence of holistic network representation, which is referred to as Collaborative Intrusion Detection System (CIDS). However, developing an effective CIDS, particularly for the IoT ecosystem raises several challenges. Recent trends and advances in blockchain technology, which provides assurance in distributed trust and secure immutable storage, may contribute towards the design of effective CIDS. In this poster abstract, we present our ongoing work on a decentralized CIDS for IoT, which is based on blockchain technology. We propose an architecture that provides accountable trust establishment, which promotes incentives and penalties, and scalable intrusion information storage by exchanging bloom filters. We are currently implementing a proof-of-concept of our modular architecture in a local test-bed and evaluate its effectiveness in detecting common attacks in IoT networks and the associated overhead.Comment: Accepted to ACM/IEEE IoTDI 202

Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.Comment: Accepted in SmartComp 202

Trust and Reputation Management for Blockchain-enabled IoT

In recent years, there has been an increasing interest in incorporating blockchain for the Internet of Things (IoT) to address the inherent issues of IoT, such as single point of failure and data silos. However, blockchain alone cannot ascertain the authenticity and veracity of the data coming from IoT devices. The append-only nature of blockchain exacerbates this issue, as it would not be possible to alter the data once recorded on-chain. Trust and Reputation Management (TRM) is an effective approach to overcome the aforementioned trust issues. However, designing TRM frameworks for blockchain-enabled IoT applications is a non-trivial task, as each application has its unique trust challenges with their unique features and requirements. In this paper, we present our experiences in designing TRM framework for various blockchain-enabled IoT applications to provide insights and highlight open research challenges for future opportunities.Comment: COMSNETS 2023 Invited Pape

Ripples in a pond: Do social work students need to learn about terrorism?

In the face of heightened awareness of terrorism, however it is defined, the challenges for social work are legion. Social work roles may include working with the military to ensure the well-being of service-men and women and their families when bereaved or injured, as well as being prepared to support the public within the emergency context of an overt act of terrorism. This paper reviews some of the literature concerning how social work responds to confl ict and terrorism before reporting a smallscale qualitative study examining the views of social work students, on a qualifying programme in the UK, of terrorism and the need for knowledge and understanding as part of their education