Ultrafast Magnetization Dynamics in Diluted Magnetic Semiconductors

We present a dynamical model that successfully explains the observed time evolution of the magnetization in diluted magnetic semiconductor quantum wells after weak laser excitation. Based on the pseudo-fermion formalism and a second order many-particle expansion of the exact p-d exchange interaction, our approach goes beyond the usual mean-field approximation. It includes both the sub-picosecond demagnetization dynamics and the slower relaxation processes which restore the initial ferromagnetic order in a nanosecond time scale. In agreement with experimental results, our numerical simulations show that, depending on the value of the initial lattice temperature, a subsequent enhancement of the total magnetization may be observed within a time scale of few hundreds of picoseconds.Comment: Submitted to PR

An overview of sustainable practices in food processing supply chain environments

Climate change has been a great challenge that the world is facing, it is a menace to the society and it is causing more damage than expected. The researchers are working tirelessly to reduce its impact on the planet in order to save the future. Mitigation of greenhouse gas emission and other sustainable practices is encouraged every day to make the world a better place to live Sustainable practice has been identified as one of the major tools to control this greenhouse gas emission especially in the emergent nations where industrialization is now growing rapidly. This paper discusses and analyzes the food security and food processing industry in the emergent nations. It also reviews literature on food processing, supply chain environments, sustainability and sustainable practices in relation to how these could help in promoting the sustainable development and environmental protection goals in the emergent nations

Benefit of irrigation water transfers in the National River Linking Project: a case study of the Ken-Betwa link

River basin managementRiver basin developmentDevelopment projectsWater transferIrrigation waterCrop productionCrop managementLivestock

Policy-based QoS management framework for software-defined networks

With the emerging trends of virtualization of cloud computing and big data applications, network management has become a challenging problem for optimizing the network state while satisfying the applications’ Quality of Service (QoS) requirements. This paper proposes a policy-based management framework over Software-Defined Networks (SDN) for QoS provisioning. The proposed approach monitors the QoS parameters of the active flows and dynamically enforces new decisions on the underlying SDN switches to adapt the network state to the current demanded high-level policies. Moreover, the proposed solution makes use of Neural Networks to identify the violating flows causing the network congestion. Upon detection of a policy violation two route management techniques are implemented, such as: rerouting and rate limiting. The proposed framework was implemented and evaluated within an experimental test bed setup. The results indicate that the proposed PBNM-based SDN framework enables QoS provisioning and outperforms the default SDN in terms of throughput, packet loss rate and latency

LearnQoS: a learning approach for optimizing QoS over multimedia-based SDNs

As video-based services become an integral part of the end-users’ lives, there is an imminent need for increase in the backhaul capacity and resource management efficiency to enable a highly enhanced multimedia experience to the endusers. The next-generation networking paradigm offers wide advantages over the traditional networks through simplifying the management layer, especially with the adoption of Software Defined Networks (SDN). However, enabling Quality of Service (QoS) provisioning still remains a challenge that needs to be optimized especially for multimedia-based applications. In this paper, we propose LearnQoS, an intelligent QoS management framework for multimedia-based SDNs. LearnQoS employs a policy-based network management (PBNM) to ensure the compliance of QoS requirements and optimizes the operation of PBNM through Reinforcement Learning (RL). The proposed LearnQoS framework is implemented and evaluated under an experimental setup environment and compared with the default SDN operation in terms of PSNR, MOS, throughput and packet loss

The Effect of Block Ownership on Future Firm Value and Performance

This paper examines the performance of the investment decisions of block owners. The block ownership data is obtained from Dlugosz, Fahlenbrach, Gompers, and Metrick (2006). We find that firm valuation (measured by Tobin's Q), operating performance (measured by changes in return on assets) and stock performance (measured by excess buy and hold returns) are positively and significantly related to the previous years' level of block ownership both in terms of the size of the ownership and the number of blockholders. Our results are robust to endogeneity concerns. Regarding whether a specific blockholder is an “insider” or an “outsider” to the firm, we find that the ownership of “outside” blockholders is a key determinant in explaining future firm performance. Note though that this category makes up about two-thirds of the aggregate amount of blockholding in Dlugosz et al. (2006) database, and also includes all blockholders not classified in other categories. In general, we attribute the superior performance to the presence of more blockholders. We also find an inverse association between the volatility in blockownership and the ex-post firm performance measures

Dissipative dynamics of an extended magnetic nanostructure: Spin necklace in a metallic environment

We study theoretically the dynamics of an ``xxz'' spin necklace coupled to a conduction electron sea, a model system for a nanostructure in a dissipative environment. We extract the long-time behavior via a mapping to a multichannel Coulomb gas problem followed by a scaling analysis. The strong quantum fluctuations of the necklace cause a nontrivial dependence of couplings on system size which we extract via an analysis involving the ``boundary condition changing operator'', and confirm via a detailed numerical evaluation of one case.Comment: 4 pages, 4 figure

Performance evaluation of routing strategies over multimedia-based SDNs under realistic environments

Most of the existing performance evaluation studies of various routing algorithms are done under limited experimental setups leading to an incomplete picture of the routing algorithm performance under dynamic network conditions. This paper presents a study that compares state-of-the-art routing algorithms over realistic multimedia-based Software Defined Networks (SDNs) with dynamic network conditions and various topology. Routing algorithms remain a key element of the networking landscape as they determine the path the data packets follow. The next-generation networking paradigm offers wide advantages over traditional networks through simplifying the management layer, especially with the adoption of SDN. However, Quality of Service (QoS) provisioning still remains a challenge that needs to be investigated especially for multimedia-based SDNs. This study investigates the impact of state-of-the-art centralized routing algorithms (e.g. MHA, WSP, SWP, MIRA) on multimedia QoS traffic u nder a realistic environment in terms of PSNR, Throughput, Packet Loss, Delay and QoS rejection

An innovative reinforcement learning-based framework for quality of service provisioning over multimedia-based SDN environments

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimediarich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks. Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches

REDO: a reinforcement learning-based dynamic routing algorithm selection method for SDN

The current increase in the Internet traffic along with the global crisis have accelerated the roll-out of the next generation 5G network and key enabling technologies. In this context, addressing the end-to-end Quality of Service (QoS) provisioning in order to guarantee a sustainable service delivery to the end-users became of paramount importance. Some of the enabling technologies that could play a key role in this regard are Software Defined Network (SDN) and Machine Learning (ML). This paper proposes REDO, a Reinforcement lEarning-based Dynamic rOuting algorithm selection method that decides on the conventional routing algorithm to be applied on the traffic flows within a SDN environment. REDO will dynamically select the most appropriate routing algorithm from a set of centralized routing algorithms (MHA, WSP, SWP, MIRA) that maximizes the reward function from the network. The proposed REDO solution is implemented and evaluated using an experimental setup based on Mininet, Floodlight controller and Open vSwitch switches. The results show that REDO outperforms other state-of-the-art solutions