Neuroimmunology: An expanding frontier in 21st century neurology

The recent discovery of functional lymphatic vessels lining the dural sinuses shattered the long held view of the absence of CNS lymphatic vas culature and provided solid neuroanatomical ground for Neuroimmunology1. Moreover, it has been shown that there is the presence of what is known as the inflammatory reflex

Energy efficiency in heterogeneous wireless access networks

In this article, we bring forward the important aspect of energy savings in wireless access networks. We specifically focus on the energy saving opportunities in the recently evolving heterogeneous networks (HetNets), both Single- RAT and Multi-RAT. Issues such as sleep/wakeup cycles and interference management are discussed for co-channel Single-RAT HetNets. In addition to that, a simulation based study for LTE macro-femto HetNets is presented, indicating the need for dynamic energy efficient resource management schemes. Multi-RAT HetNets also come with challenges such as network integration, combined resource management and network selection. Along with a discussion on these challenges, we also investigate the performance of the conventional WLAN-first network selection mechanism in terms of energy efficiency (EE) and suggest that EE can be improved by the application of intelligent call admission control policies

Control and data channel resource allocation in OFDMA heterogeneous networks

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macro cells and small cells sharing the same frequency band. Dense deployment of small cells overlaid by a macro layer is considered to be one of the most promising solutions for providing hotspot coverage in future 5G networks. The focus is to devise an optimised policy for small cells’ access to the shared spectrum, in terms of their transmissions, in order to keep small cell served users sum data rate at high levels while ensuring that certain level of quality of service (QoS) for the macro cell users in the vicinity of small cells is provided. Both data and control channel constraints are considered, to ensure that not only the macro cell users’ data rate demands are met, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. Control channel reliability is especially important as it holds key information to successfully decode the data channel. The problem is addressed by our proposed linear binary integer programming heuristic algorithm which maximises the small cells utility while ensuring the macro users imposed constraints. To further reduce the computational complexity, we propose a progressive interference aware low complexity heuristic solution. Discussion is also presented for the implementation possibility of our proposed algorithms in a practical network. The performance of both the proposed algorithms is compared with the conventional Reuse-1 scheme under different fading conditions and small cell loads. Results show a negligible drop in small cell performance for our proposed schemes, as a trade-off for ensuring all macro users data rate demands, while Reuse-1 scheme can even lead up to 40 % outage when control region of the small cells in heavily loaded

Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

A SON Solution for Sleeping Cell Detection Using Low-Dimensional Embedding of MDT Measurements

Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering an alarm for Operation and Maintenance (O&M) entity. Consequently, no SON compensation function can be launched unless SC situation is detected via drive tests or through complaints registered by the affected customers. In this paper, we present a novel solution to address this problem that makes use of minimization of drive test (MDT) measurements recently standardized by 3GPP and NGMN. To overcome the processing complexity challenge, the MDT measurements are projected to a low-dimensional space using multidimensional scaling method. Then we apply state of the art k-nearest neighbor and local outlier factor based anomaly detection models together with pre-processed MDT measurements to profile the network behaviour and to detect SC. Our numerical results show that our proposed solution can automate the SC detection process with 93 accuracy

Neuroimmunology diagnostics

Neuroimmunology has led to advanced diagnostics of previously cryptic disorders, using autoantibody testing against neurological targets. Neuropsychiatric syndromes and autoimmune encephalitis can now be routinely diagnosed using specialized antibody tests such as immunofluorescence and immunoblot assays in specialized laboratories. This helps in early and accurate diagnosis, leading to favorable patient prognosis. Here, we briefly review the diagnostics for Neuroimmunologic and related disorders including autoimmune encephalitis, demyelinating diseases, neuropathies, paraneoplastic syndromes, stiffperson syndrome, inflammatory myopathies as well as Alzheimer’s disease

Development of the Flight Dynamic Model (FDM) Using Computational Fluid Dynamic (CFD) Simulations for an Unknown Aircraft

The usage of computational fluid dynamics (CFD) has enhanced 10-fold since the last decade, especially in the area of aerospace science. In this chapter, we will focus on determining the feasibility and validity of CFD results that are plugged in flight dynamic model (FDM) to that of actual flight of an aircraft. Flight data of an actual aircraft is used to determine the aerodynamic performance of the designed FDM. In addition to this, FDM consist of various systems integration of an aircraft; however, this study will focus on aerodynamic parameter optimization. Relative analysis is carried out to validate the FDM. This will enable readers to know how CFD can be a great tool for designing FDM of an unknown aircraft

Clippers (chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids)–case report with neuroimaging

Background: CLIPPERS syndrome (chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids) is an inflammatory disease process primarily involving the pons and adjacent structures. Clinically, the patient may present with cerebellar signs such as dysarthria, gait ataxia and with cranial nerve palsies. It shows good response to steroids / immunosuppressive therapy. Pathologically, there is infiltration of T lymphocytes into the perivascular spaces of brainstem. The disease follows a relapsing and a remitting course and the earlier the treatment is started with high dose steroids and the more prolonged it is, the better the clinical outcome will be

Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance

In the era of industrialization, environmentalists are more concerned with the environment and so are continuously interested in investigating organizational factors that can facilitate the transition towards sustainability. This research systematically investigates the impact of the supply chain partner’s collaborative approach towards green practices on a firm’s sustainability performance. Stakeholder and coordination theories are used to underpin the study. A structural equation modeling technique is adopted to analyze data collected from 126 green supply chain professionals working at various manufacturing firms operating in Pakistan using a survey questionnaire. The results indicate significant and positive impacts of institution pressure and customer monitoring on the adoption of green supply chain management (GSCM) practices by organizations. This study also explains that organizational GSCM practices, external GSCM practices and performance measures have positive and significant relationships. These findings reveal that it is important for managers to address external GSCM pressures by adopting green practices and being a focal firm should undertake GSCM initiatives in collaboration with their suppliers and customers to achieve a holistic impact which ultimately leads to betterment in overall sustainability performance