Analytical and Numerical Evaluations of Flexible V-Band Rotman Lens Beamforming Network Performance for Conformal Wireless Subsystems

This paper presents the analytical design and numerical performance evaluation of novel V-band millimetre-wave (mm-wave) beamforming networks (BFNs), based on the Rotman lens array feeding concept. The devices are intended for operation in the unlicensed 60-GHz frequency band. The primary objective of this work is to study the feasibility of designing flexible V-band beamformers, based on liquid-crystal polymer (LCP) substrates. The planar Rotman lens device has been initially developed, and the output performances, in terms of the scattering parameters and accuracy, have been analysed. This is further continued with the detailed designs of the Rotman lens BFNs based on the four different proposed flexural cases, namely the concave-axial bending, the convex-axial bending, the concave-circumferential bending, and the convex-circumferential bending. Each of the flexures has been analysed, and the performance in terms of the surface currents and phase distributions, as the primary functionality indicators, has been presented. The presented flexible beamformers exhibit significant characteristics to be potentially employed as low-cost and efficient units of the mm-wave transceivers with the in-built electronic beam steering capabilities for the conformal wireless subsystems

Effectiveness Of Council Operations And Particiaption Of Inhabitants In Council Activities In Rural Mirabad, Isfahan Province Of Iran

This paper discusses council operation, public expectations of people from councils, public participation and coordination of people with councils also factors which affect this kind of participation. Public participation and coordination has been studied in Mirabad village of Tiran and Krun, 2 cities of Esfahan from Iran. This village is located 43 kilometers far from the west of Isfahan, Iran. Its population is more than 70 thousand persons and its climate is cold. The major occupation of its people is agriculture and animal husbandry in this region. The village\'s council members belong to the average class of community their educational range varies between having elementary school certificate to having licentiate\'s degree. Data gathered via questionnaire, interview and observation. 30 questionnaires were distributed among Mirabad\'s people and filled out. Most of the time, along with filling out the questionnaire, an interview was also accomplished and some observation were also made, The results show that council operations such as communicating with people, unanimity, sympathy and getting public trust were effective in receiving public participation and cooperation and also help the councils to do their tasks well. People have good participation in many fields like security maintenance, providing environmental hygiene and maintenance of public installations. The council\'s members also have good participation in agricultural affairs, providing security and hygiene and pursuing constructional plans. Keywords: participation, participation factors, councils operationJournal of Agriculture and Social Research Vol. 8 (1) 2008: pp. 106-11

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.Comment: Accepted to ECCV 2020. Code at: https://github.com/SSAW14/segmentation_membership_inferenc

A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids

Diffuse gliomas classified by 1p/19q co-deletion, TERT promoter and IDH mutation status are associated with specific genetic risk loci.

Recent genome-wide association studies of glioma have led to the discovery of single nucleotide polymorphisms (SNPs) at 25 loci influencing risk. Gliomas are heterogeneous, hence to investigate the relationship between risk SNPs and glioma subtype we analysed 1659 tumours profiled for IDH mutation, TERT promoter mutation and 1p/19q co-deletion. These data allowed definition of five molecular subgroups of glioma: triple-positive (IDH mutated, 1p/19q co-deletion, TERT promoter mutated); TERT-IDH (IDH mutated, TERT promoter mutated, 1p/19q-wild-type); IDH-only (IDH mutated, 1p/19q wild-type, TERT promoter wild-type); triple-negative (IDH wild-type, 1p/19q wild-type, TERT promoter wild-type) and TERT-only (TERT promoter mutated, IDH wild-type, 1p/19q wild-type). Most glioma risk loci showed subtype specificity: (1) the 8q24.21 SNP for triple-positive glioma; (2) 5p15.33, 9p21.3, 17p13.1 and 20q13.33 SNPs for TERT-only glioma; (3) 1q44, 2q33.3, 3p14.1, 11q21, 11q23.3, 14q12, and 15q24.2 SNPs for IDH mutated glioma. To link risk SNPs to target candidate genes we analysed Hi-C and gene expression data, highlighting the potential role of IDH1 at 2q33.3, MYC at 8q24.21 and STMN3 at 20q13.33. Our observations provide further insight into the nature of susceptibility to glioma

Integration of VR with BIM to facilitate real-time creation of bill of quantities during the design phase:a proof of concept study

As time goes on and building practices change, procedures that at one point seemed indispensable can fall by the wayside. One such example is the bill of quantities (B/Q). Research into the extant literature attributes declining use of B/Qs to a multitude of reasons, such as its complexity and potentially drawn-out time to produce, non-traditional procurement systems growing in popularity and the challenge of using its information in a construction schedule. With these issues in mind, a combined process of Building Information Modelling (BIM), Virtual Reality (VR) and including the client in the design process has been proposed as a potential solution. Following a literature review and precedent study, an experiment was carried out using this new process to simulate a client’s design decisions on window and interior furnishings, specifically. Their choices made using VR automatically updated a B/Q Revit Schedule and allowed the client to have a firm grasp on the project costs. Not only did this process give the client more confidence in a pleasing final outcome, but the technology ensured an up-to-date, accurate and easily understood B/Q. Here lies great potential savings in cost, time and gives the B/Q a newfound importance in future construction processes. The research case presented in this paper was a stepping stone in exploring new opportunities offered by VR and BIM and how they could improve the reliability and accuracy of traditional procurement within construction, specifically within the B/Q document

Design and Synthesis of Heterocyclic Cations for Specific DNA Recognition: From AT-Rich to Mixed-Base-Pair DNA Sequences

The compounds synthesized in this research were designed with the goal of establishing a new paradigm for mixed-base-pair DNA sequence-specific recognition. The design scheme starts with a cell-permeable heterocyclic cation that binds to AT base pair sites in the DNA minor groove. Modifications were introduced in the original compound to include an Hbond accepting group to specifically recognize the G-NH that projects into the minor groove. Therefore, a series of heterocyclic cations substituted with an azabenzimidazole ring has been designed and synthesized for mixed-base-pair DNA recognition. The most successful compound, 12a, had an azabenzimidazole to recognize G and additional modifications for general minor groove interactions. It binds to the DNA site −AAAGTTT− more strongly than the −AAATTT− site without GC and indicates the design success. Structural modifications of 12a generally weakened binding. The interactions of the new compound with a variety of DNA sequences with and without GC base pairs were evaluated by thermal melting analysis, circular dichroism, fluorescence emission spectroscopy, surface plasmon resonance, and molecular modeling

The NAMPT inhibitor FK866 reverts the damage in spinal cord injury

<p>Abstract</p> <p>Background</p> <p>Emerging data implicate nicotinamide phosphoribosyl transferase (NAMPT) in the pathogenesis of cancer and inflammation. NAMPT inhibitors have proven beneficial in inflammatory animal models of arthritis and endotoxic shock as well as in autoimmune encephalitis. Given the role of inflammatory responses in spinal cord injury (SCI), the effect of NAMPT inhibitors was examined in this setting.</p> <p>Methods</p> <p>We investigated the effects of the NAMPT inhibitor FK866 in an experimental compression model of SCI.</p> <p>Results</p> <p>Twenty-four hr following induction of SCI, a significant functional deficit accompanied widespread edema, demyelination, neuron loss and a substantial increase in TNF-α, IL-1β, PAR, NAMPT, Bax, MPO activity, NF-κB activation, astrogliosis and microglial activation was observed. Meanwhile, the expression of neurotrophins BDNF, GDNF, NT3 and anti-apoptotic Bcl-2 decreased significantly. Treatment with FK866 (10 mg/kg), the best known and characterized NAMPT inhibitor, at 1 h and 6 h after SCI rescued motor function, preserved perilesional gray and white matter, restored anti-apoptotic and neurotrophic factors, prevented the activation of neutrophils, microglia and astrocytes and inhibited the elevation of NAMPT, PAR, TNF-α, IL-1β, Bax expression and NF-κB activity.</p> <p>We show for the first time that FK866, a specific inhibitor of NAMPT, administered after SCI, is capable of reducing the secondary inflammatory injury and partly reduce permanent damage. We also show that NAMPT protein levels are increased upon SCI in the perilesional area which can be corrected by administration of FK866.</p> <p>Conclusions</p> <p>Our findings suggest that the inflammatory component associated to SCI is the primary target of these inhibitors.</p

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance