How Can Landscape Architecture Influence Systemic Change to Achieve Sustainable Cities and Regions

This paper presents how the ideas of landscape, design quality and drawings can influence systemic change to result in sustainable cities and regions. The research related to this paper explores project frameworks and design methods in order to reveal innovative ways and processes for creating environmentally friendly cities and regions that will have the power to adapt and mitigate climatic issues of the future. Through a series of explorations on existing landscape projects and while using a series of stakeholder engagement workshops contacted at a pan-European level the paper examines ways in which systemic change is possible and the outcomes it has in relation to the landscape. Using previously implemented and ongoing landscape projects such as the Room for the River (the Netherlands) and the West Midlands National Park (UK), the paper discusses how bold landscape-led visions influence decision making and support systemic change on a spatial scale. Drawing on experience gained during a series of stakeholder engagement workshops, where the projects of the Tame Valley Wetlands Partnership (UK) and the Urban Farming and Growing Network (UK) were selected as case studies, the research presents key findings and presents lessons learned that can build capacity and improve the understanding and management of stakeholders when it comes to spatial planning and urban design. The paper argues that a new way of thinking in design, policy or governance is not enough if these disciplines act individually. The breakthrough comes when each discipline collaborates with the aim to future proof our cities and regions. By presenting pioneering examples and models giving us tools for a systemic change, the paper aims to demonstrate that large scale developments can be brilliant examples of the new methodologies applied and lessons learnt. This research concludes that systemic change is represented across all levels, policy, decision making, governance, design and implementation if the aim is to deliver a sustainable city.

New dissidence in contemporary Russia: Students, feminism and new ethics

The essay is devoted to the specifics of the contemporary Russian opposition and civil society. We describe the characteristics of contemporary ‘intellectual activism’ and the growing network of small civil and political groups in today’s Russia. We show that Russian civil society remains fragile and fragmented; the public discussion is not focused on strategies of resistance to arbitrariness but on constructing moral categories such as the wide and vague concept of ‘new ethics’. We also show how outsiders appear among contemporary Russian dissidents, who are not supported by most independent leaders and intellectuals – these are young ‘new leftists’ and feminist activist groups. These political activists find themselves under pressure from both the siloviki and the authorities, and in the focus of criticism of opposition leaders, becoming, in fact, dissidents among dissidents in contemporary Russia.

Improving Security Using Modified S-Box for AES Cryptographic Primitives

The growing network traffic rate in wireless communication demands extended network capacity. Current crypto core methodologies are already reaching the maximum achievable network capacity limits. The combination of AES with other crypto cores and inventing new optimization models have emerged. In this paper, some of the prominent issues related to the existing AES core system, namely, lack of data rate, design complexity, reliability, and discriminative properties. In addition to that, this work also proposes a biometric key generation for AES core that constitutes simpler arithmetic such as substitution, modulo operation, and cyclic shifting for diffusion and confusion metrics which explore cipher transformation level. It is proved that in AES as compared to all other functions S-Box component directly influences the overall system performance both in terms of power consumption overhead, security measures, and path delay, etc.

Bioinformatics as Emerging Tool and Pipeline Frameworks

In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.

FDD LTE MIMO CLOSED-LOOP VS OPEN-LOOP PERFORMANCE EVALUATION IN COMMERCIAL NETWORK

With a growing network traffic Mobile Network Operators (MNO) looking for ways to increase network capacity and improve customer experience. One of the ways is to find the best parameters from the set defined by 3GPP. In the study, closed-loop MIMO was compared to open-loop MIMO on the LTE FDD network. Network performance was evaluated in 3 different scenarios: slow and fast-moving UE under different SINR levels and large scale on 2T2R and 4T4R cells. The result shows gains of using closed-loop and it is recommended to use it commercial LTE networks.

Traffic analysis for 5G network slice based on machine learning

With the rise of 5G and Internet of things, especially the key technology of 5G, network slice cuts a physical network into multiple virtual end-to-end networks, each of them can obtain logically independent network resources to support richer services. 5G mobile data and sensor data converge to form a growing network traffic. Traffic explosion evolved into a mixed network type, and network viruses, worms, network theft and malicious attacks are also involved. How to distinguish traffic types, block malicious traffic and make effective use of sensor data under the background of 5G network slice, and also the significance of this study.

Methanol-Driven Oxidative Rearrangement of Biogenic Furans – Enzyme Cascades vs. Photobiocatalysis

The oxidative ring expansion of bio-derived furfuryl alcohols to densely functionalized six-membered O-heterocycles represents an attractive strategy in the growing network of valorization routes to synthetic building blocks out of the lignocellulosic biorefinery feed. In this study, two scenarios for the biocatalytic Achmatowicz-type rearrangement using methanol as terminal sacrificial reagent have been evaluated, comparing multienzymatic cascade designs with a photo-bio-coupled activation pathway.

Reducing hERG Toxicity Using hERG Classification Model and Fragment-growing Network

Drug-induced cardiotoxicity has become one of the major reasons leading to drug withdrawal in past decades, which is closely related to the blockade of human Ether-a-go-go-related gene (hERG) potassium channel. Developing reliable hERG predicting model and optimizing model can greatly reduce the risk faced in drug discovery. In this study, we constructed eight hERG classification models, the best of which shows desirable generalization ability on low-similarity clinical compounds, as well as advantages in perceiving activity gap caused by small structural changes. Furthermore, we developed a hERG optimizer based on fragment grow strategy and explored its usage in four cases. After reinforcement learning, our model successfully suggests same or similar compounds as chemists’ optimization. Results suggest that our model can provide reasonable optimizing direction to reduce hERG toxicity when hERG risk is corresponding to lipophilicity, basicity, the number of rotatable bonds and pi-pi interactions. Overall, we demonstrate our model as a promising tool for medicinal chemists in hERG optimization attempts.

Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector

Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.