A fluid-driven soft robotic fish inspired by fish muscle architecture

Artificial fish-like robots developed to date often focus on the external morphology of fish and have rarely addressed the contribution of the structure and morphology of biological muscle. However, biological studies have proven that fish utilize the contraction of muscle fibers to drive the protective flexible connective tissue to swim. This paper introduces a pneumatic silicone structure prototype inspired by the red muscle system of fish and applies it to the fish-like robot named Flexi-Tuna. The key innovation is to make the fluid-driven units simulate the red muscle fiber bundles of fish and embed them into a flexible tuna-like matrix. The driving units act as muscle fibers to generate active contraction force, and the flexible matrix as connective tissue to generate passive deformation. Applying alternant pressure to the driving units can produce a bending moment, causing the tail to swing. As a result, the structural design of Flexi-Tuna has excellent bearing capacity compared with the traditional cavity-type and keeps the body smooth. On this basis, a general method is proposed for modeling the fish-like robot based on the independent analysis of the active and passive body, providing a foundation for Flexi-Tuna’s size design. Followed by the robot’s static and underwater dynamic tests, we used finite element static analysis and fluid numerical simulation to compare the results. The experimental results showed that the maximum swing angle of the tuna-like robot reached 20°, and the maximum thrust reached 0.185 N at the optimum frequency of 3.5 Hz. In this study, we designed a unique system that matches the functional level of biological muscles. As a result, we realized the application of fluid-driven artificial muscle to bionic fish and expanded new ideas for the structural design of flexible bionic fish.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

Tuxedo: Maximizing Smart Contract Computation in PoW Blockchains

Proof-of-Work (PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time (I) between blocks for validating smart contracts present in transactions. In such systems, block validation and PoW mining are typically performed sequentially, the former by CPUs and the latter by ASICs. A trivial increase in validation time (τ) introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and hurts fairness. Large τ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust and performance issues that are non-trivial to resolve. In this paper, we present Tuxedo, the first on-chain protocol to theoretically scale τ/I ≈1 in PoW blockchains. The key innovation in Tuxedo is to perform CPU-based block processing in parallel to ASIC mining. We achieve this by allowing miners to delay validation of transactions in a block by up to ζ blocks, where ζ is a system parameter. We perform security analysis of Tuxedo considering all possible adversarial strategies in a synchronous network with maximum end-to-end delay Δ and demonstrate that Tuxedo achieves security equivalent to known results for longest chain PoW Nakamoto consensus. Our prototype implementation of Tuxedo atop Ethereum demonstrates that it can scale τ without suffering the harmful effects of naive scaling up of τ/I in existing blockchains

Evolution of RGF/GLV/CLEL Peptide Hormones and Their Roles in Land Plant Growth and Regulation

Rooting is a key innovation during plant terrestrialization. RGFs/GLVs/CLELs are a family of secreted peptides, playing key roles in root stem cell niche maintenance and pattern formation. The origin of this peptide family is not well characterized. RGFs and their receptor genes, RGIs, were investigated comprehensively using phylogenetic and genetic analyses. We identified 203 RGF genes from 24 plant species, representing a variety of land plant lineages. We found that the RGF genes originate from land plants and expand via multiple duplication events. The lineage-specific RGF duplicates are retained due to their regulatory divergence, while a majority of RGFs experienced strong purifying selection in most land plants. Functional analysis indicated that RGFs and their receptor genes, RGIs, isolated from liverwort, tomato, and maize possess similar biological functions with their counterparts from Arabidopsis in root development. RGFs and RGIs are likely coevolved in land plants. Our studies shed light on the origin and functional conservation of this important peptide family in plant root development.

Enabling Surveillance Cameras to Navigate

Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians; however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this article, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smartphones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68 m, outperforming the state-of-the-art systems by >75%.

The Index of Evidence: speculative methodologies in response to the post-truth era

The past year has shown that even the fundamental idea of ‘evidence’ – in health contexts, but also more broadly - is coming under increasing strain. This open letter argues that the current crises of evidence and knowledge in which we find ourselves demands new speculative methodologies. It introduces the Index of Evidence – a Beacon Project funded by Exeter University’s Wellcome Centre for Cultures and Environments of Health - as one example of such a methodology, outlining its theoretical foundations and process. The key innovation of this project is to rethink the form and presentation that research can take. Using the conceptual and material affordances of the index, it merges the creative and critical in ways that aim to make an important contribution to more inter-connected, theoretically sophisticated thinking around evidence.

The operational properties of linguistic interval valued q-Rung orthopair fuzzy information and its VIKOR model for multi-attribute group decision making

As a generalization of linguistic q-rung orthopair fuzzy set (Lq-ROFS), linguistic interval valued q-Rung orthopair fuzzy set (LIVq-ROFS) is a new concept to deal with complex and uncertain decision making problems which Lq-ROFS cannot handle. Due to the lack of information in decision making process, decision makers mostly prefer to give their preferences in interval form rather than a crisp number. In this situations, LIVq-ROFS appears up as a useful tool. In this work, we define operational laws of LIVq-ROFS and prove some properties. Furthermore, we propose the conception of the LIVq-ROF weighted averaging operator and give its formula by mathematical induction. To compare two or more linguistic interval valued q-Rung orthopair fuzzy numbers (LIVq-ROFNs), the improved form of score function is also given. Considering the powerfulness of LIVq-ROFSs handling ambiguity and complex uncertainty in practical problems, the key innovation of this paper is to develop the linguistic interval-valued q-rung orthopair fuzzy VIKOR model that is significantly different from the existing VIKOR methodology. The computing steps of this newly created model are briefly presented. Finally, the effectiveness of model is verified by an example and through comparative analysis, the superiority of VIKOR method is further illustrated.

An Integrated Process Analytical Platform for Automated Monitoring of Monoclonal Antibody N-linked Glycosylation

The biopharmaceutical industry is transitioning towards adoption of continuous biomanufacturing practices that are often more flexible and efficient than traditional batch processes. Regulatory agencies such as the Food and Drug Administration (FDA) are further urging use of advanced PAT to analyze the design space to increase process knowledge and enable high quality biologics production. Post-translational modification of proteins, such as N-linked glycosylation are often critical quality attributes known to affect biologics safety and efficacy hence requiring close monitoring during manufacturing. Here, we developed an online sequential-injection based PAT system, called N-GLYcanyzer, that can rapidly monitor mAb glycosylation during upstream biomanufacturing. The key innovation includes design of an integrated mAb sampling and derivation system for antibody titer and glycoform analysis in under 2 hours. The N-GLYcanyzer process includes mAb capture, deglycosylation, fluorescent glycan labeling, and glycan enrichment for direct injection and analysis on an integrated high performance liquid chromatography (HPLC) system. Different fluorescent tags and reductants were tested to maximize glycan labeling efficiency under aqueous conditions, while porous graphitized carbon (PGC) was studied for optimum glycan recovery and enrichment. We find that 2-AB labeling of glycans with 2-picoline borane as a reducing agent, using the N-GLYcanyzer workflow, gives higher glycan labeling efficiency under aqueous conditions leading to upwards of a 5-fold increase in fluorescent products intensity. Finally, we showcase how the N-GLYcanyzer platform can be implemented at/on-line to an upstream bioreactor for automated and near real-time glycosylation monitoring of a Trastuzumab biosimilar produced by Chinese Hamster Ovary (CHO) cells.

Low-cost Large Scale Vermicompost Unit

In the current process of producing vermicompost on a large-scale, the main challenge is to keep the worms alive. This is achieved by maintaining temperature and moisture in their living medium. It is a difficult task to maintain these parameters throughout the process. Currently, this is achieved by building infrastructure but this method requires a large initial investment and long-run maintenance. Also, these methods are limited to small-scale production. For large-scale production, a unit is developed which utilises natural airflow with water and automation. The main aim of this unit is to provide favourable conditions to worms in large-scale production with very low investment and minimum maintenance in long term. The key innovation of this research is that the technology used in the unit should be practical and easy to adopt by small farmers. For long-term maintenance of the technology lesser number of parts are used.

THE TENSION BETWEEN THE REAL AND THE PAPER DEAL CONCERNING “NO ORAL MODIFICATION” CLAUSES

This article proposes a new model for the regulation of no oral modification (NOM) clauses. First, the article seeks to offer a deeper understanding of the wishes of the parties in contracts from the perspective of parties' autonomy, distinguishing between intentions focused on the legal relationships and those focused on extra-contractual relations. Second, we explain how enforcement of NOM clauses may influence the parties' relations. Third, the article includes an economic analysis clarifying the roles of efficiency and institutional considerations in the NOM phenomenon. Applying the results of our analysis, we propose a comprehensive model for regulating NOM clauses. The key innovation of the model is context-dependent regulation differentiating among sophisticated and equally powerful parties, unsophisticated parties of equal power, and relationships with power disparities. Our model also offers an auxiliary test to help distinguish between parties' legal relationships and their extra-contractual relations.