Degree Of Redundancy Research Articles

Proposing a matrix model in fog computing for expanding the efficiency of computing frameworks

Fog computing systems, by design, have a high degree of redundancy in terms of computing and storage resources. To make the most of this redundancy, coding techniques can be used to transform the data being transferred and processed. Two specific coding ideas that have been proposed for use in fog computing are minimum bandwidth codes and minimum latency codes. These codes have the potential to significantly reduce data transfer capacity utilization and computation inactivity. Minimum bandwidth codes focus on reducing the amount of data that needs to be transferred, while minimum latency codes focus on reducing the amount of time it takes for data to be processed. These two coding methods can be combined in a linked coding structure that allows for a trade-off between computation inactivity and communication load, leading to improved system performance. Fog computing has been proposed as a solution to meet the management requirements of the growing Internet of Things (IoT) and smart networks. This research breaks down the capabilities and challenges of fog computing in smart networks, presents the state-of-theart in the field, defines a fog computing-based smart grid, and provides a use case scenario for the proposed model.

가변 작업에 대한 분산 구동 메커니즘의 다목적 최적화 기반 궤적 계획

The distributed actuation mechanism is a redundant manipulator that improves the performance of an end-effector (velocity and force) by relocating the actuating position of the slider along the link without changing its posture. However, due to the higher degree of redundancy, it is difficult for trajectory planning. This study presented a trajectory planning of the distributed actuation mechanism for variable tasks through maximum performance evaluation based on multi-objective optimization. First, a multi-objective optimization was formulated to evaluate the maximum end-effector performance for targeting a three-link manipulator based on the distributed actuation mechanism (DAM-3R). For comparison, an equivalent joint actuation mechanism-based three-link manipulator (JAM-3R) was developed, and numerical simulation was performed by connecting the multi-body dynamics analysis module and the optimization module. Based on the maximum performance evaluation result, trajectory planning was performed for a given variable payload task. Simulation results confirmed that the DAM-3R was faster and can lift larger payloads than the JAM-3R. In addition, by optimally utilizing the higher degrees redundancy of the DAM-3R, the energy consumption of the DAM-3R was 64.7% lower than that of the JAM-3R. These results indicate that the proposed method effectively performed the trajectory planning for the variable payload task for the redundant manipulators.

Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson's Disease Speech Data.

As a neurodegenerative disease, Parkinson’s disease (PD) is hard to identify at the early stage, while using speech data to build a machine learning diagnosis model has proved effective in its early diagnosis. However, speech data show high degrees of redundancy, repetition, and unnecessary noise, which influence the accuracy of diagnosis results. Although feature reduction (FR) could alleviate this issue, the traditional FR is one-sided (traditional feature extraction could construct high-quality features without feature preference, while traditional feature selection could achieve feature preference but could not construct high-quality features). To address this issue, the Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model (HBD-SFREM) is proposed in this paper. The major contributions of HBD-SFREM are as follows: (1) The instance space of the deep hierarchy is built by an iterative deep extraction mechanism. (2) The manifold features extraction method embeds the nearest neighbor feature preference method to form the dual-stage feature reduction pair. (3) The dual-stage feature reduction pair is iteratively performed by the AdaBoost mechanism to obtain instances features with higher quality, thus achieving a substantial improvement in model recognition accuracy. (4) The deep hierarchy instance space is integrated into the original instance space to improve the generalization of the algorithm. Three PD speech datasets and a self-collected dataset are used to test HBD-SFREM in this paper. Compared with other FR algorithms and deep learning algorithms, the accuracy of HBD-SFREM in PD speech recognition is improved significantly and would not be affected by a small sample dataset. Thus, HBD-SFREM could give a reference for other related studies.

Rapid and safe wire tension distribution scheme for redundant cable-driven parallel manipulators

AbstractA non-iterative analytical approach is investigated to plan the safe wire tension distribution along with the cables in the redundant cable-driven parallel robots. The proposed algorithm considers not only tracking the desired trajectory but also protecting the system against possible failures. This method is used to optimize the non-negative wire tensions through the cables which are constrained based on the workspace conditions. It also maintains both actuators’ torque and cables’ tensile strength boundary limits. The pseudo-inverse problem solution leads to an n-dimensional convex problem, which is related to the robot degrees of redundancy. In this paper, a comprehensive solution is presented for a 1–3 degree(s) of redundancy in wire-actuated robots. To evaluate the effectiveness of this method, it is verified through an experimental study on the RoboCab cable robot in the infinity trajectory tracking task. As a matter of comparison, some standard methods like Active-set and sequential quadratic programming are also presented and the average elapsed time for each method is compared to the proposed algorithm.

Input redundancy: Definitions, taxonomy, characterizations and application to over-actuated systems

For input redundant systems, it is possible to construct distinct inputs producing identical outputs, from the same initial state. Thus, a system is input redundant if it is not left-invertible. In this paper, a rigorous framework is proposed to support this new definition. From the observation that state trajectories induced by the two inputs can be identical or not, an enriched taxonomy of input redundancy is also designed. A comprehensive set of characterizations is associated with the proposed definitions. A degree of redundancy is also conceived. This allows to carry out a thorough comparison with the state-of-the-art. Finally, the fact that over-actuated systems are input redundant is formally proved. This fact leads to a control design framework which can easily cope with over-actuated systems equipped with dynamical actuators.

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Environmental Efficiency Evaluation of China's Power Industry Based on the Two-Stage Network Slack-Based Measure Model.

How to achieve the continuous improvement of the environmental performance level of the power industry within the requirements of clean and low-carbon energy development is the fundamental requirement and inevitable choice for the construction of ecological civilization and sustainable development. From the perspective of environmental protection, based on the Data Envelopment Analysis (DEA) method and the internal mechanism of power system production and supply, the power industry environmental efficiency evaluation index system was constructed, and the two-stage Network Slack-based Measure (NSBM) model considering undesired output was used to calculate China’s 30 provinces and municipalities from 1998 to 2019. The environmental efficiency is divided into two links: power generation efficiency and transmission and distribution efficiency. The study found that, within the research interval, the overall environmental efficiency of China’s 30 provinces is low, and the differences between provinces and cities are large, but they have gradually developed in a better direction after 2015. The power generation efficiency of the first link in most provinces and municipalities is higher than the transmission and distribution efficiency of the second link, and the low transmission and distribution efficiency is an important reason for the low comprehensive level of environmental efficiency. The overall evolution trend of environmental efficiency in the six regions of China is roughly the same, but the regional differences are obvious, showing a trend of “high in the southeast and low in the northwest”. The economic and natural resource differences in different provinces and cities in each region have led to varying degrees of redundancy in five aspects, including investment in power assets, installed power generation capacity, and length of transmission lines, which seriously affect the environmental efficiency of the power industry. This research attempts to open the “black box” of the environmental efficiency conversion process of the power industry, which can provide directions and strategic suggestions for the improvement of the efficiency of the power industry in China.

Multidecadal changes in functional diversity lag behind the recovery of taxonomic diversity.

While there has been increasing interest in how taxonomic diversity is changing over time, less is known about how long‐term taxonomic changes may affect ecosystem functioning and resilience. Exploring long‐term patterns of functional diversity can provide key insights into the capacity of a community to carry out ecological processes and the redundancy of species’ roles. We focus on a protected freshwater system located in a national park in southeast Germany. We use a high‐resolution benthic macroinvertebrate dataset spanning 32 years (1983–2014) and test whether changes in functional diversity are reflected in taxonomic diversity using a multidimensional trait‐based approach and regression analyses. Specifically, we asked: (i) How has functional diversity changed over time? (ii) How functionally distinct are the community's taxa? (iii) Are changes in functional diversity concurrent with taxonomic diversity? And (iv) what is the extent of community functional redundancy? Resultant from acidification mitigation, macroinvertebrate taxonomic diversity increased over the study period. Recovery of functional diversity was less pronounced, lagging behind responses of taxonomic diversity. Over multidecadal timescales, the macroinvertebrate community has become more homogenous with a high degree of functional redundancy, despite being isolated from direct anthropogenic activity. While taxonomic diversity increased over time, functional diversity has yet to catch up. These results demonstrate that anthropogenic pressures can remain a threat to biotic communities even in protected areas. The differences in taxonomic and functional recovery processes highlight the need to incorporate functional traits in assessments of biodiversity responses to global change.

The hidden legacy of megafaunal extinction: Loss of functional diversity and resilience over the Late Quaternary at Hall’s Cave

AbstractAimFunctional traits mediate the interactions of species among themselves and with their environment, providing a link between diversity and ecosystem function. Crucially, the loss of biodiversity can jeopardize the functionality of ecosystems. Much focus is on predicting the impacts of current and future species loss; however, modern ecosystems have undergone biodiversity decline throughout the Late Quaternary, starting with the Pleistocene megafaunal extinctions. Thus, the fossil record offers the opportunity to investigate the long‐term legacy of biodiversity erosion and how this is affecting modern ecosystems in a cumulative manner. We aimed to investigate changes in functional diversity and redundancy of a local mammal community at Hall’s Cave, a site with a continuous record from 21,000 years ago to the present. Additionally, we included several common introduced species in the modern community to test whether they restore some lost ecological function.LocationCentral Texas.Time periodLate Pleistocene to Present.Major taxa studiedMammals.MethodsWe used eight functional traits (mass, diet, arboreality, cursoriality, soil disturbance, group size, activity period and migration habit), which, collectively, describe the ecological role of a species and its influence on ecosystem processes, to construct a multidimensional functional space. The functional richness, range and distribution of the Hall’s Cave community and the degree of functional redundancy were characterized statistically over time.ResultsWe found that declines in functional diversity were greater than expected given the decrease in species richness, implying that lost taxa contributed higher than average distinct ecological function. Functional distances between the remaining species increased through time, leading to reduced functional redundancy in younger communities. However, recently introduced taxa increased functional diversity to levels similar to those in the Holocene and partly restored the functional space occupied by Late Pleistocene fauna.Main conclusionsOur local‐scale analysis demonstrates how prolonged biodiversity erosion not only leads to functionally depauperate communities, but, crucially, lowers ecological resilience to future disturbance.

Introducing perforation when applying turbo codes

The problem of adapting the degree of redundancy introduced in the process of error-correcting coding to the changing characteristics of the data transmission channel is urgent. Turbo codes, used in a variety of digital communication systems, are capable of correcting multiple errors occurring in the data transmission channel. The article compares the decoding efficiency for various options for introducing perforation into the code sequence generated by the turbo code encoder. Based on the comparison results, recommendations were made on the most appropriate option for the introduction of perforation.

Elevated temperature may reduce functional but not taxonomic diversity of fungal assemblages on decomposing leaf litter in streams.

Mounting evidence points to a linkage between biodiversity and ecosystem functioning (B-EF). Global drivers, such as warming and nutrient enrichment, can alter species richness and composition of aquatic fungal assemblages associated with leaf-litter decomposition, a key ecosystem process in headwater streams. However, effects of biodiversity changes on ecosystem functions might be countered by the presumed high functional redundancy of fungal species. Here, we examined how environmental variables and leaf-litter traits (based on leaf chemistry) affect taxonomic and functional α- and β-diversity of fungal decomposers. We analysed taxonomic diversity (DNA-fingerprinting profiles) and functional diversity (community-level physiological profiles) of fungal communities in four leaf-litter species from four subregions differing in stream-water characteristics and riparian vegetation. We hypothesized that increasing stream-water temperature and nutrients would alter taxonomic diversity more than functional diversity due to the functional redundancy among aquatic fungi. Contrary to our expectations, fungal taxonomic diversity varied little with stream-water characteristics across subregions, and instead taxon replacement occurred. Overall taxonomic β-diversity was fourfold higher than functional diversity, suggesting a high degree of functional redundancy among aquatic fungi. Elevated temperature appeared to boost assemblage uniqueness by increasing β-diversity while the increase in nutrient concentrations appeared to homogenize fungal assemblages. Functional richness showed a negative relationship with temperature. Nonetheless, a positive relationship between leaf-litter decomposition and functional richness suggests higher carbon use efficiency of fungal communities in cold waters.

Omics-based ecosurveillance uncovers the influence of estuarine macrophytes on sediment microbial function and metabolic redundancy in a tropical ecosystem

Vertical zonation within estuarine ecosystems can strongly influence microbial diversity and function by regulating competition, predation, and environmental stability. The degree to which microbial communities exhibit horizontal patterns through an estuary has received comparatively less attention. Here, we take a multi-omics ecosurveillance approach to study environmental gradients created by the transition between dominant vegetation types along a near pristine tropical river system (Wenlock River, Far North Queensland, Australia). The study sites included intertidal mudflats fringed by saltmarsh, mangrove or mixed soft substrata habitats. Collected sediments were analyzed for eukaryotes and prokaryotes using small sub-unit (SSU) rRNA gene amplicons to profile the relative taxonomic composition. Central carbon metabolism metabolites and other associated organic polar metabolites were analyzed using established metabolomics-based approaches, coupled with total heavy metals analysis. Eukaryotic taxonomic information was found to be more informative of habitat type. Bacterial taxonomy and community composition also showed habitat-specificity, with phyla Proteobacteria and Cyanobacteria strongly linked to mangroves and saltmarshes, respectively. In contrast, metabolite profiling was critical for understanding the biochemical pathways and expressed functional outputs in these systems that were tied to predicted microbial gene function (16S rRNA). A high degree of metabolic redundancy was observed in the bacterial communities, with the metabolomics data suggesting varying degrees of metabolic criticality based on habitat type. The predicted functions of the bacterial taxa combined with annotated metabolites accounted for the conservative perspective of microbial community redundancy against the putative metabolic pathway impacts in the metabolomics data. Coupling these data demonstrates that habitat-mediated estuarine gradients drive patterns of community diversity and metabolic function and highlights the real redundancy potential of habitat microbiomes. This information is useful as a point of comparison for these sensitive ecosystems and provides a framework for identifying potentially vulnerable or at-risk systems before they are significantly degraded.

Cooperative regulation of endocytic vesicle transport by yeast Eps15-like protein Pan1p and epsins

Dynamic actin filaments are required for the formation and internalization of endocytic vesicles. Yeast actin cables serve as a track for the translocation of endocytic vesicles to early endosomes, but the molecular mechanisms regulating the interaction between vesicles and the actin cables remain ambiguous. Previous studies have demonstrated that the yeast Eps15-like protein Pan1p plays an important role in this interaction, and that interaction is not completely lost even after deletion of the Pan1p actin-binding domain, suggesting that additional proteins mediate association of the vesicle with the actin cable. Other candidates for mediating the interaction are endocytic coat proteins Sla2p (yeast Hip1R) and Ent1p/2p (yeast epsins), as these proteins can bind to both the plasma membrane and the actin filament. Here, we investigated the degree of redundancy in the actin-binding activities of Pan1p, Sla2p, and Ent1p/2p involved in the internalization and transport of endocytic vesicles. Expression of the nonphosphorylatable form of Pan1p, Pan1-18TA, caused abnormal accumulation of both actin cables and endocytic vesicles, and this accumulation was additively suppressed by deletion of the actin-binding domains of both Pan1p and Ent1p. Interestingly, deletion of the actin-binding domains of Pan1p and Ent1p in cells lacking the ENT2 gene resulted in severely defective internalization of endocytic vesicles and recruitment of actin cables to the site of endocytosis. These results suggest that Pan1p and Ent1p/2p cooperatively regulate the interaction between the endocytic vesicle and the actin cable.

Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization.

Positron emission tomography (PET) images tend to be significantly degraded by the partial volume effect (PVE) resulting from the limited spatial resolution of the reconstructed images. Our purpose is to propose a partial volume correction (PVC) method to tackle this issue. In the present work, we explore a voxel-based PVC method under the least squares framework (LS) employing anatomical non-local means (NLMA) regularization. The well-known non-local means (NLM) filter utilizes the high degree of information redundancy that typically exists in images, and is typically used to directly reduce image noise by replacing each voxel intensity with a weighted average of its non-local neighbors. Here we explore NLM as a regularization term within iterative-deconvolution model to perform PVC. Further, an anatomical-guided version of NLM was proposed that incorporates MRI information into NLM to improve resolution and suppress image noise. The proposed approach makes subtle usage of the accompanying MRI information to define a more appropriate search space within the prior model. To optimize the regularized LS objective function, we used the Gauss-Seidel (GS) algorithm with the one-step-late (OSL) technique. After the import of NLMA, the visual and quality results are all improved. With a visual check, we notice that NLMA reduce the noise compared to other PVC methods. This is also validated in bias-noise curve compared to non-MRI-guided PVC framework. We can see NLMA gives better bias-noise trade-off compared to other PVC methods. Our efforts were evaluated in the base of amyloid brain PET imaging using the BrainWeb phantom and in vivo human data. We also compared our method with other PVC methods. Overall, we demonstrated the value of introducing subtle MRI-guidance in the regularization process, the proposed NLMA method resulting in promising visual as well as quantitative performance improvements.

Type 0 ℤ2 × ℤ2 heterotic string orbifolds and misaligned supersymmetry

The [Formula: see text] heterotic string orbifold yielded a large space of phenomenological three generation models and serves as a testing ground to explore how the Standard Model of particle physics may be incorporated in a theory of quantum gravity. In this paper, we explore the existence of type 0 models in this class of string compactifications. We demonstrate the existence of type 0 [Formula: see text] heterotic string orbifolds, and show that there exist a large degree of redundancy in the space of GGSO projection coefficients when the type 0 restrictions are implemented. We explore the existence of such configurations in several constructions. The first correspond to essentially a unique configuration out of a priori [Formula: see text] discrete GGSO choices. We demonstrate this uniqueness analytically, as well as by the corresponding analysis of the partition function. A wider classification is performed in [Formula: see text]-models and [Formula: see text]-models, where the first class correspond to compactifications of a tachyonic ten-dimensional heterotic string vacuum, whereas the second correspond to compactifications of the ten-dimensional nontachyonic [Formula: see text]. We show that the type 0 models in both cases contain physical tachyons at the free fermionic point in the moduli space. These vacua are therefore necessarily unstable, but may be instrumental in exploring the string dynamics in cosmological scenarios. We analyze the properties of the string one-loop amplitude. Naturally, these are divergent due to the existence of tachyonic states. We show that once the tachyonic states are removed by hand the amplitudes are finite and exhibit a form of misaligned supersymmetry.

RETRACTED ARTICLE: Forecasting algorithm of tourism service trade based on PSO-optimized hybrid RVM model

As a comprehensive form of trade, tourism service trade has had a profound impact on the economies of various countries. This research mainly discusses the tourism service trade forecasting algorithm based on the PSO-optimized hybrid RVM model. This study extracts 8 indicators including gross national product, total fixed asset investment, total import and export, China's import and export tariff rate, the exchange rate of renminbi to the US dollar, and the global economic growth rate. The same as the impact indicators of tourism service trade, but there is a certain degree of redundancy and correlation in these indicators. In order to measure the correlation between the evaluation indicators, the autocorrelation evaluation function in MATLAB is used, and the principal component analysis method is used to extract the principal components that can represent the indicators in a larger percentage. In order to improve the prediction accuracy of the RVM model, based on the adaptive construction model structure and initial model weights, the PSO algorithm is used to optimize the RVM model weights. The optimization process takes the minimum error of the RVM model as the algorithm search target, and each represents the RVM model. The algorithm finds the value and threshold of the optimal RVM model through the particle swarm tracking search algorithm and then uses the original RVM model and the optimized RVM prediction respectively total amount of tourism service trade in City A, and compares the prediction errors of the single RVM method and the PSO-optimized RVM method, and analyzes the degree of model prediction error reduction after the PSO model optimizes the RVM model. According to the forecast result, the relative average error of 2020 is 5.7%, and the forecast result is relatively accurate. This research is helpful to provide scientific reference for my country's tourism service trade.

Frequency Data Compression for Public Transportation Network Algorithms (Extended Abstract)

Timetable information in public transportation networks exhibit a large degree of redundancy; e.g. consider a bus going from station A to station B at 6:00, 6:15, 6:30, 6:45, 7:00, 7:15, 7:30, . . . , 20:00, the very same data can be provided by a frequency-based representation as ’6:00-20:00, every 15 minutes’ in considerably less space. Nevertheless a common graph model for routing in public transportation networks is the time-expanded representation where for each arrival/departure event a single node is created. We will introduce a frequency-based graph model which allows for a significantly more compact representation of the network, resulting also in a speed-up for station-to-station queries. Moreover we will describe a new variant of Dijkstra’s algorithm, where also the labels are frequency-based. This approach allows for accelerating profile queries in public transportation networks.

Optical properties of dissolved organic matter highlight peatland-like properties in a constructed wetland

Constructing novel peatland ecosystems can help to restore the long-term carbon accumulating properties of northern soil systems that have been lost through resource extraction. Although mining companies are legally required to restore landscapes following extraction, there are limited tools to evaluate the effectiveness of restoring peat accumulating landscapes. This study analyzed the spatial patterns of the first seven years (n = 575) of dissolved organic matter (DOM) optical characteristics from a pilot watershed built to restore boreal plains peatlands on a former open pit oil sands mine. A principal component analysis (PCA) indicated a very high degree of redundancy in absorption-florescence DOM properties (PARAFAC, HIX, FI, freshness index, SUVA, and peak A, B, C, T, wavelength, and intensity ratios) at this site. The leading principal component indicated a gradient of fresh protein rich inputs, which are highest near the upland region, to older highly degraded DOM, which is highest in the lowland closest to the outlet. Two functionally different reference peatlands, a poor-fen and bog system and a moderate-rich fen, had relatively similar optical DOM characteristics indicating a high level of decomposition at these sites. Over the first seven years, in some regions of the reconstructed lowland the DOM characteristics are becoming increasingly similar to the highly decomposed DOM observed at the reference sites. When combined with carbon flux measurements these findings indicate the potential for long term organic matter accumulation at this reconstructed site.

Enhanced chemoselectivity of a plant cytochrome P450 through protein engineering of surface and catalytic residues

Cytochrome P450s (P450s) are the most versatile catalysts utilized by plants to produce structurally and functionally diverse metabolites. Given the high degree of gene redundancy and challenge to functionally characterize plant P450s, protein engineering is used as a complementary strategy to study the mechanisms of P450-mediated reactions, or to alter their functions. We previously proposed an approach of engineering plant P450s based on combining high-accuracy homology models generated by Rosetta combined with data-driven design using evolutionary information of these enzymes. With this strategy, we repurposed a multi-functional P450 (CYP87D20) into a monooxygenase after redesigning its active site. Since most plant P450s are membrane-anchored proteins that are adapted to the micro-environments of plant cells, expressing them in heterologous hosts usually results in problems of expression or activity. Here, we applied computational design to tackle these issues by simultaneous optimization of the protein surface and active site. After screening 17 variants, effective substitutions of surface residues were observed to improve both expression and activity of CYP87D20. In addition, the identified substitutions were additive and by combining them a highly efficient C11 hydroxylase of cucurbitadienol was created to participate in the mogrol biosynthesis. This study shows the importance of considering the interplay between surface and active site residues for P450 engineering. Our integrated strategy also opens an avenue to create more tailoring enzymes with desired functions for the metabolic engineering of high-valued compounds like mogrol, the precursor of natural sweetener mogrosides.

A Distributed Real-time Scheduling System for Industrial Wireless Networks

The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We have implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability.