Unidirectional Flow Of Information Research Articles

Diff-MGR: Dynamic causal graph attention and pattern reproduction guided diffusion model for multivariate time series probabilistic forecasting

Time series probability forecasting provides insight into future evolution and its inherent uncertainty from past data. In order to obtain more accurate forecasting as the reliable basis for future decision-making and planning, a new probabilistic prediction model of multivariate time series Diff-MGR is proposed in this study. Diff-MGR innovatively proposes dynamic causal graph attention blocks and pattern reproduction guided prediction blocks to predict the future patterns. Compared with the existing methods, the former captures dynamic unidirectional information flow by using causal graph structure at different periods to provide a real representation of variable relationships. The latter completes a reliable mapping of historical patterns to the future based on the prior knowledge of pattern reproduction. Furthermore, Diff-MGR proposes a novel noise prediction network that can effectively capture dependencies in predicted future patterns and generate probability distributions of future sequences using a conditional diffusion model. Extensive experiments on several real datasets verify the effectiveness of Diff-MGR's components, and show it outperforms existing models in probabilistic forecasting performance.

Global Perspectives on Innovations in Emergency Care during the COVID-19 Pandemic: A Mixed Methods Study

Abstract Introduction: In mid-2020, the Emergency Medicine Education and Research by Global Experts (EMERGE) network surveyed the emergency response to the COVID-19 pandemic. We demonstrated that innovative measures were being generated at unprecedented rates. Our aim was to build on these findings to develop the first theoretical model with utility in assessing and developing emergency innovations. Methods: Phase 1 of a two-phase, explanatory sequential mixed methods study was a secondary analysis of the original EMERGE survey. Phase 2 involved follow-up interviews of a purposeful sample of 15 of the original survey respondents, representing 12 countries and all 6 World Health Organization regions. Thematic analysis was performed by three members of the research team. Results: Innovation is a nonlinear process in which ideas are developed, implemented, refined, and spread. Ideas grew from departmental needs or the spread of successful innovations from other sites. Development was highly localized, influenced by institutional and regional factors. Obtaining buy-in from relevant stakeholders and addressing various barriers were essential to implementation. The majority of innovations then underwent a process of evolution before some were spread to other institutions through a largely unidirectional flow of information. Conclusions: This study proposes the first model for emergency innovation processes and highlights multiple areas for improvement. Understanding how to prioritize departmental needs and the barriers may streamline development and implementation. Furthermore, the apparent unidirectional flow of information demonstrated by our participants suggests both an opportunity and a need for improved information systems. We present a framework for further research and an outline for innovation management.

Hierarchical Iterative Learning Control for a Class of Distributed Hierarchical Systems

Abstract This work examines a class of distributed linear systems that fit a tree type of hierarchical structure. Therefore, unidirectional information flow from a subsystem higher in the hierarchy impacts subsystems lower in the hierarchy. While prior efforts at control focused on feedback solutions to these systems, this effort introduces Iterative Learning Control as a feedforward controller. This is termed hierarchical iterative learning control (HILC). This HILC can be implemented in parallel with feedback algorithms or can be used in a series feedforward manner assuming stable, or stabilized, subsystems. An augmentation of the standard learning update operators provides stability and monotonic convergence for the proposed approach. A simulation case study highlights the performance of the proposed design approach.

Lightweight and fast visual detection method for 3C assembly

In the context of 3C assembly scenarios, characterized by numerous semi-flexible, heterogeneous, and small slender targets, traditional target detection algorithms face significant challenges such as low accuracy, weak generalization, large model sizes, and slow inference speeds. To address these issues, this study introduces an enhanced method based on the YOLOv5 model, named YOLOv5-GTB. This method integrates Bidirectional Feature Pyramid Network (BiFPN), Ghost lightweight convolution, Vision Transformer (ViT), and adaptive activation function technologies, aiming to improve the accuracy and speed of target detection. Our approach utilizes Ghost convolution to construct a Ghost bottleneck layer, optimizing the feature extraction network while significantly reducing computational costs and enhancing the convolutional neural network’s feature extraction capabilities. Additionally, the Cross-Stage Partial Network (CSPNet) architecture is employed to effectively segment the data flow of the input feature map layer, thereby improving the efficiency of gradient processing. Furthermore, we introduce a fusion structure of CNN and Transformer, leveraging the strengths of convolutional neural networks in local feature extraction and the ViT’s capability in long-range information capture, thereby further enhancing the overall network’s feature extraction performance. Regarding feature fusion, considering the limitations of traditional top-down unidirectional information flow in effectively merging features with both location and semantic information, BiFPN is incorporated into the YOLOv5-GTB. This enhances the fusion of feature layers extracted from both the end of the backbone network and the first module’s fused feature layer, thus improving detection accuracy. Ablation and comparative experiments conducted on the 3C assembly scenario target detection dataset demonstrate the significant advantages of the YOLOv5-GTB model in terms of accuracy and speed. Ultimately, the application of this model to the 3C assembly platform successfully achieves rapid and accurate target recognition in this scenario.

Observer-Based Adaptive Consensus Protocol Design for Multi-Agent Systems with One-Sided Lipschitz Nonlinearity

This paper considers the observer-based leader–follower consensus problem of multi-agent systems with one-sided Lipschitz conditions and quadratic inner-boundedness nonlinearity. Based on the relative outputs of neighboring agents, an adaptive protocol over a directed graph is designed via assigning a time-varying coupling weight to each node. Compared with the existing protocols, the proposed protocol can not only be utilized in the unidirectional flow of information, but it does not require any global graph information for its design. It has been shown that the established criteria can not only make the observer error tendency zero, but one can also achieve the leader–follower consensus of such nonlinear multi-agent systems. Furthermore, the gains of observer and protocol parameters can be constructed by solving the linear matrix inequalities (LMIs), which are conveniently checked. The results are illustrated by numerical simulations.

To be mobile or not: the variety of reverse transcriptases and their recruitment by host genomes

Reverse transcriptases (RT), or RNA-dependent DNA polymerases, are unorthodox enzymes that originally added a new angle to the conventional view of the unidirectional flow of genetic information in the cell from DNA to RNA to protein. First discovered in vertebrate retroviruses, RTs were since re-discovered in most eukaryotes, bacteria, and archaea, spanning essentially all domains of life. For retroviruses, RTs provide the ability to copy the RNA genome into DNA for subsequent incorporation into the host genome, which is essential for their replication and survival. In cellular organisms, most RT sequences originate from retrotransposons, the type of self-replicating genetic elements that rely on reverse transcription to copy and paste their sequences into new genomic locations. Some retroelements, however, can undergo domestication, eventually becoming a valuable addition to the overall repertoire of cellular enzymes. They can be beneficial yet accessory, like the diversity-generating elements, or even essential, like the telomerase reverse transcriptases. Nowadays, ever-increasing numbers of domesticated RT-carrying genetic elements are being discovered. It may be argued that domesticated RTs and reverse transcription in general is more widespread in cellular organisms than previously thought, and that many important cellular functions, such as chromosome end maintenance, may evolve from an originally selfish process of converting RNA into DNA.

From Shanghai to Wall Street: The Influence of Chinese News Sentiment on US Stocks

This study explores the influence of Chinese news sentiment on US stocks, focusing on the potential policy shift granting Chinese retail traders direct access to US markets. Analyzing the flow of information between Chinese news sentiment and global news sentiment using transfer entropy, the findings reveal a significant influence of Chinese news sentiment on global news sentiment. Moreover, the study identifies a predominant unidirectional flow of information from news sentiment to stock returns, with Chinese news sentiment having a more substantial impact on future returns of US stocks compared to global news sentiment. This suggests that if the proposed policy changes succeed, Chinese retail traders may rely on Chinese news sentiment, potentially leading to increased volatility in the US market. Policymakers and market participants should be aware of these implications to prepare for changes in the US stock market dynamics. Further research can provide deeper insights into the interplay between news sentiment, investor behavior, and market volatility.

Design and Implementation of Broadband Services for Small Scale Organization

Abstract: Nodes are building blocks of telecommunication network which are interconnected by telecommunication links for exchanging messages and data. Broadband Integrated Services Digital Network commonly called as Broadband ISDN or BISDN is a high-speed telecommunication network that carry data, voice and video simultaneously over a single channel. It provides interactive services where there is bidirectional flow of information between one or more subscribers and a service provider or distributive services with unidirectional information flow between service provider and subscriber. Speed of B-ISDN normally ranges from 2 Mbps to 1 Gbps and transmission very much relates to Asynchronous Transfer Mode. To design BISDN services for small scale organization, resource management and organization requirements play an important role. This paper provides a design of network model and implementation of broadband services for a small-scale organization by considering advance technology requirements, the challenges faced during the implementation and total estimation of budget for entire implementation.

Engineering circuits of human iPSC-derived neurons and rat primary glia.

Novel in vitro platforms based on human neurons are needed to improve early drug testing and address the stalling drug discovery in neurological disorders. Topologically controlled circuits of human induced pluripotent stem cell (iPSC)-derived neurons have the potential to become such a testing system. In this work, we build in vitro co-cultured circuits of human iPSC-derived neurons and rat primary glial cells using microfabricated polydimethylsiloxane (PDMS) structures on microelectrode arrays (MEAs). Our PDMS microstructures are designed in the shape of a stomach, which guides axons in one direction and thereby facilitates the unidirectional flow of information. Such circuits are created by seeding either dissociated cells or pre-aggregated spheroids at different neuron-to-glia ratios. Furthermore, an antifouling coating is developed to prevent axonal overgrowth in undesired locations of the microstructure. We assess the electrophysiological properties of different types of circuits over more than 50 days, including their stimulation-induced neural activity. Finally, we demonstrate the inhibitory effect of magnesium chloride on the electrical activity of our iPSC circuits as a proof-of-concept for screening of neuroactive compounds.

Community advisory committee as a facilitator of health and wellbeing: A qualitative study in informal settlements in Nairobi, Kenya.

A range of community engagement initiatives to advance health and wellbeing are currently taking place in informal settlements in low and middle income countries (LMICs), including community and stakeholder meetings, use of radio, film, TV programs and other information, education and communication materials (IECs) organized by different stakeholders. While these initiatives tend to focus on unidirectional flow of information to communities, the need to incorporate initiatives focusing on bi or multi-directional flow of information have been identified. Despite the extensive body of literature on community engagement, the role of Community Advisory Committees (CACs) in advancing health and wellbeing in informal settlements is still a puzzle, occasioned by considerable ambiguity. A community advisory committee is a dedicated group of volunteers to support health and wellbeing needs of their community using a community approach. Researchers and project implementers work in partnership with CACs to successfully implement their activities within the target community. In this paper, using in-depth interviews, we document the roles of CACs in advancing health and wellbeing in Korogocho and Viwandani informal settlements in Nairobi, Kenya. Study participants described the role of CAC in advancing health and wellbeing through education and awareness creation, advisory roles in research and implementation goals, protecting community interests and acting as gatekeepers and collaborators to community partners. Identified barriers to achieving CAC roles include lack of finance and other field resources, being labeled as organization staff and low involvement by some upcoming and emerging local leaders on issues which involve the CAC constituents. Enablers of CACs in their roles include possession of appropriate skills and values by members; involvement of the community in the selection of members, regular consultative and advisory meetings, representativeness in the composition of CAC membership and knowledge about the community. We conclude that CACs play key roles in advancing health and wellbeing in informal settlements and that existing CACs mechanisms and operations need to be given due consideration by researchers, project implementers and local authorities right from project conceptualization. CACs need recognition beyond consultations and placations during research and project implementation to a veritable social structure for community's social viability and survival as well as partners in development for inclusive urbanization process. While CACs have contributed in advancing health and wellbeing in informal settlements, there is need for a long-term strategy to optimize their impact and reduce puzzles around their roles.

Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach

The present study used transfer entropy and effective transfer entropy to examine the asymmetric information flow between exchange rates, oil, and gold. The dataset is composed of daily data covering the period of 1 January 2018 to 31 December 2021. Further, the dataset is bifurcated for analysis for before and during COVID. The bidirectional information flow is observed between EUR/USD and Oil for the whole study period unlike before COVID. However, during COVID, there was a unidirectional information flow from Oil→EUR/USD. The study finds a significant unidirectional information flow from Gold→EUR/USD. The study estimates also indicate that before COVID, the direction of information flow was from Oil→Gold. However, the direction of information flow reversed during COVID from Gold→Oil. Overall, the direction of information flow among these three variables is asymmetric. The highest transfer entropy was observed for Gold→EUR/USD among all the pairs under consideration.

Unidirectional Information Flow and Positive Divisibility are Nonequivalent Notions of Quantum Markovianity for Noninvertible Dynamics

We construct a dynamical map which is not positive divisible and does not present information backflow either (as measured by trace norm quantifiers). It is formulated for a qutrit system undergoing noninvertible dynamics. This provides an evidence that the two definitions of quantum Markovianity based on the absence of information backflow and positive divisibility are nonequivalent for general noninvertible dynamical maps.

Quantifying Information Flows among Developed and Emerging Equity Markets

We rely on daily changes in implied volatility indices for the US stock market (VIX), developed markets excluding the US (VXEFA), stock markets in Brazil (VXEWZ), Russia (RVI), India (NIFVIX), China (VXFXI), and the overall emerging market volatility index (VXEEM) to examine the degree of information flows among the markets in the coronavirus pandemic. The study also employs the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the data into intrinsic mode functions (IMFs). Subsequently, we cluster the IMFs based on their level of frequencies into short-, medium-, and long-term horizons. The analysis draws on the concept of Rényi transfer entropy (RTE) to enable an assessment of linear as well as non-linear and tail-dependence in the markets. The study reports significant information flows from BRIC volatility indices to the overall emerging market volatility index in the short-and medium-terms and vice versa. We also document a mixture of bi-directional and uni-directional flow of high risk information and low risk information emanating from emerging equity markets and from the developed markets. We find that the transmission of high risk information is largely dominated by the developed markets (VIX and VXEFA). In the midst of high degree of contagion, our findings reveal that investors can find minimal benefits by shielding against adverse shocks from the developed markets with a combination of stocks from India and other equities in the emerging markets in the short-term, within 1–15 days. For as low as 1–5 days, the empirical evidence indicates that a portfolio consisting of stocks from Russia and Brazil also offer immunity to shocks from the VXEFA. Our study makes an important empirical contribution to the study of market integration and contagion among emerging markets and developed markets in crisis periods.

Appointed-Time Average Consensus Over Directed Networks

Current research on average consensus for multi-agent networks mainly focuses on the cases with bidirectional interaction. However, the unidirectional information flow usually exists in practical applications due to different communication ranges of individuals. To achieve state-average over directed networks, this brief introduces an extra variable for each agent, called ‘surplus’, to record the shift in the average. Then, a bank of distributed average consensus control protocols is developed by incorporating appointed-time control framework. It shows that for the multi-agent system with strongly-connected network, the presented protocol ensures that the state-average and the surplus-stability behaviour emerge at an appointed convergence time. Besides, the settling time is independent of any other parameters and can be assigned according to the task requirements. Corresponding techniques for convergence analysis involve time projection method, motion planning technique and matrix perturbation theory. Lastly, the theoretical results are examined by a numerical simulation.

Accelerated Evolution by Diversity-Generating Retroelements.

Diversity-generating retroelements (DGRs) create vast amounts of targeted, functional diversity by facilitating the rapid evolution of ligand-binding protein domains. Thousands of DGRs have been identified in bacteria, archaea, and their respective viruses. They are broadly distributed throughout the microbial world, with enrichment observed in certain taxa and environments. The diversification machinery works through a novel mechanism termed mutagenic retrohoming, whereby nucleotide sequence information is copied from an invariant DNA template repeat (TR) into an RNA intermediate, selectively mutagenized at TR adenines during cDNA synthesis by a DGR-encoded reverse transcriptase, and transferred to a variable repeat (VR) region within a variable-protein gene (54). This unidirectional flow of information leaves TR-DNA sequences unmodified, allowing for repeated rounds of mutagenic retrohoming to optimize variable-protein function. DGR target genes are often modular and can encode one or more of a wide variety of discrete functional domains appended to a diversifiable ligand-binding motif. Bacterial variable proteins often localize to cellsurfaces, although a subset appear to be cytoplasmic, while phage-encoded DGRs commonly diversify tail fiber-associated receptor-binding proteins. Here, we provide a comprehensive review of the mechanism and consequences of accelerated protein evolution by these unique and beneficial genetic elements.

Contribution to the development of a digital twin based on CMM to support the inspection process

Digital twin (DT) based on CMMs acting as a mirror between the physical and virtual measuring system and process. The inspection planning process performed on the virtual components of DT, and execution of measurement on the physical components. In this paper the measurement system with the DEA-IOTA-2203 was used as a physical twin, and a virtual machine, generated after modeling and configuring in PTC Creo software. Also, a simulation process was performed in order to check the collision and generate a measuring path for one example prismatic part. The information flow is unidirectional and flowing from the virtual to the physical inspection system based on CMM. In this way, a data format or a list of instructions for the physical machine and its movements per axes is provided. Virtual measurement system is developed for inspection of the standard types of tolerances and family of prismatic mechanical parts. The result of this paper is a new contribution to the development of the digital twin for CMM based on unidirectional information flow by.ncl (DMIS) file. The application of the results of DT development is especially pronounced when planning the inspection of prismatic parts with a large number of tolerances in industrial conditions.

Finite time observer‐based super‐twisting sliding mode control for vehicle platoons with guaranteed strong string stability

This paper presents a finite time disturbance observer (FTDO)-based super-twisting sliding mode control (SMC) structure for a vehicle platoon system with a focus on parameter uncertainties as well as external disturbances. In order to compensate the parameter uncertainties and external disturbances, a composite FTDO-based super twisting SMC method is developed under zero initial spacing deviations, where the parameter uncertainties, external disturbances, and the preceding vehicle's acceleration are estimated as a lumped disturbance by employing an FTDO. Benefiting from the FTDO, the estimation error dynamics is stable in finite time. Different from the existing SMC-based achievements, the continuity of the control signals can be achieved, which avoids the undesirable chattering phenomenon in the existing SMC-based works. Leveraging an FTDO, the preceding vehicle's acceleration information is not required, which enables the proposed control method to exploit a unidirectional information flow topology. Thus, it can reduce communication burden compared with bidirectional information flow topologies. Then, an improved time gap policy is employed to extend the developed control scheme to a more general case with nonzero initial spacing deviations. In addition, the individual stability of each vehicle is rigorously analyzed by resorting to the Lyapunov stability theory. Furthermore, the strong string stability of the whole vehicle platoon is derived with the help of the Laplace transform. Finally, numerical simulations are conducted to illustrate the effectiveness and applicability of the developed control scheme.

Adaptive CAM planning to support co-design in the building industry

Co-design describes a collaborative design process between different stakeholders. These stakeholders usually come from various domains. To enable co-design, data has to be exchanged between the stakeholders. Current approaches to data management in the building industry are usually oriented along the process chain and therefore only allow a unidirectional flow of information. This is an obstacle in the implementation of co-design within the building industry.As more and more buildings are built by utilizing prefabricated modules to accelerate the on-site building process and to reduce the cost, a major effort in terms of implementing co-design is to link the design information of the building industry with the production information of the manufacturing industry. The problem occurs as the building industry mainly uses the fndustry Foundation Classes and the manufacturing industry uses the Standard for the Exchange of Product Model Data as a data format to exchange information. This leads to a break in the flow of information. Furthermore, information about the manufacturability of products, that is generated during the computer-aided manufacturing process, cannot be easily returned to the architect who can utilize the information to adapt the design in the computer-aided design process.This paper addresses these problems by defining a data processing pipeline that enables the conversion of the geometric design information stored in IFC into the STEP data format. From there, the CAM strategy can be defined and results in an executable G-Code. The information of the CAM planning is returned to the architect, who can adapt the design by interpreting the results of the CAM processing. To close the co-design loop, the G-Code for the design adaptions can be generated automatically by reusing the predefined CAM strategy.

Traversing Excitonic and Ionic Landscapes: Reduced-Dimensionality-Inspired Design of Organometal Halide Semiconductors for Energy Applications.

At the very heart of the global semiconductor industry lies the omnipresent push for new materials discovery. New materials constantly rise and fall out of fashion in the scientific literature, with those passing an initial phase of research scrutiny becoming hotbeds of characterization and optimization efforts. Yet, innumerable hours of painstaking research have been devoted to materials that have ultimately fallen by the wayside after crossing over an indefinable threshold, whereupon historical optimism is met with newfound skepticism. Materials have to perform well, and they have to do it quickly. In the past decade, metal-halide perovskites (MHPs) have garnered widespread attention. The hegemonic view in both academic and industrial circles is that these materials could be engineered to meet the demands of the semiconductor industry. Their promise as inexpensive solar cell devices is highly attractive, and it has been nothing short of remarkable that efficiencies have risen from 3.8% in 2009 to more than 25.5% in 2021. Moreover, MHPs are poised to be revolutionary materials in more ways than one. The highest MHP LED efficiency was recently reported (23.4%), and MHPs have demonstrated promise in photodetectors, memristors, and transistors. However, the many excellent properties of MHPs are contrasted by longstanding stability and reproducibility limitations that have hindered their commercialization. Overcoming the limitations of MHPs is ultimately a materials engineering problem, which should be solved by mapping more precise relationships between structure, composition, and device performance. In 1958, Francis Crick famously developed the central dogma of molecular biology which describes the unidirectional flow of information in biological systems. In the words of Crick, "nature has devised a unique instrument in which an underlying simplicity is used to express great subtlety and versatility." In this Account, taking inspiration from the hierarchical organization of nature, we describe a hierarchical approach to materials engineering of organic metal-halide semiconductors. We demonstrate that organo-metal halide semiconductors' dimensionality, composition, and morphology dictate their optoelectronic properties and can be exploited in defining more explicit relationships between structure and function. Here, we traverse three-dimensional (3D), two-dimensional (2D), and one-dimensional (1D) organo-metal halide semiconductors, detailing the morphological and compositional differences in each and the implications that can be drawn within each domain on the engineering process. Control over ion migration pathways via morphology engineering as well as control over charge formation in organic-inorganic semiconductors is demonstrated. Fundamental insights into the amount of static and dynamic disorder in the MHP lattice are provided, which can be continuously tuned as a function of composition and morphology. Using electroabsorption spectroscopy on 2D MHPs, a disorder-induced dipole moment in the exciton proportional to the summed value of static and dynamic disorder is measured. Spectroscopic isolation of exciton features in 2D MHP electroabsorption spectra allows us to obtain precise, model-independent measurements of exciton binding energies to study the effect of chemical substitutions, such as Sn2+ → Pb2+, on the value of the exciton binding energy. Finally, we conclude that this multidimensional platform, with the aid of machine learning and robotics, will be foundational in accurately predicting structure-property-device relationships in organo-metal halide semiconductors in the future.

Integrative data processing for cyber-physical off-site and on-site construction promoting co-design

The term co-design describes a collaborative design process, usually across the boundaries of individual areas. A major aspect, and often an obstacle in the implementation of the co-design approach, is the establishment of a centralized data management system. In the past, the data management concept has been approached from different perspectives. Current approaches usually are oriented along the process chain and thus reflect a unidirectional flow of information. Neither the information feedback nor the reaction to adjustments in upstream model steps is supported, which makes continuous collaboration more difficult and at best reduces the co-design approach for continuous data usage. This paper addresses these weaknesses while outlining an integrative data processing concept enabling co-design between the domains of construction and industrial prefabrication.Geometrical information generated during architectural design and construction planning is reused for the production of components, manufacturing-related design adjustments are transferred back into the domain of construction. Changes throughout the entire process are centrally recorded. To ensure independence from the software tools used and to consider their great diversity, data exchange is largely based on standards, namely industry foundation classes and the standard for the exchange of product model data. In order to counteract the sequential processing according to a waterfall model and to support the co-design approach, the industrial prefabrication toolchain, consisting of computer-aided manufacturing software and downstream postprocessors, is largely automated, so that reprocessing of a workpiece for minor geometric adjustments no longer requires any interaction of the production planner.