Breaking the challenge of signal integrity using time-domain spoof surface plasmon polaritons

In modern integrated circuits and wireless communication systems/devices, three key features need to be solved simultaneously to reach higher performance and more compact size: signal integrity, interference suppression, and miniaturization. However, the above-mentioned requests are almost contradictory using the traditional techniques. To overcome this challenge, here we propose time-domain spoof surface plasmon polaritons (SPPs) as the carrier of signals. By designing a special plasmonic waveguide constructed by printing two narrow corrugated metallic strips on the top and bottom surfaces of a dielectric substrate with mirror symmetry, we show that spoof SPPs are supported from very low frequency to the cutoff frequency with strong subwavelength effects, which can be converted to the time-domain SPPs. When two such plasmonic waveguides are tightly packed with deep-subwavelength separation, which commonly happens in the integrated circuits and wireless communications due to limited space, we demonstrate theoretically and experimentally that SPP signals on such two plasmonic waveguides have better propagation performance and much less mutual coupling than the conventional signals on two traditional microstrip lines with the same size and separation. Hence the proposed method can achieve significant interference suppression in very compact space, providing a potential solution to break the challenge of signal integrity

System Dynamics Based Simulation Study On Storage and Distribution Integration of Electronic Commerce Enterprise

With the strong advocacy of national policies and the rapid development of electronic commerce, offline logistics operation has become the key to efficient and fast e-commerce. This paper will use the system dynamic method to build an integrated warehousing and distribution system of e-commerce, applying the computer simulation to analyze the change of each parameter after the target inventory and delay time have changed. Suggestions will be put forward at last: building of an info-sharing mechanism, reducing the delay time via active coordination, predicting the target inventory of distribution center on time. Through these to reduce the average cost and the possibility of short supply at distribution center, and thus guarantee the delivery quality and speed, optimize buyers’ shopping experience, form a virtuous circle and enhance the overall competence of the supply chain

NAD tagSeq reveals that NAD+-capped RNAs are mostly produced from a large number of protein-coding genes in Arabidopsis.

The 5' end of a eukaryotic mRNA transcript generally has a 7-methylguanosine (m7G) cap that protects mRNA from degradation and mediates almost all other aspects of gene expression. Some RNAs in Escherichia coli, yeast, and mammals were recently found to contain an NAD+ cap. Here, we report the development of the method NAD tagSeq for transcriptome-wide identification and quantification of NAD+-capped RNAs (NAD-RNAs). The method uses an enzymatic reaction and then a click chemistry reaction to label NAD-RNAs with a synthetic RNA tag. The tagged RNA molecules can be enriched and directly sequenced using the Oxford Nanopore sequencing technology. NAD tagSeq can allow more accurate identification and quantification of NAD-RNAs, as well as reveal the sequences of whole NAD-RNA transcripts using single-molecule RNA sequencing. Using NAD tagSeq, we found that NAD-RNAs in Arabidopsis were produced by at least several thousand genes, most of which are protein-coding genes, with the majority of these transcripts coming from <200 genes. For some Arabidopsis genes, over 5% of their transcripts were NAD capped. Gene ontology terms overrepresented in the 2,000 genes that produced the highest numbers of NAD-RNAs are related to photosynthesis, protein synthesis, and responses to cytokinin and stresses. The NAD-RNAs in Arabidopsis generally have the same overall sequence structures as the canonical m7G-capped mRNAs, although most of them appear to have a shorter 5' untranslated region (5' UTR). The identification and quantification of NAD-RNAs and revelation of their sequence features can provide essential steps toward understanding the functions of NAD-RNAs

Travel Demand Forecasting: A Fair AI Approach

Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.Comment: improved the methodology; updated new content

Gene expression variations are predictive for stochastic noise

Fluctuations in protein abundance among single cells are primarily due to the inherent stochasticity in transcription and translation processes, such stochasticity can often confer phenotypic heterogeneity among isogenic cells. It has been proposed that expression noise can be triggered as an adaptation to environmental stresses and genetic perturbations, and as a mechanism to facilitate gene expression evolution. Thus, elucidating the relationship between expression noise, measured at the single-cell level, and expression variation, measured on population of cells, can improve our understanding on the variability and evolvability of gene expression. Here, we showed that noise levels are significantly correlated with conditional expression variations. We further demonstrated that expression variations are highly predictive for noise level, especially in TATA-box containing genes. Our results suggest that expression variabilities can serve as a proxy for noise level, suggesting that these two properties share the same underlining mechanism, e.g. chromatin regulation. Our work paves the way for the study of stochastic noise in other single-cell organisms