information channel
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Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we show that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain due to time integration while also keeping the loss due to stochasticity in translation relatively low (< 0.5 bits).

Indonesia has held simultaneous regional elections on 9th December 2020. In contrast to previous years, when the campaign became a moment for citizens to gather with their prospective leaders, the Covid-19 pandemic forced the limitation of face-to-face meetings in order to implement Covid-19 health protocols. The government through the General Elections Commission (KPU) has issued General Election Commission Regulation (PKPU) 13/2020 which has explicitly encouraged candidate to use digital media in political campaigns. This is an effort to encourage candidates to take advantage of the digital space in campaigning. Currently, the candidates already have social media, but its use has not become the main information channel in political communication. During this campaign period, the candidates have used social media as a channel of political communication, but the social media used is limited to conveying invitations or information that the candidate has attended an activity, so social media has not become the main information channel in campaigning. The lack of organizing an online campaign by this candidate viewed from a philosophical perspective of egoism, based on the idea that the public or prospective voters are more focused on themselves. Participating in a online campaign for prospective voters requires extra sacrifices such as paying for internet quota fees, it is more troublesome because prospective voters have to learn to operate a online video application, and there are no direct benefits. The challenges faced in implementing an online campaign include, firstly, because it is preferred by the community, it is believed that the community prefers to meet face-to-face with the prospective leader directly because it provides direct benefits to the community, secondly, it is right on target because an online election campaign will not attract people, new voters because it will only be followed by voters who firmly support the candidate. Thirdly, because of the lack of creativity from the campaign team due to the lack of innovation from the candidates for not building a team that campaigns boldly, in addition to innovation, infrastructure problems that the evaluation of signal interference and the uneven distribution of digital infrastructure in the regions have hampered the implementation of bold campaigns.

With the development of society, e-commerce competition has become increasingly intense and has ascended to the level of the ecosystem. Therefore, it is extremely significant to study the mechanism of evolution and balance for the e-commerce ecosystem. Simultaneously, blockchain technology is essentially a consensus mechanism, the core idea of which is decentralization, but it is actually the deconstruction of privileges and authority. Especially, the influence on the e-commerce ecosystem cannot be underestimated. Blockchain technology ultimately changes not only technology, but a comprehensive reconstruction of various industries. Building an e-commerce information ecosystem based on blockchain can promote the healthy and sustainable development of e-commerce information ecology. This work combines the definition and technical characteristics of blockchain, discusses the blockchain-based e-commerce information ecosystem model, and discusses how to achieve the ecological balance and system evolution of e-commerce under the background of blockchain. According to the internal problems of the e-commerce ecosystem, three evolutionary paths are proposed in this work. First, consider the timeliness of the information and construct a full-process information channel. Second, remove central nodes and build a safe and efficient block payment. Third, solve the blind zone in the field of logistics and create efficient and transparent intelligent logistics. This work can provide an effective reference for the development of e-commerce.

This paper introduces a new attack against the Kirchhoff–Law–Johnson-Noise (KLJN) secure key exchange scheme. The attack is based on the nonlinearity of the noise generators. We explore the effect of total distortion ([Formula: see text]) at the second order ([Formula: see text]), third order ([Formula: see text]) and a combination of the second and third orders ([Formula: see text]) on the security of the KLJN scheme. It is demonstrated that as little as 1% results in a notable power flow along the information channel, which leads to a significant information leak. We also show that decreasing the effective temperature (that is, the wire voltage) and, in this way reducing nonlinearity, results in the KLJN scheme approaching perfect security.

AbstractDepression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

Despite evidence about the informal advisors of the presidents in Latin America, literature on this topic is still incipient. This article investigates the informational scenario that surrounded the Brazilian president, Jair Bolsonaro, from January to April 2020, a period of presidential decision-making on the measures to face the Covid-19 pandemic. In-depth case study of interactions established by the Brazilian president is developed based on data from the Brazilian President’s Daily Diary. Social Network Analysis (SNA) tools are used to analyze this evidence. The findings suggest that the Ministry of Health (MOH) was not the main information channel for the president at the beginning of the pandemic despite its central role in the national governance structure of public health emergencies. In addition, the analysis shows the president's choice to use the structures of the Presidency as main informational support, including strengthening them through unilateral administrative measures. Finally, the results indicate that there is no evidence that the president combined formal and informal advisory as a strategy to access alternative information to the MOH. The findings should be pondered regarding the partly reliable nature of the President’s Daily Diary as a source of relational data. The study provides a conceptual and methodological framework to identify and measure the presidential informal advisory strategy, contributing to the advance of research on presidential advising in Latin America.  

Social networks, e.g. Twitter, have been proved to be almost real-time systems for spreading information, that provide a valuable information channel in emergencies, e.g. disasters. This paper presents a framework designed to distill actionable tweets. The framework tackles the diversity, large volume, and noise of tweets for providing users live information for quick responses. To do that, our framework first retrieves a large number of tweets to ensure the diversity. It next removes irrelevant and indirect tweets for reducing the volume, divides informative tweets into predefined classes for quick navigation, and groups tweets in a class into topics to preserve the diversity. Finally, it ranks tweets in each topic to extract important tweets for the user’s quick scan. For ranking, the framework utilizes event extraction to enrich the semantics and reduce the noise of tweets. After that, the framework builds event graphs for ranking to find out important tweets. To validate the efficiency of our framework, we took Twitter as a case study. Experimental results on five disaster datasets show that our framework achieves promising results compared to strong methods in disaster scenarios.

In this study, we explore the inverted U-shaped association between internal control quality and firm operational efficiency. Although effective internal controls can facilitate and improve operational efficiency, excessive internal controls can negatively affect operational efficiency by (1) influencing management energy, attention, risk-taking, and innovation motivations; (2) hindering employees' creativity, enthusiasm, and trust. Our findings support the inverted U-shaped association. We further explore and prove the two channels through which internal controls affect firm operational efficiency: the "information channel" (the quality of internal management reports), and the "application channel" (the enforcement of internal controls). Additionally, we show that the inverted U-shaped association only exists in non-state-owned firms. We do not find significant association between internal control quality and operational efficiency in state-owned firms. Overall, this study suggests that firms should not only establish an optimal level of internal controls, but also enforce the internal controls effectively to achieve their intended goals.

This content analysis examines print media coverage of Toronto's waterfront development to determine whether story frames perpetuate the dominant social paradigm. Articles from 8 newspapers are analysed in two content dimensions, the sub-issues which surround waterfront development and the ways of understanding the environment presented as relevant to Toronto's waterfront development. Findings show presence of conflict, use of a non-routine information channel and broad source mix do not result in more diverse content. Likewise, characteristics such as a news organization's conventionality (i.e., alternative or mainstream), size and ownership (i.e., independent or group-owned) exert limited influence over story content. Organized around the competitive city concept described by Kipfer and Keil's (2002), this research examines whether media coverage aligns with the capitalist urbanization process, concluding story frames in news discourse de-emphasize the environment as an issue and rely on the least-progressive environment paradigms when reporting on Toronto's waterfront development.