Different Types Of Information Research Articles

Illuminating Transparency in Digital Historical Visualizations: Notre-Dame of Paris, St Stephen’s of Westminster, and Visualizing Venice

Illuminating Transparency in Digital Historical Visualizations: Notre-Dame of Paris, St Stephen’s of Westminster, and Visualizing Venice

Individual differences in depression are reflected in negative self-evaluations when imagining future events

Emotional disorders, including depression, are associated with deficits in retrieving past and imagining future autobiographical events. Imagining future events requires accessing different types of information, from general conceptual knowledge to specific event details. Here, we tested the hypothesis that depression levels within a community sample are most strongly reflected in how conceptual information about the self (i.e., self-schemas) are accessed. In an online experiment, we collected ratings of depression as well as anxiety, which often presents alongside depression, in a group of participants who then completed a trait judgment task in which they judged whether positive and negative traits reflected the self or another person, followed by an event imagination task in which participants generated specific future events for the self or another person. A second experiment was run on a separate group of participants who performed these same tasks in reversed order. Across experiments, we found that depression but not anxiety levels were associated with greater endorsement of negative traits only for the self, was not related to the ability to imagine specific future events but did alter how these events were evaluated. An exploratory analysis revealed greater endorsement of negative traits for the self when the trait judgement task came before imagining events. These results provide new insights into how depression levels in a subclinical sample are associated with changes in autobiographical knowledge, enhancing negative self-schemas, when imagining future events.

Comparative study of the cost of implementing wireless technologies for IoT and M2M for the last mile: A case study

The need to determine which wireless IoT technology, currently available on the market, will be able to support a sizeable simultaneous load of information, types of electrical emergency alarms, required bandwidths, delays-latency, area coverage, and economic cost to serve areas of 600 square kilometers of surface and several users of around two million homes. This article compares Sigfox, LoRa, NB-IoT, and WiMAX technologies as possible solutions for last-mile communications. For this purpose, a network of hundreds of smart meters has been simulated, each transmitting different types of information. In conclusion, it has been obtained that, for a context with the priority of alarms and a large amount of information transport in massive last-mile communications in smart grids and for electrical metering traffic, it is highly recommended that it be done through WiMAX, which in the evaluation shows the best result compared to the other technologies.

What Does Northern European Travel Means to South Koreans? Focusing on Travel Decision Process

The main purpose of this study is to analyze different types of information that may have diverse effects on the travel decision-making process. We collected the most frequently used words related to “Northern European travel” from Korean news sources and blogs, to determine whether there were any systematic differences through network text analysis and CONCOR analysis. We found that Koreans are exposed to words such as “nature” and “cruise activities”, which may lead them to perceive traveling in Northern Europe as a special experience. While political and diplomatic issues are frequently discussed in the news, practical information is discussed in blogs.

Fabrication of Gold Nanostructures on Quartz Crystal Microbalance Surface Using Nanoimprint Lithography for Sensing Applications.

A quartz crystal microbalance (QCM) is a sensor that uses the piezoelectric properties of quartz crystals sandwiched between conductive electrodes. Localized surface plasmon resonance (LSPR) is an analytical technique that uses the collective vibration of free electrons on metal surfaces. These measurements are known as analysis techniques that use metal surfaces and have been applied as biosensors because they allow for the label-free monitoring of biomolecular binding reactions. These measurements can be used in combination to analyze the reactions that occur on metal surfaces because different types of information can be obtained from them. However, as different devices are used for these measurements, the results often contain device-to-device errors and are not accurately evaluated. In this study, we directly fabricated gold nanostructures on the surface of a QCM to create a device that can simultaneously measure the mass and refractive index information of the analyte. In addition, the device could be easily fabricated because nanoimprint lithography was used to fabricate gold nanostructures. As a proof of concept, the nanoparticle adsorption on gold nanostructures was evaluated, and it was observed that mass and refractive index information were successfully obtained without device-to-device errors.

Trend of social media use by undergraduate medical students; a comparison between medical students and educators

PurposeSocial media (SM) is one of the most powerful tools of communication and learning in the recent era. Different types of information can be shared through these social networking sites in the form of texts, videos, pictures, audios, and references (contacts). Due to the constant increase in the use of these social networking sites in our daily routine life especially during the COVID 19 pandemic, their use in teaching and learning has become inevitable. Social media has immense potential to enhance its role in educational settings. Both the students and educators use it for communication, education, sharing and expressing knowledge, and recreation. Therefore, the present study aims to find out the most frequently used social network sites for learning and easy communication between medical students and educators. ObjectiveThis study sought to explore the most frequently used social networking sites by the medical students and educators at Batterjee Medical College. MethodsA cross-sectional study was carried out to assess the trends of usage of SM as an extracurricular way of enhancing learning and teaching experience among medical students and educators in Batterjee Medical College; Saudi Arabia from November 2020 to March 2021.A pre-validated self-administrated questionnaire was built using Google Drive forms and distributed to medical students and educators via emails and WhatsApp. Convenient sampling was used to collect the data. ConclusionSocial media has immense potential to enhance its role in educational settings. Students in our study preferred YouTube and WhatsApp for their learning and communication especially, during COVID 19 pandemic. However, to further enhance their utility choosing the right platform, the amount and quality of the information shared to ensure optimal benefit, providing ethical guides, and professional standards for SM use at institutional levels are the few challenges that need to address.

Multiple effects of ICT investment on carbon emissions: evidence from China.

Little research has been conducted on the multiple possible future environmental effects of different types of information and communication technology (ICT) investment. This paper innovatively calculates the ICT productive capital stock (PCS) in China from 2007 to 2018 and explores the multiple effects of ICT PCS on carbon emissions. The results show that (1) ICT PCS is conducive to carbon emission reduction; furthermore, ICT software PCS has a significant carbon emission reduction effect, while ICT hardware PCS has the opposite outcome. (2) The spatial effect demonstrates that ICT and its hardware and software PCS can significantly reduce carbon emissions in surrounding areas. (3) The ICT PCS indirectly affects carbon emissions through the digital economy and energy efficiency, but the role of the influence mechanism varies according to the type of ICT PCS. (4) There is a nonlinear relationship between all ICT PCS and carbon emissions due to differences in green productivity and ICT PCS levels. Finally, this study provides valuable references for optimizing ICT investment and promoting low-carbon development.

Multiple types of navigational information are independently encoded in the population activities of the dentate gyrus neurons

The dentate gyrus (DG) plays critical roles in cognitive functions, such as learning, memory, and spatial coding, and its dysfunction is implicated in various neuropsychiatric disorders. However, it remains largely unknown how information is represented in this region. Here, we recorded neuronal activity in the DG using Ca2+ imaging in freely moving mice and analyzed this activity using machine learning. The activity patterns of populations of DG neurons enabled us to successfully decode position, speed, and motion direction in an open field, as well as current and future location in a T-maze, and each individual neuron was diversely and independently tuned to these multiple information types. Our data also showed that each type of information is unevenly distributed in groups of DG neurons, and different types of information are independently encoded in overlapping, but different, populations of neurons. In alpha-calcium/calmodulin-dependent kinase II (αCaMKII) heterozygous knockout mice, which present deficits in spatial remote and working memory, the decoding accuracy of position in the open field and future location in the T-maze were selectively reduced. These results suggest that multiple types of information are independently distributed in DG neurons.

Give Up Flights? Psychological Predictors of Intentions and Policy Support to Reduce Air Travel.

Concerted, timely action for mitigating climate change is of uttermost importance to keep global warming as close to 1.5°C as possible. Air traffic already plays a strong role in driving climate change and is projected to grow—with only limited technical potential for decarbonizing this means of transport. Therefore, it is desirable to minimize the expansion of air traffic or even facilitate a reduction in affluent countries. Effective policies and behavioral change, especially among frequent flyers, can help to lower greenhouse gas emissions. For both, a positive evaluation and public support is indispensable. This study contributes to understanding air travel behavior and the perception of regulative policies. We examined the role of attitudes, perceived behavioral control, efficacy, global identity, and justice concerns for intentions to avoid flights and aviation-related environmental policy support. We conducted an online survey study with a quota sample of N = 2,530 participants in Germany. The strongest positive predictors of intentions to refrain from flying and policy support were perceived behavioral control to travel without flying, efficacy beliefs that avoiding air travel contributes to climate change mitigation, and intergenerational justice concerns; pro-travel attitude was a negative predictor. Moreover, we tested whether the provision of additional information on climate impact, global and intranational inequalities as well as subsidies (implying intranational inequality) affected the intention to avoid air travel and policy support. We found no effects of the different types of information. Nor did we find an interaction between the type of information provided and global or national identity. Our results highlight the need for a shift within the mobility sector that facilitates attractive and accessible transport alternatives in order to strengthen people’s behavioral control to choose other means than planes and their efficacy perceptions. Moreover, raising awareness of the impacts of climate change on future generations and developing strategies to promote people’s concern for intergenerational justice might motivate people to reduce air travel and thereby contribute to a livable future for new generations.

Explanations and Causal Judgments Are Differentially Sensitive to Covariation and Mechanism Information.

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation – even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition.

Together they shall not fade away: Opportunities and challenges of self-tracking for dementia care

Dementia is a major public health concern, and mobile technologies have been identified as having the potential to improve the quality of life for individuals with dementia and their caregivers. Recent research, however, suggests that technology-based solutions are frequently driven by and beneficial to caregivers, not both. Emerging personal technologies that are equipped with self-tracking are an ideal option for accommodating person-centered care in light of the collaborative nature of dementia care. However, there is a lack of investigation on how tracking occurs in the context of dementia care. This article presents a thematic analysis of the types of tracking-related information desired in dementia care online communities, as well as how and by whom they are desired through adapting the Conceptual Model of Shared Health Informatics (CoMSHI) for tracking in chronic illness management. Our findings show that four types of tracking metrics are desired for nine types of information: safety alert for wandering, falls, and strangers, reminders of daily life activities and medical and health related activities, monitoring data related to daily life activities and data related to health status, and remote control for patients' daily life activities and financial safety. Family members, caregivers, community members, and persons with dementia involved in work with tracked data face specific challenges. We recommend that self-tracking technologies be implemented in dementia care through collaboration, with the recognition of different types of information as well as different roles involved and with particular attention paid to different types of data work and related roles in data work.

ИНФОРМАЦИОННАЯ ЗНАЧИМОСТЬ ФИНАНСОВОГО АНАЛИЗА ЭКОНОМИЧЕСКИХ СУБЪЕКТОВ

The real result of the financial analysis of an economic entity directly depends on the accuracy and quality of the information used. Undoubtedly, the quality of the analysis cannot exceed the quality of the information used. That is why different types of information are used in the analysis. In some studies, the information used in the analysis is limited to financial information only. Along with financial information, when conducting financial analysis, production, technical, social, environmental and macroeconomic information is used, as well as conditional information is necessary when interpreting the results and developing development prospects. Limiting the analysis to financial information reduces the analytical potential of an economic entity for an objective assessment of its production potential, its opportunities for intensification and various trends in the sector in which this entity operates, as well as at the regional and national levels.

Preparing for the unknown: How working memory provides a link between perception and anticipated action

What mechanisms underlie the transfer of a working memory representation into a higher-level code for guiding future actions? Electrophysiological correlates of attentional selection and motor preparation processes within working memory were investigated in two retrospective cuing tasks. In the first experiment, participants stored the orientation and location of a grating. Subsequent feature cues (selective vs. neutral) indicated which feature would be the target for later report. The oscillatory response in the mu and beta frequency range with an estimated source in the sensorimotor cortex contralateral to the responding hand was used as correlate of motor preparation. Mu/beta suppression was stronger following the selective feature cues compared to the neutral cue, demonstrating that purely feature-based selection is sufficient to form a prospective motor plan. In the second experiment, another retrospective cue was included to study whether knowledge of the task at hand is necessary to initiate motor preparation. Following the feature cue, participants were cued to either compare the stored feature(s) to a probe stimulus (recognition task) or to adjust the memory probe to match the target feature (continuous report task). An analogous suppression of mu oscillations was observed following a selective feature cue, even ahead of task specification. Further, a subsequent selective task cue again elicited a mu/beta suppression, which was stronger after a continuous report task cue. This indicates that working memory is able to flexibly store different types of information in higher-level mental codes to provide optimal prerequisites for all required action possibilities.

Inscriptions in Science Teaching: From Realism to Abstraction

This article attempts to highlight inscriptions, i.e., photographs, drawings, diagrams, or graphs as autonomous carriers of meaning that can illuminate the different dimensions of a scientific concept. In addition, the article examines the inherent potential of diverse types of inscriptions to be combined with each other creating conceptual sequences and thus, crafting a narrative for the formation of a concept. For the formation of conceptual sequences the proper synthesis of both naturalistic and abstractive inscriptions that hold different types of information and complement each other is suggested. That is, inscriptions such as photographs that hold morphological relevance with their referent describing at the same time the everyday knowledge, as well as inscriptions such as graphs or equations that, from a morphological perspective, have a no linear connection to their referent and are related to the typical visual code of school knowledge. Thus, existing, transformed, or novel inscriptions can create conceptual continuums offering logical connections between visual codes from everyday experience and the codes of diagrams, graphs, and equations. From both the teachers and the students, when inscriptions cooperate with the human body, oral language, and other elements of the space, constitute a critical aspect in multimodal communication within school classrooms.

Online robot guidance and navigation in non-stationary environment with hybrid Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can be easily combined. Since there is not a single learning method that can benefit all targets, hybrid Hierarchical Reinforcement Learning (hHRL) was proposed to use various methods to optimize the learning with different types of information and objectives in one application. The previous hHRL method, however, requires manual task-specific designs, which involves engineers’ preferences and may impede its transfer learning ability. This paper, therefore, proposes a systematic online guidance and navigation method under the framework of hHRL, which generalizes training samples with a function approximator, decomposes the state space automatically, and thus does not require task-specific designs. The simulation results indicate that the proposed method is superior to the previous hHRL method, which requires manual decomposition, in terms of the convergence rate and the learnt policy. It is also shown that this method is generally applicable to non-stationary environments changing over episodes and over time without the loss of efficiency even with noisy state information.

Online financing without FinTech: Evidence from online informal loans

We present the first comprehensive dataset on an online informal micro-lending community. These informal loans are small, short duration and high-cost. Using our unique micro data, and the Covid-19 pandemic as a laboratory, we uncover different types of information contained on loan terms and on the narratives of market participants. First, loan terms reflect the aggregate economic context of borrowers and lenders. Second, narratives among market participants contain additional and timely information about aggregate and individual borrower circumstances. Third, lenders imperfectly screen on both loan terms and narrative information. These findings highlight the role of data in FinTech. Transparency on micro-loans can improve the efficiency of the credit market, democratizing access to finance for borrowers, while protecting lenders.

Avis sur les restaurants en ligne: détection et analyse quantitative

The study examines, from various perspectives, restaurant evaluations written by customers and published online. From a manual observation of the corpus, we propose a model to study evaluations of experiences in restaurants; we show that evaluations can have several functions and contain different types of information: reviewers write reviews to give opinions, make suggestions, express intentions, and describe their experiences. We conduct various quantitative analyses using corpus linguistics methods which offer the possibility to identify the keywords of the reviews as well as their grammatical and topologocial characteristics, including keyword analysis, the distribution of parts of speech and the positions of evaluation categories within reviews. We finally try to automatically detect the evaluation categories in our corpus. We test and validate a supervised learning approach based on our model completed with other linguistic and textual features.

INVESTIGATION OF ENVIRONMENTAL DETERMINANTS FOR AGRITOURISM DEVELOPMENT IN ALMATY REGION OF KAZAKHSTAN

This study aims to comprehend relations between environmental determinants and agritourism by example of the Almaty region of Kazakhstan. As a basis for the research of this specific content the representation about agrarian recreational-tourist complexes (ARTCs) was developed and applied (ARTCs are special territorial and intersectoral integrities which largely depend on environmental determinants). Agritourism development is presented as the most important prerequisite for different successful tourism activities in Kazakhstan. With use of the developed instruments, information of different types and cartographical data 15 ARTCs were identified within the Almaty region as well as the influence of the environmental determinants for agritourism was studied. In bare outlines, the methods of functional agritourism and ecological agritourism analysis of the ARTCs’ territory based on analysis of the environmental determinants are described. The results of the research can be used for the establishment of prospects for agritourism development within the ARTCs as well as for the development of address recommendation system.

Single Image Dehazing Based on Two-Stream Convolutional Neural Network

Objective The haze weather environment leads to the deterioration of the visual effect of the image, and it is difficult to carry out the work of the advanced vision task. Therefore, dehazing the haze image is an important step before the execution of the advanced vision task. Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors, but the effect is unstable when dealing with complex scenes. In the method based on convolutional neural network, the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image, and the image spatial information is lost in the encoding stage. In order to overcome these problems, this paper proposes a novel end-to-end two-stream convolutional neural network for single image dehazing. Method The network model is composed of a spatial information feature stream and a high-level semantic feature stream. The spatial information feature stream retains the detailed information of the dehazing image, and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image. A spatial information auxiliary module is designed between the feature streams. This module uses the attention mechanism to construct a unified expression of different types of information, and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network. A parallel residual twicing module is proposed, which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images. Result The peak signal-to-noise ratio and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image. The structure similarity and peak signal-to-noise ratio of the method in this paper reached 0.852 and 17.557dB on the Hazerd dataset, which were higher than all comparison algorithms. On the SOTS dataset, the indicators are 0.955 and 27.348dB, which are sub-optimal results. In experiments with real haze images, this method can also achieve excellent visual restoration effects.

The effect of recycled water information providing on public acceptance of recycled water:An Eye-Tracking Experiment

Public acceptance plays a key role in promoting recycled water. This study attempts to explore how information providing affects public acceptance of recycled water and whether people pay different levels of attention to different types of information for various uses of recycled water. Two experimental groups were provided with information of recycled water for toilet flushing and drinking, respectively. We surveyed the acceptance of recycled water by questionnaires, and compared the acceptance of participants in two experimental groups with those without information. We found that information providing increased the acceptance of recycled water for drinking but decreased that for flushing. An eye-tracking experiment was conducted to explore whether there were differences in the levels of attention to the various types of information for different uses. Results showed that for drinking used recycled water, people spent more time on processing new information, and resolving the incongruity between information and existing schema.