Different Types Of Information Research Articles

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.

Role of Human-AI Interaction in Selective Prediction

Recent work has shown the potential benefit of selective prediction systems that can learn to defer to a human when the predictions of the AI are unreliable, particularly to improve the reliability of AI systems in high-stakes applications like healthcare or conservation. However, most prior work assumes that human behavior remains unchanged when they solve a prediction task as part of a human-AI team as opposed to by themselves. We show that this is not the case by performing experiments to quantify human-AI interaction in the context of selective prediction. In particular, we study the impact of communicating different types of information to humans about the AI system's decision to defer. Using real-world conservation data and a selective prediction system that improves expected accuracy over that of the human or AI system working individually, we show that this messaging has a significant impact on the accuracy of human judgements. Our results study two components of the messaging strategy: 1) Whether humans are informed about the prediction of the AI system and 2) Whether they are informed about the decision of the selective prediction system to defer. By manipulating these messaging components, we show that it is possible to significantly boost human performance by informing the human of the decision to defer, but not revealing the prediction of the AI. We therefore show that it is vital to consider how the decision to defer is communicated to a human when designing selective prediction systems, and that the composite accuracy of a human-AI team must be carefully evaluated using a human-in-the-loop framework.

Competitive Differentiation through Intangibles – A Case Study of Maruti Suzuki Ltd.

Contemporary environment is very dynamic and challenging, a company who want to survive and grow only mantra is minimization of competition with differentiation in offering (whether their product or service) for retention of customer to enhance brand loyalty. Competitive differentiation is a new strategy when your product/solution is unique and no one has anything “on the truck” that can compete against your offering at all. Competitive Differentiation is one step ahead from normal differentiation; indicates how a company is going to minimize competition using differentiation as a tool in the light of competitive strategy. Competitive differentiation is possible by unique intangibles in current era. Unique intangible assets are those intangibles which are not common in nature or different status for value creation process and specific advantages offers to a corporate. According to the nature of different industries and corporate’s different types of information’s, internal structures, agreements are exist which are main source of wealth creation. This research paper explicates conceptual understanding of Competitive differentiation, value creation & role of intangibles in general for top Indian corporate’s and as a special case study for Maruthi Suzuki ltd as an automobile company. Researcher analyzes their product offerings, service, brand image which creates a competitive differentiation in the contemporary market. Applying content analysis as tool, finally researcher finds some specific initiatives such as brand management and facelift of models, strong R&D network and skilled engineers, creation of patent every year, strong sales & service outlets, focus on customer’s engagement initiative, Digitalisation and Innovation and accessories etc. are the important intangible segment which creates competitive differentiation for Maruthi Suzuki ltd.

An Investigation of Artificial Life to Examine Systems Related to Natural Life, Its Processes, And Its Evolution, Through the Use of Simulations with Computer Models, Robotics, and Biochemistry

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

An Evaluation of Cyber Physical System (CPS) or Intelligent System in Which a Mechanism is Controlled or Monitored by Computer Based Algorithms

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

A Cross-Chain Trusted Reputation Scheme for a Shared Charging Platform Based on Blockchain

With the development of electric vehicles, the shortage of charging piles (CPs) has gradually been exposed. In response to this situation, CP operators have taken private CPs into the shared charging system. Due to the lack of maintenance personnel for private CPs that join shared charging, users often face the problems of damaged CPs and poor service attitudes of CP owners. Reputation solutions based on third-party platforms face a problem of single-point failures and reputation solutions based on blockchain face problems of storage and query efficiency. To improve storage and query efficiency, this article proposes a multichain charging model that stores different types of information on different blockchains. However, it faces the problem of unreliable information called across chains, when calculating reputation across chains. Therefore, this article proposes a cross-chain trusted smart contract ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{2}T$ </tex-math></inline-formula> smart contract) to ensure the authenticity, real-time, and interchain write mutual exclusion of cross-chain information, making reputation calculation in the multichain charging model more convenient and more accurate. Especially, we propose a data mutual trust mechanism based on Merkle proof to ensure the authenticity of cross-chain information and prevent forged information from participating in calculating reputation. Furthermore, we present a data structure composed of multiple counting Bloom filters (MCBFs) to verify the real time of information and filter out non-real-time information, thereby ensuring the real time of the calculated reputation. In addition, we put forward an algorithm to guarantee the interchain write mutual exclusion by hash mutexes, making the reputation calculation process more accurate and complete. The security analysis and experimental results demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{2}T$ </tex-math></inline-formula> smart contract is feasible in practice.

Language shapes children's attitudes: Consequences of internal, behavioral, and societal information in punitive and nonpunitive contexts.

Research has probed the consequences of providing people with different types of information regarding why a person possesses a certain characteristic. However, this work has largely examined the consequences of different information subsets (e.g., information focusing on internal vs. societal causes). Less work has compared several types of information within the same paradigm. Using the legal system as an example domain, we provided children (N = 198 6- to 8-year-olds) with several types of information-including information highlighting internal moral character, internal biological factors, behavioral factors, and societal factors-about why a specific outcome (incarceration) might occur. We examined how such language shaped children's attitudes. In Study 1, children reported the most positivity toward people who were incarcerated for societal reasons and the least positivity toward people who were incarcerated for their internal moral character; attitudes linked with behavioral information fell between these extremes. Studies 2a-2b suggested that Study 1's effects could not be fully explained by participants drawing different inferences about individuals in Study 1. Study 3 replicated Study 1's results and showed that information linking incarceration with internal biological factors led to more positivity than information linking incarceration with internal moral character. Finally, Study 4 suggested that the patterns found in Studies 1 and 3 generalize to nonpunitive contexts. Moreover, Study 4 found that the effects in Studies 1 and 3 emerged regardless of whether information was communicated via explanations or descriptions. These results demonstrate that how we express our beliefs about social phenomena shape the realities in which others live. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Online Agro Product Shop

Abstract: In this Online Agro product Shop Project we will build a user-friendly website in order to buy and sell agriculture products online. The main purpose to build this store is to facilitate farmers to buy seeds, pesticides, and fertilizers from anywhere through internet connectivity. To facilitate remote area farmers to buy agriculture products, there is a need to build an online agriculture product store. Through the Internet, customers and business owners can communicate with each other, share a different type of information and resource.

Design of Proactive Interaction for In-Vehicle Robots Based on Transparency.

Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combinations of information in different channels of the in-vehicle robot, based on a driving simulator to collect subjective and objective data, which focuses on users’ safety, usability, trust, and emotion dimensions under driving conditions. The results show that appropriate transparency expression is able to improve drivers’ driving control and subjective evaluation and that drivers need a different amount of transparency information in different types of tasks.