Different Types Of Information Research Articles

A review of imaging methods for detection of photoluminescence in field-installed photovoltaic modules

Abstract It is predicted that the photovoltaic energy conversion will be the largest installed power capacity by 2027. The least costly option for new electricity generation in many of the world’s countries will be the utility-scale solar photovoltaic electricity generation. Accurate monitoring of solar plants for localizing and detecting faults is expected to be one of the critical tasks facing the energy industry. Imaging of photovoltaic modules for the purpose of fault detection can be more efficient and accurate compared to measurements of electrical parameters. Different spectral regions provide different types of information about a faulty module. Detection of photoluminescence, that is, radiation emitted upon band-to-band recombination after charge carrier excitation with an illumination source, has shown a great potential in the laboratory setting. In the recent years, the first approaches in the outdoor setting have been conducted on silicon modules with the Sun and a LED module as excitation source. The present study sums up the different methods for outdoor photoluminescence imaging and emphasizes their differences regarding filtering of the reflected light from the photoluminescence signal. The different types of photoluminescence images obtained from each method and the image processing algorithms are described. Finally, the interpretation of the different types of photoluminescence images is addressed.

Use of antimicrobials and other medical products in an ethnic minority context of South-Central Vietnam: A qualitative study of vulnerability.

Despite the global threat of antimicrobial resistance (AMR), evidence on the use and quality of medicines at community level is limited, particularly in impoverished, rural areas where prevalence of (bacterial) infections is high. To better understand the processes that drive vulnerability to AMR' effects, this study aimed to assess social factors underpinning access to-and use of-medical products and healthcare, among people from the Raglai ethnic minority in Ninh Thuan Province, Vietnam. We conducted ethnographic research in eight villages in 2018-2019, using interviewing and participant observation methods for data collection. Different types of informants (including community members and healthcare providers) were selected using purposive sampling strategies and analysis was retroductive. Our findings show that, despite the existence of a government-funded health insurance scheme, Raglai people's flexible therapeutic itineraries did not systematically start with formal healthcare. Different types of care (private/informal, public, shamanic) were combined in parallel or in alternation, determined by distance to the provider, cost, workload, perceived diagnostic capacity, perceived severity and aetiology of the illness, and trust in the provider. Available medicines were often tablets dispensed in plastic bags containing labelled tablets, unlabelled tablets (in bulk) or tablets ground to powder. Treatment was often considered effective when it relieved symptoms, which led to abandonment of the treatment course. When symptoms did not speedily abate, the illness aetiology would be reinterpreted, and "stronger" medicines would be sought. The precarious socio-economic status of some Raglai drove them in cycles of severe poverty when additional unforeseen factors such as illness, animal disease or loss of crops arose, hampering access to (in)formal healthcare providers and/or appropriate diagnosis and treatment. We conclude that Raglai communities are structurally unable to buffer themselves against the threat and consequences of AMR. Despite this vulnerability, they are among the least targeted by efforts to optimize antibiotic use, which are concentrated in secondary and tertiary healthcare facilities targeted at urban populations.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text. Although most of the existing methods unify the semantic feature extraction and hash function learning modules for end-to-end learning, they ignore this issue and do not use information-rich modalities to support information-poor modalities, leading to suboptimal results, although they unify the semantic feature extraction and hash function learning modules for end-to-end learning. Furthermore, previous methods learn hash functions in a relaxed way that causes nontrivial quantization losses. To address these issues, we propose a new method called graph convolutional network (GCN) discrete hashing. This method uses a GCN to bridge the information gap between different types of data. The GCN can represent each label as word embedding, with the embedding regarded as a set of interdependent object classifiers. From these classifiers, we can obtain predicted labels to enhance feature representations across modalities. In addition, we use an efficient discrete optimization strategy to learn the discrete binary codes without relaxation. Extensive experiments conducted on three commonly used datasets demonstrate that our proposed method graph convolutional network-based discrete hashing (GCDH) outperforms the current state-of-the-art cross-modal hashing methods.

Multi-task convex combination interpolation for meta-learning with fewer tasks

Meta-learning methods try to enhance the generalization of the meta-learning model by various tasks. Diverse tasks can provide sufficient knowledge to assist the model in understanding and capturing different types of information. The sufficient number of meta-training tasks is a crucial factor in ensuring the generalization of meta-learning algorithms. However, obtaining a sufficient number of labeled meta-training tasks is often a challenging endeavor in real-world scenarios. Some scholars have addressed the issue of inadequate tasks only by interpolating between two tasks, but they do not take into account the problem of task diversity. To resolve this concern, we propose a Multi-task Convex Combination Interpolation (MCCI) method that simultaneously considers both task quantity and task diversity issues. First, we increase the number of tasks based on interpolation techniques, which provides the possibility to extend it from 2-task interpolation to n-task interpolation. Second, we randomly select multiple tasks and perform convex combination interpolation in the n-task space, in order to increase the task diversity. Third, we prove the positive effect of the convex combination interpolation on the generalization ability and noise resistance of meta-learning algorithms in theory. Finally, we verify the generalization ability and noise resistance of the proposed MCCI on seven datasets. The extensive experimental results show the superior performance of the proposed method compared to the state-of-art methods.

The neural and cognitive basis of expository text comprehension

As science and technology rapidly progress, it becomes increasingly important to understand how individuals comprehend expository technical texts that explain these advances. This study examined differences in individual readers’ technical comprehension performance and differences among texts, using functional brain imaging to measure regional brain activity while students read passages on technical topics and then took a comprehension test. Better comprehension of the technical passages was related to higher activation in regions of the left inferior frontal gyrus, left superior parietal lobe, bilateral dorsolateral prefrontal cortex, and bilateral hippocampus. These areas are associated with the construction of a mental model of the passage and with the integration of new and prior knowledge in memory. Poorer comprehension of the passages was related to greater activation of the ventromedial prefrontal cortex and the precuneus, areas involved in autobiographical and episodic memory retrieval. More comprehensible passages elicited more brain activation associated with establishing links among different types of information in the text and activation associated with establishing conceptual coherence within the text representation. These findings converge with previous behavioral research in their implications for teaching technical learners to become better comprehenders and for improving the structure of instructional texts, to facilitate scientific and technological comprehension.

Entangled brains and the experience of pains.

The International Association for the Study of Pain (IASP) revised its definition of pain to "an unpleasant sensory and emotional experience." Three recent recommendations for understanding pain if there are no clear brain correlates include eliminativism, multiple realizability, and affordance-based approaches. I adumbrate a different path forward. Underlying each of the proposed approaches and the new IASP definition is the suspicion that there are no specific correlates for pain. I suggest that this basic assumption is misguided. As we learn more about brain function, it is becoming clear that many areas process many different types of information at the same time. In this study, I analogize how animal brains navigate in three-dimensional space with how the brain creates pain. Underlying both cases is a large-scale combinatorial system that feeds back on itself through a diversity of convergent and divergent bi-directional connections. Brains are not like combustion engines, with energy driving outputs via the structure of the machine, but are instead more like whirlpools, which are essentially dynamic patterns in some substrates. We should understand pain experiences as context-dependent, spatiotemporal trajectories that reflect heterogeneous, multiplex, and dynamically adaptive brain cells.

Audio–visual representation learning for anomaly events detection in crowds

In recent years, anomaly events detection in crowd scenes attracts many researchers’ attentions, because of its importance to public safety. Existing methods usually exploit visual information to analyze whether any abnormal events have occurred due to only visual sensors are generally equipped in public places. However, when an abnormal event in crowds occurs, sound information may be discriminative to assist the crowd analysis system to determine whether there is an abnormality. Compared with vision information that is easily occluded, audio signals have a certain degree of penetration. Thus, this paper attempt to exploit multi-modal learning for modeling the audio and visual signals simultaneously. To be specific, we design a two-branch network to model different types of information. The first is a typical 3D CNN model to extract temporal appearance feature from video clips. The second is an audio CNN for encoding Log Mel-Spectrogram of audio signals. Finally, by fusing the above features, the more accurate prediction will be produced. We conduct the experiments on SHADE dataset, a synthetic audio–visual dataset in surveillance scenes, and find introducing audio signals effectively improves the performance of anomaly events detection and outperforms other state-of-the-art methods. Furthermore, we will release the code and the pre-trained models as soon as possible.

Подход к оценке технического состояния электрооборудования с использованием взвешенных нечётких правил

To ensure the smooth operation of all existing electric power systems, it is necessary to periodically diagnose electrical equipment using various methods and models that take into account all possible parameters and factors affecting the condition. The paper considers an approach to assessing the technical condition of electrical equipment using weighted fuzzy rules, taking into account different types of information (measuring, expert). The novelty of the approach is the representation of parameters in the form of fuzzy triangular numbers and the formation of importance weight vectors of term sets parameter values. This makes it possible to accurately assess the technical condition of electrical equip-ment in the context of different types of information to predict the condition of the equipment; to quickly identify pa-rameters which values are outside acceptable limits, thereby determining the preliminary cause of equipment failure, as well as making informed diagnostic decisions regarding the condition of electrical equipment.

The Effect of Macroeconomic Uncertainty on Household Spending

We use randomized treatments that provide different types of information about the first and/or second moments of future economic growth to generate exogenous changes in the perceived macroeconomic uncertainty of treated households. The effects on their spending decisions relative to an untreated control group are measured in follow-up surveys. Our results indicate that, after taking into account first moments, higher macroeconomic uncertainty induces households to significantly and persistently reduce their total monthly spending in subsequent months. Changes in spending are broad based across spending categories and apply to larger durable good purchases as well. (JEL D12, D81, D84, E21, E23, G51)

Implementation of Building Information Modelling (BIM) Technology for Interior Design Industry in Shandong

Building Information Modelling (BIM) is a well-known application in architecture sectors. However, not many people in the related sectors are using BIM for the interior design of a building. Some companies and their interior designers are not having good collaboration towards enhancing BIM for the interior design projects. This study explores the benefits of BIM technology in interior design of Shandong Province, to identify and examine the current status and challenges to propose strategies for an effective BIM implementation in interior design. The study employed a mixed method with the use of primary quantitative data and secondary qualitative data to provide different types of information to meet the aims and objectives of the study. A survey was conducted with 50 engineers working in the construction and interior design sectors who have the experience of using BIM. The respondents were selected through a random sampling method to provide valuable findings to the interior designers and future researchers. The outcome of the research found out that BIM helped to develop their construction knowledge; it allows the interior designers to conceptualise their thoughts and transform it into reality from the process of executing, planning and development phases of a project and ease to change the planning according to the client’s needs. However, not all respondents are equally enthusiastic or in agreement with it. The study recommends future studies to include more sampling through a purposive sampling method with inclusion of more respondents for a more intensified outcome.

Hierarchical processing of feature, egocentric and relational information for spatial orientation in domestic chicks.

Animals can use different types of information for navigation. Domestic chicks (Gallus gallus) prefer to use local features as a beacon over spatial relational information. However, the role of egocentric navigation strategies is less understood. Here, we tested domestic chicks' egocentric and allocentric orientation abilities in a large circular arena. In experiment 1, we investigated whether domestic chicks possess a side bias during viewpoint-dependent egocentric orientation, revealing facilitation for targets on the chicks' left side. Experiment 2 showed that local features are preferred over viewpoint-dependent egocentric information when the two conflict. Lastly, in experiment 3, we found that in a situation where there is a choice between egocentric and allocentric spatial relational information provided by free-standing objects, chicks preferentially rely on egocentric information. We conclude that chicks orient according to a hierarchy of cues, in which the use of the visual appearance of an object is the dominant strategy, followed by viewpoint-dependent egocentric information and finally by spatial relational information.

Exploring relationality in African knowledge systems as a contribution to decoloniality in sustainability science

ABSTRACT The current solutions offered by Western sustainability science to address prevailing global environmental destruction and social injustice are still largely embedded in the Western knowledge system established by colonisation, limiting the efficacy of these solutions for a large part of the planet. Conversely, it may be reasonable to imagine that the concept of relationality is beneficial in all cultures and knowledge systems. Relationality, elementally referring to a web of relationships, as considered from an African Indigenous and local knowledge perspective, could play an important role in decolonising Western sustainability science. Two valuable approaches, namely ubuntu (humanness) and ukama (relatedness), as predominantly observed in southern Africa, are essentially immersed in human-nature relationality. This type of relationality considers everything as interconnected, and therefore that nothing happens in isolation, and that the well-being of humans is inextricable from the well-being of nature. The way relationality is approached in African Indigenous knowledge systems is inclusive, holistic and perpetual, broadening its usefulness to a large audience, making it a sensible contributor to decoloniality in sustainability science. A collective knowledge could emerge, including cooperative, multidirectional interactions with different types of information from diverse human and non-human sources, increasingly eradicating the relational divide among knowledge systems caused by persistent colonial discourse and attitudes.

Preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology.

In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.

Economics of Information Search and Financial Misreporting

ABSTRACTI examine how investors’ search for different types of information affects managers’ reporting decisions. I distinguish investors’ search for information about firm fundamentals (“fundamental search”) from their search for information about managers’ incentives (“incentive search”). Based on a parsimonious model of misreporting, I predict that fundamental search reduces the earnings response coefficient, which reduces managers’ benefits from misreporting, resulting in less misreporting. In contrast, incentive search increases the earnings response coefficient, which increases the benefits from misreporting, resulting in more misreporting. I test these predictions using an empirical technique that classifies EDGAR downloads as fundamental search or incentive search. Consistent with my theoretical predictions, I find that fundamental (incentive) search is negatively (positively) related to the earnings response coefficients and intentional restatements. I confirm my findings in two distinct empirical settings where the costs of information search exogenously changed: the adoption of XBRL and the electronic filing mandate of Form 4s. Collectively, the results show that investors’ information demand can shape managers’ reporting decisions, and its effects can vary depending on the type of information investors search for.

Impact of social networking sites (SNSs) on stock market: Review, synthesis and direction for future research

Social networking sites (SNSs) provide a platform for disseminating different types of information, including stock market information. Many studies in the literature discuss the influence of social media on the stock market. However, little effort has been made in the past to present a literature review. This exploratory study aims to review the literature on social media and the stock market published from 2004 to 2018 and present insights and directions for future research. The study collects data from Scopus and ISI Web of Science databases and subjectively selects 126 papers, and conducts metadata analysis. In addition, the selected papers were analyzed to present the insights and classifications of the literature based on content analysis. This review classified the selected papers into different domains: reaction (e.g., investor behavior, market behavior, corporate governance and financial performance), data analysis techniques (mathematical, econometric models, data and text mining), and application of pre-established models. It identifies influential authors, top journals, top contributing nations, top contributing institutions, top citations, most used words, top publishers and contributions by fields. It develops a framework showing how the information of SNSs is disseminated, processed and responded to. It also finds that most of the paper outcomes support online social media as important information dissemination platforms for investment decisions in the stock market. The study will offer a novel opportunity for future assessment and contribution to the behavioral finance literature.

Citizens' Access to Online Health Information - An International Survey of IMIA Member Countries.

Citizens' access to their online health information is pivotal. Therefore, this study examines citizens' access to their online health information across countries and healthcare settings. The study is based on a survey design targeting the 98 IMIA representatives of the national societies. Results indicate that Test results and Medications are the two types of online information that citizens in most cases have access to. Ten countries provide citizens access to all the different types of information included in the study. That relatively few countries provide citizens access to all the included types of online health information underscores the importance of continuous emphasis on accessibility and research within this field.

Health information-seeking experience in people with head and neck neoplasms undergoing treatment: a qualitative study.

To describe the health information-seeking experience and its influencing factors of people with head and neck neoplasms undergoing treatment. This was a descriptive phenomenology study. Participants were recruited by purposive sampling. The semistructured interviews and all observation results were recorded. The data were analysed using Colaizzi's method. Fourteen participants were selected. We identified four themes that illustrate factors that influence the health information-seeking behaviour of participants: patients' awareness of health information needs, patients' competence, doctor-patient communication, and online advertising interference. We also determined the value of different types of information and patients' information needs and sources. These findings can help professionals understand patients' behaviours and think about how to deliver practical information support in a network environment to guide patients in continuous information seeking while taking specific factors into account.

A workload identification method of industrial robot combining dynamic model and convolutional neural network

The motion joint system of industrial robot has obvious nonlinear characteristics, high dimensional running data and limited number of experimental samples. If mature neural network algorithm is used for identifying the workload of robots, it is easy to have over-learning problems, which seriously restricts the generalization ability of load identification method. In this paper, a load identification method combining the dynamic model of industrial robots with the neural network data model is proposed. A UR5 robot is used for carrying out multiple dynamic load testings to verify the effectiveness of the proposed identification method. Firstly, the workload identification by CNN algorithm is given, and the influence of parameters of prediction model and different neutral network are analyzed. Then the classical dynamic model of industrial robots with multi-degrees of freedom is established. The identified workload by dynamic model is also analyzed. At last, the deterministic information such as velocity and displacement is extracted from the calculation results of the dynamic model as the initial anchoring value. Then convolutional neural network is applied for compensating the residual highly nonlinear information. An improved mixing combination method is also proposed. This method can effectively deal with the interaction of different types of information in the data, and preliminarily cooperate the dynamic model and convolutional neural network. This provides a basic framework and method for solving the problem of parameter identification in multi-degree-of-freedom systems with small samples.

Soft, hard, and digital skills for managers in the digital age: Business requirements and the need to master them

Digitalisation in Ukraine and the world changes products, services, and business processes, affecting the number and quality of jobs due to the need for digital skills. Employers are requiring new skills from candidates in job profiles for existing managerial positions. The purpose of this study was to investigate the impact of soft skills, hard skills, and digital skills on managers in the era of digitalisation based on the analysis of business requirements. The following methods were used: theoretical generalisation and comparison (disclosure of the content of each group of skills), analysis (skills most frequently and most demanded by employers), statistical method (summary and grouping of data collected from the job search portal), synthesis (combination of different types of information), and concretisation (identification of problems in the legislative and regulatory framework of Ukraine). The study established the ratio of skill groups in the analysed positions: sales manager, HR manager, and logistics manager mainly need the following skill groups: soft and hard, but to varying degrees. Soft skills are essential for the positions of sales manager and HR manager, while hard skills prevail for the position of logistics manager. Only project managers need hard skills and digital skills because of their specificity. To provide a better understanding and visualisation of complex information about competencies or skill levels, the authors first introduced a “three-zone competency stoplight” and a colour matrix of the result of soft, hard, and digital skills requirements of employers by position. Three skills groups are proposed to be represented in different colours: orange (soft), pink (hard), and green (digital), which will allow businesses to use this visualisation to see the zones that correspond to their job offers and understand what skills they will require from candidates for the relevant position and to what extent. The practical significance of the study is the possibility of using its results in the development of educational programmes for planning the development of necessary disciplines

The information content of stress test announcements

We exploit institutional features of the U.S. bank stress tests to disentangle different types of information garnered by market participants when the stress test results are released. By examining the reaction of different asset prices, we find evidence that market participants value the stress test announcements not only for the information on possible future capital distributions but also for the signals about bank resilience. These results back the use of stress tests by central banks to inform the broader public about the soundness of the banking system.