Relevant Information Research Articles

Relevance of Accounting Information in Earnings Management to Stock Prices as a Source of Investment Decisions

This study investigates the influence of earnings management on stock prices within the banking sector companies listed on the Indonesia Stock Exchange (IDX) from 2016 to 2023. By employing secondary data from annual financial reports and utilizing statistical analysis through SPSS, the study aims to explore whether accounting information specifically in the form of earnings management plays a significant role in investment decisions. The analysis employs various statistical tests including ANOVA and t-tests to determine the significance of earnings management in influencing stock price movements. The results indicate that earnings management has a significant impact on stock prices, suggesting that it serves as a crucial signal to the market. This study contributes to the literature on accounting information's relevance in investment decisions, offering insights for investors and policymakers in understanding the potential effects of earnings manipulation on financial markets.

Privacy-Preserving Text Summarization Using Semantic Similarity With Biobert And Clinicalbert For Multiple Medical Documents Leveraging Parallelized High-Performance Computing

The enormous volume of textual data produced by medical documents in the healthcare industry provides insightful information, but it also presents serious privacy, data security, and computational complexity issues. Through the use of parallelized high-performance computing (HPC), this research presents a unique framework for the privacy-preserving text summarization of various medical records utilizing semantic similarity algorithms driven by modified BioBERT and ClinicalBERT. In order to maximize productivity, the framework uses distributed computing environments and secure computation approaches to satisfy the demand for summarizing sensitive medical data while maintaining anonymity. This study shows that the method offers quick and privacy-compliant summarization, protecting patient privacy without sacrificing the information's relevance and semantic accuracy.

Tools for planning large-scale measurement surveys for the assessment of indoor environmental pollutants: the case of radon

Risks from indoor pollutants require the implementation of effective policies to prevent and reduce exposure. To take decisions and actions, competent authorities need relevant information. In the case of indoor radon exposure, surveys are carried out by installing detectors in buildings (homes, workplaces) that measure radon concentration in rooms. Conducting national or large-scale surveys in buildings requires addressing both technical complexities of implementation and economic costs. In order to support the implementation of large-scale radon surveys, procedure and tools are described for defining the sample size of buildings to be measured according to available resources, for planning the sampling of buildings in relation to the specific objectives of the survey, and for obtaining a tool for management and control of sample recruitment and installation of radon detectors in buildings, reducing sources of bias. A particular application is shown for the case of the EU radon regulation.

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI.

In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. One example in neuroscience is the stratification of connections between different cortical depths or "laminae", which is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm 3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between areas and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial depths of the cortex dominated information transfer and deeper depths drove clustering. These differences were largest in frontotemporal and limbic regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information; thus, multilayer connectomics could provide a methodological framework for studies on how information flows across this stratification.

РОЛЬ И ЗНАЧЕНИЕ ФИНАНСОВОЙ И НЕФИНАНСОВОЙ ИНФОРМАЦИИ ДЛЯ УСПЕШНОЙ РЕАЛИЗАЦИИ ИНФРАСТРУКТУРНЫХ ПРОЕКТОВ

The article shows the importance of finan-cial and non-financial information in various areas, examines the historical aspects of the formation of requirements for integrated reporting as a brief form of communication about the strategy, management, perfor-mance results and prospects of the organi-zation, taking into account its external envi-ronment. The authors substantiate that the importance of relevant and reliable infor-mation increases within the framework of project activities, and especially in the de-velopment and implementation of infra-structure projects. The availability of inte-grated financial and non-financial infor-mation ensures transparency in interaction between project participants, facilitates the task of monitoring the situation with the implementation of the project, allows for effective management of the impact of the project on the environment and ensures the minimization of negative consequences.

Propagation Structure Fusion for Rumor Detection Based on Node-Level Contrastive Learning.

With the rise of social media, the rapid spread of rumors online has resulted in numerous negative effects on society and the economy. The methods for rumor detection have attracted great interest from both academia and industry. Given the widespread effectiveness of contrastive learning, many graph contrastive learning models for rumor detection have been proposed by using the event propagation structure as graph data. However, the existing contrastive models usually treat the propagation structure of other events similar to the anchor events as negative samples. While this design choice allows for discriminative learning, on the other hand, it also inevitably pushes apart semantically similar samples and, thus, degrades model performance. In this article, we propose a novel propagation fusion model called propagation structure fusion model based on node-level contrastive learning (PFNC) for rumor detection based on node-level contrastive learning. PFNC first obtains three augmented propagation structures by masking the text of each node in the propagation structure randomly and perturbing some edges in the propagation structure based on the importance of edges. Then, PFNC applies the node-level contrastive learning method between every two augmented propagation structures to prevent the samples with similar propagation structure from far away. Finally, a convolutional neural network (CNN)-based model is proposed to capture the relevant information that is consistent and supplementary among three augmented propagation structures by regarding the propagation structure of the event as a color picture, three augmented propagation structures as color channels, and each node as a pixel. The experimental results on real datasets show that the PFNC significantly outperforms the state-of-the-art models for rumor detection.

Unveiling the Latest Breakthroughs: A Comprehensive Review of the Therapeutic Activity and Safety Profile of Aloe vera.

The use of herbal drugs as alternative and complementary medicine has increased in popularity, raising concerns about their safety profile. Aloe vera, a plant with diverse therapeutic properties, has been extensively used for centuries. This review aims to assess the therapeutic activity and safety profile of Aloe vera. A comprehensive literature search was conducted to gather relevant information from various biomedical databases. The chemical composition, mechanism of action, and therapeutic activities of Aloe vera were analyzed. Aloe vera contains numerous active components such as vitamins, enzymes, minerals, sugars, lignin, saponins, and anthraquinones. Its mechanisms of action involve collagen synthesis, anti-inflammatory effects, immune modulation, laxative properties, and antiviral activity. Aloe vera has demonstrated potential therapeutic benefits in wound healing, diabetes management, liver and kidney protection, and glycemic control. However, it is essential to consider potential side effects, such as skin irritation and allergic reactions. This review provides evidence-based information to improve patient safety and promote informed decisions regarding the use of Aloe vera as a therapeutic agent.

Topical Anti-ulcerogenic Effect of the Beta-adrenergic Blockers on Diabetic Foot Ulcers: Recent Advances and Future Prospectives.

Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes. The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs. Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review. The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures. There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

Molecular Targets of Valeric Acid: A Bioactive Natural Product for Endocrine, Metabolic, and Immunological Disorders.

Postbiotics produced by gut microbiota have exhibited diverse pharmacological activities. Valeric acid, a postbiotic material produced by gut microbiota and some plant species like valerian, has been explored to have diverse pharmacological activities. This narrative review aims to summarise the beneficial role of valeric acid for different health conditions along with its underlying mechanism. In order to get ample scientific evidence, various databases like Science Direct, PubMed, Scopus, Google Scholar and Google were exhaustively explored to collect relevant information. Collected data were arranged and analyzed to reach meaningful a conclusion regarding the bioactivity profiling of valeric acid, its mechanism, and future prospects. Valeric acid belongs to short-chain fatty acids (SCFAs) compounds like acetate, propionate, butyrate, pentanoic (valeric) acid, and hexanoic (caproic) acid. Valeric acid has been identified as one of the potent histone deacetylase (HDAC) inhibitors. In different preclinical in -vitro and in-vivo studies, valeric acid has been found to have anti-cancer, anti-diabetic, antihypertensive, anti-inflammatory, and immunomodulatory activity and affects molecular pathways of different diseases like Alzheimer's, Parkinson's, and epilepsy. These findings highlight the role of valeric acid as a potential novel therapeutic agent for endocrine, metabolic and immunity-related health conditions, and it must be tested under clinical conditions to develop as a promising drug.

An Update on Glutathione's Biosynthesis, Metabolism, Functions, and Medicinal Purposes.

Glutathione (GSH) has been the focus of increased scientific interest in the last decades. It plays a crucial role in all major physiological processes by supplying antioxidant defenses through participating in cellular redox reactions in the human body and other living organisms. GSH also participates in detoxifying xenobiotics, protecting protein thiols from crosslinking and oxidation, regulating the cell cycle, storing cysteine, etc. The significant role of GSH in the most important physiological processes has been highlighted, such as maintaining the redox balance and reducing oxidative stress due to its ability to inactivate the reactive oxygen, nitrogen, and sulfur species. It can also enhance metabolic detoxification and regulate the function of the immune system. All of these characteristics make it a universal biomarker since its proper balance is essential for improving health and treating some age-related disorders. This review presents a current concept of the synthesis and metabolism of GSH; its main functions in a living organism, and as a precursor and cofactor; data on the use of GSH for medicinal purposes in the prevention and treatment of some diseases, as well as a nutritional strategy to maintain a normal pool of GSH in the body. The data were gathered by searching relevant information in multiple databases, such as PubMed, Scopus, ScienceDirect, and Google Scholar.

Open-Ended Online Learning for Autonomous Visual Perception.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn" among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization

Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared, which is then analyzed by human experts to determine the potential risk of remaining landmines. In this article, we propose RELand system to support these tasks, which consists of three major components. We (1) provide general feature engineering and label assigning guidelines to enhance datasets for landmine risk modeling, which are widely applicable to global demining routines, (2) formulate landmine presence as a classification problem and design a novel interpretable model based on sparse feature masking and invariant risk minimization, and run extensive evaluation under proper protocols that resemble real-world demining operations to show a significant improvement over the state-of-the-art, and (3) build an interactive web interface to suggest priority areas for demining organizations. We are currently collaborating with a humanitarian demining NGO in Colombia that is using our system as part of their field operations in two areas recently prioritized for demining. The resulting dataset and developed code can be found here .

External Nonfinancial Measures in Substantive Analytical Procedures: Contributions of Weather Information

ABSTRACT Recent studies and new standards suggest that auditors can use information from expanded external sources to verify their clients’ financial information. We propose advanced substantive analytical procedures with external nonfinancial measures derived from weather information to test whether it helps detect misstatements. Using computational simulations and daily store-level sales data, we test whether the proposed procedures with weather indicators outperform the procedures without such indicators in substantive analytical procedures to identify overstated daily store sales. For the multilocation retail firm examined in this study, we find that the models with one or more weather indicators perform better at detecting misstatements than the models without them. When the reliability of relevant internal information is in question, the usefulness of weather indicators is apparent. Overall, our results provide evidence suggesting the potential value of external nonfinancial measures in auditing.

Orthogonal neural representations support perceptual judgements of natural stimuli.

In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides.

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

High volume online hemodiafiltration: a global perspective and the Brazilian experience.

Online hemodiafiltration (HDF) is a rapidly growing dialysis modality worldwide. In Brazil, the number of patients with private health insurance undergoing HDF has exceeded the number of patients on peritoneal dialysis. The achievement of a high convection volume was associated with better clinical imprand patient - reported outcomes confirming the benefits of HDF. The HDFit trial provided relevant practical information on the implementation of online HDF in dialysis centers in Brazil. This article aims to disseminate technical information to improve the quality and safety of this new dialysis modality.

Information extraction from automotive reports for ontology population

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling ( OCD), 1 1 industryportal.enit.fr/ontologies/OCD , 2 2 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the [Formula: see text] ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).