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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.

We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.

The global prevalence of childhood and adolescent obesity is a major concern due to its association with chronic diseases and long-term health risks. Artificial intelligence technology has been identified as a potential solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study highlights the importance of early identification and prevention of obesity-related health issues. To develop effective algorithms for the prediction of obesity rates and provide personalized feedback, factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information must be taken into account. Therefore, by collecting health datasets from 321 adolescents who participated in Would You Do It! application, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. Statistical analysis revealed that the performance of the proposed deep learning framework was more statistically significant (p 0.001) compared to the other general models. The proposed system has the potential to effectively address childhood and adolescent obesity.

Pathology imaging is routinely used to detect the underlying effects and causes of diseases or injuries. Pathology visual question answering (PathVQA) aims to enable computers to answer questions about clinical visual findings from pathology images. Prior work on PathVQA has focused on directly analyzing the image content using conventional pretrained encoders without utilizing relevant external information when the image content is inadequate. In this paper, we present a knowledge-driven PathVQA (K-PathVQA), which uses a medical knowledge graph (KG) from a complementary external structured knowledge base to infer answers for the PathVQA task. K-PathVQA improves the question representation with external medical knowledge and then aggregates vision, language, and knowledge embeddings to learn a joint knowledge-image-question representation. Our experiments using a publicly available PathVQA dataset showed that our K-PathVQA outperformed the best baseline method with an increase of 4.15% in accuracy for the overall task, an increase of 4.40% in open-ended question type and an absolute increase of 1.03% in closed-ended question types. Ablation testing shows the impact of each of the contributions. Generalizability of the method is demonstrated with a separate medical VQA dataset.

Knowledge of results (KR), particularly its informational role, has often been regarded as redundant for learning interception-like tasks, such as coincidence–anticipation timing tasks. However, it is possible that the KR’s guiding effect might be detrimental to motor adaptation, instead of only redundant, leading to a dependency on KR and steering the sensorimotor system away from relevant information of the task. In this study, we aimed to investigate KR’s effect on learning a coincidence–anticipation timing tasks and on the adaptation to unpredictable perturbations. Two groups of participants practiced a coincidence–anticipation timing tasks with or without KR on 1 day and underwent testing the next day for learning (Retention test) and for adaptation to unpredictable perturbations (Exposure phase). Both groups exhibited similar learning results but failed to adapt to the perturbations, contradicting the assumption of negative guidance effects of KR and the positive effects of relying solely on intrinsic information (no KR). These findings suggest that motor adaptation may require specific information during the acquisition process, highlighting for more systematic analyses to understand this phenomenon better. Such insights could have practical implications in contexts like sports and rehabilitation, by providing learners with appropriate information for acquiring adaptive internal representations of tasks.

Through experimental research and theoretical analysis, this study primarily aimed to compare the behavior of cement screeds made in a traditional manner with those made with the addition of microreinforcement. The study also explored the possibility of using electrofilter ash as a component of screeds, examining the advantages and disadvantages of partial substitution of cement with fly ash. The contribution of this article is the experimental research on the characteristics of fresh and hardened cement composites, as well as the parameters influencing the structure and behavior of cement screeds during their use. It has been determined that by using electrofilter ash as a partial replacement for cement, satisfactory values of physical–mechanical and deformation characteristics of fine-grained cement composite can be achieved. Through analysis of the obtained results and influential parameters of these composites, the optimal design approach has been explored. This relevant information could potentially provide reliable recommendations to designers and contractors for the production of quality and durable cement screeds.

The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion. The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically. Little is known about its capabilities to identify motivation states and provide appropriate information to facilitate behavior change progression. We evaluated 3 GAs, ChatGPT, Google Bard, and Llama 2 in identifying motivation states following the TTM stages of change. GAs were evaluated using 25 validated scenarios with 5 health topics across 5 TTM stages. The relevance and completeness of the responses to cover the TTM processes to proceed to the next stage of change were assessed. 3 GAs identified the motivation states in the preparation stage providing sufficient information to proceed to the action stage. The responses to the motivation states in the action and maintenance stages were good enough covering partial processes for individuals to initiate and maintain their changes in behavior. However, the GAs were not able to identify users' motivation states in the precontemplation and contemplation stages providing irrelevant information, covering about 20%-30% of the processes. GAs are able to identify users' motivation states and provide relevant information when individuals have established goals and commitments to take and maintain an action. However, individuals who are hesitant or ambivalent about behavior change are unlikely to receive sufficient and relevant guidance to proceed to the next stage of change. The current GAs effectively identify motivation states of individuals with established goals but may lack support for those ambivalent towards behavior change.

Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1536.