Determinants of S.M.E.s capital structure in the Visegrad group

In this article, we investigate the capital structure determinants of the Visegrad group, namely the Czech Republic, Slovakia, Hungary, and Poland for small- and medium-sized enterprises (S.M.E.s) from 2011 to 2018. We compare the capital structures of S.M.E.s across all mentioned countries and define how these may impact capital structure choices. The results show S.M.E.s in these countries determine their capital structure in similar ways and all the factors analysed in this study (except for growth opportunity) provide robust explanatory power for companies across all four countries. We find that profitability, liquidity, firm size, assets structure, and non-debt tax (depreciation of total assets) have a significant negative impact on capital structure for all four countries. Our study should be of interest to policymakers and companies who want to optimise their capital structure in order to improve company performance

Factors Affecting Prosocial Sharing Health-related Information on Social Media During a Health Crisis

During a health crisis, prosocial sharing of health-related information (HRI) on social media can help to deliver early warnings about new diseases, raise social awareness, exchange support, and spread health policies. Current literature has mainly focused on the factors of general sharing of HRI under normal conditions but neglected those motivations under the health crisis context. This research aims to investigate factors that influence online users’ prosocial sharing of HRI during a health crisis. To obtain the objective, this study developed a dual helping-protecting motivation model from the fear appeal model and social exchange theory. The partial least squares analysis, carried out on the surveyed data of 326 participants, revealed that prosocial sharing intention is affected by protecting factors (i.e., sharing efficacy, response efficacy) and helping factors (i.e., reciprocity expectation). Additionally, both perceived health risk and perceived information quality risk were found to influence the sharing intention via motivational factors

An Efficient Transmission Power Design for SWIPT Multi-antenna Network Integrated by an Intelligent Reflecting Surface

In this work, intelligent reflecting surface (IRS) is integrated to improve the transmission power in the simultaneous wireless information and power transfer (SWIPT) system with hybrid time-switching (TS) users. The considered scenario includes one base station (BS), one IRS, and multiple TS users, where the BS transmits the information and energy signals to the receivers with IRS assistance. The sum transmission power minimization problem is formulated under the quality-of-service constraints of data rate and energy harvesting amount at the TS users and the equal time-switching periods. The successive convex approximation and alternating optimization methods are exploited to construct efficient algorithms for finding the suboptimal precoding beamforming vectors at the BS and the phase shifts at the IRS elements. Finally, the numerical results show convergence and significant improvement in performance as compared to conventional baseline schemes

Predicting the activity of chemical compounds based on machine learning approaches

Exploring methods and techniques of machine learning (ML) to address specific challenges in various fields is essential. In this work, we tackle a problem in the domain of Cheminformatics; that is, providing a suitable solution to aid in predicting the activity of a chemical compound to the best extent possible. To address the problem at hand, this study conducts experiments on 100 different combinations of existing techniques. These solutions are then selected based on a set of criteria that includes the G-means, F1-score, and AUC metrics. The results have been tested on a dataset of about 10,000 chemical compounds from PubChem that have been classified according to their activit

ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions

Mathematical questioning is crucial for assessing students problem-solving skills. Since manually creating such questions requires substantial effort, automatic methods have been explored. Existing state-of-the-art models rely on fine-tuning strategies and struggle to generate questions that heavily involve multiple steps of logical and arithmetic reasoning. Meanwhile, large language models(LLMs) such as ChatGPT have excelled in many NLP tasks involving logical and arithmetic reasoning. Nonetheless, their applications in generating educational questions are underutilized, especially in the field of mathematics. To bridge this gap, we take the first step to conduct an in-depth analysis of ChatGPT in generating pre-university math questions. Our analysis is categorized into two main settings: context-aware and context-unaware. In the context-aware setting, we evaluate ChatGPT on existing math question-answering benchmarks covering elementary, secondary, and ternary classes. In the context-unaware setting, we evaluate ChatGPT in generating math questions for each lesson from pre-university math curriculums that we crawl. Our crawling results in TopicMath, a comprehensive and novel collection of pre-university math curriculums collected from 121 math topics and 428 lessons from elementary, secondary, and tertiary classes. Through this analysis, we aim to provide insight into the potential of ChatGPT as a math questioner.Comment: Accepted at the 39th ACM/SIGAPP Symposium On Applied Computing (SAC 2024), Main Conferenc

Exploring Value Co-Destruction Process in Customer Interactions with AI-Powered Mobile Applications

Background: Mobile applications have emerged as important touchpoints for addressing service requests and optimizing human resources. Within the service industry, the integration of artificial intelligence (AI) into these applications has enabled the inference of product demand, provision of personalized service offers, and enhancement of overall firm value. Customers now engage with these apps to stay informed, seek guidance, and make purchases. It is important to recognize that the interactive and human-like qualities of AI can either foster the co-creation of value with customers or potentially lead to the co-destruction of customer value. Although prior research has examined the process of value co-creation, the present study aims to investigate the underlying factors contributing to the value co-destruction process, specifically within AI-powered mobile applications. Method: Our research employs topic modelling and content analysis to examine the value co-destruction process that occurs when customers engage with AI apps. We analyze 7,608 negative reviews obtained from eleven AI apps available on Google Play and App Store AI apps. Results: Our findings reveal six distinct types of value - utilitarian, hedonic, symbolic, social, epistemic, and economic value - that can be co-destroyed during the process. System failure, self-threat and privacy violation are some contributing factors to this value co-destruction process. These values change over time and vary depending on the type of app. Conclusion: Theoretically, our findings extend the concept of value co-destruction in the context of AI apps. We also offer practical recommendations for designing an AI app in a more service-friendly way

Evolution of the Continental Margin of South to Central Vietnam and Its Relationship to Opening of the South China Sea (East Vietnam Sea)

The continental margin of south to central Vietnam is notable for its high elevation plateaus many of which are covered by late Cenozoic basalt flows. It forms the westernmost margin of a wide continental rift of the South China Sea (East Vietnam Sea), and uplift has been considered a result of either rifting or younger intraplate basalt magmatism. To investigate margin development apatite thermochronometry was applied to a dense array of samples collected from across and along the margin of south to central Vietnam. Results, including thermal history models, identified a distinct regional episode of fast cooling between c. 37 and 30 Ma after which cooling rates remained low. The fast cooling coincides with a period of fast extension across the South China Sea (East Sea) region that preceded continental break-up recorded by Oligocene grabens onshore. A thermal model is used test different processes that might influence the inferred cooling including a distinct pulse of exhumation; a decrease in exhumation followed by an associated transient decrease in geothermal gradients and, underplating coincident with rifting. Thermal relaxation following Mesozoic arc magmatism is ruled out as geotherms returned to background rates within 20 Myrs of emplacement, well before the onset of fast cooling. Models support fast cooling attributed to accelerated erosion during early stages of rifting. Some additional heating from either underplating, and/or hot mantle upwellings is also possible. No evidence was found to support regional uplift associated with the intraplate magmatism, enhanced monsoon-driven erosion or seafloor spreading dynamics

Robust Reflection Detection and Removal in Rainy Conditions using LAB and HSV Color Spaces

In the field of traffic monitoring systems, shadows are the main causes of errors in computer vision-based vehicle detection and classification. A great number of  research have been carried out to detect and remove shadows. However, these research works only focused on solving shadow problems in daytime traffic scenes. Up to now, far too little attention has been paid to the problem caused by vehicles’ reflections in rainy conditions. Unlike shadows in the daytime, which are homogeneous gray shades, reflection shadows are inhomogeneous regions of different colors. This characteristic makes reflections harder to detect and remove. Therefore, in this paper, we aim to develop a reflection detection and removal method from single images or video. Reflections are detected by determining a combination of L and B channels from LAB color space and H channel from HSV color space. The reflection removal method is performed by determining the optimal intensity of reflected areas so that they match with neighbor regions. The advantage of our method is that all reflected areas are removed without affecting vehicles’ textures or details