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This exploratory study aims to investigate the use of Facebook as a dialogic accounting tool for promoting citizen engagement in Italian regions.

This study adopts a mixed methodology. Indeed, it first collects some quantitative data to construct an engagement index for the Facebook pages of the Italian regions, and then it performs a content analysis of some posts while also examining the tenor of the related comments and the level of interaction between regions and citizens.

The Italian regions have mainly used their Facebook pages for public communication purposes rather than for public participation. Therefore, they have conceived social pages more as an instrument of self-legitimisation and thus monologic accounting and have rarely considered them as a tool for engaging citizens who, in turn, showed low interest in participating in online debates. Nature and environment, tourism promotion and sport were the most engaging content types. Findings also confirm that posting many messages does not automatically increase engagement.

This study is one of the first to investigate the potential of social media from a dialogic accounting perspective, especially in the public sector. Additionally, it focuses on regions which are understudied in the literature, although they are critical actors in implementing public policies. Last but not least, this study offers a framework that integrates the literature on the use of social media for citizen engagement and research on such platforms as dialogic accounting tools.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

The purpose of this study is to review the role of knowledge-sharing and its association with the Theory of Planned Behavior (TPB). TPB is the most used theory in understanding knowledge-sharing behavior in many contexts.

Based on the bibliometric approach, this study extracted and analyzed 229 journal articles on the Web of Science. In addition, two analyses (bibliographic coupling and co-word) were performed to provide science mapping in presenting the knowledge structure on the present and future research direction on knowledge-sharing and TPB.

The findings of this study suggest that two stand-out clusters are determinants and antecedents of knowledge-sharing behavior based on TPB and the role of the virtual platform and social media in facilitating knowledge-sharing among users.

This study will benefit researchers and scholars in studying individual behavioral traits underpinning TPB in achieving organizational excellence.

This study extends the findings of the previous review because of their limitations on methods. This study confirms the determinants of knowledge-sharing intention and behavior. However, this study suggests integrating TPB with other theories to provide more insights into knowledge-sharing behavior and use online and technology-based platforms to facilitate knowledge-sharing behavior.

In this study, it is aimed to develop cooling models for the efficient use of batteries and to show how important the busbar material is. Batteries, which are indispensable energy sources of electric aircraft, automobiles and portable devices, may eventually run out. Battery life decreases over time; the most critical factor is temperature. The temperature of batteries should not exceed the safe operating temperature of 313 K and it is recommended to have a balanced temperature distribution through the battery.

In this study, the effect on the battery temperature caused by using different busbar materials to connect batteries together was investigated. Gold, copper and titanium were chosen as the different busbar material. The Air velocities used were 1 m/s and 2 m/s, the air inlet temperatures were 295 and 300 K and the discharge rates 1.0–1.5–2.0–2.5C were chosen for cooling the batteries.

The best busbar material was identified as copper. Because these studies are long-term studies, it is also suggested to estimate the data obtained with ANN (Artificial Neural Networks). The purpose of ANN is to enable the solution of many different complex problems by creating systems that do not require human intelligence. Four different program (BR-LM-CGP-SCG) were used to estimate the data obtained with ANN. It was found that the most reliable algorithm was BR18. The R2 size of the BR18 algorithm in the test phase was 0.999552, the CoV size was 0.007697 and the RMSE size was 0.005076.

When the literature is considered, the cooling part of the battery modules has been taken into consideration during the temperature observation of the battery modules, but busbar materials connecting the batteries have always been ignored. In this study, various busbar materials were used and it was noticed how the temperature of the battery model changed under the same working conditions. These studies are very time-consuming and costly studies. Therefore, an estimation of the data obtained with artificial neural networks (ANN) was also evaluated.

The selective laser melting (SLM) technique, as a typical additive manufacturing process, is widely used for the fabrication of metallic biomedical components. In terms of biodegradability, zinc and its alloys represent an emerging generation of metallic materials for biomedical implants. The purpose of this paper is to obtain the Zn and Zn10Mg alloys with high mechanical properties using the SLM technology. The relationship between the processing parameters and the porosity of pure Zn and Zn10Mg alloy samples was investigated.

The samples were fabricated using SLM technology working in an inert gas closed chamber. Preliminary experiments were conducted to analyze the laser power and gas flow on evaporation, single track form and porosity. To evaluate the influence of factors on relative density, the response surface methodology was applied.

The satisfactory results of the proposed method were achieved, in which the relative density of the components reached up to 99.63%, and compression strength reached 214 ± 13 MPa under optimal processing conditions.

Zinc is categorized by its low melting and boiling point, which leads to the high porosity of the components. It is difficult to prepare the Zn alloy samples with high relative density using SLM technology. This work successfully achieved dense Zn and Zn10Mg samples and investigated their microstructure, mechanical properties and corrosion behavior.

The effectiveness of a message and its attributes have become important for digital media. This study aims to investigate how different elements of a website including both argument-oriented and emotional stimuli based on the elaboration likelihood model (ELM) can affect the issue involvement and change the attitude of the website visitors of a healthcare service provider.

The Ministry of Health and Education (MOHME) website was selected to explore how its content and design can persuade visitors. An online survey was conducted on 355 adults engaging in health protection behaviors during the COVID-19 pandemic.

Structural equation modeling (SEM) analysis showed that one design element, i.e. website navigation and one social cue, i.e. social connectedness, have positive impact on issue involvement, while social presence and website satisfaction have a negative effect on issue involvement because of the random fluctuation suppressor effect. In addition, prior knowledge significantly influenced the issue's involvement. Further, website satisfaction has impacted attitudes directly. There was no significant relationship between argument quality and issue involvement.

Previous works have studied health-related behaviors in offline contexts; however, the scholars have not focused on the individuals' persuasion using ELM regarding the healthcare services provided in online communities. The results of the current study have theoretical and practical implications for scholars, website designers and policymakers.