Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among the population. It can be used particularly to create a new automated approach to calculate DMF (Decay, Missing, Filled) index score. In this paper, an autonomous diagnostic approach for detecting dental cavities in photos is developed using deep learning algorithms and ensemble methods. The proposed technique employs a set of pretrained models including Xception, VGG16, VGG19, and DenseNet121 to extract essential characteristics from photographs and to classify images as either normal or caries. Then, two ensemble learning methods, E- majority and E-sum, are employed based on majority voting and sum rule to boost the performances of the individual pretrained model. Experiments are conducted on 50 images with data augmentation for normal and caries images, the employed E-majority and E-sum achieved an accuracy score of 96% and 97%, respectively. The obtained results demonstrate the superiority of the proposed ensemble framework in the detection of caries. Furthermore, this framework is a step toward constructing a fully automated, efficient decision support system to be used in the dentistry area.

The main objectives of this research are to identify significant spatial and temporal components associated with diarrhea and provide an accurate forecast. Using data from the Ban-dung city health surveillance system, the analysis reveals a decreasing trend in both the number of incidences and the estimated relative risks of diarrhea in most districts. Key factors contributing to diarrhea variation include temporally structured, spatially structured, and unstructured effects of space-time interaction Type I. No clear seasonal pattern is observed in diarrhea incidence among children under five, emphasizing the need for consistent vigilance and preventive measures. Spatial clustering was observed in the eastern and western parts of Bandung city. The forecasting model predicts a continued decline in diarrhea incidence and relative risk throughout 2022.

The topic of robots is gaining traction in both academic discourse and popular media due to their growing prevalence in the hospitality sector. The hospitality industry is likely to encounter practical challenges in adopting the increased use of robots. The primary objective of the present research was to conduct an empirical investigation into a theoretical framework that explores how employees view robot-caused unemployment, with particular attention given to their perceptions of robot adoption. The study utilised structural equation modelling to analyse data obtained from 401 service employees in Jordan through online questionnaires. The findings indicate that the perception of robot-induced unemployment among employees is significantly influenced by their social skills, perceived risk, awareness, and trust in using service robots. The present study established a theoretical framework for investigating user perceptions of robot-induced unemployment within the particular setting of hospitality robots. The findings offer valuable insights for guiding future development and research efforts in the hotel service robot industry as well as informing marketing strategies for hotel managers. Ultimately, these efforts may contribute to the sustainable growth of service robots and associated sectors, including the hotel service sector.

In dynamic socio-economic environments, public sector organizations and companies with strong financial resilience are better equipped to adapt to economic changes, socio-economic fluctuations, and shifts in the business landscape with greater flexibility. Financial resilience also has implications for an organization's liquidity. One of the critical factors influencing financial resilience in public sector organizations is the quality of its audit. Ensuring high-quality audits is vital for assessing the accuracy and reliability of an organization's financial statements. This study aims to investigate the impact of audit quality and internal control on financial security, with information quality serving as a mediating factor. Quantitative research methods were employed to collect and statistically analyze the data. The study gathered information through questionnaires distributed to the Auditors of the Supreme Audit Board, with a sample size of 321 participants. The data was then processed using SmartPLS software. The research findings demonstrate a significant relationship between audit quality and internal control, positively influencing the organization’s financial resilience. Furthermore, the study reveals that information quality acts as a crucial mediator, linking audit quality and internal control to financial security. The analysis shows that audit quality significantly affects information quality. However, the direct impact of audit quality on financial resilience is not significant. On the other hand, internal control significantly influences both information quality and the organization’s financial resilience. Additionally, the quality of information also has a significant effect on the organization’s financial resilience.

This study investigated the relationship between the digital gap and the intention of Jordanians towards e-government usage. It focused on three components of the digital divide namely access, skills, and innovativeness. In addition, the research investigated how socio-demographic factors influence this connection. Data comprised 620 valid replies to questionnaires issued to 700 Jordanian citizens aged 22 and older who resided in urban and rural areas. Statistical Package for the Social Sciences (SPSS) version 26 and Analysis of Moment Structures (AMOS) version 24 were employed in data analyses and hypotheses testing. The results showed that all three aspects of the digital divide had a significant influence (access, skills, and innovativeness) on the intent of Jordanian citizens to utilize e-government, and the dimension of access imparted the strongest impact, followed by the dimension of skills and then the dimension of innovativeness. Additionally, it was discovered that gender, age, and education were the socio-demographic factors that could weaken the impact of the digital divide on the intentions of user to access e-government services. Contrariwise, the factor of income did not show a similar impact. Furthermore, perceived security significantly impacted the propensity towards e-government usage. Trust played a significant role in mediating the link between perceived security and the intention to engage in e-government.

Nonhomogeneous Continuous-Time Markov Chain (NH-CTMC) is a stochastic process that can be used to model problems where the future state depends only on the current state and is independent of the past. The transition intensity in NH-CTMC is not constant but is a function of time. In this paper, NH-CTMC is employed to model information dissemination on Twitter, where transitions occur only from followee groups to follower groups. Information is considered spread on Twitter when followers retweet posts from their followees. The tweet-retweet process on Twitter satisfies the Markov property, as a retweet from a follower depends only on the tweet posted just before by the corresponding followee. The probability of a tweet spreading is determined by the transition intensity, assumed to be a Sigmoid function whose parameters are estimated using Maximum Likelihood Estimation (MLE). This method is applied to Twitter data from Indonesia related to discussions on Covid-19 vaccination. The results indicate that information about Covid-19 vaccination on Twitter spreads rapidly from followees to followers in the first 20 hours, and then slows down after 40 hours. The NH-CTMC model outperforms the Homogeneous Continuous-Time Markov Chain (H-CTMC) approach, where the transition intensity (tweet spreading intensity) is assumed to be constant.

Cordyceps are a form of herb that is nurtured and propagated utilizing organic farming techniques. This study investigated consumers’ intention towards adopting the organic herb cordyceps, also referred to as “Thungchao” in Thai language. The research was driven by increased health awareness among people, and skewness towards use of organic as compared to conventional medication. In Thailand, the herb has been consumed traditionally for years, because of its medical benefits. The Theory of Planned Behavior (TPB) was applied to investigate the outcomes of users’ perceptions (perceived experience with organic product, perceived health benefit, information awareness) and TPB aspects (subjective norm, perceived behavioral control, attitude towards organic herb) to examine intentions of consumers’ towards adopting codyceps. The research collected primary data from 452 Thai respondents. Collected data were evaluated utilizing Structural Equation Modeling analysis. Results revealed that for the perceived customers’ experience, intention to use organic herb cordyceps was positively and significantly influenced by perceived experience with organic hers and information awareness. All the TPB variables – subjective norm, perceived behavior control, and attitude towards organic herbs – significantly and positively influence consumers’ intention towards organic herb cordyceps. The research recommended that organic herb cordyceps “Thungchao” has many medical benefits, and its consumption should be encouraged. Information awareness and sharing of the knowledge of the herb’s medical requirement and benefits, resources and dosage are necessary to encourage or rather influence consumers intention.

Online shopping has developed rapidly in Indonesia since the Covid-19 outbreak, making potential customers frequently browse e-commerce. In e-commerce there is a social commerce construct which is a construction originating from social commerce such as online reviews. Depth from online reviews on a product cannot necessarily be trusted and prospective customers also cannot use other people's experiences as an assessment of product quality. Customer review also considers perceived value from a utilitarian and hedonic perspective. Therefore, this study analyzes the effect of perceived value on browsing behavior in C2C e-commerce with the antecedent depth of review in Indonesia. This study uses SOR (stimulus-organism-response) theory. The population of this study are individuals who live in Indonesia and have done shopping online in one commerce which has facilities online review such as Tokopedia, Blibli, Amazon, Alibaba, and JD.ID. Sampling technique nonprobability sampling by using techniques convenience sampling total 137 samples. The data collection method uses the survey method, while the data analysis method used is PLS-SEM. The results of the study show that depth review affects perceived utilitarian and hedonic values, and perceived utilitarian and hedonic values also affect browsing. Thus, all hypotheses are accepted, which means that there is an influence of perceived value on browsing behavior in C2C e-commerce with an antecedent of review depth. This research can be used as a reference for further studies by digging deeper into the effect of the depth of review on other variables that can have an impact on the viability of a seller's business in e-commerce. This research can be used as a reference for sellers to evaluate and create strategies to encourage customers to give positive reviews so that they can influence other readers when browsing e-commerce where it is hoped that purchases will occur. This research pioneered the study of perceived value of browsing behavior in C2C e-commerce with antecedents of depth of review in Indonesia.

The term “metaverse” refers to a virtual reality setting where users may engage in sustained and immersive interactions with other users and digital information. The metaverse offers new potential for entertainment, education, commerce, sociability, and creativity; therefore, it is anticipated to play a significant role in the future of the digital economy. However, the metaverse presents additional difficulties in resolving conflicts that can develop between its users, producers, and providers. Intellectual property rights, privacy, contract enforcement, fraud, harassment, and cybercrime are some of the concerns that may be raised in these conflicts. The existing legal system for settling these conflicts is disjointed and insufficient since it does not consider the metaverse’s unique qualities and complexity. This study investigates the present legal framework to provide fair and effective conflict resolution in the metaverse. It then establishes tenable fundamentals within the context of scientific and legal foundations. and propose a theoretical model named Hybrid Legal Framework for Metaverse Dispute Resolution (HLFMDR).

The present study attempted to determine the elements affecting users' opinions about using electronic auctions in Jordan. The study's target audience was Jordanian university students. We randomly selected 600 students from three public universities in Jordan after 600 users from each of the three universities responded to the surveys. The primary data for this study were gathered using a specially created questionnaire that was based on earlier research. SEM software (smart PLS 4.0.8.3) was employed in the evaluation of data. This study looked into the factors that led Jordanians to use electronic auctions and found four indirect significant relationships and ten direct significant relationships. First, user attitudes and independent variables (usefulness, awareness, ease of use, and techno trust) are directly significant antecedents of utilizing e-auctions in Jordan. The use of e-auctions is related to the independent variable in five direct and significant ways: usefulness, awareness, ease of use, digital divide, and techno trust. Thirdly, there are direct and statistically significant correlations between user attitudes and the independent variables (usefulness, awareness, ease of use, and techno trust). Fourthly, there appears to be a clear and significant link between user attitudes and the intention to use e-auctions. Fifth, there are four mediated meaningful indirect correlations between the intention to use e-auctions and the independent variable. The findings of the current study are based on a number of factors that affect the demand for e-commerce in Jordan. They also include some recommendations for academics and decision-makers in e-marketing affairs. The current study also contributes in identifying challenges and suggestions that would overcome obstacles related to shopping operations through e-auctions. The factors examined in this study have been the subject of prior research. The data gathered in this study are valuable to Jordanian decision-makers and web developers, as the data can be used to create efficient policies and strategies to launch work on electronic auction platforms.