User Confidence Research Articles

Exploring Electronic Personal Health Record usage in Northern Italy: a cross-sectional study

Abstract Background The Electronic Personal Health Record (EPHR) is a patient-centered tool that enables citizens to manage their health information. The EPHR has been implemented worldwide, including in Italy, and holds immense potential, but it has shown a generalized underutilisation. Therefore, this paper aims to identify its fulfillment and the reasons behind its poor use, potentially uncovering the key to unlocking its benefits. Methods A survey was distributed on social media and in the waiting rooms of vaccination facilities in a Region of Italy. Multivariable regression models were conducted to detect a connection between the general population’s usage of the EPHR and specific independent variables. Results Of the 1634 respondents, 710 activated the EPHR, especially women, Italians, highly educated, unemployed, with good health but affected with chronic diseases. Amongst those who activated the EPHR, the multivariate analysis showed that the number of accesses to the platform was significantly associated with lower age (adjusted Odds Ratio (adjOR) 0.96 p = 0.015), higher level of education (adjOR 2.17 p = 0.029), unemployment (adjOR 5.61 p = 0.008) and considering the information on the EPHR complete (adjOR 3.83 p = 0.001). As age increases, the perceived ease of access to the EPHR decreases (adjOR=0.98 p = 0.049). Having a higher level of education (adjOR=0.41 p = 0.042), autonomy in reading health materials (adjOR=0.32 p = 0.043), and less self-confidence in filling out medical forms (adjOR=0.61 p = 0.033) were linked to a decrease in perceived ease of access. Conclusions The study showed that the EPHR is more utilised by specific categories of citizens, especially the young and those with higher education. More efforts should be made to increase access to the EPHR for the whole community by making it more intuitive and user-friendly. The present study provides valuable foundations to foster the implementation of a platform for sharing health data at a European level. Key messages • The Electronic Personal Health Record (EPHR) is underutilised despite its potential; it’s mainly accessed by young and highly educated individuals. • Efforts to increase EPHR usage must focus on improving accessibility and ease of access, as well as enhancing user confidence and autonomy in managing health information.

Systematic review of the Health Literacy Questionnaire (HLQ) for global health literacy development

Abstract Background The Health Literacy Questionnaire (HLQ) is used to inform programs and policies in more than 80 countries. It is a key needs assessment in the Ophelia (Optimising Health Literacy and Access) process, a codesign process to build effective interventions. Ophelia is applied in the European Commission Joint Action on Cardiovascular Diseases and Diabetes initiative (25 projects/13 countries). Given growing demand, an Ophelia digital platform is being developed, and confirmation of robust HLQ psychometric evidence is needed for user confidence in the interpretation and use of HLQ scores in their contexts. This study is a review of the HLQ. Methods Developed in Australia in 2013, the HLQ measures nine health literacy domains and is translated to 40 languages. A March 2024 PRISMA systematic search (PROSPERO CRD42022361966) identified 257 articles, which were screened for evidence about internal structure and reliability. Results Across 15 countries/languages, 22 articles were included. Study samples included diverse groups, settings and contexts. Out of the 18 studies that used factor analysis, three used the Bayesian approach. All 15 classical factor analysis studies reported satisfactory fit indices in restricted 9-factor models (range CFI=0.841 to 0.995, TLI=0.826 to 0.969, RMSEA=0.048 to 0.084), except two required correlated residuals for acceptable fit indices. Factor loadings were significant except for five items across five studies (range .20 to .99). Three studies used Rasch modelling and found all domains had unidimensionality except for Domain 9 in one study. Most studies reported Cronbach’s alpha (range .57 to .95) across all domains, with only three studies reporting α<.7. Conclusions The HLQ has exceptional robust psychometric properties across cultures and languages, paving the way for users to have confidence in HLQ psychometric validity evidence on the upcoming Digital Ophelia platform to support scaling up of health literacy development. Key messages • The HLQ has robust psychometric properties across countries and languages. • The HLQ 9-domain profiles can inform intervention development for equitable health outcomes.

Development of the bank of russia’s fast payment system in modern conditions

The financial market development in Russia and in the world is currently characterized by a number of processes such as emergence of a large number of new products and new formats implementation for their provision, expansion of the geography of financial institutions presence, national boundaries erasure in acquisition and provision of financial services, sales channels improvement for financial products and services, including remote ones, using modern information technologies. The article studies issues related to the current state and prospects of the fast payment system (hereinafter referred to as FPS) development considering the synergetic impact of payment services digitalization and sanctions restrictions. It analyzes the changes taking place in the FPS service in recent years, focusing on innovative business models, new functions implementation, technological transformations, as well as regulatory and risk aspects. The study pays special attention to the financial technology impact on development and functioning of the system as a key element of the payment industry. The potential of FPS in the context of ensuring the Russian payment infrastructure security represents an important direction for strengthening user confidence and system sustainability. The paper considers a number of promising areas for further development of FPSs, including their applicability to cross-border payments.

The Effect Of E-Service Quality And E-Trust On Buying Interest In Shopee Pay Users In The Millennial Generation

The use of e-wallet is increasing as online transactions increase in Indonesia. This research is an explanatory study that aims to find out the influence of E-service quality and E-trust on the interest of using ShopeePay when shopping online among Millennials in DKI Jakarta. The population of this study is the entire Generation of Millennials in DKI Jakarta who have used ShopeePay at least three times when making online transactions and the number is unknown. Sampling technique used is purposive sampling techniques on condition samples are Millennial Generation, live in the territory of DKI Jakarta and have used ShoppePay at least 3 times. Data collected through the spread of the google form questionnaire to the Millennial Generation. Data analysis using SmartPLS 3.0 Partial Least Squares Structural Equation Modeling (PLS-SEM). Research results show that E-Service Quality and E-Trust have a positive and significant influence on purchasing interest using ShopeePay on the millennial generation in DKI Jakarta. According to research results, 92.9% of Millennial Generation interest in using ShoppePay is influenced by E-Service Quality and E-Trust. The findings confirm that it is important for e-wallet service providers to focus on improving the quality of electronic services and building user confidence to boost purchasing interest.

The Influence of Thoracic Ultrasound on the Clinical Practice of Pulmonologists in Portugal.

Thoracic ultrasound (TUS) has garnered increased recognition, both in aiding pleural procedures and as a complement to physical examination. Operator ability is essential to obtain reliable findings. This study aimed to inquire pulmonology specialists and residents about their confidence with the technique and to determine future needs. A self-administered online survey, including a total of 24 questions, was sent to pulmonology specialists and residents registered as members of the Portuguese Pulmonology Society. Participation was pseudoanonymized and voluntary. One hundred fourteen valid answers were obtained. Most responders had prior TUS training. The primary method for knowledge consolidation was unsupervised practice. The majority had an ultrasound machine available in their daily practice; only 17% performed TUS more than 3 times per week. Overall confidence in TUS usage for diagnostic purposes (median: 7/10), disease monitoring (median: 7/10), and invasive procedures (median: 8/10) were high. There was a significant relation between higher levels of confidence and regularly attending TUS courses and performing TUS more than 3 times per week. The main reasons cited as preventing regular use of TUS were the availability/cost of courses and the availability/cost of equipment. Regular TUS training and utilization are essential to boost user's confidence degree. Its use is already a regular feature in the daily practice of pulmonologists in Portugal, but further and better structured training is required to meet the needs of clinicians. The inclusion of mandatory TUS training in residency programs should be considered in the future.

XAIEnsembleTL-IoV: A New eXplainable Artificial Intelligence Ensemble Transfer Learning for Zero-Day Botnet Attack Detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100%, precision of 100%, recall of 100%, F1-score of 100%, and Matthew Correlation Coefficient of 100% in binary-class situations for internal vehicular (CAN) networks and achieved 99.88% accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

An energy analysis methodology for residential heat pump retrofits

The government has targets to upscale domestic heat pump retrofit as part of the strategy to decarbonise heat. There are significant barriers to uptake including installation cost together with a lack of expertise in predicting and reporting energy and carbon performance. As a result, end user confidence in heat pumps can be low. This paper presents analysis of two monitored domestic properties using high resolution data. The aim is to develop reliable prediction and in-use diagnostic tools to improve transparency and reliability of heat pump performance. This paper focuses on a method drawn from theory as set out in CIBSE TM41, and shows how real building heat loss coefficients can be determined from regression analysis over different time frame resolutions. Practical application This research aims to provide residential heat pump retrofit providers with cost effective methods for assessing the suitability of dwellings to accept heat pump retrofit solutions, and to extend these techniques for the effective monitoring and performance evaluation of heat pumps post installation.

Acceptance of artificial intelligence: key factors, challenges, and implementation strategies

This research paper investigates the key factors influencing AI acceptance, focusing on elements such as technological readiness, perceived usefulness, and ease of use, along with the organizational and societal impacts. It identifies the significant obstacles to AI adoption, including ethical concerns, data privacy issues, and the potential for job displacement. The study also explores the importance of trust and transparency in promoting AI acceptance, highlighting the necessity for explainable AI (XAI) to build user confidence. Strategies for enhancing AI acceptance are examined, emphasizing the need for robust regulatory frameworks, ongoing education, and skill development to mitigate resistance and boost user engagement. The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns. Additionally, it underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance. By offering a thorough analysis of the factors affecting AI acceptance and the associated challenges, this paper provides valuable insights and actionable strategies for stakeholders aiming to navigate the complex landscape of AI integration effectively.

THE INFLUENCE OF AUDITOR ETHICS ON AUDIT QUALITY IN PINRANG DISTRICT INSPECTORATE OFFICE

Audit quality is defined as the probability of an Auditor determining and reporting fraud that occurs in the client's accounting system. The higher the audit quality produced by the auditor, the higher the confidence of information users in using financial reports. This research aims to determine the influence of auditor ethics on audit quality at inspectorate offices. This type of research is quantitative research in the form of a questionnaire with uses an exploratory approach using data collection techniques, namely primary in the form of questionnaires and secondary data obtained indirectly which is tested using descriptive statistical analysis tests, validity tests, reliability tests, classical assumption tests, hypothesis tests. The results of this research show that: (1) Auditor integrity partially influences audit quality, this is proven by a significant value of 0.033, which is <0.05. (2) Objectivity partially influences audit quality, this is proven by a significant value of 0.000 where <0.05. (3) Partial competency has no effect on audit quality, this is proven by a significant value of 0.449, which is > 0.05. (4) Auditor confidentiality does not partially influence audit quality, this is proven by a significant value of 0.179, which is > 0.05. (5) The auditor's professional behavior partially influences audit quality, this is proven by a significant value of 0.000, where <0.05. Integrity (X1), Objectivity (X2), Competence (X3), Creativity (X4) and Professional Behavior (X5) simultaneously influence audit quality, this is proven by the F valuecount 47,563 > Ftable2.53 and a significant value of 0.000 <0.05.

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

ASSESSING HEALTHCARE PROVIDERS’ ACCEPTANCE OF THE HOSPITAL MANAGEMENT SYSTEM IN A PRIVATE HOSPITAL IN SELANGOR, MALAYSIA

Introduction: In the current technological era, the implementation and use of information and communication technology are crucial for productivity and organizational management in the health sector. This study aimed to investigate factors influencing staff acceptance of the Hospital Management System (HMS) using the Unified Theory of Acceptance and Use of Technology (UTAUT) in a private hospital in Selangor, Malaysia. Methods: Semi-structured interviews were conducted with 25 healthcare professionals, including doctors, nurses, and administrative staff, to explore their experiences and perceptions of the HMS. Data collection was performed through interviews and observations and analyzed using thematic analysis with ATLAS.ti. Results: The study identified five key themes influencing HMS acceptance: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Participants emphasized the importance of system reliability, speed, and user-friendly interfaces for efficient workflow. Peer support and a technology-promoting organizational culture positively influenced system use. Comprehensive training and continuous technical support were crucial for user confidence. Despite recognizing the system's benefits, participants highlighted challenges such as inadequate system integration and the need for simultaneous digital and traditional record-keeping. Conclusion: Addressing these issues and tailoring the HMS to meet specific departmental needs can enhance user satisfaction and system effectiveness. The study underscores the importance of understanding user needs and providing robust support systems to facilitate the successful implementation and adoption of HMS in healthcare settings.

Integration of LIME Explainable AI to Enhance Interpretability of Deep Learning Models in Box Palette Classification

In the food production system, manual box palette arrangement often encounters errors, such as incorrect stacking patterns, mismatched box quantities, and improper mixing of product variants. This issue occurs in a soy sauce production company with limited infrastructure, leading to the mixing of two product variants with the same box size in a single production line. This results in disruptions to the production flow and significant potential losses. This study proposes a solution using deep learning to detect box palette arrangement patterns. The Convolutional Neural Network (CNN) method is chosen because it has proven effective in image classification. Additionally, this study implements Explainable AI (XAI) to provide explanations related to classification results, increasing user confidence in the system. The Local Interpretable Model-agnostic Explanations (LIME) technique will be used to provide interpretations. This research produces the development of a deep learning model to classify box palette arrangements. Furthermore, the implementation of LIME in this study successfully provides interpretations of the model's predictions. The result is evident in the result where it shows that MobileNetV2 give an F1-Score of 100%, and LIME fidelity score of 0.2 and stability score of 0.2.

Can we trust explainable artificial intelligence in wind power forecasting?

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper aims to investigate the interpretability of AI models, which is crucial but usually overlooked in wind power forecasting. Specifically, four model-agnostic explainable artificial intelligence (XAI) techniques (i.e., Shapley additive explanations, permutation feature importance, partial dependence plot, and local interpretable model-agnostic explanations) are tailored to provide global and instance interpretability for AI models in wind power forecasting. Then, several metrics are proposed to evaluate the trustworthiness of interpretations provided by XAI techniques. Simulation results demonstrate that the proposed XAI techniques can not only identify important features from wind power datasets, but also enable the understanding of the contribution of each feature to the forecast power output for a specific sample. Furthermore, the proposed evaluation metrics aid users in comprehensively assessing the trustworthiness of XAI techniques in wind power forecasting, enabling them to judiciously select suitable XAI techniques for their AI models.

Whole-Body SPECT/CT: Protocol Variation and Technical Consideration-A Narrative Review.

Introducing a hybrid imaging approach, such as single-photon emission computerized tomography with X-ray computed tomography (SPECT)/CT, improves diagnostic accuracy and patient management. The ongoing advancement of SPECT hardware and software has resulted in the clinical application of novel approaches. For example, whole-body SPECT/CT (WB-SPECT/CT) studies cover multiple consecutive bed positions, similar to positron emission tomography-computed tomography (PET/CT). WB-SPECT/CT proves to be a helpful tool for evaluating bone metastases (BM), reducing equivocal findings, and enhancing user confidence, displaying effective performance in contrast to planar bone scintigraphy (PBS). Consequently, it is increasingly utilized and might substitute PBS, which leads to new questions and issues concerning the acquisition protocol, patient imaging time, and workflow process. Therefore, this review highlights various aspects of WB-SPECT/CT acquisition protocols that need to be considered to help understand WB-SPECT/CT workflow processes and optimize imaging protocols.

Artificial intelligence drivers' effect on willingness to adopt the human capital supply chain in manufacturing firms: an empirical investigation from developing countries – a mediation model

PurposeThis study tries to examine the effect of artificial intelligence (AI) drivers on the willingness to adopt the human capital supply chain (HCSC) in manufacturing firms (MFs) in developing countries (DCs) including Jordan, Saudi Arabia, Bahrain, Qatar and the United Arab Emirates, which are listed in the Chambers of Industry of these countries.Design/methodology/approachThe quantitative methodology with a simple random sampling method was adopted using a questionnaire survey-based approach to collect data from 233 out of 1,055 participants (human resource (HR) managers and information technology (IT) senior managers) from various MFs (private and commercial), representing a 22% response rate. Covariance-based structural equation modeling (CB-SEM) was used to analyze the raw data using Amos V.25.FindingsThe results of this study showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs. At the same time, there is no significant effect on a firm’s infrastructure readiness (FIRs), in addition to the indirect effect of UC in the relationship between CPs and FIRs on the willingness to adopt AI in HCSC.Originality/valueSuch findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive AI drivers' effect on willingness to adopt the HCSC in the MFs in DCs as emerging economies. Additionally, the managers might focus on the existence of a significant positive indirect effect of UC as a mediating factor in the relationship between FIRs and willingness to adopt AI and its applications in HCSC systems and departments.

Activities-specific balance confidence scale: a Rasch evaluation of the Arabic version in lower-limb prosthetic users

Purpose To evaluate the psychometric properties of the Arabic version of the Activities-Specific Balance Confidence Scale using the 5-option response categories for individuals with lower limb amputation (ABC-5/Ar). Materials and methods This was a methodological study on a convenience sample of individuals with unilateral lower-limb amputation attending outpatient rehabilitation centres in Saudi Arabia and Turkey (N = 155). Rasch analysis (WINSTEPS version 4.6.5) was used to examine the 5-categories rating scale structure, item fit, item difficulty hierarchy, person separation index, unidimensionality, local item dependency, and differential item functioning. Results The ABC-5/Ar 5-response option demonstrated an appropriate model fit. Most items fit the Rasch model, except for item #12 “walk in a crowded mall,” which showed an overfitting value of 0.63. The person separation indices 2.95 (Cronbach’s α = 0.96). Principal component analysis of residuals confirmed the unidimensionality of the scale; however, local dependency was detected between item #14 “Ride in escalator holding rail,” and item #15 “Ride in escalator not holding rail.” Conclusions The findings suggest that the ABC-5/Ar shows promise in assessing balance confidence in Arabic-speaking lower-limb prosthesis users. However, further studies with larger sample sizes and in diverse clinical contexts are needed to confirm its effectiveness in various clinical settings.

ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

Navigating the ever-expanding sea of scientific literature presents a daunting challenge for researchers seeking relevant and up-to-date information. Traditional citation recommendation systems, while well-intentioned, often fall short due to their limited focus on text-based features and lack of contextual awareness. In this paper we introduce the ICA-CRMAS (Intelligent Context-Aware Approach for Citation Recommendation based on Multi-Agent System), an intelligent system that leverages the power of deep learning, semantic analysis, and multimodal learning to overcome these limitations. ICA-CRMAS goes beyond the surface, delving into the rich tapestry of information within academic papers, including figures, which often hold vital contextual clues. By weaving this contextual data directly into its recommendation models, ICA-CRMAS generates highly personalized and relevant suggestions. This comprehensive approach unlocks enhanced accuracy, diversity, and serendipity, enabling researchers to effectively discover papers aligning with their interests and research objectives. ICA-CRMAS illuminates its reasoning. Instead of opaque suggestions, the system provides clear explanations that justify and illustrate recommended citations. This transparency builds user confidence, allowing researchers to critically engage with and trust the system’s recommendations. Evaluation experiments conducted on real-world academic datasets demonstrate that ICA-CRMAS outperforms existing approaches across various metrics. it surpassing its closest competitor by a margin of 7.53 on accuracy, 6.07% on MRR and by 5.87 on Recall. User feedback further reinforces its effectiveness, with an Overall System Usability Scale (SUS) score of 76.73, exceeding benchmark scores for comparable systems.

An Advanced Cardiac Life Support Application Improves Performance during Simulated Cardiac Arrest.

Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial. Forty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards (N = 22) or the guided app (N = 24). The primary outcome was successful return of spontaneous circulation (ROSC). Secondary outcomes included code leader stress and confidence, AHA ACLS guideline adherence, and errors. A focus group of 22 residents provided feedback. Statistical analysis included two-sided t-tests and Fisher's exact tests. App users showed significantly higher ROSC rate (50 vs. 18%; p = 0.024), correct thrombolytic administration (54 vs. 23%; p = 0.029), backboard use (96 vs. 27%; p < 0.001), end-tidal CO2 monitoring (58 vs. 27%; p = 0.033), and confidence compared with baseline (1.0 vs 0.3; p = 0.005) compared with controls. A focus group of 22 residents indicated unanimous willingness to use the app, with 82% preferring it over AHA pocket cards. Our guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice.

Credible calculation method for regional corrected carbon emission factors taking into account the nearby consumption of renewable energy

Abstract The carbon emission factor of the power grid is a key data indicator for measuring the carbon emission level on the user side. Currently, the carbon emission factor of the power grid is not sufficient to accurately express the regional carbon emission level, and especially cannot accurately reflect the regional new energy consumption level, which affects user confidence in emission reduction and the accuracy of carbon emission quota issuance. Therefore, this article utilizes blockchain technology and carbon emission flow theory to realize the impact of user-side self-generated and self-used photovoltaics (PV) on carbon emission factors. We have constructed a residual power grid correction model, designed a blockchain intelligent terminal module and functional requirements, ensured the correctness of the model and results from the source, solved the problem of data tampering and insufficient data credibility generated by terminal devices at the source, and ultimately helped achieve the dual carbon goal.

Fort-Trust: Safeguarding online transaction by machine learning

In the digital age, the safety of online transactions has become an important situation for both customers and groups. The increasing sophistication of cyber-attacks necessitates the development of robust and sensible protection mechanisms. "Citadel-believe: Safeguarding online Transactions through device gaining knowledge of" proposes a complete solution leveraging machine studying techniques to decorate the safety of online financial transactions. This method aims to stumble on and mitigate fraudulent activities in real time, presenting a further layer of safety past conventional techniques. e-commerce has completely changed the way people shop, it has also made people more susceptible to online transaction fraud. This problem is addressed by the innovative framework Fort-Trust, which uses the XGBoost algorithm for fraud detection. Fort-Trust incorporates feature correlation analysis to solve a prevalent problem in this field: imbalanced datasets. This strategy aims to maximize detection accuracy while minimizing false positives. The high precision rate that XGBoost delivers, according to the results, is essential for lowering financial losses and increasing user confidence. All things considered, Fort-Trust strengthens the security and dependability of online transactions by providing a strong and useful solution for real-world fraud detection