Assessment Approach Research Articles

Risk assessment of dietary exposure to aflatoxins in nuts and spices. A pilot study in Yerevan, Armenia

The study aimed to assess aflatoxin (AFB1, AFB2, AFG1, AFG2) exposure and potential health risks through nuts and spices consumption by adults (age 18-65, n = 554) in the capital city of Yerevan, where one-third of the Armenian population resides. AFs occurrence in nuts (peanut, almond, pistachio) and spices (black pepper, red pepper, mix of spices) was detected using HPLC. Daily consumption of products among the adult population was collected using a method of Food Frequency Questionnaire. The deterministic approach for risk assessment involved calculating the estimated daily intake and margin of exposure (MOE) of AFs, based on the EFSA benchmark dose of 0.4 μg/kg bw/day. AFs contents in products ranged from 0.011 μg/kg (mixed spices) to 0.391 μg/kg (ground red pepper), all below the EU regulatory limits. Ground black pepper was the main contributor to estimated exposure and a health concern (i.e. MOE<10000) was identified for high consumers in the worst-case scenario. Moreover, a health concern was present for individuals with a consumption of all studied food items (41% of the sample). Comparison with studies from neighboring countries and global data emphasizes the need for continuous monitoring and regulation to mitigate aflatoxin exposure risks in countries of the Caucasus area and globally.

Bio-organic substitution in tobacco (Nicotiana tabacum L) cultivation: Optimum strategy to lower carbon footprint and boost net ecosystem economic benefit

The partial substitution of organic manure for chemical nitrogen fertilizers, known as organic substitution, is widely regarded as a cleaner and more sustainable production strategy. However, few studies have quantified greenhouse gas emissions, product income and net ecosystem economic benefit (NEEB) using a life cycle assessment (LCA) approach, particularly for typical tobacco (Nicotiana tabacum L.) production. Here, we quantified the yield and quality of a typical tobacco production in Qujing, Yunnan, China, through field experiments and calculated its carbon footprint and NEEB using the LCA approach. Four organic substitution strategies were established with equal nitrogen inputs, including synthesized chemical fertilizer (SN), farmyard organic manure (NF), commercial organic manure (NC), and bio-organic (Trichoderma viride Pers.) manure (NT), each substituting 15% of synthesized nitrogen fertilizer. Compared to the SN strategy, the NT strategy significantly increased yield and income by 10.3% and 9.6%, respectively. In contrast, the NF strategy significantly reduced income, while the NC strategy showed no significant difference. Both the NC and NT strategies significantly reduced N2O cumulative emissions (by 15.9% and 8.0%, respectively), increased δSOC (by 38.4% and 15.0%, respectively), and decreased carbon footprint compared to the SN strategy. However, the NF strategy significantly increased the income-scaled carbon footprint, even though it also notably reduced N2O cumulative emissions (by 22.6%) and increased δSOC (by 7.9%). The NT strategy achieved a win-win scenario of low environmental risk and high economic returns of tobacco production with significantly increased NEEB (by 10.6%) compared to the SN strategy (37.60 × 103 CNY yr−1). This suggests that the bio-organic Trichoderma manure substituting 15% synthesized nitrogen fertilizer is the best organic substitution strategy for sustainable tobacco production.

Traditional, complementary and integrative medicine therapies for the treatment of mild/moderate acute COVID-19: protocol for a systematic review and network meta-analysis

BackgroundIncreasing evidence has emerged for traditional, complementary and integrative medicine (TCIM) to treat COVID-19 which requires systematic summaries of the net benefits of interventions against standard care and one another....

Dynamic Energy Management and Control of Networked Microgrids based on Load to Grid Services and Incentive-Based Demand Response Programs: A Multi-Agent Deep Reinforcement Learning Approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Phase angle as a marker of muscle quality: A systematic review and meta-analysis

Background & aimsPhase angle (PhA) is a biomarker derived from raw bioelectrical impedance analysis (BIA) values: resistance (R) and reactance (Xc). PhA reflects cellular membrane integrity and, as a result, has been considered a marker of fluid distribution, making it a potential prognostic indicator. A growing body of research demonstrates independent associations between PhA and muscle strength, mass, and composition. In this context, PhA has the extra potential to serve as a marker of muscle quality. However, the evidence supporting its use for this purpose is not well established. This study aimed to investigate the relationship between PhA and markers of muscle quality. MethodsThis systematic review and meta-analysis (Internal Prospective Register of Systematic Reviews – PROSPERO on a registration code: CRD42024507853) focused on observational studies assessing the relationship between PhA and markers of both concepts of muscle quality: the muscle quality index (MQI: strength by a unit of mass) and the muscle composition (i.e., skeletal muscle radiodensity [SMD], muscle echogenicity, muscle fat fraction, inter- and intramuscular adiposity). Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), while the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Meta-analyses with a random-effects model were conducted. ResultsSeventeen studies were included in this systematic review, encompassing 2,710 participants. Meta-analyses demonstrated that PhA had a moderate positive correlation coefficient with SMD (4 studies, 924 participants; r = 0.54, 95% confidence interval (CI) 0.38 to 0.69, heterogeneity (I2) = 92%) and good accuracy (85%) for classifying low SMD (2 studies, 390 participants; Area Under the Curve – AUC pooled 0.85, 95% CI 0.78 to 0.92, I2 = 0%). PhA was inversely-moderately correlated with muscle echogenicity (8 studies, 1,401 participants; r = - 0.42, 95% CI - 0.57 to - 0.24, I2 = 82%) and positively-weakly correlated with MQI (2 studies, 191 participants; r = 0.36, 95% CI 0.21 to 0.49, I2 = 17%). All studies had a higher risk of bias. The certainty of evidence ranged from low to very low. ConclusionDespite technical challenges, this study demonstrates the potential of PhA as a surrogate marker for muscle quality, particularly expressing muscle composition (SMD). Future studies should utilize BIA with standardized protocols to potentially establish specific cutoff values for PhA, thereby enhancing its diagnostic accuracy and clinical applicability. These studies could additionally explore the mechanisms underlying the associations between PhA and muscle quality aspects. In cases where technical factors are not easily controlled, the use of standardized PhA (SPhA), which converts PhA into Z-scores, could be beneficial. Although this warrants investigation, this approach (SPhA) has the potential to account for variables such as age, sex, device differences, and health status.

2024 Chinese guidelines for the diagnosis and treatment of rheumatoid arthritis

Rheumatoid arthritis(RA) is an autoimmune disease characterized by erosive arthritis, which is an important cause of disability in Chinese population. It is of great significance to formulate and update RA diagnosis and treatment guidelines that meet the standards of international guidelines and clinical practice in China. The update of the Chinese guidelines for the diagnosis and treatment of RA was initiated by National Clinical Research Center for Dermatologic and Immunologic Diseases, jointly with the Chinese Association of Rheumatology and Immunology Physicians, the Rheumatology and Immunology Professional Committee of Chinese Rehabilitation Medical Association, the Rheumatology and Immunology Professional Committee of Chinese Research Hospital Association, and the Rheumatology and Immunology Branch of Beijing Association of Holistic Integrative Medicine. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and the Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist were followed to update the guidelines. The guidelines provide evidence-based recommendations on 10 clinical issues which were concerned by Chinese rheumatologists. The aim is to improve the level of diagnosis and standard treatment of RA in China, and to improve the quality of life and prognosis of patients.

Median nerve electrical stimulation for restoring consciousness in patients with traumatic brain injury: study protocol for a systematic review and meta-analysis

IntroductionTraumatic brain injury (TBI) is one of the prevalent critical illnesses encountered in clinical practice, often resulting in a spectrum of consciousness disorders among survivors. Prolonged states of impaired consciousness...

Does preprocedural ultrasound prior to lumbar neuraxial anesthesia or analgesia increase first-pass success in adults with obesity? A systematic review.

Preprocedural ultrasound (PPU) reduces the risk of technical failure in non-obese patients and when technical difficulty is predicted. We conducted this review to determine if PPU improves first-pass needle insertion success for neuraxial anesthesia in patients with obesity. We conducted a systematic review without meta-analysis, due to the small number of included studies. The study protocol was registered (PROSPERO: CRD42022368271). We conducted searches in MEDLINE, Embase, PubMed, and Cochrane Library from January 1, 1980 to October 1, 2022 for peer-reviewed randomized controlled or observational studies comparing PPU versus landmark techniques in patients with body mass index >30 kg/m2. The quality of evidence was assessed using the revised Cochrane risk-of-bias tool for randomized trials and Grading of Recommendations Assessment, Development, and Evaluation approach. There were nine randomized controlled studies, comprising 866 patients having lumbo-sacral neuraxial techniques. Three studies utilized a small handheld ultrasound device called Accuro™ and six utilized non-handheld ultrasound devices. Certainty of evidence was low for improving the first-pass success rate. There was evidence (moderate certainty) that PPU decreased number of passes, increased first insertion attempt success, and reduced number of insertion attempts. There was no evidence that PPU affected identifying time, needling time, or overall procedural time. There was no evidence that PPU influenced procedural failure rate (very low certainty evidence) and insufficient evidence to suggest that artificial intelligence-supported handheld devices were superior to conventional ultrasound devices. In patients with obesity, there is evidence of very-low to moderate certainty that PPU improves markers of insertion success, with no indication of increased adverse effects. PPU should be used to reduce attempts. Further studies adhering to standardized outcome definitions are required for definitive recommendations. The study protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022368271).

Assessment IS learning: developing a student-centred approach for assessment in Higher Education.

Assessment and the associated feedback from those assessments are powerful factors in the development of students' learning. We have seen a shift within the Higher Education sector to conceptualise assessment as being more than summative assessment 'of' learning. Instead, there has been a greater emphasis on assessment 'as' learning, or assessment 'for' learning, through the enhanced use of formative assessments. Centralising assessment within the learning process highlights that assessment IS learning and cannot be separated from other elements of the learning process. In particular, assessment has a vital role to play in the development of students' self-regulated learning skills and the development of independence in learners. However, for assessments to effectively support learning, they need to be meaningful, engaging, well-integrated into the learning activities and 'student-focused'. Placing student skills development and personal development at the centre of assessment design has the potential to empower students through assessment. This review focuses on the potential of assessment to support student learning and development, using the 'Equity, Agency, Transparency' ('EAT') framework as a lens for effective assessment and feedback practices. We suggest ways in which we can make our assessment and feedback practices more inclusive, meaningful and authentic to the students' learning needs.

Improving Network Resilience Against DDoS Attacks: A Fuzzy TOPSIS-based Quantitative Assessment Approach

Improving Network Resilience Against DDoS Attacks: A Fuzzy TOPSIS-based Quantitative Assessment Approach

Assessing the quality of the time delay estimate in acoustic leak localisation

The problem considered in this paper is assessing the quality of the time delay estimate between leak signals measured on water pipes. This is practically important, as a quantitative assessment of the accuracy of time delay estimation (TDE) results makes it possible to infer the reliability of acoustic leak localisation results in a given situation. Three quality assessment approaches are developed by considering the statistical properties of the cross-correlation function (CCF): information-criterion, processing gain, and statistical approaches. In the information-criterion approach, the Bayes factor (BF) is employed to decide the most likely probability distribution of observed CCF peak values. The processing gain approach determines the quality of the time delay estimate using indices that indicate detectability of the CCF peak, namely, the peak-to-side lobe ratio (PSR) and the peak-to-mean ratio (PMR). In the statistical approach, an index termed inconsistency score (ICS) is used to describe the quality of TDE results based on root-mean square of deviations of time delay estimates from their statistical mode. Experimental results show that the proposed approaches provide effective means of assessing the accuracy of the time delay estimate in acoustic leak detection applications. Also, the proposed indices can be employed as figures of merit for selecting best parameters for TDE, for example, filter cut-off frequencies.

Assessing urban fire risk: An ensemble learning approach based on scenarios and cases

Urban fires represent a significant hazard to people’s lives and property, which makes it critical to estimate the risk adequately. Existing urban fire evaluation methods lack applicability because they do not take into account individual scene components and previous cases. As a result, this study offers the scenario- and case-based urban fire risk assessment approach (SCBUFRA), which seeks to achieve a more thorough and accurate urban fire risk assessment. First, the technique uses fire case and scenario data, as well as the recursive feature elimination method, to pick the elements utilized to assess urban fire risk. Second, the data-driven empowerment technique and stability analysis are utilized to determine the precise fire risk value and correctly quantify the fire danger level in each part of the city. Next, the Affinity Propagation (AP) technique is used to cluster scene elements. Ensemble learning is then used to create a risk prediction model by refining the weighting strategy of R2. Finally, Shapley additive explanations are used to investigate the elements causing urban fires. The findings show that SCBUFRA outperforms popular machine learning methods, that the number of crimes, gross population, and house price are the most important variables for fire prediction, and that the research is applicable to urban fire risk management and firefighting resource allocation.

A health system assessment approach to analysis of political parties' health proposals, Portugal.

Comparing health policy measures before elections and identifying potential gaps in the health policy debate can be challenging. We explored the use of the Health System Performance Assessment for Universal Health Coverage framework to analyse health policy proposals by classifying health policy measures outlined in political manifestos into four health system functions: governance, financing, resource generation and service delivery. As a case study, we analysed the political manifestos of all Portuguese parties with parliamentary representation ahead of the election in March 2024. We calculated the share of measures per health system function for individual political manifestos and identified potential gaps in the health policy debate. When required, we used additional classification criteria and local expertise on political and institutional knowledge. A snap general election was announced in Portugal in November2023, following an alleged corruption scandal, and political parties began publishing their manifestos on their websites in January2024. We identified and classified 350 health-related measures across the four functions: governance, 29.7% (104 measures); financing, 16.9% (59 measures); resource generation, 33.4% (117 measures); and service delivery, 20.0% (70 measures). These findings enabled characterization of the priorities of parties, facilitated cross-party comparisons and identified missing topics in the political debate. We show that the framework can be adapted to analyse political manifestos, providing a systematic method for comparing and synthesizing health policy proposals. We further demonstrate the potential for extending the framework's applicability beyond health system performance assessment, opening new avenues for policy analysis.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

State-of-the-art nonstationary hypersurface damage assessment approach for energy harvesters

Since EH (Energy Harvesters) constitute nowadays a vital part of renewable energy engineering, experimental research is required in addition to numerical modeling, serving reliable structural design and ensuring prolonged device service time. The performance of GPEH (GalloPing EH) has been examined in this case study, utilizing comprehensive laboratory wind tunnel tests, carried out under realistic windspeed conditions. Novel structural multivariate risks assessment methodology, presented here, being feasible for nonstationary nonlinear GPEH dynamic systems, that had been either physically measured over a representative period, providing jointly quasi-ergodic time-series, or directly numerically MCS (Monte Carlo Simulated). Based on laboratory-measured GPEH dynamics, the presented analysis demonstrates that the proposed multivariate hypersurface methodology offers robust predictions of the structural failure/damage risks. Furthermore, when dealing with raw measured timeseries, representing the high-dimensional dynamic system, existing risk assessment techniques struggle to handle nonlinear inter-correlations between GPEH critical components. This case study's main objective has been to validate and benchmark the novel multimodal risk assessment methodology, which utilizes multivariate nonstationary lab-recorded time histories to extract relevant design information from the underlying GPEH dynamics.The proposed state-of-the-art nonstationary hypersurface reliability approach being of a generic nature, offering additional capacity for damage/failure risks prognostics for a wide range of nonlinear multidimensional nonstationary systems. Forecasted damage and failure risks have been supplied with confidence bands, demonstrating the experimental setup's robustness, as well as the useful design features of the presented nonstationary hypersurface risks assessment methodology. It should be noted that the presented reliability methodology being mathematically exact, and it does not rely on simplifying assumptions.

Combined Molecular Mismatch Approaches to Predict Immunological Events Within the First Year After Renal Transplantation.

Several molecular mismatch assessment approaches exist, but data on their combined use are limited. In this study, we aimed to define distinct risk groups for rejection based on the combination of three molecular mismatch assessment approaches (i.e., eplet mismatch count, the number of highly immunogenic eplets and PIRCHE-II score) in 439 consecutive immunological standard risk transplantations. For each molecular mismatch assessment approach, ROC analyses were used to define cut-offs for prediction of (sub) clinical rejection according to Banff 2019 classification within the first year post-transplant as a reference. If all three scores were below the cut-off, the patient was assigned to the low-risk group (19% of patients); if all three scores were above the cut-off, the patient was assigned to the high-risk group (21% of patients). The one-year incidence of (sub) clinical rejection was 12% in the low-risk group and 33% in the high-risk group (p = 0.003). Internal validation of the assigned risk groups for prediction of other outcomes revealed a high consistency: clinical rejection (6% vs. 24%; p = 0.004), ATG-treated rejection (1% vs. 16%; p < 0.001) and development of de novo HLA-DSA at 5 years post-transplant (6% vs. 25%; p = 0.003). The molecular mismatch risk group was an independent predictor for (sub) clinical rejection (high-risk vs. low-risk: hazard ratio 3.11 [95%-CI 1.50-6.45]; p = 0.002). We conclude that combining molecular mismatch approaches allows us to distinguish low- and high-risk groups among standard renal allograft recipients. Independent validation in other patient populations and different ethnicities is required.

Assessment of Water-Related Ecosystem Services and Beneficiaries in the Hainan Tropical Rainforest National Park

Tropical rainforests are of vital importance to the environment, as they contribute to weather patterns, biodiversity and even human wellbeing. Hence, in the face of tropical deforestation, it becomes exigent to quantify and assess the contribution of ecosystem services associated with tropical rainforests to the environment and especially to the people. This study adopted a nuanced approach, different from traditional economic valuations, to estimate the water-related ecosystem services (WRESs) received by the people from 2010 to 2020 in the Hainan Tropical Rainforest National Park (HTRNP). The study focused on water yield, soil conservation, and water purification using InVEST, the SCS-CNGIS model, and spatial analysis. The results show (1) significant land cover changes within the HTRNP, as forest decreased by 4433 ha and water bodies increased by 4047 ha, indicating the active presence of human activities. However, land cover changes were more pronounced within the 5 km buffer area around the HTRNP, suggesting the effectiveness of the tropical rainforest conservation efforts in place. (2) The water yield of the HTRNP in the years studied decreased by 307.03 km3, based on the water yields in 2010 and 2020, which were 5625.7 km3 and 5318.7 km3, respectively. (3) Change detection showed that runoff mitigation in the rainforest has a negative mean (−0.21), indicating a slight overall decrease in soil conservation and runoff mitigation in the rainforest from 2010 to 2020; however, the higher curve number indicates areas susceptible to surface runoff. (4) The ecological effectiveness of water purification to absorb and reduce nitrogen load was better in 2020 (145,529 kg/year), as it was reduced from 506,739 kg/year in 2010, indicating improved water quality. (5) Population growth is more pronounced in areas with high water yields. Overall, the proposed framework has shown that the water yield potential of the HTRNP can meet the water consumption demands of people and industries situated within the buffer area. However, analysis of the study shows that it does not meet the crop water requirements. This study provides insights for decision makers in identifying potential beneficiaries and the essence of effective area-based conservation measures, and the proposed framework can be applied to any area of interest, offering a different approach in ecosystem services assessment.

Evaluating neonatal pain via fusing vision transformer and concept-cognitive computing

In clinical nursing, neonatal pain assessment is a challenging task for preventing and controlling the impact of pain on neonatal development. To reduce the adverse effects of repetitive painful treatments during hospitalization on newborns, we propose a novel method (namely pain concept-cognitive computing model, PainC3M) for evaluating facial pain in newborns. In the fusion system, we first improve the attention mechanism of vision transformer by revising the node encoding way, considering the spatial structure, edge and centrality of nodes, and then use its corresponding encoder as a feature extractor to comprehensively extract image features. Second, we introduce a concept-cognitive computing model as a classifier to evaluate the level of pain. Finally, we evaluate our PainC3M on various open pain data sets and a real clinical pain data stream, and the experimental results demonstrate that our PainC3M is very effective for dynamic classification and superior to other comparative models. It also provides a good approach for pain assessment of individuals with aphasia (or dementia).

Integrasi Penilaian Tes dan Non-Tes dalam Pendidikan Agama Islam: Menuju Evaluasi Holistik untuk Pembelajaran Berkelanjutan

This study discusses the development and implementation of an assessment model for Islamic Education (PAI) that integrates both test and non-test techniques. The study is conducted through a comprehensive literature review encompassing various academic sources related to PAI assessment. The findings reveal that test techniques, such as multiple-choice questions and essays, are highly effective in assessing students' cognitive understanding of Islamic concepts. However, these techniques have limitations in evaluating the affective and psychomotor domains, which are also crucial in shaping students' character. On the other hand, non-test techniques, including observations, portfolios, and projects, are better suited for assessing affective and psychomotor aspects, providing a more holistic view of students' development. The combination of test and non-test techniques allows for a more comprehensive, fair, and holistic evaluation of all aspects of student learning, encompassing knowledge, attitudes, and skills. The implications of this study emphasize the importance of a holistic assessment approach in Islamic education, as well as the need for teacher training in the application of non-test techniques to ensure objectivity and validity in assessments. Thus, this research makes a significant contribution to improving the quality of Islamic education through assessments that are more adaptive, responsive, and relevant to students' needs in the modern era

Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content

User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question–answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, and images), multimodal representation of UGC has become increasingly popular. This article reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the article presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the article provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal UGC are presented.